地表水体遥感制图及动态变化特征

No abstract available

The elevations of water surfaces hold important information on the earth's oceans and land surface waters. Ocean sea surface height is related to the internal change of the ocean's density and mass associated with ocean circulation and its response to climate change. The flow rates of rivers and volume changes of lakes are crucial to freshwater supplies and the hazards of floods and drought resulting from extreme weather and climate events. The Surface Water and Ocean Topography (SWOT) Mission is a new satellite using advanced radar technology to make headway in observing the variability of the elevation of water surfaces globally, providing fundamentally new information previously not available to the study of earth's waters. Here, we provide the first results of SWOT over oceans, rivers, and lakes. We demonstrate the potential of the mission to address science questions in oceanography and hydrology.

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Abstract This review presents a comprehensive examination of recent advancements in utilizing multi-date satellite data to analyze spatial and temporal variations in seasonal and inter-annual surface water dynamics within arid environments of Africa. Remote sensing offers continuous, precise, and long-term datasets for surface water research. Various sensors with differing spatial resolutions are discussed, with high-resolution multispectral sensors providing superior spatial resolution but at higher costs. Conversely, dual-sensor approaches, incuding optical sensors (Sentinel-2 and Landsat), radar satellites (Sentinel-1 and RADARSAT) and UAVs were investigated. The review further examines the efficiency and applicability of traditional algorithms such as the modified normalized difference water index (MNDWI), normalized difference water index (NDWI), and automated water extraction index (AWEI) in detecting and delineating surface water resources. Additionally, machine learning (ML) algorithms, including support vector machines (SVM), Random Forest (RF), deep learning and emerging methodologies like recurrent tranformer networks, have been explored. Therefore, we recommend that future research endeavours focus on leveraging high-resolution satellite imagery and integrating physical models with deep learning techniques, artificial intelligence, and online big data processing platforms to improve surface water mapping capabilities.

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Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource—if not overexploited—sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.

Water body feature extraction using a remote sensing technique represents an important tool in the investigation of water resources and hydrological drought assessment. Nuntasi-Tuzla Lake, a component of the Danube Delta Natural Reserve, is located on the Romanian Black Sea littoral. On account of an event in summer 2020, when the lake surface water decreased significantly, this study aims to identify the variation of the Nuntasi-Tuzla Lake surface water over a long-term period in correlation with human intervention and climate change. To this end, it provides an analysis in the period 1965–2021 via hydrological drought indices and data mining classification. The latter approach is based on several water indices derived from Landsat TM/ETM+/OLI and MODIS full-time series datasets: Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index (NDVI), Modified NDWI (MNDWI), Weighted Normalized Difference Water Index (WNDWI), and Water Ratio Index (WRI). The experimental results indicate that the proposed classification methods can extract relevant features from waterbodies using remote sensing imagery with a high accuracy. Moreover, the study shows a similarity in the evolution of surface water cover identified with the data mining classification and the drought periods detected in the flow data series for the Nuntasi and Sacele Rivers that supply the Nuntasi-Tuzla Lake. Overall, the results of our investigation show that human intervention and hydrological drought had an extensive impact on the long-term changes in surface water of the Nuntasi-Tuzla Lake.

Dongting Lake plays an important role in water regulation and biodiversity protection, but it is vulnerable to climate change and human activities. To quantify surface water changes and factors driving them, long-term surface water variation in Dongting Lake was investigated using the multiple spectral indices method based on a decision tree classification for full time-series Landsat and MODIS datasets. Factors influencing surface water changes were explored by combining the annual maximum surface water and annual permanent occurrent water with meteorological and hydrological data. The results showed that both annual maximum surface water and annual permanent water decreased from 1975 to 2019 and the trends of rainfall and runoff at three outlets also changed. The annual maximum surface water surface of Dongting Lake increased during the 1990s due to high rainfall but decreased again after 2000. A significant change in both the hydrological stage and surface water sequence from 1986 to 2019 occurred in 2003, which coincided with the beginning of work to construct the Three Gorges Dam (TGD). The surface water decreased by about 360 km2 and runoff at the three outlets decreased by about 150 × 108 m3 after the impoundment of the TGD, which was likely the main cause of surface water changes after 2003. The area of surface water that changed from permanent water in the pre-TGD period into seasonally occurring water in the post-TGD periods is 209 km2, while the area of surface water that changed from seasonally occurring water in the pre-TGD period into permanent occurrent water in the post-TGD period is 31 km2. Meteorological elements and human activities have had a comprehensive impact on surface water changes in Dongting Lake. Rainfall and temperature account for about one-third of the influence on long-term changes of surface water, and rainfall is the main meteorological driving factor of surface water in the wet season, while temperature is the main factor in the dry season. Runoff at three outlets, four rivers and the Chenglingji explain about half of the change in surface water; the three outlets runoff is mainly affected by human activities and is the main hydrological driving factor of surface water. The monthly maximum surface water fluctuates regularly and Dongting Lake has a strong seasonal characteristic. Indeed, the seasonal changes are significantly altered when drought or flooding occurs, the causes of which are diverse and complex.

The availability of surface water in global drylands is essential for both human society and ecosystems. However, the long-term drivers of change in surface water storage, particularly those related to anthropogenic activities, remain unclear. Here we use multi-mission remote sensing data to construct monthly time series of water storage changes from 1985 to 2020 for 105,400 lakes and reservoirs in global drylands. An increase of 2.20 km3 per year in surface water storage is found primarily due to the construction of new reservoirs. For lakes and old reservoirs (constructed before 1983), conversely, the trend in storage is minor when aggregated globally, but they dominate surface water storage trends in 91% of individual global dryland basins. Further analysis reveals that long-term storage changes in these water bodies are primarily linked to anthropogenic factors—including human-induced warming and water-management practices—rather than to precipitation changes, as previously thought. These findings reveal a decoupling of surface water storage from precipitation in global drylands, raising concerns about societal and ecosystem sustainability. This study quantifies and attributes the changes of surface water storage in global dryland basins over 1985–2020, indicating that long-term changes are mainly linked to anthropogenic factors rather than precipitation.

Climate variability and recurrent droughts have caused remarkable strain on water resources in most regions across the globe, with the arid and semi-arid areas being the hardest hit. The impacts have been notable on surface water resources, which are already under threat from massive abstractions due to increased demand, as well as poor conservation and unsustainable land management practices. Drought and climate variability, as well as their associated impacts on water resources, have gained increased attention in recent decades as nations seek to enhance mitigation and adaptation mechanisms. Although the use of satellite technologies has, of late, gained prominence in generating timely and spatially explicit information on drought and climate variability impacts across different regions, they are somewhat hampered by difficulties in detecting drought evolution due to its complex nature, varying scales, the magnitude of its occurrence, and inherent data gaps. Currently, a number of studies have been conducted to monitor and assess the impacts of climate variability and droughts on water resources in sub-Saharan Africa using different remotely sensed and in-situ datasets. This study therefore provides a detailed overview of the progress made in tracking droughts using remote sensing, including its relevance in monitoring climate variability and hydrological drought impacts on surface water resources in sub-Saharan Africa. The paper further discusses traditional and remote sensing methods of monitoring climate variability, hydrological drought, and water resources, tracking their application and key challenges, with a particular emphasis on sub-Saharan Africa. Additionally, characteristics and limitations of various remote sensors, as well as drought and surface water indices, namely, the Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Normalized Difference Vegetation (NDVI), Vegetation Condition Index (VCI), and Water Requirement Satisfaction Index (WRSI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Land Surface Water Index (LSWI+5), Modified Normalized Difference Water Index (MNDWI+5), Automated Water Extraction Index (shadow) (AWEIsh), and Automated Water Extraction Index (non-shadow) (AWEInsh), and their relevance in climate variability and drought monitoring are discussed. Additionally, key scientific research strides and knowledge gaps for further investigations are highlighted. While progress has been made in advancing the application of remote sensing in water resources, this review indicates the need for further studies on assessing drought and climate variability impacts on water resources, especially in the context of climate change and increased water demand. The results from this study suggests that Landsat-8 and Sentinel-2 satellite data are likely to be best suited to monitor climate variability, hydrological drought, and surface water bodies, due to their availability at relatively low cost, impressive spectral, spatial, and temporal characteristics. The most effective drought and water indices are SPI, PDSI, NDVI, VCI, NDWI, MNDWI, MNDWI+5, AWEIsh, and AWEInsh. Overall, the findings of this study emphasize the increasing role and potential of remote sensing in generating spatially explicit information on drought and climate variability impacts on surface water resources. However, there is a need for future studies to consider spatial data integration techniques, radar data, precipitation, cloud computing, and machine learning or artificial intelligence (AI) techniques to improve on understanding climate and drought impacts on water resources across various scales.

No abstract available

In this study, a data-driven approach employed by utilizing the product called JRC-Global surface water mapping layers V1.4 on the Google Earth Engine (GEE) to map and monitor the effects of climate change on surface water resources. Key climatic variables affecting water bodies, including air temperature (AT), actual evapotranspiration (ETa), and total precipitation, were analyzed from 2000 to 2021 using the temperature-vegetation index (TVX) and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The findings demonstrate a clear association between global warming and the shrinking of surface water resources in the LUB. According to the results, an increase in AT corresponded to a decrease in water surface area, highlighting the significant influence of AT and ETa on controlling the water surface in the LUB (partial rho of − 0.65 and − 0.68, respectively). Conversely, no significant relationship was found with precipitation and water surface area (partial rho of + 0.25). Notably, the results of the study indicate that over the past four decades, approximately 40% of the water bodies in the LUB remained permanent. This suggests a loss of around 30% of the permanent water resources, which have transitioned into seasonal water bodies, accounting for nearly 13% of the total. This research provides a comprehensive framework for monitoring surface water resource variations and assessing the impact of climate change on water resources. It aids in the development of sustainable water management strategies and plans, supporting the preservation and effective use of water resources.

No abstract available

This letter summarizes substantial modifications made to the Surface Water Microwave Product Series (SWAMPS), a coarse-resolution (~25 km) global inundated area fraction data record derived from active and passive microwave remote sensing. SWAMPS is the most temporally dense, long-term record of global surface water dynamics publicly available today. This update improves upon the original release by: 1) incorporating a customized, consistent resampling and assembly of the Special Sensor Microwave Imager and Special Sensor Microwave Imager Sounder brightness temperature record; 2) eliminating signal contamination from ocean waters along coastlines; 3) inclusion of permanent surface waters as a component of the data record; and 4) reducing anomalous inundation retrievals over arid and semiarid regions. This update provides for the enhanced scientific utility of the full 25+ years of data records. Remaining uncertainties in the surface water fraction retrievals are principally in areas with bare, sandy surface cover and in areas with dense vegetation cover that diminishes radiometric sensitivity to surface water. This data record and associated documentation are freely available through the Alaska Satellite Facility, Fairbanks, AK, USA.

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To limit the abstract to 250 words, please change it to “The Amazon basin, one of the world's largest and most vital ecosystems, presents a formidable challenge for accurately mapping its extensive surface water extent and dynamic seasonal and long-term variations. This challenge arises from the region's dense vegetation and persistent cloud coverage, limiting the applicability of conventional remote sensing technologies. Spaceborne global navigation satellite system reflectometry (GNSS-R) offers a promising avenue to complement existing techniques, due to its distinctive sensitivity to surface water, coupled with its vegetation and cloud penetration and high spatio-temporal resolution. A trackwise method is developed for mapping surface water using data from cyclone GNSS (CYGNSS). Over a period of more than six years, from April 2017 to May 2023, monthly 3-km binary water/land maps covering the entire basin are produced. Comparative analyses are conducted using a comprehensive set of classification metrics with datasets derived from optical, passive microwave, and radar missions. The findings revealed that these products tend to underestimate the full extent of inundation across the basin. In contrast, CYGNSS, while exhibiting a slight tendency to overestimate surface water due to its heightened sensitivity to water and wet soils, provides valuable insights into inundation dynamics even in densely vegetated areas. Furthermore, monthly inundated area estimates are compared with water levels measured by four gauging stations across the basin. The results demonstrated a strong agreement between them and a maximum correlation coefficient of 0.74. Overall, the monthly water maps produced in this study hold significant promise for a wide range of hydrological investigations and practical applications.

