全天候遥感地表温度估算

… (skin temperature). This study proposes a framework for the retrieval of all-weather LST at a … Compared to the MODIS LST product, the all-weather LST reflects the spatial variations in …

Abstract. Land surface temperature (LST) serves as a crucial variable in characterizing climatological, agricultural, ecological, and hydrological processes. Thermal infrared (TIR) remote sensing provides high temporal and spatial resolutions for obtaining LST information. Nevertheless, TIR-based satellite LST products frequently exhibit missing values due to cloud interference. Prior research on estimating all-weather instantaneous LST has predominantly concentrated on regional or continental scales. This study involved generating a global all-weather instantaneous and daily mean LST product spanning from 2000 to 2020 using XGBoost. Multisource data, including Moderate-Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) observations, surface radiation products, and reanalysis data, were employed. Validation using an independent dataset of 77 individual stations demonstrated the high accuracy of our products, yielding root mean squared errors (RMSEs) of 2.787 K (instantaneous) and 2.175 K (daily). The RMSE for clear-sky conditions was 2.614 K for the instantaneous product, which is slightly lower than the cloudy-sky RMSE of 2.931 K. Our instantaneous and daily mean LST products exhibit higher accuracy compared to the MODIS official LST product (instantaneous RMSE = 3.583 K; daily 3.105 K) and the land component of the fifth generation of the European ReAnalysis (ERA5-Land) LST product (instantaneous RMSE = 4.048 K; daily 2.988 K). Significant improvements are observed in our LST product, notably at high latitudes, compared to the official MODIS LST product. The LST dataset from 2000 to 2020 at the monthly scale, the daily mean LST on the first day of 2010 can be freely downloaded from https://doi.org/10.5281/zenodo.4292068 (Li et al., 2024), and the complete product will be available at https://glass-product.bnu.edu.cn/ (last access: 22 August 2024).

Abstract Land surface temperature (LST) is among the most important variables in monitoring land surface processes. LST is often retrieved from thermal infrared remote sensing data, which have a tradeoff between the spatial and temporal resolutions and are spatially incomplete due to cloud contamination. Land surface model (LSM) output can reflect LST under all-weather conditions, but the spatial resolution is relatively coarse. In this study, a two-step LST data fusion framework was proposed for generating MODIS-like LST (at the satellite overpass time for each day) at a 1 km spatial resolution under all-weather conditions. First, MODIS LST on clear days (i.e., all MODIS LST pixels are cloud-free) for a given study region (e.g., 80 km × 80 km) and China Land Data Assimilation System (CLDAS) LST at a spatial resolution of ~7 km × 7 km were fused using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). Second, systematic biases of the fused LST estimates were corrected by MODIS LST for clear pixels on cloudy days. Results indicate that the fused LST after bias correction (fused LSTcorr) under all-weather conditions is highly consistent with in situ LST measurements made at three sites with different land cover types in the north of China, in terms of mean absolute errors of 2.20–3.08 K, root mean square errors of 2.77–3.96 K, and coefficients of determination of 0.93–0.95. The accuracy of these results is comparable to that of the MODIS LST retrievals at all testing sites. In addition, the fused LSTcorr can well reflect spatial heterogeneity and temporal variability in LST under all-weather conditions, and there is no significant difference in the accuracy of the fused LSTcorr between the clear and cloudy pixels on cloudy days. The developed approach maximizes the potential of quality MODIS LST retrievals and LSM LST output, and generates LST under all-weather conditions without using ancillary remote sensing and in situ data. The generated MODIS-like LST data under all-weather conditions are valuable in soil moisture downscaling and evapotranspiration estimation for better water resources management.

A new all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. It is the first all-weather LST product based on visible and infrared observations combining clear-sky LST retrieved from the Spinning Enhanced Visible and Infrared Imager on Meteosat Second Generation (MSG/SEVIRI) infrared (IR) measurements with LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a microwave (MW)-based LST product derived from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which offers the potential to obtain a global, nearly gap-free LST product.

Abstract. Land surface temperature (LST) is a key variable within Earth's climate system and a necessary input parameter required by numerous land–atmosphere models. It can be directly retrieved from satellite thermal infrared (TIR) observations, which contain many invalid pixels mainly caused by cloud contamination. To investigate the spatial and temporal variations in LST in China, long-term, high-quality, and spatiotemporally continuous LST datasets (i.e., all-weather LST) are urgently needed. Fusing satellite TIR LST and reanalysis datasets is a viable route to obtain long time-series all-weather LSTs. Among satellite TIR LSTs, the MODIS LST is the most commonly used, and a few corresponding all-weather LST products have been reported recently. However, the publicly reported all-weather LSTs were not available during the temporal gaps of MODIS between 2000 and 2002. In this study, we generated a daily (four observations per day) 1 km all-weather LST dataset for China's landmass and surrounding areas, the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST, which begins on the first day of the new millennium (1 January 2000). We used the enhanced reanalysis and thermal infrared remote sensing merging (E-RTM) method to generate the TRIMS LST dataset with the temporal gaps being filled, which had not been achieved by the original RTM method. Specifically, we developed two novel approaches, i.e., the random-forest-based spatiotemporal merging (RFSTM) approach and the time-sequential LST-based reconstruction (TSETR) approach, respectively, to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps. We also conducted a thorough evaluation of the TRIMS LST. A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. The results outside the temporal gap show consistent comparisons of the TRIMS LST with the MODIS LST and the Advanced Along-Track Scanning Radiometer (AATSR) LST, with a mean bias deviation (MBD) of 0.09/0.37 K and a standard deviation of bias (SD) of 1.45/1.55 K. Validation based on the in situ LST at 19 ground sites indicates that the TRIMS LST has a mean bias error (MBE) ranging from −2.26 to 1.73 K and a root mean square error (RMSE) ranging from 0.80 to 3.68 K. There is no significant difference between the clear-sky and cloudy conditions. For the temporal gap, it is observed that RFSTM and TSETR perform similarly to the original RTM method. Additionally, the differences between Aqua and Terra remain stable throughout the temporal gap. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island modeling. The TRIMS LST is freely and conveniently available at https://doi.org/10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021).

Land surface temperature (LST) is crucial for understanding surface energy budgets, hydrological cycling, and land–atmosphere interactions. However, cloud cover leads to numerous data gaps in existing remote sensing thermal infrared (TIR) LST products, seriously restricting their applications. This article provides a comprehensive review concerning both LST recovery methodologies and 26 emerging all-weather products derived from polar-orbiting and geostationary (GEO) satellites. Clarifying product distinctions will enable end users to select suitable options for diverse research. Methodologies are categorized into spatiotemporal interpolation, surface energy balance (SEB)-based physical estimation, passive microwave (PMW)-based methods, and simulated temperature-based approaches. Historical research trajectories, strengths, limitations, and potential research directions of the methodologies and products are discussed. The review reports that existing all-weather LST products generally exhibit root-mean-square errors (RMSEs) of <4 (2.5) K at instantaneous (daily mean) scales based on extensive ground measurements, comparable to clear sky retrievals. Deep learning (DL) models prominently feature in state-of-the-art interpolation and fusion approaches [e.g., long short-term memory (LSTM) and extreme gradient boosting (XGBoost)]. Product intercomparisons in various application scenarios reveal that interpolation-based products offer better texture details; however, noticeable biases exist compared to fusion-based products, especially in arid and semiarid regions, despite the high availability of clear sky samples. The bias shifts to negative at higher latitudes, due to ignored cloud radiative effects. The review emphasizes the underexplored recovery of diurnal temperature cycles (DTCs) from GEO satellites. This focus will benefit heat exposure monitoring for public health, understanding circadian rhythm responses of ecosystems to environmental changes, and harmonizing existing and forthcoming high-resolution TIR missions.

Land surface temperature (LST) is used as a critical indicator for various environmental issues because it links land surface fluxes with the surface atmosphere. Moderate-resolution imaging spectroradiometers (MODIS) 1 km LSTs have been widely utilized but have the serious limitation of not being provided under cloudy weather conditions. In this study, we propose two schemes to estimate all-weather 1 km Aqua MODIS daytime (1:30 p.m.) and nighttime (1:30 a.m.) LSTs in South Korea for humid summer days. Scheme 1 (S1) is a two-step approach that first estimates 10 km LSTs and then conducts the spatial downscaling of LSTs from 10 km to 1 km. Scheme 2 (S2), a one-step algorithm, directly estimates the 1 km all-weather LSTs. Eight advanced microwave scanning radiometer 2 (AMSR2) brightness temperatures, three MODIS-based annual cycle parameters, and six auxiliary variables were used for the LST estimation based on random forest machine learning. To confirm the effectiveness of each scheme, we have performed different validation experiments using clear-sky MODIS LSTs. Moreover, we have validated all-weather LSTs using bias-corrected LSTs from 10 in situ stations. In clear-sky daytime, the performance of S2 was better than S1. However, in cloudy sky daytime, S1 simulated low LSTs better than S2, with an average root mean squared error (RMSE) of 2.6 °C compared to an average RMSE of 3.8 °C over 10 stations. At nighttime, S1 and S2 demonstrated no significant difference in performance both under clear and cloudy sky conditions. When the two schemes were combined, the proposed all-weather LSTs resulted in an average R2 of 0.82 and 0.74 and with RMSE of 2.5 °C and 1.4 °C for daytime and nighttime, respectively, compared to the in situ data. This paper demonstrates the ability of the two different schemes to produce all-weather dynamic LSTs. The strategy proposed in this study can improve the applicability of LSTs in a variety of research and practical fields, particularly for areas that are very frequently covered with clouds.

Land surface temperature (LST) is a key variable at the land–atmosphere boundary. For many research projects and applications an all-weather LST product at moderate spatial resolution (e.g., 1 km) would be highly useful, especially in frequently cloudy areas. Merging thermal infrared (TIR) and microwave (MW) observations is able to overcome shortcomings of single-source remote sensing to derive such an LST. However, in current merging methods, models adopted for downscaling MW LST fail to quantify the effect of temporal variation of LST. Thus, accuracy of the merged LST can be deteriorated and therefore remain a major impediment for these methods to be generalized over large areas. In this context, we propose a new practical method to merge TIR and MW observations from a perspective of decomposition of LST in temporal dimension. The physical basis of the method is decomposing LST into three temporal components: annual temperature cycle component, diurnal temperature cycle component prescribed by solar geometry, and weather temperature component driven by weather change. The method was applied to MODIS and AMSR-E/AMSR2 data to generate an 11-year record of 1-km all-weather LST over Northeast China: the resulting merged LST has an accuracy of 1.29–1.71 K when validated against in situ LST; besides, no obvious differences in accuracy of the merged LST were found between clear-sky and unclear-sky conditions. Furthermore, the proposed method outperforms the previous method in both accuracy and image quality, indicating its good capability to generate daily 1-km all-weather LST, which will benefit continuous monitoring of earth’s surface temperature.

In the face of rapid global climate change and the increasing occurrence of extreme weather events, acquiring seamless land surface temperature (LST) with high spatial and temporal resolution on a global scale has become increasingly crucial. However, the limited ability of thermal infrared (TIR) remote sensing to penetrate cloud cover has hindered the widespread application of TIR LST datasets. To address this limitation, we propose a novel reconstruction approach for cloud-covered pixels, which is established based on the annual surface temperature cycle. It shifted the previous reconstruction from directly modeling LST to indirectly modeling the residual term derived from the LST observations and the annual temperature cycle (ATC) model fit values. A random forest regression (RFR) was used to build this estimation model and the model was applied to cloud-covered pixels to derive their LSTs. Taking the Iberian Peninsula as the study area, the proposed method was applied to generate the all-weather LST product for the whole year 2021. The visual assessment demonstrates its robust performance across different seasons and weather conditions. Additionally, through the validation with the masked clear-sky LST observations, it reveals that the proposed method achieves a stable estimation accuracy, with the average value of the coefficient of determination ( ${R} ^{2}$ ) and root mean squared error (RMSE) of above 0.8 and 1.08 K under different climatic conditions. In comparison, the validation with the ERA-5 land reanalysis data also indicates a relatively good consistency between the performance of the reconstructed LST and the clear-sky LST, although with a slight decline in ${R} ^{2}$ and RMSE. Additionally, the indirect validation with near-surface air temperature (NSAT) also shows the comparable ability of the reconstructed LST in NSAT estimation as the clear-sky LST, with an increase of RMSE no more than 0.95 K. In general, the proposed method shows good potential in reconstructing cloud-covered LSTs with relatively stable performance under different cloud-cover conditions and it can be applied for generating all-weather LST products.

Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.

如何获取全天候地表温度对促进相关研究具有十分重要的意义。卫星热红外遥感地表温度虽然在反演理论方法和科学数据产品等方面已相对成熟，但热红外难以穿透云雾的特点导致反演得到的地表温度在云下有大量缺失；被动微波遥感虽能获取云下地表温度，但由于物理机制和成像方式的限制，存在空间分辨率不足、精度较低、轨道间隙较大等问题。通过卫星单源遥感难以直接获取中等空间分辨率、不受云雾影响的全天候地表温度。从原理、方法、产品和应用方面回顾并归纳了当前全天候地表温度的研究进展和面临的主要问题。基于有效观测重构和多源数据集成是获取全天候地表温度的两种基本途径，前者可分为时空插值和基于能量平衡方程插值两类，后者则可分为热红外与被动微波遥感集成、热红外与再分析资料集成。多源数据集成可以整合热红外遥感、被动微波遥感、再分析资料各自的优势，具有较大的研究价值和潜力。在产品方面，分析了当前学术界已公开发布的5种全天候地表温度产品。在应用方面，虽然部分全天候地表温度产品已在土壤湿度、地表蒸散发估算与同化方面取得了一些应用成果，但其在其他领域的应用亟待挖掘。此外，对全天候地表温度的未来研究方向和重点进行了讨论和展望。

… Land surface temperature (LST) is one of the … the Earth’s surface and the atmosphere, which affects the energy exchange and water-heat balance processes between the land surface …

The high-frequency all-weather land surface temperature (LST) product generated from the thermal-infrared (TIR) observations of the geostationary meteorological satellite is of great significance to study the diurnal variations in the LST and the land surface energy balance. However, the TIR sensor cannot penetrate the clouds and obtain the desired LST under cloudy conditions. In this study, we developed a physical-based framework for generating high-frequency (hourly) all-weather LST data by synchronizing geostationary satellite TIR observations and simulations of the land surface model (LSM). There are three parts to the developed framework. First, the clear-sky LST was retrieved from the Advanced Himawari Imager (AHI) onboard the geostationary satellite Himawari-8 using our newly developed temperature and emissivity separation algorithm. Second, the Advanced Microwave Scanning Radiometer 2 (AMSR2) observations were assimilated into the Noah land surface model with multiple parameterization (Noah-MP) options’ model to generate the all-weather LST. Finally, the retrieved clear-sky AHI LST and Noah-MP assimilated LST were fused using the ensemble Kalman filter (EnKF) algorithm. In situ measurements from three networks were collected to evaluate the Noah-MP assimilated LST and EnKF fused LST. The bias/RMSE of the Noah-MP assimilated LST and EnKF fused LST were–0.16/3.01 K and 0.15/2.68 K, respectively, under all-weather conditions. Compared to the Noah-MP free-run LST, the absolute values of the bias were reduced by 0.64 K and 0.68 K for the Noah-MP assimilated LST and EnKF fused LST, while the RMSEs were reduced by 0.33 K and 0.65 K, respectively. In addition, the spatial distribution of EnKF fused LST was in good agreement with the retrieved clear-sky AHI LST. The proposed framework in this study was demonstrated to be capable of obtaining accurate high-frequency (hourly) all-weather LST data.