The annual flood and the alteration in hydrological regimes are the most vital concerns in the Vietnamese Mekong Delta (VMD). Although synthetic aperture radar (SAR) Sentinel-1 imagery is widely used for water management, only a few studies have used Sentinel-1 data for mapping surface water and monitoring flood events in the VMD. This study developed an algorithm to implement (i) automatic Otsu threshold on a series of Sentinel-1 images to extract surface water and (ii) time series analyses on the derived surface water maps to detect flood water extent in near-real-time (NRT). Specifically, only cross-polarized VH was selected after an assessment of different Sentinel-1 polarizations. The dynamic Otsu thresholding algorithm was applied to identify an optimal threshold for each pre-processed Sentinel-1 VH image to separate water from non-water pixels for producing a time series of surface water maps. The derived Sentinel-1 surface water maps were visually compared with the Sentinel-2 Full Resolution Browse (FRB) and statistically examined with the Sentinel-2 Multispectral Instrument (MSI) surface water maps, which were generated by applying the Otsu threshold on the normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) images. The visual comparison showed a strong correspondence between the Sentinel-1 surface water maps and Sentinel-2 FRB images in three periods, including rice’s sowing season, flood period, and rice’s maturation stage. A good statistical agreement suggested that the performance of the dynamic Otsu thresholding algorithm on Sentinel-1 image time series to map surface water is effective in river areas (R2 = 0.97 and RMSE = 1.18%), while it is somewhat lower in paddy field areas (R2 = 0.88 and RMSE = 3.88%). Afterward, a flood mapping algorithm in NRT was developed by applying the change-detection-based time series analyses on the derived Sentinel-1 surface water maps. Every single pixel at the time is respectively referred to its state in the water/non-water and flooded/non-flooded maps at the previous time to be classified into a flooded or non-flooded pixel. The flood mapping algorithm enables updates at each time step to generate temporal flood maps in NRT for monitoring flood water extent in large-scale areas. This study provides a tool to rapidly generate surface water and flood maps to support water management and risk reduction in the VMD. The future improvement of the current algorithm is discussed.

Surface water mapping is crucial for preserving and strategically managing water resources. However, extracting water bodies and estimating their extent from satellite images faces challenges due to the similar spectral responses of different land types. The task of identifying optimal features from satellite images and designing deep neural networks with reduced computational complexity is also demanding. In this paper, the Machine Learning (ML) with conventional, Pixel Level Feature (PLF), and Multi Scale Feature Extraction Network (MSFEN) feature extraction methods and Lightweight Attention-based Multiscale Neural Network (LWAMNNet) are compared for surface water mapping using satellite images. Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGB) are the ML classifiers considered for this study. The Linear Imaging Self-Scanning Sensor-III (LISS-III) images from the Resourcesat-2 satellite are used for experimentation. The results indicate that LWAMN-Net outperforms traditional ML techniques. Among the ML techniques, the MSFEN feature extraction method demonstrates superior performance, particularly when combined with SVM (MSFEN-SVM), outperforming other ML techniques.

ABSTRACT Optical satellite imaging for surface water mapping often encounters significant challenges owing to persistent spatial data gaps caused by clouds, shadows, and sensor errors. This study presents a novel Stepwise Gap-Filling (SGF) method, designed to enhance the monthly surface water mapping and monitoring. The SGF method leverages temporal similarities and spatial correlations to reconstruct gap pixels originally classified as invalid observations. We validated this approach against historical high-resolution Google Earth images from 2887 sample points in the Siling Co Basin of the Tibetan Plateau. The results demonstrated substantial improvements in mapping accuracy, achieving an overall accuracy of 98.93%, a producer’ accuracy of 98.59%, and a user’ accuracy of 99.11%, markedly reducing the uncertainties in the original dataset. Importantly, the SGF method offers detailed insights into monthly surface water dynamics, which are closely aligned with annual trends. This study highlights the effectiveness of the SGF method for filling data gaps and its potential for widespread application in the monitoring and management of global water resources.

Surface water is a highly dynamical object on the earth’s surface. At present, satellite remote sensing is the most effective way to accurately depict the temporal and spatial variation characteristics of surface water on a large scale. In this study, a region-adaptive random forest algorithm is designed on the Google Earth Engine (GEE) for automatic surface water mapping by using data from multi-sensors such as Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-1 SAR images as source data, and China as a case study region. The visual comparison of the mapping results with the original images under different landform areas shows that the extracted water body boundary is consistent with the water range in the image. The cross-validation with the JRC GSW validation samples shows a very high precision that the average producer’s accuracy and average user’s accuracy of water is 0.933 and 0.998, respectively. The average overall accuracy and average kappa is 0.966 and 0.931, respectively. The independent verification results of lakes with different areas also prove the high accuracy for our method, with a maximum average error of 2.8%. These results show that the method is an ideal way for large-scale surface water mapping with a high spatial–temporal resolution.

ABSTRACT Efficient and continuous monitoring of surface water is essential for water resource management. Much effort has been devoted to the task of water mapping based on remote sensing images. However, few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner. Moreover, water area statistics are sensitive to the satellite image observation quality. This study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine (GEE) with a supervised random forest classifier. A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence products. The samples for permanent and seasonal water were mapped and collected separately, so that the supplement of seasonal samples can increase the spectral diversity of the sample space. To reduce the uncertainty of the derived water occurrences, temporal correction was applied to repair the classification maps with invalid observations. Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results. Comparative tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods, as well as the GSWD and GLAD datasets.

Mapping high-spatial-resolution surface water bodies in urban and suburban areas is crucial in understanding the spatial distribution of surface water. Although Sentinel-2 images are popular in mapping water bodies, they are impacted by the mixed-pixel problem. Sub-pixel mapping can predict finer-spatial-resolution maps from the input remote sensing image and reduce the mixed-pixel problem to a great extent. This study proposes a sub-pixel surface water mapping method based on morphological dilation and erosion operations and the Markov random field (DE_MRF) to predict a 2 m resolution surface water map for heterogeneous regions from Sentinel-2 imagery. DE_MRF first segments the normalized difference water index image to extract water pixels and then detects the mixed pixels by using combined morphological dilation and erosion operations. For the mixed pixels, DE_MRF considers the intra-pixel spectral variability by extracting multiple water endmembers and multiple land endmembers within a local window to generate the water fraction images through spectral unmixing. DE_MRF was evaluated in the Jinshui Basin, China. The results suggested that DE_MRF generated a lower commission error rate for water pixels compared to the comparison methods. Because DE_MRF considers the intra-class spectral variabilities in the unmixing, it is better in mapping sub-pixel water distribution in heterogeneous regions where different water bodies with distinct spectral reflectance are present.

Accurate surface water mapping is crucial for monitoring and protecting ecosystem environments. During the past decades, several water extraction methods have been presented and significant progress has been made. However, most of the existing research has mainly focused on reducing the interference factors, such as clouds and cloud shadows, building shadows, and mountain shadows. There are relatively few studies concentrated on the fine extraction of water body boundaries, which is of equal importance for surface water mapping. Therefore, in this article, we developed a novel boundary enhancement network, to improve its ability to extract water boundary information. The proposed BE-Net consists of three modules, i.e., SE, SA, and MFF, which are used to focus on water features, water boundary information, and fuse features of different levels, respectively. In addition, the Sobel edge loss function is adopted. The proposed model was tested on six regions with significant differences in water body morphology and the contribution of the Sobel edge loss function and the three modules were investigated. The results demonstrated that: 1) compared with the state-of-the-art methods, the proposed BE-Net achieved the best accuracy in all testing areas and was all above 97%; 2) Compared with the commonly used loss function, the Sobel edge loss function can better capture the detail boundary information, resulting in higher accuracy; and 3) by integrating the SE, SA, and the MFF modules, a robust boundary enhancement network is constructed, and the water extraction accuracy can be significantly improved.

Abstract Satellite remote sensing plays an important role in mapping the location and extent of surface water. A variety of approaches are available for mapping surface water, but deep learning approaches are not commonplace as they are ‘data hungry’ and require large amounts of computational resources. However, with the availability of various satellite sensors and rapid development in cloud computing, the remote sensing scientific community is adapting modern deep learning approaches. The new integration of cloud-based Google AI platform and Google Earth Engine enables users to deploy calculations at scale. In this paper, we investigate two methods of automatic data labeling: 1. the Joint Research Centre (JRC) surface water maps; 2. an Edge-Otsu dynamic threshold approach. We deployed a U-Net convolutional neural network to map surface water from Sentinel-1 Synthetic Aperture Radar (SAR) data and tested the model performance using different hyperparameter tuning combinations to identify the optimal learning rate and loss function. The performance was then evaluated using an independent validation data set. We tested 12 models overall and found that the models utilizing the JRC data labels showed a better model performance, with F1-scores ranging from 0.972 to 0.986 for the training test and validation efforts. Additionally, an independently sampled high-resolution data set was used to further evaluate model performance. From this independent validation effort we observed models leveraging JRC data labels produced F1-Scores ranging from 0.9130.922. A pairwise comparison of models, through varying input data, learning rates, and loss functions constituents, revealed the JRC Adjusted Binary Cross Entropy Dice model to be statistically different than the 66 other model combinations and displayed the highest relative evaluations metrics including accuracy, precision score, Cohen Kappa coefficient, and F1-score. These results are in the same range as many of the conventional methods. We observed that the integration of Google AI Platform into Google Earth Engine can be a powerful tool to deploy deep-learning algorithms at scale and that automatic data labeling can be an effective strategy in the development of deep-learning models, however independent data validation remains an important step in model evaluation.

Water resources are an important component of ecosystem services. During long periods of cloudiness and precipitation, when a ground-based sample is not available, the water bodies are detected from satellite SAR (synthetic-aperture radar) data using threshold methods (e.g., Otsu and Kittler–Illingworth). However, such methods do not enable to obtain the correct threshold value for the backscattering coefficient (σ0) of relatively small water areas in the image. The paper proposes and substantiates a method for the mapping of the surface of water bodies, which makes it possible to correctly identify water bodies, even in “water”/“land” class imbalance situations. The method operates on a principle of maximum compliance of the resulting SAR water mask with a given reference water mask. Therefore, the method enables the exploration of the possibilities of searching and choosing the optimal parameters (polarization and speckle filtering), which provide the maximum quality of SAR water mask. The method was applied for mapping natural and industrial water bodies in the Pohjois-Pohjanmaa region (North Ostrobothnia), Finland, using Sentinel-1A and -1B ground range detected (GRD) data (ascending and descending orbits) in 2018–2021. Reference water masks were generated based on optical spectral indices derived from Sentinel-2A and -2B data. The polarization and speckle filtering parameters were chosen since they provide the most accurate σ0 threshold (on average for all observations above 0.9 according to the Intersection over Union criterion) and are resistant to random fluctuations. If a reference water mask is available, the proposed method is more accurate than the Otsu method. Without a reference mask, the σ0 threshold is calculated as an average of thresholds obtained from previous observations. In this case, the proposed method is as good in accuracy as the Otsu method. It is shown that the proposed method enables the identification of surface water bodies under significant class imbalance conditions, such as when the water surface covers only a fraction of a percent of the area under study.