Spatiotemporally continuous land surface temperature (LST) is crucial for monitoring extreme weather and providing disaster warnings. It captures abnormal temperature fluctuations, offering timely early warning and response for sudden climate events and natural disasters. However, cloud cover and satellite observation gaps often limit the spatial completeness of LST, while previous reconstruction methods seldom consider the effects of solar radiation and cloud cover on LST. To address these challenges, this study proposed the all-weather real estimation (AWRE) method, which integrated thermal infrared (TIR) and passive microwave (PMW) data with environmental factors to estimate the LST under all-weather conditions. By incorporating deep learning and land surface energy balance (SEB) models, and analyzing the impact of clouds on temperature fluctuations, the proposed method retrieves all-weather LST. Applied to the 2022 data of China, the AWRE method demonstrated high accuracy in estimating LST. The overall average root mean square error (RMSE) and Bias were 2.90 and 0.56 K, respectively, with daytime and nighttime RMSEs of 2.97 and 2.83 K, respectively. Specifically, for daytime (nighttime) conditions, the RMSEs under clear sky were 2.94 K (2.58 K), partially cloudy 3.08 K (2.76 K), and fully cloudy 2.9 K (3.14 K). The estimated all-weather LST effectively captured diurnal and seasonal variations, with accuracy comparable to in situ LST measurements, maintaining temporal continuity. This approach improves the detection of extreme heat events and addresses spatiotemporal coverage gaps, providing more accurate data for climate models, weather monitoring, and public health decisions.

ABSTRACT The need for cross-comparison and validation of all-weather Land Surface Temperature (LST) products has arisen due to the release of multiple such products aimed at providing comprehensive all-weather monitoring capabilities. In this study, we focus on validating two well-established all-weather LST products (i.e. MLST-AS and TRIMS LST) against in-situ measurements obtained from four high-quality LST validation sites: Evora, Gobabeb, KIT-Forest, and Lake Constance. For the land sites, MLST-AS exhibits better accuracy, with RMSEs ranging from 1.6 K to 2.1 K, than TRIMS LST, the RMSEs of which range from 1.9 K to 3.1 K. Because MLST-AS pixels classified as “inland water” are masked out, the validation over Lake Constance is limited to TRIMS LST: it yields a RMSE of 1.6 K. Furthermore, the validation results show that MLST-AS and TRIMS LST exhibit better accuracy under clear-sky conditions than unclear-sky conditions across all sites. Since the accuracy of the all-weather LST products is considerably affected by the input clear-sky LST products, we further compare the all-weather LST with the corresponding input clear-sky LST to conduct an error source analysis. Considering the clear-sky pixels on MLST-AS directly using the estimates from MLST, the error source analysis is limited to examining TRIMS LST and its input (i.e. MODIS LST). The findings indicate that TRIMS LST is highly correlated with MODIS LST. The investigation and validation of the two selected all-weather LST products objectively evaluate their accuracy and stability, which provides important information for applications of these all-weather LST products.

… Land surface temperature (LST) has been used in many applications as its strong relationships with land surface processes… the utility of proposed models to reconstruct all-weather LST. …

With the continuous improvement of urbanization levels in the Lhasa area, the urban heat island effect (UHI) has seriously affected the ecological environment of the region. However, the satellite-based thermal infrared land surface temperature (LST), commonly used for UHI research, is affected by cloudy weather, resulting in a lack of continuous spatial and temporal information. In this study, focusing on the Lhasa region, we combine simulated LST data obtained by the Weather Research and Forecasting (WRF) model with remote sensing-based LST data to reconstruct the all-weather LST for March, June, September, and December of 2020 at a resolution of 0.01° while using the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST as a reference (in terms of accuracy). Subsequently, based on the reconstructed LST, an analysis of the UHI was conducted to obtain the spatiotemporal distribution of UHI in the Lhasa region under all-weather LST conditions. The results demonstrate that the reconstructed LST effectively captures the expected spatial distribution characteristics with high accuracy, with an average root mean square error of 2.20 K, an average mean absolute error of 1.51 K, and a correlation coefficient consistently higher than 0.9. Additionally, the heat island effect in the Lhasa region is primarily observed during the spring and winter seasons, with the heat island intensity remaining relatively stable in winter. The results of this study provide a new reference method for the reconstruction of all-weather LST, thereby improving the research accuracy of urban thermal environment from the perspective of foundational data. Additionally, it offers a theoretical basis for the governance of UHI in the Lhasa region.

Abstract The land surface temperature can be estimated from satellite passive microwave observations, with limited contamination from the clouds as compared to the infrared satellite retrievals. With ∼60% cloud cover in average over the globe, there is a need for “all weather,” long record, and real‐time estimates of land surface temperature (Ts) from microwaves. A simple yet accurate methodology is developed to derive the land surface temperature from microwave conical scanner observations, with the help of precalculated land surface microwave emissivities. Different tests are conducted to optimize the algorithms. The method is applied to the Special Sensor Microwave/Imagers (SSM/I) observations over 2 years, regardless of the cloud cover. The results are compared to infrared estimates from International Satellite Cloud Climatology Project (ISCCP) and from Advanced Along Track Scanning Radiometer (AATSR), under clear‐sky conditions. Limited biases are observed (∼0.5 K for both comparisons) with a root‐mean‐square error (RMSE) of ∼5 K, to be compared to the RMSE of ∼3.5 K between ISCCP et AATSR. Cloud contamination in the AATSR estimates have been evidenced, and a simple filtering has been proposed. The microwave surface temperatures have also been carefully compared to in situ Ts time series from a collection of more than 20 stations over a large range of environments. Very good agreement is obtained for well‐controlled stations in vegetated environments (down to RMSE of ∼2.5 K for several stations), but the methodology encounter difficulties under cold conditions due to the large variability of snow and ice surface emissivities.

Earth’s surface temperature (EST), encompassing both land surface temperature (LST) and sea surface temperature (SST), serves as a crucial indicator of climate change. This study introduces a groundbreaking framework for the daily estimation of Earth’s Surface Temperature (EST), integrating reanalysis data with thermal infrared remote sensing data merging (RTM) techniques and employing machine learning methods. The spatial distribution of the generated all-weather EST aligns effectively with MODIS EST, showcasing its capability to recover EST values in cloudy regions and estimate missing values in orbital gap areas. Validation results for all-weather LST and SST demonstrate commendable accuracy, with minimal variations observed under both clear-sky and cloudy conditions. The Root Mean Square Error (RMSE) for LST ranges from 1.69 to 2.84 K, while for SST, it spans from 0.38 °C to 0.59 °C. The framework exhibits adaptability to diverse weather conditions, maintaining consistent relative trends across different geographical locations. In summary, this innovative approach provides a robust solution for generating all-weather ESTs, effectively addressing challenges associated with conventional Thermal Infrared (TIR) data.

Land surface temperature (LST) is one of the most valuable variables for applications relating to hydrological processes, drought monitoring and climate change. LST from satellite data provides consistent estimates over large scales but is only available for cloud-free pixels, greatly limiting applications over frequently cloud-covered regions. With this study, we propose a method for estimating all-weather 1 km LST by combining passive microwave and thermal infrared data. The product is based on clear-sky LST retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) thermal infrared measurements complemented by LST estimated from the Advanced Microwave Scanning Radiometer Version 2 (AMSR2) brightness temperature to fill gaps caused by clouds. Terrain, vegetation conditions, and AMSR2 multiband information were selected as the auxiliary variables. The random forest algorithm was used to establish the non-linear relationship between the auxiliary variables and LST over the Tibetan Plateau. To assess the error of this method, we performed a validation experiment using clear-sky MODIS LST and in situ measurements. The estimated all-weather LST approximated MODIS LST with an acceptable error, with a coefficient of correlation (r) between 0.87 and 0.99 and a root mean square error (RMSE) between 2.24 K and 5.35 K during the day. At night-time, r was between 0.89 and 0.99 and the RMSE was between 1.02 K and 3.39 K. The error between the estimated LST and in situ LST was also found to be acceptable, with the RMSE for cloudy pixels between 5.15 K and 6.99 K. This method reveals a significant potential to derive all-weather 1 km LST using AMSR2 and MODIS data at a regional and global scale, which will be explored in the future.