No abstract available

Satellite remote sensing plays an important role in the monitoring of surface water for historical analysis and near real-time applications. Due to its cloud penetrating capability, many studies have focused on providing efficient and high quality methods for surface water mapping using Synthetic Aperture Radar (SAR). However, few studies have explored the effects of SAR pre-processing steps used and the subsequent results as inputs into surface water mapping algorithms. This study leverages the Google Earth Engine to compare two unsupervised histogram-based thresholding surface water mapping algorithms utilizing two distinct pre-processed Sentinel-1 SAR datasets, specifically one with and one without terrain correction. The resulting surface water maps from the four different collections were validated with user-interpreted samples from high-resolution Planet Scope data. It was found that the overall accuracy from the four collections ranged from 92% to 95% with Cohen’s Kappa coefficients ranging from 0.7999 to 0.8427. The thresholding algorithm that samples a histogram based on water edge information performed best with a maximum accuracy of 95%. While the accuracies varied between methods it was found that there is no statistical significant difference between the errors of the different collections. Furthermore, the surface water maps generated from the terrain corrected data resulted in a intersection over union metrics of 95.8%–96.4%, showing greater spatial agreement, as compared to 92.3%–93.1% intersection over union using the non-terrain corrected data. Overall, it was found that algorithms using terrain correction yield higher overall accuracy and yielded a greater spatial agreement between methods. However, differences between the approaches presented in this paper were not found to be significant suggesting both methods are valid for generating accurate surface water maps. High accuracy surface water maps are critical to disaster planning and response efforts, thus results from this study can help inform SAR data users on the pre-processing steps needed and its effects as inputs on algorithms for surface water mapping applications.

The Global Navigation Satellite System reflectometry (GNSS-R) technique has demonstrated its potential for terrestrial applications. Over inland water bodies, the dominance of coherent components in GNSS-R has been widely recognized. Nevertheless, little attention is given to GNSS-R speckle, which is inherent to coherent imaging systems. In this study, taking the multiplicative speckle into account, we regard GNSS-R coherent scattering as a statistical distribution. First, the expression of the statistical distribution is identified and parameterized using observations from the Cyclone Global Navigation Satellite System (CyGNSS). The results suggest that the power tends to obey a three-degrees-of-freedom distribution model. Second, the multilook statistics of CyGNSS, such as the mean value and the coefficient of variation (CV), are analyzed on different spatial–temporal scales. Finally, we realize surface water mapping using multilook statistics. Comparison with the state-of-the-art algorithms shows that the proposed method can effectively improve the goodness of water mapping, with higher overall accuracies (~0.97) and F1 scores (~0.60). This study provides new insights into future GNSS-R land observations.

Surface water is a fundamental resource in urban environments. Monitoring the spatio-temporal distribution of urban surface water from remotely sensed images is crucial for urban planning and management. Unfortunately, due to the limitation of spatial resolution, the method based on low/medium resolution images is difficult to extract small water bodies accurately. Recently, very high resolution (VHR) images have shown considerable potential for urban compositions mapping. However, fewer spectral bands, shadows, and high spectral heterogeneity of VHR images hinder the application of traditional methods. In this article, we proposed an urban surface water mapping method called sparse superpixel-based water extraction (SSWE) from VHR images. The method includes three steps: clustering water bodies into sparse targets at the object level by an improved scale-adaptive simple non-iterative clustering (SA-SNIC) superpixel segmentation; generating new bands with additional spectral, spatial, and derived features, to increase the dimensions of original data and enhance the separability between water bodies and background covers; and constructing a positive-negative constrained energy minimization multitarget sparse detector to highlight the water bodies while suppressing shadows. The proposed method was applied to GF-2 multispectral images of four cities in China. The results showed that SSWE achieved the highest accuracy compared with other methods, with an average OA of 98.91% and an average kappa coefficient of 0.942. Furthermore, the separability analysis also indicated that SSWE could effectively distinguish urban water bodies from shadows and other land covers. Stable results can be acquired by the suggested parameters and thresholds of SSWE.

Abstract Satellite remote sensing provides an efficient pathway to map inland surface water extent across different spatial and temporal scales. However, how to monitor the surface water distribution and its spatiotemporal variability via combining optical and radar remote sensing datasets still faces substantial challenges. In this study, we propose a Seamless Surface Water Mapping Framework (SSWMF) which synergizes both optical (MODIS, Landsat 8, Sentinel-2) and SAR (Sentinel-1) imageries. The validity of SSWMF was first proved over the middle and lower reaches of the Yangtze River of China with abundant lake resources, showing an overall accuracy of 90.72%, and the results indicate that SSWMF can provide surface water map with higher spatial and temporal continuity compared to the Joint Research Centre Global Surface Water dataset. Multi-source validation showed that the SSWMF-derived surface water maps can well capture the temporal fluctuation and spatial heterogeneity of water resources over China, with an overall accuracy of 92.39%. Overall, our results suggest that the proposed water mapping framework is promising and is readily applicable to large-scale water resource management and drought/flood monitoring.

Continuous monitoring of surface water resources is often challenging due to the lack of monitoring systems in remote areas and the high spatial distribution of water bodies. The Google Earth Engine (GEE) platform, which houses a large set of remote sensing datasets and geospatial processing power, has been applied in various aspects of surface water resources monitoring to solve some of the challenges. PyGEE-SWToolbox is a freely available Google Earth Engine-enabled open-source toolbox developed with Python to be run in Jupyter Notebooks that provides an easy-to-use graphical user interface (GUI) that enables the user to obtain time series of Landsat, Sentinel-1, and Sentinel-2 satellite imagery, pre-process them, and extract surface water using water indices, such as the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEI), and Dynamic Surface Water Extent (DSWE). The validation of the toolbox is carried out at four reservoir and lake locations: Elephant Butte Lake, Hubbard Creek Reservoir, Clearwater Lake, and Neversink Reservoir in the United States. A time series of the water surface area generated from PyGEE-SWToolbox compared to the observed surface areas yielded good results, with R2 ranging between 0.63 and 0.99 for Elephant Butte Lake, Hubbard Creek Reservoir, and Clearwater Lake except the Neversink Reservoir with a maximum R2 of 0.52. The purpose of PyGEE-SWToolbox is to provide water resource managers, engineers, researchers, and students a user-friendly environment to utilize the GEE platform for water resource monitoring and generation of datasets. The toolbox is accompanied by a step-by-step user manual and Readme documentation for installation and usage.

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

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ABSTRACT The Surface Water and Ocean Topography (SWOT) satellite, launched in December 2022, marks a significant breakthrough in hydrological monitoring. Equipped with a Ka-band radar interferometer, nadir altimeter, and radiometers, SWOT enables simultaneous measurements of water levels, widths, and slopes with unprecedented spatiotemporal resolution. This manuscript systematically reviews SWOT's satellite parameters, scientific missions, algorithm processes, and application products to provide a more comprehensive understanding of its capabilities. Validation with ICESat-2 and other reference datasets shows that SWOT’s WSE products achieve an accuracy of up to 0.18 m for inland water surface elevation measurements, thereby significantly improving the precision of water body monitoring. Additionally, SWOT demonstrates substantial potential in flood monitoring by capturing flood inundation dynamics with high spatiotemporal resolution, thereby enhancing rapid response and early warning capabilities. These advancements address critical limitations in existing satellite missions, improving global hydrological observation and supporting water resource management. SWOT’s data products offer essential insights into surface water dynamics, contributing to disaster mitigation and a better understanding of climate change impacts on water resources.

Mining affects groundwater and surface water both during an active mining operation and after its termination. Continuous monitoring and both quantitative and qualitative assessment of water dynamics are crucial for the sustainable management of the mining and post-mining environment. This paper provides an extensive overview of water in the mining industry and of remote sensing methods for surface water monitoring. Moreover, selected spectral water indices are compared to assess their performance and usefulness in surface water monitoring. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI) are applied to different case study areas affected by mining-induced multitemporal surface water changes. All the selected indices were found useful as proxies for surface water identification; however, their effectiveness and accuracy varied in subsequent case studies.

In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data-filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall classification accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

Small and medium-sized reservoirs play an important role in water systems that need to cope with climate variability and various other man-made and natural challenges. Although reservoirs and dams are criticized for their negative social and environmental impacts by reducing natural flow variability and obstructing river connections, they are also recognized as important for social and economic development and climate change adaptation. Multiple studies map large dams and analyze the dynamics of water stored in the reservoirs behind these dams, but very few studies focus on small and medium-sized reservoirs on a global scale. In this research, we use multi-annual multi-sensor satellite data, combined with cloud analytics, to monitor the state of small (10–100 ha) to medium-sized (> 100 ha, excluding 479 large ones) artificial water reservoirs globally for the first time. These reservoirs are of crucial importance to the well-being of many societies, but regular monitoring records of their water dynamics are mostly missing. We combine the results of multiple studies to identify 71,208 small to medium-sized reservoirs, followed by reconstructing surface water area changes from satellite data using a novel method introduced in this study. The dataset is validated using 768 daily in-situ water level and storage measurements (r2 > 0.7 for 67% of the reservoirs used for the validation) demonstrating that the surface water area dynamics can be used as a proxy for water storage dynamics in many cases. Our analysis shows that for small reservoirs, the inter-annual and intra-annual variability is much higher than for medium-sized reservoirs worldwide. This implies that the communities reliant on small reservoirs are more vulnerable to climate extremes, both short-term (within seasons) and longer-term (across seasons). Our findings show that the long-term inter-annual and intra-annual changes in these reservoirs are not equally distributed geographically. Through several cases, we demonstrate that this technology can help monitor water scarcity conditions and emerging food insecurity, and facilitate transboundary cooperation. It has the potential to provide operational information on conditions in ungauged or upstream riparian countries that do not share such data with neighboring countries. This may help to create a more level playing field in water resource information globally.

Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decades have seen a steady decline in in situ hydrological monitoring and the availability of the growing volume of environmental data from free and open satellite systems is increasingly being recognized as an essential tool for largescale monitoring of water resources. The scientific literature holds many promising studies on satellite-based surface-water mapping, but a systematic evaluation has been lacking. Therefore, a round robin exercise was organized to conduct an intercomparison of 14 different satellite-based approaches for monitoring inland surface dynamics with Sentinel-1, Sentinel-2, and Landsat 8 imagery. The objective was to achieve a better understanding of the pros and cons of different sensors and models for surface water detection and monitoring. Results indicate that, while using a single sensor approach (applying either optical or radar satellite data) can provide comprehensive results for very specific localities, a dual sensor approach (combining data from both optical and radar satellites) is the most effective way to undertake largescale national and regional surface water mapping across bioclimatic gradients.

In water‐stressed hyperarid basins, questions mount over the impacts of anthropogenic groundwater extraction and climate‐driven perturbations on groundwater‐surface water interactions and the resilience of ecosystem‐critical surface water. Coupling groundwater with surface water observations from Sentinel‐2 data provides an unprecedented opportunity to evaluate surface water connectivity with local aquifers following intense precipitation events in arid basins. Surface water area and groundwater level data were analyzed for trends following precipitation, including peak lag time, post‐peak recession rates, and changes in hydraulic gradients. Results indicate variable connectivity following large precipitation events between surface water change and groundwater level fluctuations in the upgradient freshwater aquifer, whereas the downgradient brine‐to‐brackish area of the aquifer indicated virtually no connectivity with the aquifer. Comparison between precipitation and surface water response indicate distinct responses based on the physical relationship of the surface water body with the brine‐to‐brackish area of the aquifer. Lumped parameter modeling of surface water inundation also constrains the possible hydrologic dynamics of the post‐precipitation response. While modeled influx to surface water seems primarily controlled by watershed hydraulics rather than direct hydraulic connectivity of the aquifers, the relationship between surface water and adjacent groundwater levels coupled with surface water area indicates that local aquifers are primarily connected to the surface water bodies through discharge via subsurface infiltration. Modeling results imply that the existence of brine‐adjacent surface water in arid basins relies on upgradient discharge from freshwater aquifers. Our results further support that marginal surface water systems can serve as a critical recharge mechanism to local aquifers.