Thermal infrared (TIR) land surface temperature (LST) products derived from geostationary satellites have a high temporal resolution in a diurnal cycle, but they have many missing values under cloudy-sky conditions. Therefore, it is pressing to obtain all-weather LST (AW LST) with a high temporal resolution by filling the gap of TIR LST. In this study, a method integrating reanalysis data and TIR data from geostationary satellites (RTG) was proposed for reconstructing hourly AW LST. Then, taking the Tibetan Plateau (TP), which is a focus of climate change as a case, RTG was applied to the Chinese Fengyun-4A (FY-4A) TIR LST and China Land Surface Data Assimilation System (CLDAS) data. Validation based on the in-situ LST shows that the accuracy of the AW LST is better than the FY-4A LST and CLDAS LST under clear-sky, cloudy-sky, and all-weather conditions. The mean RMSEs are 3.02 K for clear-sky conditions, 3.94 K for cloudy-sky conditions, and 3.57 K for all-weather conditions. Uncertainty and coarse resolution of the original FY-4A and CLDAS data affect the accuracy of the obtained AW LST. The results of the LST time series comparison also show that the reconstructed AW LST is consistent with in-situ LST. The reconstructed AW LST also has the good image quality and provides reliable spatial patterns. RTG is practical in obtaining high temporal resolution AW LST from the Chinese FY-4A to satisfy related applications. It can also be extended to other geostationary satellites and reanalysis datasets.

Land surface temperature (LST) is a key parameter in the interaction of the land-atmosphere system. Nevertheless, on the regional scale, the actual weather is cloudy for half a year in most regions. Therefore, receiving all-weather LST from thermal-infrared remote sensing is necessary and urgent. In this paper, an approach with multi-temporal and spatial neighboring-pixels in combination with diurnal solar radiation and surface temperature evolution is proposed to estimate daytime all-weather LST using FY-2D data. Evaluation of the accuracy of the algorithm is performed against the simulated data and the in situ measurements. The root mean square error (RMSE) between the actual and estimated LSTs under cloud-free conditions is approximately 1.84 K for the simulated data, while the RMSE of LST under cloud-free conditions varies from 3.42 to 5.1 K for the in situ measurement, and RMSE of LST under cloudy sky is approximately 7 K. The results indicate that the new algorithm is practical for retrieving the daytime all-weather LST at high-temporal resolution without any auxiliary field measurement, although some uncertainties exist.

It is urgently needed to obtain the hourly near-real-time all-weather land surface temperature (NRT-AW LST) for immediately monitoring the disaster and environmental changes. Nevertheless, studies on estimating hourly NRT-AW LST are in the preliminary stage. In this study, we proposed a Spatio-TEmporal Fusion (STEF) method for fusing the reanalysis dataset derived from the China Land Surface Data Assimilation System (CLDAS) and thermal infrared (TIR) data derived from the Chinese Fengyun-4A (FY-4A) geostationary satellite to estimate the hourly NRT-AW LST with 0.04° resolution. The STEF method can produce NRT-AW LST without relying on the data after the target moment. STEF is tested in the Tibetan Plateau (TP). Validation results on DOY 215–366 of 2020 indicate that STEF has good accuracy: root-mean-square errors (RMSEs) and mean bias error (MBEs) under clear-sky, cloudy-sky, and AW conditions vary from 2.74 K (−1.06 K) to 3.77 K (0.14 K), from 3.31 K (−1.40 K) to 4.46 K (−0.22 K), and from 3.10 K (−1.11 K) to 3.87 K (−0.22 K), respectively. The STEF method can improve the accuracies of FY-4A LST, and RMSEs are reduced by about 0.77–1.82 K. The NRT-AW LSTs estimated by STEF have better accuracies than CLDAS LSTs under AW conditions. The SETF also exhibited similar results in 2021. We believe that the proposed STEF method can meet the requirements of NRT-AW LST estimation and contribute to improving the timeliness of regional monitoring and related parameter estimations.

ABSTRACT Land surface temperature (LST) is an essential parameter in environmental monitoring. However, due to the cloud contamination and the limitations of sensors, existing thermal infrared LST products are challenging to provide all-weather LST with high spatiotemporal resolution. Therefore, this paper presents a framework for reconstructing hourly LST under clouds based on the moderate resolution imaging spectroradiometer (MODIS) LST products. This framework consists of two main steps: (1) Instantaneous LST estimation for MODIS using a modified annual temperature cycle (ATCM) model and (2) Hourly LST reconstruction under all-weather conditions from atmospheric reanalysis data and MODIS instantaneous LST with the linear model (LM). The proposed framework is evaluated with the full year data of 2019 with tile number H26V05. The results show that the spatial distribution of the estimated LST is similar with the MODIS LST. Additionally, it could provide an accurate indication of the spatial and temporal variability of LST. Comparing the MODIS LST of the selected area for tile number H26V05 in 2019 with the estimated LST of the ATCM, the root mean squared errors (RMSEs) of the model are around 1K ~ 3K. The hourly LST is then reconstructed based on the LM method, and the RMSE is around 2K during the daytime and around 1K during the night-time. Finally, to verify the robustness of the hourly LST reconstruction method, the accuracy of the hourly LST was evaluated by the MODIS LST at different situations in the day. The results show that the missing data at one moment will cause the accuracy of the model decreasing by 0.09 ~ 1.67K at the corresponding moment. In general, the proposed framework has the potential to reconstruct the 1 km all-weather hourly LST with high accuracy and a certain level of robustness.

… derived all-weather LST through remote sensing data fusion or the assimilation of retrieved … on intermediate LST retrieval products and their associated retrieval uncertainties, while …

… limited to a lower retrieval accuracy and spatial resolution. Therefore, the applications of PMW and TIR LST products are strongly limited in areas where all-weather LST products at a …

Land surface temperature (LST) plays a crucial role in the physical and chemical processes of the land–atmosphere system. Remote sensing technology has greatly advanced the measurement of thermal infrared LST (TIR LST), which is the most widely utilized surface temperature product. However, cloud cover and mist often cause significant data loss in TIR LST. To address this issue and reconstruct the MYD11A1 LST under cloudy conditions, this study proposes an all-weather LST generation method based on the extreme gradient boosting (XGBoost) model. This method incorporates spatial-seamless passive microwave LST (PMW LST) to capture the nonlinear relationship between TIR LST and other variables. Compared to the MYD11A1 LST, the generated all-weather LST provides continuous spatial texture information without a significant boundary reconstruction effect, improving the accuracy of spatiotemporal variations in LST in China. In situ validation demonstrated the high accuracy of the generated all-weather LST, with mean $R^{2}$ , bias, and unbiased root-mean-square error (ubRMSE) of 0.96 (0.91), 1.08 K (3.61 K), and 2.92 K (4.54 K) under clear (cloudy) daytime conditions, and 0.92 (0.95), −0.93 K (−2.96 K), and 3.09 K (3.04 K) under clear (cloudy) nighttime conditions. These results indicate the feasibility and reasonableness of the all-weather LST generation method developed in this study and affirm its ability to generate highly accurate all-weather LST.

Land surface temperature (LST) is an important variable in the physics of land–surface processes controlling the heat and water fluxes over the interface between the Earth’s surface and the atmosphere. Space-borne remote sensing provides the only feasible way for acquiring high-precision LST at temporal and spatial domain over the entire globe. Passive microwave (PMW) satellite observations have the capability to penetrate through clouds and can provide data under both clear and cloud conditions. Nonetheless, compared with thermal infrared data, PMW data suffer from lower spatial resolution and LST retrieval accuracy. Various methods for estimating LST from PMW satellite observations were proposed in the past few decades. This paper provides an extensive overview of these methods. We first present the theoretical basis for retrieving LST from PMW observations and then review the existing LST retrieval methods. These methods are mainly categorized into four types, i.e., empirical methods, semi-empirical methods, physically-based methods, and neural network methods. Advantages, limitations, and assumptions associated with each method are discussed. Prospects for future development to improve the performance of LST retrieval methods from PMW satellite observations are also recommended.

… The precision of the retrieved land surface temperature is expected to be better than 2.5 K for forests and 3.5 K for low vegetation. This method can be used to complement existing …

… for retrieval of land surface parameters (soil moisture, vegetation water content, and surface temperature) using satellite microwave … by the Advanced Microwave Scanning Radiometer (…

… This study developed a physical algorithm for retrieving land surface temperatures (LST) by using the special sensor microwave imager (SSM/I) brightness temperature measurements …

… microwave-derived surface temperatures are compared to synchronous in situ air and ground surface temperatures … Over dense vegetation, the microwave-derived surface temperature …

… multiple-instruments in Aqua satellite, we build land surface temperature retrieval algorithm by utilizing the MODIS LST product and different brightness temperatures of AMSR-E, which …

… Land surface temperatures can be retrieved with pas sive microwave brightness temperatures … snow, or rain are excluded from the land surface temperature retrieval in this analysis. The …

… of land surface temperature (LST) an important research subject in China. Many methodologies have been established to retrieve … LST retrieval algorithms for each land surface type, …

The land surface temperature (LST) is a key parameter for energy balance, evapotranspiration and climate change. In this study, two new methods of LST retrieval from passive microwave data are developed: one is deriving LST only using single-channel dual-polarized data based on the relationship between the emissivity and microwave polarization difference index (MPDI) (denoted as Method 1); the other one is deriving LST using the traditional multi-channel method with prior knowledge of the normalized difference vegetation index (NDVI) (denoted as Method 3). Taking Moderate Resolution Imaging Spectroradiometer (MODIS) LST products as the actual LSTs, the coefficients for these algorithms are determined. From the results for the year 2008, it is demonstrated that the root mean square errors (RMSEs) for the LST retrieval using Method 3 are the smallest and range from 2.92 K to 3.44 K, the RMSEs for the LST retrieval using traditional multi-channel method (denoted as Method 2) range from 3.07 K to 4.05 K, and the worst results come from Method 1, whose RMSEs range from 3.11 K to 4.13 K at a frequency of 89 GHz. This could be caused by the fact that the NDVI provides substantial emissivity knowledge in Method 3, and much richer vegetation could result in a more accurate emissivity estimation.

ABSTRACT Satellite remote sensing provides a unique way to measure land surface temperature (LST) at regional and global scales. Algorithms using thermal infrared (TIR) data provide a reliable way to retrieve LST. However, they are limited to clear-sky conditions due to their inability to penetrate clouds. As an alternative for LST retrieval, passive microwave data are much less affected by clouds and water vapour than TIR data. In this study, we presented an improved physically based algorithm for the retrieval of LST under cloudy atmospheric conditions from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature measurements at 18.7 and 23.8 GHz vertically polarized channels based on the assumption that the emissivity relationship between the two adjacent frequencies is linear. The performance of the algorithm was firstly evaluated using simulation data, with a root mean square error (RMSE) of approximately 2.1 K. Moreover, the RMSE value reduces with precipitable water vapour (PWV) increasing. This algorithm was further applied to AMSR2 measurements. The retrieved cloudy LST was compared with ground-based air temperature over China in 2016. The bias varies from approximately 2 K to 4 K and the RMSE from approximately 4 K to 6 K during daytime and night-time. To eliminate the systematic bias between the retrieved LST and the ground-based air temperature, a linear adjustment was performed to the retrieved LST during daytime and nighttime, respectively. The accuracies for the adjusted LST are nearly the same during daytime and night-time, with an RMSE of approximately 3.6 K. The combination of this physically based LST retrieval algorithm with TIR LST algorithm is attractive for generating an all-weather LST product at global scale.

… (LST) plays an important role in land surface processes, and it is a key input for … retrieve cloudy LST values from passive microwave data based on the relationship among surface …

Land surface temperature (LST) is an important parameter for the study related to land–air coupled systems. Satellite-based passive microwave (PMW) sensor is a significant approach to retrieving LSTs, which can penetrate the atmosphere conditions. However, soil moisture is one of the variables affecting PMW brightness temperature, yet few existing methods have considered it in the procedure of retrieving LSTs. On this basis, we propose a retrieval algorithm for PMW-based LST that comprehensively considers soil moisture changes and land scenarios (RA-PLST-SM) to take the soil moisture into account. Additionally, two fusion strategies one of which fuses the landform and soil moisture, and the other further fusing with land cover are proposed to construct the land environment description. Besides, we also propose a fusion strategy to integrate the LSTs from the time-interval-based models of month, quarter, and year. The mean root-mean-squared error (RMSE) referring to MODIS LSTs shows that RA-PLST-SM LSTs gain 3.13 and 2.32 K at day/night time. Also, referring to advanced LST data product, RA-PLST-SM LSTs present a yearly mean STD of 4.12 and 2.48 K at day/night time. Furthermore, the metric results according to six in situ stations demonstrate that RA-PLST-SM LST is significantly enhancing the consistency of actual LSTs, obtaining a mean RMSE of 4.42 K/3.48 K at day/night time. Moreover, the LST fusion strategy proposed in this article can effectively improve the quality of results. The progress of this article can promote the research fields to acquire cloudy LSTs and even provide the technique reference for PMW-based LST retrieval.

To ensure optimal and consistent algorithm usage within climate studies utilizing satellite-derived Land Surface Temperature (LST) datasets, an algorithm intercomparison exercise was undertaken to assess the various operational and scientific LST retrieval algorithms in use. This study was focused on several LST products including single-sensor products for AATSR, Terra-MODIS, SEVIRI, SSM/I and SSMIS; a Climate Date Record (CDR), which is a combined dataset drawing from AATSR, SLSTR and MODIS; and finally a merged low Earth orbit/geostationary product using data from AATSR, MODIS and SEVIRI. Therefore, the analysis included 14 algorithms: seven thermal infrared algorithms and seven microwave algorithms. The thermal infrared algorithms include five split-window coefficient-based algorithms, one optimal estimation algorithm and one single-channel inversion algorithm, with the microwave focusing on linear regression and neural network methods. The algorithm intercomparison assessed the performance of the retrieval algorithms for all sensors using a benchmark database. This approach was chosen due to the lack of sufficient in situ validation sites globally and the bias this limited set engendered on the training of particular algorithms. A simulated approach has the ability to test all parameters in a consistent, fair manner at a global scale. The benchmark database was constructed from European Centre for Medium-Range Weather Forecasts Re-analysis 5 (ERA5) atmospheric data, Combined ASTER and MODIS Emissivity for Land (CAMEL) infrared emissivity data, and Tool to Estimate Land Surface Emissivities at Microwave frequencies (TELSEM) emissivity data for the period of 2013–2015. The best-performing algorithms had biases of under 0.2 K and standard deviations of approximately 0.7 K. These results were consistent across multiple sensors. Areas of improvement, such as coefficient banding, were found for all algorithms as well as lines for further inquiry that could improve the global and regional performance.

Land surface temperature (LST) and soil moisture (SM) are two important parameters in land surface ecosystem at the regional and global scale. The accurate acquisition of LST and SM can benefit various fields, including agriculture and climate which are closely related to human life. The independent retrievals of LST and SM from passive microwave observations are mutually restricted and highly dependent on auxiliary data. To solve this problem, a simulation retrieval method of LST and SM was proposed based on the characteristics of multifrequency and dual-polarization. The simultaneous solution of LST and SM was realized by approximating and correcting the radiative transfer equation (RTE). The performance of the proposed method was evaluated using simulated data, resulting in a root mean square error (RMSE) of approximately 1.63 K and 0.063 $\text{m}^{3}/\text{m}^{3}$ . This method was further used to retrieve LST and SM from advanced microwave scanning radiometer for EOS (AMSR-E) observations. The retrieved LST was compared to the MODIS LST product under clear sky, with an RMSE of 5.68 K. The retrieved LST was validated using the Integrated Surface Database (ISD) air temperature under cloudy sky, with an RMSE of 4.29 K. The accuracy of retrieved LST changes with the variation of vegetation. The retrieved SM was evaluated using the Climate Change Initiative (CCI) SM product and in situ observations. The result shows that the accuracy ranges from 0.0157 to 0.1115 $\text{m}^{3}/\text{m}^{3}$ with the change of vegetation. This study gives a feasible method to retrieve LST and SM simultaneously with reasonable accuracy.