Abstract. The North American Prairie Pothole Region (PPR) represents a large system of wetlands with great importance for biodiversity, water storage and flood management. Knowledge of seasonal and inter-annual surface water dynamics in the PPR is important for understanding the functionality of these wetland ecosystems and the changing degree of hydrologic connectivity between them. Optical sensors that are widely used for retrieving such information are often limited by their temporal resolution and cloud cover, especially in the case of flood events. Synthetic aperture radar (SAR) sensors can potentially overcome such limitations. However, water extent retrieval from SAR data is often impacted by environmental factors, such as wind on water surfaces. Hence, robust retrieval methods are required to reliably monitor water extent over longer time periods . The aim of this study was to develop a robust approach for classifying open water extent in the PPR and to analyse the obtained time series covering the entire available Sentinel-1 observation period from 2015 to 2020 in the hydrometeorological context. Open water in prairie potholes was classified by fusing dual-polarised Sentinel-1 data and high-resolution topographical information using a Bayesian framework. The approach was tested for a study area in North Dakota. The resulting surface water maps were validated using high-resolution airborne optical imagery. For the observation period, the total water area, the number of waterbodies and the median area per waterbody were computed. The validation of the retrieved water maps yielded producer’s accuracies between 84 % and 95 % for calm days and between 74 % and 88 % for windy days. User’s accuracies were above 98 % in all cases, indicating a very low occurrence of false positives due to the constraints introduced by topographical information. The observed dynamics of total water area displayed both intra-annual and inter-annual patterns. In addition to differences in seasonality between small (<1 ha) and large (>1 ha) waterbodies due to the effect of evaporation during summer, these size classes also responded differently to an extremely wet period from 2019 to 2020 in terms of the increase in the number of waterbodies and the total area covered. The results demonstrate the potential of Sentinel-1 data for high-resolution monitoring of prairie wetlands. Limitations of this method are related to wind inhibiting the correct water extent retrieval and to the rather long acquisition interval of 12 d over the PPR, which is a result of the observation strategy of Sentinel-1.

Earth Observation (EO) data is a critical information source for mapping and monitoring water resources over large inaccessible regions where hydrological in-situ networks are sparse. In this paper, we present a simple yet robust method for fusing optical and Synthetic Aperture Radar (SAR) data for mapping surface water dynamics over mainland China. This method uses a multivariate logistic regression model to estimate monthly surface water extent over a four-year period (2017 to 2020) from the combined usages of Sentinel-1, Sentinel-2 and Landsat-8 imagery. Multi-seasonal high-resolution images from the Chinese Gaofen satellites are used as a reference for an independent validation showing a high degree of agreement (overall accuracy 94%) across a diversity of climatic and physiographic regions demonstrating potential scalability beyond China. Through inter-comparison with similar global scale products, this paper further shows how this new mapping technique provides improved spatio-temporal characterization of inland water bodies, and for better capturing smaller water bodies (< 0.81 ha in size). The relevance of the results is discussed, and we find this new enhanced monitoring approach has the potential to advance the use of Earth observation for water resource management, planning and reporting.

Abstract Climate change and urbanization are jointly impacting on the open surface water area of the Irtysh River in the Arctic Circle. This will affect the economic development and ecology of the countries in the Irtysh River basin and the hydrological cycle of the Arctic Circle. However, the long-term changes of open surface water bodies in the Irtysh River Basin have not been well quantified. To address this, 89,000 Landsat 4,5,7 and 8 images from 1990 to 2019 were used to extract the surface water body area of the Irtysh River Basin and integrated the assessment of open surface water area dynamics. The geographical detector model was used to quantify the factors that affect the area of open surface water bodies. The results show that the total open surface water area of the Irtysh River Basin is an increasing trend over the past 30 years, with a total increase of 88,790 km2, of which the permanent water body area decreased by 20,800 km2 and the seasonal water body area increased by 109,590 km2. In addition, the factors that contributed the most to the dynamics of the surface water area in the Irtysh River Basin were evapotranspiration, precipitation and snow water equivalent, whose contribution rates could reach 72%, 66% and 61%, respectively, with important interactions between factors. This suggests that monitoring dynamics in surface water area requires comprehensive consideration of all factors. Results obtained from this study offer the latest information for fully understanding the spatio-temporal variation of surface water body area and its driving factors in this basin, which could be used to effectively manage water resources for possibly reducing international water disputes and protecting the fragile ecology in the Arctic.

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Dynamics of surface water is of great significance to understand the impacts of global changes and human activities on water resources. Remote sensing provides many advantages in monitoring surface water; however, in large scale, the efficiency of traditional remote sensing methods is extremely low because these methods consume a high amount of manpower, storage, and computing resources. In this paper, we propose a new method for quickly determining what the annual maximal and minimal surface water extent is. The maximal and minimal water extent in the year of 1990, 2000, 2010 and 2017 in the Middle Yangtze River Basin in China were calculated on the Google Earth Engine platform. This approach takes full advantage of the data and computing advantages of the Google Earth Engine’s cloud platform, processed 2343 scenes of Landsat images. Firstly, based on the estimated value of cloud cover for each pixel, the high cloud covered pixels were removed to eliminate the cloud interference and improve the calculation efficiency. Secondly, the annual greenest and wettest images were mosaiced based on vegetation index and surface water index, then the minimum and maximum surface water extents were obtained by the Random Forest Classification. Results showed that (1) the yearly minimal surface water extents were 14,751.23 km2, 14,403.48 km2, 13,601.48 km2, and 15,697.42 km2, in the year of 1990, 2000, 2010, and 2017, respectively. (2) The yearly maximal surface water extents were 18,174.76 km2, 20,671.83 km2, 19,097.73 km2, and 18,235.95 km2, in the year of 1990, 2000, 2010, and 2017, respectively. (3) The accuracies of surface water classification ranged from 86% to 93%. Additionally, the causes of these changes were analyzed. The accuracy evaluation and comparison with other research results show that this method is reliable, novel, and fast in terms of calculating the maximal and minimal surface water extent. In addition, the proposed method can easily be implemented in other regions worldwide.

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This study develops a new, highly efficient method to produce accurate, high‐resolution surface water maps. The “active‐passive surface water classification” method leverages cloud‐based computing resources and machine learning techniques to merge Sentinel 1 synthetic aperture radar and Landsat observations and generate monthly 10‐m‐resolution water body maps. The skill of the active‐passive surface water classification method is demonstrated by mapping surface water change over the Awash River basin in Ethiopia during the 2015 East African regional drought and 2016 localized flood events. Errors of omission (water incorrectly classified as nonwater) and commission (nonwater incorrectly classified as water) in the case study area are 7.16% and 1.91%, respectively. The case study demonstrates the method's ability to generate accurate, high‐resolution water body maps depicting surface water dynamics in data‐sparse regions. The developed technique will facilitate better monitoring and understanding of the impact of environmental change and climate extremes on global freshwater ecosystems.

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High temporal resolution water distribution maps are essential for surface water monitoring because surface water exhibits significant inner-annual variation. Therefore, high-frequency remote sensing data are needed for surface water mapping. Dongting Lake, the second-largest freshwater lake in China, is famous for the seasonal fluctuations of its inundation extents in the middle reaches of the Yangtze River. It is also greatly affected by the Three Gorges Project. In this study, we used Sentinel-1 data to generate surface water maps of Dongting Lake at 10 m resolution. First, we generated the Sentinel-1 time series backscattering coefficient for VH and VV polarizations at 10 m resolution by using a monthly composition method. Second, we generated the thresholds for mapping surface water at 10 m resolution with monthly frequencies using Sentinel-1 data. Then, we derived the monthly surface water distribution product of Dongting Lake in 2016, and finally, we analyzed the inner-annual surface water dynamics. The results showed that: (1) The thresholds were −21.56 and −15.82 dB for the backscattering coefficients for VH and VV, respectively, and the overall accuracy and Kappa coefficients were above 95.50% and 0.90, respectively, for the VH backscattering coefficient, and above 94.50% and 0.88, respectively, for the VV backscattering coefficient. The VV backscattering coefficient achieved lower accuracy due to the effect of the wind causing roughness on the surface of the water. (2) The maximum and minimum areas of surface water were 2040.33 km2 in July, and 738.89 km2 in December. The surface water area of Dongting Lake varied most significantly in April and August. The permanent water acreage in 2016 was 556.35 km2, accounting for 19.65% of the total area of Dongting Lake, and the acreage of seasonal water was 1525.21 km2. This study proposed a method to automatically generate monthly surface water at 10 m resolution, which may contribute to monitoring surface water in a timely manner.

At high latitudes, lake and river ecosystems are predominant and these ecosystems are undergoing significant changes due to climate change. Although many scientists have studied lakes and rivers in the Arctic region, the inland water dynamics in this region at the continental scale remain unknown. In this study, the dynamics of the Arctic water were analyzed at the continental scale using Landsat ortho-rectified surface reflectance products of fine spatial and temporal resolutions for the period of 2000–2016, using the random forests method. The results of this study produced the following revelations: (i) the water area is decreasing year by year in the long term; (ii) the water loss and gain always show the same dynamic pattern spatially and temporally; (iii) the spatial distribution of the water budget is strongly linked to permafrost, which implies that permafrost determines the distribution pattern of the water dynamics more than climatic factors; and (iv) the dynamics of the water show a certain rule with surface temperature, but the pattern of the dynamics cannot be explained by temperature alone.

The Surface Water and Ocean Topography (SWOT) mission represents a major advance in satellite hydrology by extending conventional nadir altimetry to two-dimensional wide-swath observations of the inland-water surface elevation, extent, width, and slope. This review combines a bibliometric analysis of 556 publications indexed in the Web of Science Core Collection from January 2019 to April 2026 with a thematic synthesis of representative post-launch studies. In contrast to earlier reviews centered largely on mission preparation or individual application domains, we examine the evolution of SWOT research together with study-level evidence on data products, water-surface-elevation retrieval, river-discharge estimation, hydrological applications, uncertainty, and multi-mission integration. The literature shows a clear transition from pre-launch algorithm development and simulation toward post-launch validation and application. Reported water-surface-elevation performance is strongly context dependent: representative studies include a mean RMSE of 0.29 m across Yangtze River stations, MAE below 0.10 m for Tibetan Plateau lakes against ICESat-2, and MAE of 6.7 cm for 1 km2-averaged elevations in herbaceous wetlands. These values should be interpreted as study-specific evidence rather than as single mission-wide accuracy because the validation design, spatial support, environmental conditions, and processing strategies differ substantially among studies. River-discharge estimation remains more uncertain because the bathymetry, roughness, channel geometry, slope estimation, and temporal sampling introduce additional uncertainty. Major remaining challenges concern observation constraints, processing and product uncertainty, transferability, operational implementation, and uneven validation evidence. Future progress will depend on standardized and reproducible processing, uncertainty-aware validation, cross-regional benchmarking, and the integration of SWOT with complementary satellite observations and hydrological or hydraulic models.

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The quantity and quality of surface water are inherently connected yet are overwhelmingly studied separately in the field of remote sensing. Remotely observable water quantity (e.g., water extent, water elevation, lake/reservoir volume, and river discharge) and water quality (e.g., color, turbidity, total suspended solids, chlorophyll a, colored dissolved organic matter, and temperature) parameters of inland waterbodies interact through a series of hydrological and biogeochemical processes. In this review, we analyzed trends in remote sensing publications to understand the prevalence of studies on the quantity versus quality of open‐surface inland waterbodies (rivers, streams, lakes, and reservoirs) as well as identified opportunities for integrating both water quality and quantity sensing in future work. Our bibliometric analysis found that despite the increasing number of publications using remote sensing for inland waterbodies, few studies to date have used remote sensing tools or approaches to simultaneously study water quantity and quality. Ultimately, by providing insights into potential integration of the water quality and quantity studies, we aim to identify a pathway to advance the understanding of inland water dynamics and freshwater resources through remote sensing.

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Wetlands in drylands represent distinct ecosystems characterized by unique assemblages of fauna and flora that coexist in a fluctuating hydrological regime. These habitats provide essential resources, including water and vegetation, which support fauna and thus concentrate a complex web of ecological interactions that rely on the presence of water, especially during the dry season. Understanding the alternating dynamics of recharge and drying is therefore critical, as these processes directly affect the ecological interactions that depend on water availability. To better understand these dynamics, we developed a Landsat‐based remote sensing methodology using a 37‐year‐long time series of images to assess the frequency of water presence in known wetlands in drylands in Hwange National Park, Zimbabwe, in relation to climate data obtained from ERA5. Annual and monthly frequency of water presence was calculated using a thresholding single water index based on the modified normalized detection water index. We assessed inter‐ and intra‐annual variations in water presence. This methodology provides a month‐by‐month assessment of water presence and anomalies from the average between 1986 and 2022. The filling and drying of wetlands showed a temporal lag of 2 months between precipitation and water presence, thus allowing the assessment of the ecological drought intensity in the dry season. Overall, the results allow us to evaluate the drying and filling dynamics of wetlands in drylands as well as their surface variability, giving crucial insights into the water availability dynamics and their resilience to climate forcing and variations.

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Abstract Wetlands are considered one of the most valuable ecosystems around the world and provide numerous environmental services, including water purification, flood protection, and habitat for a variety of species. Wetlands loss is an increasing trend due to anthropogenic activities and natural processes. As such, spatial knowledge regarding the extent and dynamics of surface water is demanding for wetland conservation and protection. The Landsat program, with five decades of historical Earth observation data, has a unique advantage for monitoring wetland surface water changes and dynamics with 30 m spatial resolution. We monitored 266 Ramsar wetland sites in North America for the past 40 years using the open-access Landsat data within the Google Earth Engine cloud computing platform. Landsat Collection 2 Level-2 surface reflectance products were preprocessed and cloud-screened, and a time series of spectral bands and indices were created. The unsupervised Dynamic Surface Water Extent method classified each image into water classes with different confidence levels. An average overall agreement of 92,97% and an average F-score of 96.31% were achieved in this study. Water occurrence maps, in addition to inundation class and change maps, were created for the entire North America, and quantified spatial information was calculated for Ramsar wetland sites.

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Despite successful land reclamation efforts, post-mining areas are still prone to secondary effects of mineral extraction. These effects include surface deformations, damage to infrastructure and buildings, and periodic or permanent changes to surface water resources. This study focused on analyzing a former copper mine in southwest Poland in terms of surface water changes, which may be caused by the restoration of groundwater conditions in the region after mine closure. The main objective of the study was to detect areas with statistically significant changes in surface water between 2015 and 2024, as well as to identify the main factors influencing the observed changes. The methodology integrated open remote sensing datasets from Landsat and Sentinel-1 missions for deriving spectral indices—Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Moisture Index (NDMI), as well as Surface Soil Moisture index (SSM); spatial statistics methods, including Emerging Hot Spot analysis; and regression models—Random Forest Regression (RFR) and Geographically Weighted Regression (GWR). The results obtained indicated a general increase in vegetation water content, a reduction in the extent of surface water, and minor soil moisture changes during the analyzed period. The Emerging Hot Spot analysis revealed a number of new hot spots, indicating regions with statistically significant increases in surface water content in the study area. Out of the investigated regression models, global regression (RFR) outperformed local (GWR) models, with R2 ranging between 74.7% and 87.3% for the studied dependent variables. The most important factors in terms of influence were the distance from groundwater wells, surface topography, vegetation conditions and distance from active mining areas, while surface geology conditions and permeability had the least importance in the regression models. Overall, this study offers a comprehensive framework for integrating multi-source data to support the analysis of environmental changes in post-mining regions.

Spatio-temporal changes to our world’s surface water resources are escalating. Translating how these changes impact communities and ecosystems requires time-varying data of Global Surface Water Extents (GSWE). Traditionally, GSWE mapping has been limited to static estimates, with recent efforts focusing on annual averages, frequency and occurrence of long-term variations. Building upon these foundational capabilities, we harnessed remotely sensed Sentinel-2 based near real-time Dynamic World (DW) land cover products to produce the first-of-its-kind 10 m resolution GSWE dataset representing 2015–2023. Our dataset estimated 2.5 million km2 of permanent waters and 8 million km2 of seasonal waters worldwide. Comparing our Sentinel-2 based data to contemporary Landsat-based GSWE, we observed that our data mapped less water within the >50% probability of occurrence range, suggesting a lower presence of open permanent water especially in high latitudes, deviating from what we previously learnt from Landsat data. Statistical analysis compared to well-established observational products and widely used GSWE datasets across some of the world’s most ecologically significant regions, including Pantanal in South America and Haor in South Asia, supports the overall physical realism of our data in predicting global open surface water dynamics. Our key contribution is a prototype Open Science operational framework that extracts routinely available DW products, runs geospatial analytics, and creates actionable water information for educators, researchers, and stakeholders at any scale of practical interest. We present examples of this operational capability through instant mapping of flood in Spain and drought in Lake Urmia, Central Asia, frequent monitoring of river extent changes at the Ganges–Brahmaputra confluence, and above all, interoperability with other existing GSWE applications.

With the acceleration of global warming and the intensification of anthropogenic activities, numerous lakes worldwide are experiencing reductions in their water surface areas. Yilong Lake, a typical shallow plateau lake located on the Yunnan–Guizhou Plateau in China, serves as a crucial water resource for local human production, daily life, and ecosystem services. Hence, long-term comprehensive monitoring of its dynamic changes is essential for its effective protection. However, previous studies have predominantly utilized remote sensing data with limited temporal resolution, thus failing to reflect the long-term variations in Yilong Lake’s water body. This study employs high temporal resolution monitoring, utilizing multi-source satellite data (e.g., KeyHole, Landsat, HJ-1 A/B) images spanning from 1965 to 2022 to investigate the changes in Yilong Lake’s surface area, analyzing the influencing factors and ecological impacts of these changes. The results indicate that from 1965 to 2022, Yilong Lake’s water surface area decreased by 8.33 km2, with a maximum surface area of 40.49 km2 on 7 January 1986, and a minimum surface area of 10.64 km2 on 20 April 2013. These changes are characterized by three significant phases: (1) a rapid shrinking phase (1965–1979); (2) a fluctuating shrinking period (1986–2016); and (3) an expanding recovery phase (2016–2022). Spatially, the most significant shrinkage was observed along the southern and southwestern shores of the lake. The driving factors varied across different periods: sunshine duration was the dominant influence during the rapid shrinking phase (1965–1979), accounting for 82% of the changes; population and cropland area were the main drive factors during the fluctuating shrinking period (1986–2016), accounting for 56% of the changes; and during the expanding recovery phase (2016–2022), the population accounted for 75% of the changes in the lake’s surface area. Currently, the protection of Yilong Lake depends on water supplementation and strict regulation of outflow, resulting in the lake exhibiting characteristics similar to a reservoir. This long-term investigation provides baseline information for future lake monitoring. Our research findings can also guide decision-makers in urban water resource management and environmental protection, ensuring the scientific and rational use of watershed water resources, effectively curbing the shrinkage of Yilong Lake, and achieving long-term sustainable restoration of the lake’s ecology.

Ecological sluices were constructed along the Tarim River to supplement the ecosystem’s water supply. However, the impact of water regulation by these sluices on the surface water area (SWA) and its relationship with the vegetation response remain unclear. To increase the efficiency of ecological water use, it is crucial to study the response of SWA to water control by the ecological gates and its relationship with vegetation restoration. We utilized the Google Earth Engine (GEE) cloud platform, which integrates Landsat-5/7/8 satellite imagery and employs methods such as automated waterbody extraction via mixed index rule sets, field investigation data, Sen + MK trend analysis, mutation analysis, and correlation analysis. Through these techniques, the spatiotemporal variations in SWA in the middle reaches of the Tarim River (MROTR) from 1990–2022 were analyzed, along with the relationships between these variations and vegetation restoration. From 1990–2022, the SWA in the MROTR showed an increasing trend, with an average annual growth rate of 12.47 km2 per year. After the implementation of ecological gates water regulations, the SWA significantly increased, with an average annual growth rate of 28.8 km2 per year, while the ineffective overflow within 8 km of the riverbank notably decreased. The NDVI in the MROTR exhibited an upward trend, with a significant increase in vegetation on the northern bank after ecological sluice water regulation. This intervention also mitigated the downward trend of the medium and high vegetation coverage types. The SWA showed a highly significant negative correlation with low-coverage vegetation within a 5-km range of the river channel in the same year and a significant positive correlation with high-coverage vegetation within a 15-km range. The lag effect of SWA influenced the growth of medium- and high-coverage vegetation. These findings demonstrated that the large increase in SWA induced by ecological gate water regulation positively impacted vegetation restoration. This study provides a scientific basis for water resource regulation and vegetation restoration in arid regions globally.

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Timely monitoring of surface water is essential for water management in arid and semi-arid re gions. This study evaluates freely available Landsat imagery processed in Google Earth Engine to monitor water extent across f ive major Algerian reservoirs. Satellite-derived surface areas were compared with in-situ measurements from the National Ag ency for Dams and Transfers (ANBT), obtained from topographic and bathymetric surveys. Results show strong agreement bet ween satellite-derived and in-situ water surface areas, with correlation coefficients exceeding 0.97. Normalized errors, calcula ted relative to the normal reservoir surface area, remained low, with MAE ranging from 4.97% to 7.68% and RMSE from 5.70% to 8.37%. Linear regression models of the form Y = aX were developed for each reservoir, with slopes ranging from 1.0643 to 1.1093. These models enable reconstruction of historical surface areas and estimation of missing in-situ observations, especially before 2001. For ungauged reservoirs, the average slope provides a practical predictive equation. Leave-one-year-out cross-vali dation confirmed model robustness, while residual analyses showed no systematic bias. Overall, the approach provides a scalab le, cost-effective framework for operational reservoir monitoring, supporting near-real-time assessment, drought management, and long-term evaluation of reservoir dynamics under climate change, and facilitating evidence-based decisions for reservoir o peration, planning, and regional strategic water security.

The Qinghai–Tibet Plateau is rich in water resources with numerous lakes, rivers, and glaciers, and, as a source of many rivers in Central Asia, it is known as the Asian Water Tower. Under global climate change, it is critical to understand the current influencing factors on surface water area in this region. Although there are numerous studies on surface water mapping, they are still limited by temporal/spatial resolution and record length. Moreover, the complicated topographic condition makes it challenging to map the surface water accurately. Here, we proposed an automatic two-step annual surface water classification framework using long time-series Landsat images and topographic information based on the Google Earth Engine (GEE) platform. The results showed that the producer accuracy (PA) and user accuracy (UA) of the surface water map in the Qinghai–Tibet Plateau in 2020 were 99% and 90%, respectively, and the Kappa coefficient reached 0.87. Our dataset showed high consistency with high-resolution images, indicating that the proposed large-scale water mapping method has great application potential. Furthermore, a new annual surface water area dataset on the Qinghai–Tibet Plateau from 2000 to 2020 was generated, and its relationship with climate, vegetation, permafrost, and glacier factors was explored. We found that the mean surface water area was about 59 481 km2, and there was a significant increasing trend (=322 km2/year, $p < 0.01$ ) during 2000–2020 in the plateau. Greening, warming, and wetting climate conditions contributed to the increase of surface water area. Active layer thickness and permafrost types may be the most related to the decrease of surface water area. This study provides important information for ecological assessment and protection of the plateau and promotes the implementation of sustainable development goals related to surface water resources.

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SDGSAT-1, the first scientific satellite dedicated to advancing the United Nations 2030 Agenda for Sustainable Development, brings renewed vigor and opportunities to water resource monitoring and research. This study evaluates the effectiveness of SDGSAT-1 in extracting water bodies in comparison to Sentinel-2 multi-spectral imager (MSI) data. We applied a confidence thresholding method to delineate river water from land, utilizing the Normalized Differential Water Body Index (NDWI), Normalized Difference Water Index (MNDWI), and Shaded Water Body Index (SWI). It was found that the SWI works best for SDGSAT-1 while the NDWI works best for Sentinel-2. Specifically, the NDWI demonstrates proficiency in delineating a broader spectrum of water bodies and the MNDWI effectively mitigates the impact of shadows, while SDGSAT-1’s SWI extraction of rivers offers high precision, clear outlines, and shadow exclusion. SDGSAT-1’s SWI overall outperforms Sentinel-2’s NDWI in water extraction accuracy (overall accuracy: 90% vs. 91%, Kappa coefficient: 0.771 vs. 0.416, and F1 value: 0.844 vs. 0.651), likely due to its deep blue bands. This study highlights the comprehensive advantages of SDGSAT-1 data in extracting river water bodies, providing a theoretical basis for future research.