… The results of this study provide inputs for land surface models and a … retrieval method for land surface temperature and fraction of water surface determination from satellite microwave …

High-spatial-resolution land-surface temperature is required for several applications such as hydrological or climate studies. Global estimates of surface temperature are available from sensors observing in the infrared (IR), but without ‘all-weather’ observing capability. Passive microwave (MW) instruments can also be used to provide surface-temperature measurements but suffer from coarser spatial resolutions. To increase their resolution, a downscaling methodology applicable over different land environments and at any time of the day is proposed. The method uses a statistical relationship between clear sky-predicting variables and clear-sky temperatures to estimate temperature patterns that can be used in conjunction with coarse measurements to create high-resolution products. Different predicting variables are tested showing the need to use IR-derived information on vegetation, temperature diurnal evolution, and a temporal information. To build a true ‘all-weather’ methodology, the effect of clouds on surface temperatures is accounted for by correcting the clear-sky diurnal cycle amplitude, using cloud parameters from meteorological reanalysis. Testing the method on a coarse IR synthetic data at ∼25 km resolution yields a Root Mean Square Deviations (RMSD) between the ∼5 km high-resolution and downscaled temperatures smaller than 1 ∘C. When applied to observations by the Special Sensor Microwave Imager Sounder (SSMIS) at ∼25 km resolution, the downscaling to ∼5 km yields a smaller RMSD compared to IR observations. These results demonstrate the relevance of the methodology to downscale MW land-surface temperature and its potential to spatially enhanced the current ‘all-weather’ satellite monitoring of surface temperatures.

… the SST-SWT over land surfaces. Nevertheless, provided we accept a somewhat reduced accuracy, the SWT could be adapted to land surface temperature retrieval. In the specific case …

Land surface temperature (LST) is a crucial parameter that reflects land–atmosphere interaction and has thus attracted wide interest from geoscientists. Owing to the rapid development of Earth observation technologies, remotely sensed LST is playing an increasingly essential role in various fields. This review aims to summarize the progress in LST estimation algorithms and accelerate its further applications. Thus, we briefly review the most‐used thermal infrared (TIR) LST estimation algorithms. More importantly, this review provides a comprehensive collection of the widely used TIR‐based LST products and offers important insights into the uncertainties in these products with respect to different land cover conditions via a systematic intercomparison analysis of several representative products. In addition to the discussion on product accuracy, we address problems related to the spatial discontinuity, spatiotemporal incomparability, and short time span of current LST products by introducing the most effective methods. With the aim of overcoming these challenges in available LST products, much progress has been made in developing spatiotemporal seamless LST data, which significantly promotes the successful applications of these products in the field of surface evapotranspiration and soil moisture estimation, agriculture drought monitoring, thermal environment monitoring, thermal anomaly monitoring, and climate change. Overall, this review encompasses the most recent advances in TIR‐based LST and the state‐of‐the‐art of applications of LST products at various spatial and temporal scales, identifies critical further research needs and directions to advance and optimize retrieval methods, and promotes the application of LST to improve the understanding of surface thermal dynamics and exchanges.

… In this paper we review the current status for deriving land surface temperatures (LSTs) by remote sensing from satellites in the thermal infrared. Because of its widespread use and …

… window coefficients that can be used to retrieve land surface temperature (LST) from thermal infrared sensors onboard the most popular remote-sensing satellites: ERS-ATSR2, …

… [2] Land surface temperature and emissivity (LST&E) are … of thermal infrared energy radiated from the Earth’s surface according … types of Earth surface processes and surfaceatmosphere …

… Land surface temperature (LST) is the key parameter for characterizing the water and energy balance of the Earth’ surface. At present, thermal infrared (TIR) remote sensing provides …

… for recovering surface kinetic temperature from multispectral thermal infrared data acquired over land. … The first two methods have been widely used with data from the thermal infrared …

… thermal imagery. This strategy for utilizing radiometric surface temperature in land surface … the utility of thermal infrared remote sensing for monitoring land surface fluxes from local to …

… and near infrared (VNIR), short wave infrared (SWIR), mid-infrared (MIR), and thermal infrared (… feasibility of retrieving land surface temperature from the 10 AHS thermal infrared bands, …

… For example, we compared land surface temperature retrieved using different MODIS bands … in the middle infrared bands, we do not derive the land surface temperature in bands 20, 22 …

… (0.4 to 2.4 mm) and thermal infrared region (10.3 to 12.3 mm). This paper is focused on the land surface temperature retrieval from SPECTRA thermal infrared data. In the first part of the …

… Minimum configuration of thermal infrared bands for land surface temperature and emissivity estimation in the context of potential future missions . Remote Sensing of Environment , 148 …

… temperature profiles were used with a radiative transfer model for retrieving land surface temperature (LST) from thermal infrared … Retrieved LSTs were compared to concurrent ground …

… In particular, land surface temperature (LST) is a key variable to be retrieved from TIR data, … -channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data,…

The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of the extremely hot weather, a frequently-occurring phenomenon of an abnormal heat flux process in summer along the Yangtze River Basin, had been thoroughly analyzed. This successful application suggested that the IMW algorithm presented in the study could be used as an efficient method for LST retrieval from the Landsat 8 TIRS Band 10 data.

This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by Jimenez-Munoz and Sobrino (2003), which was particularized to the thermal-infrared (TIR) channel (band 6) located in the Landsat-5 Thematic Mapper (TM) sensor. The SC algorithm relies on the concept of atmospheric functions (AFs) which are dependent on atmospheric transmissivity and upwelling and downwelling atmospheric radiances. These AFs are fitted versus the atmospheric water vapor content for operational purposes. In this paper, we present updated fits using MODTRAN 4 radiative transfer code, and we also extend the application of the SC algorithm to the TIR channel of the TM sensor onboard the Landsat-4 platform and the enhanced TM plus sensor onboard the Landsat-7 platform. Five different atmospheric sounding databases have been considered to create simulated data used for retrieving AFs and to test the algorithm. The test from independent simulated data provided root mean square error (rmse) values below 1 K in most cases when atmospheric water vapor content is lower than 2 g middotcm-2. For values higher than 3 g middotcm-2, errors are not acceptable, as what occurs with other SC algorithms. Results were also tested using a land surface temperature map obtained from one Landsat-5 image acquired over an agricultural area using inversion of the radiative transfer equation and the atmospheric profile measured in situ at the sensor overpass time. The comparison with this ldquoground-truthrdquo map provided an rmse of 1.5 K.

Abstract Land surface temperature (LST) is an important parameter in many research fields. Many algorithms have been developed to retrieve LST from satellite thermal infrared (TIR) measurements; of these, the most widely used are the split window (SW) and temperature–emissivity separation (TES) methods. However, the performance of the SW and TES methods can be limited by the difficulty in obtaining sufficiently accurate prior knowledge—specifically, input land surface emissivity (LSE) for the SW method and atmospheric parameters for the TES method. In this study, a procedure was proposed for selecting specific channel pairs in the TIR spectral region to accurately retrieve ground brightness temperatures without prior atmospheric knowledge, using a method similar to the SW method. Subsequently, the TES method is applied to the retrieved ground brightness temperatures to separate the LST and LSE. In numerical simulations, the three ground brightness temperatures corresponding to 8.6 μm, 9.0 μm, and 10.4 μm are acquired with an accuracy of about 0.65 K by using five channels centered at 8.6 μm, 9.0 μm, 10.4 μm, 11.3 μm, and 12.5 μm, each with a width of 0.1 μm. When inputting the three retrieved ground brightness temperatures into TES method, LST could be recovered with an accuracy of 0.87 K. Sensitivity analysis shows that LST retrieval accuracy is less affected by channel width and atmospheric downwelling radiance than by the channel center locations and channel noise. Finally, the proposed method is preliminarily applied to actual satellite data from the Atmospheric InfraRed Sounder (AIRS) and the retrieved results are compared with the pixel-aggregated Moderate Resolution Imaging Spectroradiometer (MODIS) LST product. For the study area of Australia, discrepancies between our result and the MODIS LST product appear to be about 1.6 K during the day and 1.0 K at night, indicating that the new channel configuration can be used to retrieve accurate LST from satellite measurements.