This study evaluates various water extraction indices to identify surface features using Sentinel-2 satellite images. Eight study sites were selected with three indices employed in this study, such as the water ratio index (WRI), the modified normalized difference water index (MNDWI), and the normalized difference water index (NDWI). The results of the accuracy assessment revealed that the producer accuracy (PA) for the NDWI ranged from 81.11 to 94.56%, that for the MNDWI ranged from 85.3 to 96.7%, and that for the WRI ranged from 96.98 to 97.98%. The user accuracies for the NDWI, MNDWI, and WRI results are 68.1–81.6%, 74.81–86.14%, and 80.33–88.78%, respectively. This study reveals that the WRI has greater PA (97.98%), user accuracy (88.78%), and kappa statistic (0.88) than the MNDWI and NDWI. The WRI is particularly suitable for extracting surface bodies surrounded by bare lands with minimal vegetation cover. Conversely, the NDWI is more applicable in areas with moderate vegetation and shadows, such as agricultural regions or wetlands. The MNDWI is suitable to avoid confusion water bodies with built-up areas and used to extract water bodies with various land cover. Therefore, establishing optimal thresholds is essential for policymakers and planners to effectively monitor water surface features and changes in water sources.

Reliable mapping of vegetation and surface water from satellite imagery remains challenging, as common spectral indices can saturate at high biomass, show limited sensitivity across ecosystems, and confuse targets with soil, shadows, or built-up surfaces. We present two indices, the Symbolic Regression Vegetation Index (SRVI) and the Symbolic Regression Water Index (SRWI), discovered with a data-driven symbolic regression framework applied to Sentinel-2 Level-2A reflectance and guided by ESA WorldCover labels. Expressions were evolved from physically interpretable building blocks using non-linear combinations of visible, NIR, and SWIR bands. Indices were derived on a spectrally complex Mediterranean site and evaluated on eleven independent regions spanning diverse biomes. Performance was assessed with the Jeffries–Matusita distance, averaged across months to account for phenology, and compared against established vegetation indices (NDVI, EVI, SAVI, MSAVI2, NDRE) and water indices (NDWI, MNDWI, AWEI, TCW, WI2015). SRVI improves separability between vegetation and non-vegetation and shows higher discrimination among vegetation types relative to all benchmarks. SRWI yields more consistent water delineation with reduced confusion with built-up and shadowed surfaces, outperforming standard alternatives on the same datasets. Results indicate that symbolic regression can produce compact, interpretable indices that generalise across regions and seasons, offering practical gains for global vegetation and water mapping.

Vegetation-covered water bodies (VCW) are a vital component of wetlands, and their distribution information is crucial for studying the dynamic interactions between vegetation and water. However, due to vegetation obstruction, optical remote sensing has limitations in extracting such water bodies, as it typically identifies only open water areas effectively. In contrast, microwave remote sensing, with its vegetation-penetrating capability and specular reflection characteristics, provides a more comprehensive identification of wetland water bodies. Previous studies have shown that the additional water body areas (SW) identified by SAR but not by optical sensors are often accompanied by significant vegetation cover. However, a systematic assessment of SW’s potential in mapping VCW is still lacking. This study uses the Caohai Wetland in Guizhou, China, as an example, leveraging Sentinel-2A and RadarSat-2 imagery from adjacent periods and multiple water body extraction methods to extract SW and explore its performance in mapping VCW during the dry season. Results show that during the initial stage of vegetation senescence (7 January 2019), the use of SW achieved high accuracy in mapping VCW, with overall accuracy, kappa coefficient, and F1 score reaching 84.2%, 68.4%, and 85.3%, respectively. However, as vegetation senescence deepened (12 January 2020), these metrics dropped to 76.2%, 60.7%, and 87%, respectively, indicating a significant decline in accuracy. During the vegetation regrowth stage (7 April 2020), the overall accuracy, kappa coefficient, and F1 score were 71.1%, 57.2%, and 70.9%, respectively. As vegetation continued to grow (21 April 2019), these metrics improved to 79.4%, 67.2%, and 86.6%. In summary, SW extracted from high-resolution optical and SAR imagery can preliminarily map VCW during the dry season. Furthermore, its identification accuracy improves significantly with increasing vegetation density. This study provides a novel perspective for wetland water body monitoring and the study of vegetation-water interactions.

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River water extent is critical for understanding river discharge or its hydrological conditions. Although numerous methods have been proposed to map river water from either optical or synthetic aperture radar (SAR) remotely sensed images, uncertainties still exist broadly. In this study, we developed an image fusion method that integrates Landsat-8, Sentinel-1 and Sentinel-2 images simultaneously for river water mapping with two major steps. Firstly, a posterior probability support vector machine model was adopted to generate water probability maps from each individual image; and second, a Multi-dimensional Weighted Fusion Method (MDWFM) was developed to fuse these probability maps. Four reaches with different characteristics were selected as case study sites. High resolution aerial images were acquired and used as the reference to evaluate our results. We found the fusion process not only improves the quality of river water mapping, but also excludes the cloud interference. The fused river water maps become more reliable after the conflicts from difference images being solved by the proposed MDWFM method that contains a proportional conflict redistribution rule. The weighted root mean square difference was reduced to 0.066, and the Area Under the ROC curve reached up to 0.984. The Critical Success Index, Kappa Coefficient, and F-measure reached up to 0.810, 0.836 and 0.895, respectively. These stable and accurate river extent mapping results obtained through fusing multiple images with high spatial resolution (10 m) and short revisit interval (0.4~4.4 days) are of great significance for enriching the data and methodology of hydrological studies.

Accurate surface water mapping is crucial for watershed planning and safeguarding regional water resources. The study aimed to extract extent of seasonal surface water, focusing on selected districts of Manipur, northeast India from 2016 to 2021, utilized Sentinel-2 data in the Google Earth Engine (GEE) platform. Employing multiple indices and the Random Forest classifier, the methodology addressed challenges such as cloud and shadow interference, particularly in high-altitude regions. Results revealed Bishnupur with the maximum surface water extent (124 km2) and Tengnoupal with the minimum (0.24 km2) during the study period. A notable 6% gain in Bishnupur surface water was observed from pre- to post-monsoon in 2016, while changes in other districts were negligible. Conversely, a maximum loss of 7% occurred in Bishnupur during pre-monsoon from 2016 to 2021. Overall, post-monsoon expansion exceeded that of pre-monsoon in all districts. Discrepancies were evident in both seasons in 2021. The applied techniques proved reliable and innovative, ensuring accurate surface water extent mapping. The GEE platform facilitated enhanced access to satellite data, significantly expediting processing through machine learning algorithms. The findings of this study have the potential to inform surface water planning and management, offering valuable insights for efficient resource utilization.

Surface water is a critical natural resource, but its mapping accuracy is vulnerable to cloud cover, snow, shadows, and diverse roofing materials. Recognizing the limitations of a single threshold segmentation method that fails to achieve high-precision extraction of surface water in complex terrain areas, this study introduces a multiple threshold water detection rule (MTWDR) method to improve water extraction results. This method uses the multi-band reflectance characteristics of ground features to construct a water index and combines brightness features with the Otsu algorithm to eliminate interference from highly reflective ground features like ice, snow, bright material buildings, and clouds. The Yunan–Guizhou Plateau was selected as the study area due to its complex terrain and multiple types of surface water, and experiments were conducted using Sentinel-2 data on the Google Earth Engine (GEE). The results demonstrate that: (1) The proposed method achieves an overall accuracy of 94.08% and a kappa coefficient of 0.8831 in mountainous areas. In urban areas, the overall accuracy reaches 95.15%, accompanied by a kappa coefficient of 0.8945. (2) Compared to five widely used water indexes and rules, the MTWDR method improves accuracy by more than 3%. (3) It effectively overcomes interference from highly reflective ground features while maintaining the integrity and accuracy of water boundary extraction. In conclusion, the proposed method enhances extraction accuracy across different types of surface water within complex terrain areas, and can provide significant theoretical implications and practical value for researching and applying surface water resources.

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All-weather satellite Synthetic Aperture Radar (SAR) images are widely used to identify surface water bodies when clouds and shadows cause detection errors from optical data. Algorithms for mapping water bodies are often based on determining a threshold value from a backscattering coefficient histogram. The threshold cannot always be found in the case if the “water” and “land” classes are imbalanced. An approach to backscattering coefficient threshold selection is proposed and substantiated in this paper. It is based on a maximum similarity between SAR water mask and the reference mask generated from an optical multispectral satellite image. The parameters of SAR data preprocessing are selected to ensure the best compliance between the optical and SAR water masks. It has been substantiated that the optimal threshold, determined on a reference date, provides high accuracy of water bodies detection on the validation date of observation.

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Frequent observations of surface water at fine spatial scales will provide critical data to support the management of aquatic habitat, flood risk and water quality. Sentinel-1 and Sentinel-2 satellites can provide such observations, but algorithms are still needed that perform well across diverse climate and vegetation conditions. We developed surface inundation algorithms for Sentinel-1 and Sentinel-2, respectively, at 12 sites across the conterminous United States (CONUS), covering a total of >536,000 km2 and representing diverse hydrologic and vegetation landscapes. Each scene in the 5-year (2017–2021) time series was classified into open water, vegetated water, and non-water at 20 m resolution using variables from Sentinel-1 and Sentinel-2, as well as variables derived from topographic and weather datasets. The Sentinel-1 algorithm was developed distinct from the Sentinel-2 model to explore if and where the two time series could potentially be integrated into a single high-frequency time series. Within each model, open water and vegetated water (vegetated palustrine, lacustrine, and riverine wetlands) classes were mapped. The models were validated using imagery from WorldView and PlanetScope. Classification accuracy for open water was high across the 5-year period, with an omission and commission error of only 3.1% and 0.9% for the Sentinel-1 algorithm and 3.1% and 0.5% for the Sentinel-2 algorithm, respectively. Vegetated water accuracy was lower, as expected given that the class represents mixed pixels. The Sentinel-2 algorithm showed higher accuracy (10.7% omission and 7.9% commission error) relative to the Sentinel-1 algorithm (28.4% omission and 16.0% commission error). Patterns over time in the proportion of area mapped as open or vegetated water by the Sentinel-1 and Sentinel-2 algorithms were charted and correlated for a subset of all 12 sites. Our results showed that the Sentinel-1 and Sentinel-2 algorithm open water time series can be integrated at all 12 sites to improve the temporal resolution, but sensor-specific differences, such as sensitivity to vegetation structure versus pixel color, complicate the data integration for mixed-pixel, vegetated water. The methods developed here provide inundation at 5-day (Sentinel-2 algorithm) and 12-day (Sentinel-1 algorithm) time steps to improve our understanding of the short- and long-term response of surface water to climate and land use drivers in different ecoregions.

Accurate water mapping depends largely on the water index. However, most previously widely-adopted water index methods are developed from 30-m resolution Landsat imagery, with low-albedo commission error (e.g., shadow misclassified as water) and threshold instability being identified as the primary issues. Besides, since the shortwave-infrared (SWIR) spectral band (band 11) on Sentinel-2 is 20 m spatial resolution, current SWIR-included water index methods usually produce water maps at 20 m resolution instead of the highest 10 m resolution of Sentinel-2 bands, which limits the ability of Sentinel-2 to detect surface water at finer scales. This study aims to develop a water index from Sentinel-2 that improves native resolution and accuracy of water mapping at the same time. Support Vector Machine (SVM) is used to exploit the 10-m spectral bands among Sentinel-2 bands of three resolutions (10-m; 20-m; 60-m). The new Multi-Spectral Water Index (MuWI), consisting of the complete version and the revised version (MuWI-C and MuWI-R), is designed as the combination of normalized differences for threshold stability. The proposed method is assessed on coincident Sentinel-2 and sub-meter images covering a variety of water types. When compared to previous water indexes, results show that both versions of MuWI enable to produce native 10-m resolution water maps with higher classification accuracies (p-value < 0.01). Commission and omission errors are also significantly reduced particularly in terms of shadow and sunglint. Consistent accuracy over complex water mapping scenarios is obtained by MuWI due to high threshold stability. Overall, the proposed MuWI method is applicable to accurate water mapping with improved spatial resolution and accuracy, which possibly facilitates water mapping and its related studies and applications on growing Sentinel-2 images.

Song, A.; Kim, Y., and Kim, Y., 2019. Change detection of surface water in remote sensing images based on fully convolutional network. In: Lee, J.L.; Yoon, J.-S.; Cho, W.C.; Muin, M., and Lee, J. (eds.), The 3rd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 91, pp. 426-430. Coconut Creek (Florida), ISSN 0749-0208.This study presents a new approach based on fully convolutional networks (FCN) to detect changes in surface water. The proposed method can be divided into three steps: (1) training the FCN using color-infrared (CIR) images from the Coastwide Reference Monitoring System (CRMS) dataset with two classes, such as water and land; (2) passing the multitemporal images respectively through the pre-trained FCN and generating a difference image (DI) from score maps of the last prediction layers; and (3) determining optimal threshold values using fuzzy entropy and discriminating between changed and unchanged pixels in the DI. This method has the advantage of effectively learning the spatial and spectral characteristics of water bodies from large remote-sensing datasets, and it would be helpful to analyze and monitor changes in newly obtained images without ground truth. The experimental results obtained using the multitemporal CRMS data demonstrated the effectiveness of this deep-learning approach for detecting changes in remote-sensing images, as compared other traditional methods for change detection.

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Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000–2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and 8-OLI images. In doing so, the applicability of different satellite-derived indexes including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) were investigated for the extraction of surface water from Landsat data. Overall, the NDWI was found superior to other indexes and hence it was used to model the spatiotemporal changes of the lake. In addition, a new approach based on Principal Components of multi-temporal NDWI (NDWI-PCs) was proposed and evaluated for surface water change detection. The results indicate an intense decreasing trend in Lake Urmia surface area in the period 2000–2013, especially between 2010 and 2013 when the lake lost about one third of its surface area compared to the year 2000. The results illustrate the effectiveness of the NDWI-PCs approach for surface water change detection, especially in detecting the changes between two and three different times, simultaneously.

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This presentation introduces a deep learning framework for automated detection of beaver-influenced floodplain inundation using multi-temporal aerial RGB imagery. A semantic segmentation model is applied to high-resolution data to map surface water expansion across forested and wetland environments. The approach improves detection of inundation patterns in complex landscapes where water is partially obscured by vegetation. This work demonstrates how airborne optical imagery and deep learning can support scalable monitoring of wildlife-driven hydrologic change and broader ecosystem dynamics over time.

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Coastal erosion along the Visakhapatnam shoreline of Andhra Pradesh, India, poses a growing threat to infrastructure, livelihoods, and natural ecosystems. This paper presents an end-to-end, computationally lightweight, satellite-based framework for quantifying historical shoreline dynamics and forecasting future shoreline positions along this 50 km Bay of Bengal coastline. Multitemporal Landsat-8 and Landsat-9 Level-2 Surface Reflectance imagery spanning 2016–2024 was acquired from the USGS Earth Explorer portal. Shorelines were extracted at 23 shore-normal transects using the Normalized Difference Water Index (NDWI) with sub-pixel sigmoid edge detection. A physics-based tidal correction model eliminated tidal bias from cross-shore measurements, reducing non-morphological positional scatter by 66%. A Random Forest (RF) classifier trained on four coastal land-cover classes achieved an overall classification accuracy of 93% with a Cohen's Kappa coefficient of κ = 0.91. Per-transect Long Short-Term Memory (LSTM) networks trained on the tide-corrected time series generated ten-year shoreline forecasts (2027– 2037) with a mean Root Mean Square Error (RMSE) of 2.88 m on a held-out test set. Forecasted annual change rates classified 30% of the coastline as high erosion risk, predominantly in the port-proximal southern zone, while 30% of northern transects exhibit measurable accretion. An interactive Flask-based web dashboard integrates all pipeline outputs to support accessible coastal decision-making without cloud computing infrastructure.

Aquaculture is currently one of the fastest growing food production systems globally, and shrimp is considered one of the most highly valued products. Our study area is the lower Jaguaribe River sub-basin (LJRSB), located in the northeastern part of Ceará in Brazil. The aquaculture activity in this area began in the early 1990s and is currently one of the largest shrimp producers in Brazil. This study generated a spatial-temporal analysis of vegetation index and land use and land cover (LULC) using remote sensing images from Landsat satellites processed using geographic information systems (GIS). The findings showed an increase in the water bodies class where shrimp farms are found. In addition, to help us discuss the results, data from the Global Surface Water Explorer was also used to understand this change throughout intra and interannual water variability. Besides shrimp farms’ intensification, agricultural areas in the LJRSB also increased, mainly in the irrigated perimeter lands (IPLs), causing a loss in the Caatinga native vegetation. In summary, over recent years, significant changes have been noticeable in the LJRSB coastal region, caused by an increase in shrimp farms mainly located on the Jaguaribe River margins, destroying the native riparian forest.

Geospatial technologies now allow routine observation of lakes and wetlands across large areas, but turning those observations into timely, actionable insight still requires scalable computing. Google Earth Engine (GEE) provides a web-based platform that brings multi-sensor Remote Sensing (RS) archives and parallel processing together in one environment. This review synthesizes how artificial intelligence (AI), machine learning (ML) and deep learning (DL) have been paired with GEE to map and monitor surface water quantity and quality. We summarize recent methods, compare model families commonly used on GEE, and discuss frequent processing pitfalls. To ground the review, we include a case study of three Nebraska lakes (2022–2023) that demonstrates month-to-month tracking of water extent and indicators of water quality. The results demonstrated the effectiveness of GEE in providing timely and accurate insights for surface water monitoring and assessment while also revealing current limitations and opportunities for improvement. Overall, we find that coupling AI methods with GEE can strengthen operational surface water assessment and inform decision-making under increasing environmental pressures. Unlike previous reviews, this study systematically compares the accuracy of AI-GEE combinations across different water quality indicators and highlights common processing pitfalls. We review how machine and deep learning models are used in Google Earth Engine to make surface-water quality and quantity assessments more accurate and scalable. Showcase a three-lake case study from Nebraska that demonstrates, in practice, how GEE effectively monitors both water quality and water quantity over time. Unlike previous reviews, this study systematically compares the accuracy of AI-GEE combinations across different water quality indicators and highlights common processing pitfalls. We review how machine and deep learning models are used in Google Earth Engine to make surface-water quality and quantity assessments more accurate and scalable. Showcase a three-lake case study from Nebraska that demonstrates, in practice, how GEE effectively monitors both water quality and water quantity over time.

Water is an essential necessity for maintaining the life cycle on Earth. These resources are continuously changing because of human activities and climate-related factors. Hence, adherence to effective water management and consistent water policy is vital for the optimal utilization of water resources. Water resource monitoring can be achieved by precisely delineating the borders of water surfaces and quantifying the variations in their areas. Since Lake Van is the largest lake in Turkey, the largest alkaline lake in the world, and the fourth largest terminal lake in the world, it is very important to determine the changes in water surface boundaries and water surface areas. In this context, the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Automatic Water Extraction Index (AWEI) were calculated from Landsat-8 satellite images of 2014, 2017, 2020 and 2023 in June, July, and August using the Google Earth Engine (GEE) platform. Water pixels were separated from other details using the Canny edge detection algorithm based on the calculated indices. The Otsu thresholding method was employed to determine water surfaces, as it is the most favored technique for calculating NDWI, AWEI, and MNDWI indices from Landsat 8 images. Utilizing the Canny edge detection algorithm and Otsu threshold detection approaches yielded favorable outcomes in accurately identifying water surfaces. The AWEI demonstrated superior performance compared to the NDWI and MNDWI across all three measures. When the effectiveness of the classification techniques used to determine the water surface is analyzed, the overall accuracy, user accuracy, producer accuracy, kappa, and f score evaluation criteria obtained in 2014 using CART (Classification and Regression Tree), SVM (Support Vector Machine), and RF (Random Forest) algorithms as well as NDWI and AWEI were all 100%. In 2017, the highest producer accuracy, user accuracy, overall accuracy, kappa, and f score evaluation criteria were all 100% with the SVM algorithm and AWEI. In 2020, the SVM algorithm and NDWI produced the highest evaluation criteria values of 100% for producer accuracy, user accuracy, overall accuracy, kappa, and f score. In 2023, using the SVM and CART algorithms as well as the AWEI, the highest evaluation criteria values for producer accuracy, user accuracy, overall accuracy, kappa, and f score were 100%. This study is a case study demonstrating the successful application of machine learning with Canny edge detection and the Otsu water surfaces thresholding method.

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Understanding the spatial and seasonal dynamics of surface water bodies is imperative for addressing water security challenges in water-scarce regions. This study aimed to evaluate the efficacy of multi-date Sentinel-2-derived spectral indices, specifically the normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and Sentinel 2 Water Index (SWI), in conjunction with landscape metrics for mapping spatial and seasonal fluctuations in surface water bodies. Google Earth Engine (GEE) was employed for this assessment. The research achieved impressive overall accuracies, ranging from 96 to 100% for both dry and wet seasons, highlighting the robustness of the methodology. The study revealed significant differences in water bodies in terms of size and coverage between the dry and wet seasons. Surprisingly, the dry season exhibited a higher prevalence of water bodies when compared to the wet season, indicating unexpected patterns of water availability in the region and the substantial heterogeneity of water bodies. Meanwhile, the wet season was characterized by extensive coverage. These findings challenge conventional assumptions about water resource availability during different seasons. Based on the findings, the study recommends that water resource management strategies in semi-arid regions consider the observed seasonal variability in water bodies. Policymakers and stakeholders should adopt adaptive management approaches to address the unique challenges posed by differing water body dynamics in dry and wet seasons. Future research endeavors should explore the underlying factors driving these seasonal fluctuations and assess the potential long-term impacts on water availability. This can help to develop more resilient and sustainable water security strategies to cope with changing climate conditions in semi-arid tropical environments.

ABSTRACT This paper demonstrates the feasibility of large-scale, reliable monitoring of inland water surface levels through the analysis of the data collected by the Global Ecosystem Dynamics Investigation (GEDI) altimeter. In particular, we propose an automated and worldwide operational workflow, implemented within Google Earth Engine (GEE), benefiting from the availability of the whole GEDI time series in this geospatial cloud platform. Leveraging the massive computational capabilities of GEE, we were able to analyze millions of GEDI footprints and assess the potential of the sensor – in terms of precision and accuracy – to serve as an efficient and reliable remote hydrometer. The workflow is based on a rigorous spatio-temporal outlier rejection procedure and a spatial aggregation of the remaining high-quality footprints, for the robust estimation of a per-epoch median water level and its precision for the considered lake surface. A comprehensive precision and accuracy assessment was performed by comparing the GEDI retrieved water-level time series with in situ gauge data for 11 lakes of variable extent (from tens to several thousand km2) located in three continents. A rather homogeneous precision of GEDI water levels across the lakes was found, with a mean value of 14 cm. Additionally, a good agreement with the reference gauge stations was observed, showing an overall accuracy of 35 cm, a slight overestimation bias (6 cm), and a correlation of 0.76. It is important to note that these results are affected by the uncertainties of the transformation among GEDI reference frame and gauge stations reference frames. The proposed workflow can be easily applied to provide reliable inland water-level time series for all those lakes for which GEDI data is available, offering a generally higher temporal resolution than other altimeters. This approach lays the foundations for integrating GEDI within the set of remote-sensing instruments for water cycle monitoring on a large scale, enhancing our understanding of water storage dynamics in lakes, particularly in remote areas where it is not possible to install and maintain hydrometric gauges.