… This study investigates the effects of soil moisture (SM) on thermal infrared (TIR) land surface emissivity (LSE) using field- and satellite-measurements. Laboratory measurements were …

… land surface temperature (hereinafter referred to as T s or LST) from remote sensing data in the thermal infrared … equation (RTE), which can be written in the thermal infrared region as …

… Experimental access to effective surface parameters, surface temperature, and emissivity, relevant to satellite investigations in the thermal infrared band (FIR), is addressed and …

… profiles for each pixel with the surface air temperature and a scaling factor of the total water … both LST and emissivity from multispectral thermal infrared imagery in this study. Both …

Land surface temperature (LST) data are essential for environmental monitoring, climate change research, and ecological studies. Thermal infrared (TIR) data offer fine spatial resolution but are easily affected by clouds, resulting in much lower temporal resolution in practice than expected (e.g., the time intervals between available cloud-free moderate resolution imaging spectroradiometer (MODIS) LST observations are usually much greater than one day at the local scale). In contrast, passive microwave (PMW) data provide daily observations unaffected by atmospheric conditions but at a coarser spatial resolution. Spatiotemporal fusion presents a feasible solution to this issue. However, the substantial differences in modality between TIR and PMW data pose a major challenge. To address these issues, a deep learning-based spatiotemporal fusion network, feature-aware attention and deep-aggregated supervision (FAD-STF), is proposed to directly blend advanced microwave scanning radiometer (AMSR) brightness temperature (BT), PMW-based, and MODIS LST (TIR-based) data in this article. FAD-STF employs feature-aware attention (FA) to capture complementary features from AMSR and MODIS datasets adaptively and uses deep supervision in a decomposing way to improve training stability. Experimental results under clear-sky conditions (with simulated missing LST data) demonstrate that FAD-STF outperforms five spatiotemporal fusion methods by effectively utilizing all 14 AMSR BT bands simultaneously, with root mean square error (RMSE) and mean absolute error (MAE) around 2 K and correlation coefficient (CC) close to 0.85. Furthermore, FAD-STF also achieves superior accuracy under cloudy-sky conditions by presenting greater consistency with in situ observations.

Land surface temperature (LST) is a crucial parameter in the circulation of water, exchange of land-atmosphere energy, and turbulence. Currently, most LST products rely heavily on thermal infrared remote sensing, which is susceptible to cloud and rain interference, leading to inferior temporal continuity. Microwave remote sensing has the advantage of being available "all-weather" due to strong penetration capability, which provides the possibility to simulate time-continuous LST data. In addition, the continuous increase in high-density station observations (>10,000 stations) provides reliable measured data for the remote sensing monitoring of LST in China. This study aims to adopt the "Earth big data" generated from high-density station observation and microwave remote sensing data to monitor LST based on deep learning (U-Net family) for the first time. Given the significant spatial and temporal variability of LST and its sensitivity to various factors according to radiation transmission equations, this study incorporated climatic, anthropogenic, geographical, and vegetation datasets to facilitate a multi-source data fusion approach for LST estimation. The results showed that the U-Net++ model with modified skip connections better minimized the semantic discrepancy between the feature maps of the encoder and decoder subnetworks for 0.1° daily LST mapping across China than the U-Net and U2-Net deep learning models. The accuracy of the LST simulation exhibited favorable outcomes in the spatial and temporal dimensions. The station density met the requirements of monitoring air-ground integration monitoring in China. Additionally, the temporal change in the simulation accuracy fluctuated in a W-shape owing to the limited simulation capability of deep learning in extreme scenarios. Anthropogenic factors had the largest influence on LST changes in China, followed by climate, geography, and vegetation. This study highlighted the application of deep learning in remote sensing monitoring against the background of "big data" and provided a scientific foundation for the response of climate change to human activities, ecological environmental protection, and sustainable social and economic development.

Land surface temperature (LST) is an important parameter in various fields including hydrology, climatology, and geophysics. Its derivation by thermal infrared remote sensing has long tradition but despite substantial progress there remain limited data availability and challenges like emissivity estimation, atmospheric correction, and cloud contamination. The annual temperature cycle (ATC) is a promising approach to ease some of them. The basic idea to fit a model to the ATC and derive annual cycle parameters (ACP) has been proposed before but so far not been tested on larger scale. In this study, a new global climatology of annual LST based on daily 1 km MODIS/Terra observations was processed and evaluated. The derived global parameters were robust and free of missing data due to clouds. They allow estimating LST patterns under largely cloud-free conditions at different scales for every day of year and further deliver a measure for its accuracy respectively variability. The parameters generally showed low redundancy and mostly reflected real surface conditions. Important influencing factors included climate, land cover, vegetation phenology, anthropogenic effects, and geology which enable numerous potential applications. The datasets will be available at the CliSAP Integrated Climate Data Center pending additional processing.

Land surface temperature (LST) data in the thermal infrared (TIR) band measured by the moderate-resolution imaging spectroradiometer (MODIS) instrument are critical for studying surface urban heat islands (SUHIs); however, these acquired TIR LST data are contaminated by clouds, so it is crucial to develop a method to generate cloud-free LST products. In this article, employing Tianjin as the research area, we combined the weather research and forecasting model with a random forest and a spatial optimization algorithm to propose a cloud-free MODIS-like model (WRFFM). The model can reconstruct cloud-free MODIS-like LSTs and SUHIs are studied. The spatial patterns of the WRFFM LSTs and the MODIS LSTs are consistent; the correlation coefficients in July and December range from 0.8 to 0.91 and 0.8 to 0.93, respectively, and the root mean square errors range from 0.5 to 3.8 K and 0.4 to 1.8 K, respectively, indicating that the modeled results are accurate. We use these WRFFM LSTs to study SUHIs and evaluate the deviations between the MODIS SUHIs and WRFFM SUHIs. When the proportion of clear-sky pixels is below 30%, the deviation is above 3 K, and when the proportion of clear-sky pixels is above 80%, the deviation is below 0.6 K. The results indicate that the developed model can be applied to improve the study of SUHIs and that the number of clear-sky pixels for a city is an important factor that affects the bias relative to the actual SUHI .

… In addition, for all types of land use, the error of wetland and water are … of land surface temperature is mainly considered in this paper, and there are few water bodies in the study area, …

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

… Seasonal (horizontal axis) and diurnal (vertical axis) variation of land surface temperature. Data … At each grid, cloud-free ratio is defined as the ratio of cloud-free pixels to total available …

Abstract. Conventional methods to estimate land surface temperature (LST) from space rely on the thermal infrared (TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive-microwave (MW) observations. The MW-LST product is informed by six polar-orbiting satellites to create a global record with up to eight observations per day for each 0.25° resolution grid box. For days with sufficient observations, a continuous diurnal temperature cycle (DTC) was fitted. The main characteristics of the DTC were scaled to match those of a geostationary TIR-LST product. This paper tests the cloud tolerance of the MW-LST product. In particular, we demonstrate its stable performance with respect to flux tower observation sites (four in Europe and nine in the United States), over a range of cloudiness conditions up to heavily overcast skies. The results show that TIR-based LST has slightly better performance than MW-LST for clear-sky observations but suffers an increasing negative bias as cloud cover increases. This negative bias is caused by incomplete masking of cloud-covered areas within the TIR scene that affects many applications of TIR-LST. In contrast, for MW-LST we find no direct impact of clouds on its accuracy and bias. MW-LST can therefore be used to improve TIR cloud screening. Moreover, the ability to provide LST estimates for cloud-covered surfaces can help expand current clear-sky-only satellite retrieval products to all-weather applications.