Dynamic monitoring of surface water bodies is essential for understanding global climate change and the impact of human activities on water resources. Satellite remote sensing is characterized by large-scale monitoring, timely updates, and simplicity, and it has become an important means of obtaining the distribution of surface water bodies. This study is based on a long time-series Landsat satellite images and the Google Earth Engine (GEE) platform, focusing on Anhui Province in China, and proposes a method for extracting surface water that combines water indices, Bias-Corrected Fuzzy Clustering Method (BCFCM), and OTSU threshold segmentation. The spatial distribution of surface water in Anhui Province was obtained from 1984 to 2021, and further analysis was conducted on the spatiotemporal characteristics of surface water in each city and three major river basins within the province. The results indicated that the overall accuracy of water extraction in this study was 94.06 %. Surface water in Anhui was most abundant in 1998 and least in 2001, with more distribution in the south than in the north. Northern Anhui is dominated by rivers, while southern Anhui has more lakes. Permanent surface water with an inundation frequency of above 75 % covered approximately 4341 km2, accounting for 32.03 % of the total water, while seasonal water with an inundation frequency between 5 % and 75 % covered about 6661 km2, accounting for 49.15 % of the total water, others were considered temporary surface water. By comparing our results with the global annual surface water released by the Joint Research Centre (JRC), we found that our study performed better in extracting lakes and rivers in terms of completeness, but the extraction results for aquaculture areas were slightly less than the JRC dataset. Overall, the long-term surface water dataset established in this study can effectively supplement the existing datasets and provide important references for regional water resource investigation, management, as well as flood monitoring.

Rapid land use transformations driven by urbanization can profoundly affect surface water resources, altering hydrological processes and water availability. This study focuses on the Upper Bhima River basin in India, analyzing three decades of land use/land cover (LULC) change (1990–2020) using an integrated remote sensing and GIS approach on the Google Earth Engine (GEE) platform. High-resolution satellite data enabled accurate classification of LULC (overall accuracies 94.92% for 1990 and 93.31% for 2020; Kappa 0.93 and 0.92) and detection of major landscape transitions. In this 6717.6 km2 study area, a significant land-use change occurred between 1990 and 2020. Barren land decreased drastically, from 67.1% to just 7.7%, as it was largely converted into agricultural land, which expanded from 7.6 to 47%, and built-up areas, which grew from 0.65 to 14.8%. Surface water dynamics, analyzed using the Joint Research Centre’s Global Surface Water (GSW) dataset, revealed that 94.76 km2 remained as permanent water bodies, 23.0 km2 emerged as new permanent water, while 0.50 km2 of permanent water was lost. Seasonal water bodies occupied 48.00 km2, with 37.31 km2 newly classified as seasonal and 9.72 km2 lost. These extensive land and water transitions indicate intensified surface runoff, diminished groundwater recharge potential, and escalating stress on water resources. The findings provide essential insights for guiding sustainable land use planning and adaptive water resource management strategies in the Upper Bhima basin under the pressures of ongoing urbanization.

The article is devoted to analyzing the dynamics of spatio-temporal changes in water bodies within the Semipalatinsk Test Site using remote sensing data based on the Google Earth Engine platform, with the aim of developing a map of surface water and irrigation systems to support landscape planning of the area. Landsat and Sentinel-2 satellite imagery, ERA5-Land climatic data, and the SRTM digital elevation model were used as the primary input datasets. Water surfaces were extracted using the Modified Normalized Difference Water Index (MNDWI) method. The results revealed that the largest extents of water surfaces were observed in years characterized by cold and snowy winters (2000 and 2010), whereas the smallest extents occurred during warm and low-snow periods (1996, 2012, and 2024). The proportion of temporary water bodies exceeds 60%, reflecting the instability of the hydrological regime and its strong dependence on climatic variability. The use of modern platforms such as Google Earth Engine for identifying spatio-temporal changes in water bodies has proven to be effective and demonstrates great potential for water resource management and dynamic assessment. The obtained results can be applied for environmental monitoring and landscape planning in the development of the former test site areas for agricultural purposes.

The use of remote sensing (RS) data is associated with many difficulties that lead to high labor and computer resource costs. The use of cloud computing greatly simplifies this work. The aim of this study was to demonstrate the possibilities of Google Earth Engine (GEE) for assessing the state of Lake Baikal surface water. An overview of the datasets on the GEE platform was made. The available data processing algorithms were reviewed. It was noted that, in addition to standard methods of stationary processing of satellite images, the GEE platform offers quite complex machine learning methods. For example, Random Forest method significantly improves the accuracy of the results. In addition, the possibilities of constructing a composite from Sentinel-2, Landsat-8, MODIS satellite images, calculating spectral indices to determine the boundaries of water bodies, estimating surface temperature, chlorophyll-a concentration, Secchi disk transparency in selected areas of the water body are shown. It is necessary to control the accuracy of the data and compare it with field research data. The results of RS data processing can be used as additional material during monitoring the state of water bodies. Processing is carried out in the cloud using the JavaScript programming language. It is possible to create applications for interactive use. It is advisable to use GEE possibilities for operational monitoring and analysis of changes in water bodies based on large data sets available on the platform.

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No abstract available

Understanding the dynamics of water bodies is crucial for managing water resources and protecting ecosystems, especially in regions prone to climatic extremes. The Tumen River Basin, a transboundary area in Northeast Asia, has seen significant water body changes influenced by natural and anthropogenic factors. Using Landsat 8 and Sentinel-1 data on Google Earth Engine, we systematically analyzed the spatiotemporal variations and drivers of water body changes in this basin from 2015 to 2023. The water body extraction process demonstrated high accuracy, with overall precision rates of 95.75% for Landsat 8 and 98.25% for Sentinel-1. Despite observed annual fluctuations, the overall water area exhibited an increasing trend, notably peaking in 2016 due to an extraordinary flood event. Emerging Hot Spot Analysis revealed upstream areas as declining cold spots and downstream regions as increasing hot spots, with artificial water bodies showing a growth trend. Utilizing Random Forest Regression, key factors such as precipitation, potential evaporation, population density, bare land, and wetlands were identified, accounting for approximately 81.9–85.3% of the observed variations in the water body area. During the anomalous flood period from June to September 2016, the Geographically Weighted Regression (GWR) model underscored the predominant influence of precipitation, potential evaporation, and population density at the sub-basin scale. These findings provide critical insights for strategic water resource management and environmental conservation in the Tumen River Basin.

The surface water area and types in the Aral Sea Basin (ASB) have undergone extensive changes due to the impacts of climate change and anthropogenic activities. This study explores the changes in the surface water area in the ASB based on the Google Earth Engine cloud platform. Then, we integrate multi-source data to identify 1559 lakes and 196 reservoirs from the Joint Research Centre Global Surface Water (JRC GSW) dataset. Our results indicate that the lake area (34,999.61 km2) is about 10 times that of the reservoir area (3879.08 km2) in the ASB. The total area of surface water in the ASB decreased by 23,194.35 km2 or 34.58% from 1992 to 2020. Specifically, the areas of permanent water shrunk at a rate of 1278.6 km2/year, while the areas of seasonal water increased at a rate of 522.5 km2/year. The proportion of lakes and reservoirs in the total surface water has decreased from 79.33% (during 1992–2000) to 75.21% (during 2000–2010) to 63.94% (during 2010–2020). The water that should have flowed into the Aral Sea to maintain its permanent water may have been converted into two parts. Part of it might continue to be permanent water but show up in other regions, while part of it might convert to seasonal water (especially in the Aral Sea itself and the ASB plain area). Our study bridges the limitations of previous studies that have ignored seasonal water change and builds a water area list for 1755 lakes/reservoirs (≥0.1 km2) for the first time. The results can serve as important knowledge for water resource management and sustainable river basin development in ASB.

Reservoirs are facing increasing hydrological pressure, making continuous and accurate monitoring of these resources essential for sustainable management. In this study, we utilized a method involving Google Earth Engine (GEE), a platform with strong data processing capabilities for big data, to analyze and interpret satellite images. The Otsu method was applied to automatically determine the threshold value for extracting the water surface of the Song Hinh reservoir using Landsat 5, 8, and 9 satellite imagery, and to assess changes in the reservoir’s surface area. The research results indicated that the water surface area of the Song Hinh reservoir initially increased 4.4 times (1999-2000) and then remained relatively stable (2000-2024). However, during the 2000-2015 period, the water surface area experienced minor expansions and contractions, while during the 2015-2024 period, the surface area expanded insignificantly, with less contraction than in the previous period. Additionally, the analysis results of water surface area changes were used to support the development of Earth Engine Apps, also known as WebGIS, as a tool for monitoring surface water changes in the Song Hinh reservoir. In summary, the results obtained in this study are highly useful as a foundation for developing effective monitoring measures and sustainable resource management for the Song Hinh reservoir area.

Surface water is an important parameter for water resource management and terrestrial water circulation research that is closely related to human production and livelihood. With the rapid development of remote sensing technology and cloud computing platforms, the use of remote sensing technology for large-scale and long-term surface water monitoring and investigation has become a research trend. Based on the Google Earth Engine (GEE) cloud platform and Landsat series satellite data, in this study, the Emergency Geomatics Service (EGS) operational surface water mapping algorithm and water index masking were utilized to extract the spatial scope of the water body. The validated models of the Secchi disk depth (SDD), chlorophyll-a (Chl-a) and suspended solids (SS) concentration were applied to water quality parameter inversion and water quality evaluation. Surface water extent extraction and water quality maps were created to analyze the spatial distribution of the water body and the spatial–temporal evolution characteristics of the water quality parameters. A verification experiment was carried out with the surface water in Zhejiang Province as the research object. The results show that the surface water in the study area from 1990 to 2022 could be accurately extracted. The kappa coefficients were all greater than 0.90, and the overall accuracies of the extractions were greater than 95.31%. From 1990 to 2022, the total surface water area in Zhejiang Province initially decreased and then increased. The minimum water area of 2027.49 km2 occurred in 2005, and the maximum water area of 2614.96 km2 occurred in 2020, with an annual average variation of 193.92 km2. Since 2015, the proportion of high SS and Chl-a concentrations, and low SDD water bodies in Zhejiang Province have decreased, and the proportion with better water quality has increased significantly. The spatial distribution map of the surface water and the inversion results of the water quality parameters obtained in this study provide a valuable reference and guidance for regional water resource management, disaster monitoring and early warning, environmental protection, and aquaculture.

Background This study focuses on Dhaka City and its impact on urban surface water. Cities, ecosystems, and agriculture need surface water. It is crucial for water resource planning and environmental preservation. The primary aim is to study how urbanization has affected surface water in Dhaka City over 30 years using satellite imagery. Methods The study analyzed three decades of urban surface water shifts using Landsat 5 TM and Landsat 8 OLI/TIRS satellite imagery and Google Earth Engine (GEE) with JavaScript code for water ratio index detection. To investigate water level changes, field observation surveys and secondary data analysis were conducted. This integrated methodology simplified surface water data extraction and analysis, making remote sensing easier and allowing cloud-based satellite data processing. Results The study demonstrates that the amount of surface water in cities is going down, from 36.23 km^2 in 1990 to 5.83 km^2 in 2021, which is an enormous decrease. This means that about 20 square kilometers, or 45 percent of the water's surface, have been lost in the last 30 years. The main reasons for the drop are unplanned expansion of cities, accelerated real estate development, and more trade and economic activities in the study area. Conclusions The GEE algorithms provide useful insights into surface water's maximum and minimum extent, enabling appropriate planning and management. These findings aid Dhaka City's water resource management and environmental protection.