… In this study, LST values of cloud-free and cloud-shadow pixels are obtained from Landsat 8 … pixels are estimated from those of spatially adjacent cloud-free and cloud-shadow pixels, …

Land surface temperature (LST) serves as an important climate variable which is relevant to a number of studies related to energy and water exchanges, vegetation growth and urban heat island effects. Although LST can be derived from satellite observations, these approaches rely on cloud‐free acquisitions. This represents a significant obstacle in regions which are prone to cloud cover. In this paper, a graph‐based propagation method, referred to as GraphProp, is introduced. This method can accurately obtain LST values which would otherwise have been missing due to cloud cover. To validate this approach, a series of experiments are presented using synthetically obscured Landsat acquisitions. The validation takes place over scenarios ranging from between 10% and 90% cloud cover across six urban locations. In presented experiments, GraphProp recovers missing LST values with a mean absolute error of less than 1.1°C, 1.0°C and 1.8°C in 90% cloud cover scenarios across the studied locations respectively.

Land surface temperature (LST) plays a critical role in land surface processes. However, as one of the effective means for obtaining global LST observations, remote sensing observations are inherently affected by cloud cover, resulting in varying degrees of missing data in satellite-derived LST products. Here, we propose a solution. First, the data interpolating empirical orthogonal functions (DINEOF) method is used to reconstruct invalid LSTs in cloud-contaminated areas into ideal, clear-sky LSTs. Then, a cumulative distribution function (CDF) matching-based method is developed to correct the ideal, clear-sky LSTs to the real LSTs. Experimental results prove that this method can effectively reconstruct missing LST data and guarantee acceptable accuracy in most regions of the world, with RMSEs of 1–2 K and R values of 0.820–0.996 under ideal, clear-sky conditions and RMSEs of 4–7 K and R values of 0.811–0.933 under all weather conditions. Finally, a spatiotemporally continuous MODIS LST dataset at 0.05° latitude/longitude grids is produced based on the above method. Measurement(s) land surface temperature Technology Type(s) satellite imaging Sample Characteristic - Environment planetary surface Sample Characteristic - Location global Measurement(s) land surface temperature Technology Type(s) satellite imaging Sample Characteristic - Environment planetary surface Sample Characteristic - Location global

Land surface temperature (LST) is a key parameter in the interaction of the land-atmosphere system. However, clouds affect the retrieval of LST data from thermal-infrared remote sensing data. Thus, it is important to determine a method for estimating LSTs at times when the sky is overcast. Based on a one-dimensional heat transfer equation and on the evolution of daily temperatures and net shortwave solar radiation (NSSR), a new method for estimating LSTs under cloudy skies (Tcloud) from diurnal NSSR and surface temperatures is proposed. Validation is performed against in situ measurements that were obtained at the ChangWu ecosystem experimental station in China. The results show that the root-mean-square error (RMSE) between the actual and estimated LSTs is as large as 1.23 K for cloudy data. A sensitivity analysis to the errors in the estimated LST under clear skies (Tclear) and in the estimated NSSR reveals that the RMSE of the obtained Tcloud is less than 1.5 K after adding a 0.5 K bias to the actual Tclear and 10 percent NSSR errors to the actual NSSR. Tcloud is estimated by the proposed method using Tclear and NSSR products of MSG-SEVIRI for southern Europe. The results indicate that the new algorithm is practical for retrieving the LST under cloudy sky conditions, although some uncertainty exists. Notably, the approach can only be used during the daytime due to the assumption of the variation in LST caused by variations in insolation. Further, if there are less than six Tclear observations on any given day, the method cannot be used.

… cold spots as compared with pixels under the cloud free condition. Therefore, the difference … a group A), while 58 images were identified as cloud-free (assigned to a group B). For further …

Abstract There is considerable demand for satellite observations that can support spatiotemporally continuous mapping of land surface temperature (LST) because of its strong relationships with many surface processes. However, the frequent occurrence of cloud cover induces a large blank area in current thermal infrared-based LST products. To effectively fill this blank area, a new method for reconstructing the cloud-covered LSTs of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) daytime observations is described using random forest (RF) regression approach. The high temporal resolution of the Meteosat Second Generation (MSG) LST product assisted in identifying the temporal variations in cloud cover. The cumulative downward shortwave radiation flux (DSSF) was estimated as the solar radiation factor for each MODIS pixel based on the MSG DSSF product to represent the impact from cloud cover on incident solar radiation. The RF approach was used to fit an LST linking model based on the datasets collected from clear-sky pixels that depicted the complicated relationship between LST and the predictor variables, including the surface vegetation index (the normalized difference vegetation index and the enhanced vegetation index), normalized difference water index, solar radiation factor, surface albedo, surface elevation, surface slope, and latitude. The fitted model was then used to reconstruct the LSTs of cloud-covered pixels. The proposed method was applied to the Terra/MODIS daytime LST product for four days in 2015, spanning different seasons in southwestern Europe. A visual inspection indicated that the reconstructed LSTs thoroughly captured the distribution of surface temperature associated with surface vegetation cover, solar radiation, and topography. The reconstructed LSTs showed similar spatial pattern according to the comparison with clear-sky LSTs from temporally adjacent days. In addition, evaluations against Global Land Data Assimilation System (GLDAS) NOAH 0.25° 3-h LST data and reference LST data derived based on in-situ air temperature measurements showed that the reconstructed LSTs presented a stable and reliable performance. The coefficients of determination derived with the GLDAS LST data were all above 0.59 on the four examined days. These results indicate that the proposed method has a strong potential for reconstructing LSTs under cloud-covered conditions and can also accurately depict the spatial patterns of LST.

… Thermal infrared land surface temperature (LST) data from satellites often contain extensive missing values due to high cloudiness degree, which severely hinders their use in …

Environment Canada meteorological station hourly sampled air temperatures Tair at four stations in the southwest Yukon were used to identify cloud contamination in the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra clear-sky daytime land surface temperature (LST) and emissivity daily level-3 global 1-km grid product (MOD11A1, Collection 5) that is not flagged by the MODIS quality algorithm as contaminated. The additional cloud masking used qualitative ground-based sky condition observations, collected at two of the four stations, and coincident MODIS quality flag information. The results indicate that air temperature observed at a variety of discrete spatial locations having different land cover is highly correlated with MODIS LST collected at 1-km grid spacing. Quadratic relationships between LST and air temperature, constrained by ground observations of “clear” sky conditions, show less variability than relationships found under “mainly clear” and “mostly cloudy” sky conditions, and the more clouds observed in the sky coincides with a decreasing y intercept. Analysis of MODIS LST and its associated quality flags show a cold bias (&amp;lt;0°C) in the assignment of the ≤3-K-average LST error, indicating MODIS LST has a maximum average error of ≤2 K over a warm surface (&amp;gt;0°C). Analysis of two observation stations shows that unidentified clouds in MODIS LST are between 13% and 17%, a result that agrees well with previous studies. Analysis of daytime values is important because many processes are dependent on daylight and maximum temperature. The daytime clear-sky LST–Tair relationship observed for the good-quality confirmed cloud-free-sky MODIS LST quality flag can be used to discriminate cloud-contaminated grid cells beyond the standard MODIS cloud mask.

… Land Surface Temperature (LST) datasets play a crucial role in understanding the complex … of a machine learning framework based on CatBoost and XGBoost models in estimating LST …

A high spatio-temporal resolution land surface temperature (LST) is necessary for various research fields because LST plays a crucial role in the energy exchange between the atmosphere and the ground surface. The moderate-resolution imaging spectroradiometer (MODIS) LST has been widely used, but it is not available under cloudy conditions. This study proposed a novel approach for reconstructing all-sky 1 km MODIS LST in South Korea during the summer seasons using various data sources, considering the cloud effects on LST. In South Korea, a Local Data Assimilation and Prediction System (LDAPS) with a relatively high spatial resolution of 1.5 km has been operated since 2013. The LDAPS model’s analysis data, binary MODIS cloud cover, and auxiliary data were used as input variables, while MODIS LST and cloudy-sky in situ LST were used together as target variables based on the light gradient boosting machine (LightGBM) approach. As a result of spatial five-fold cross-validation using MODIS LST, the proposed model had a coefficient of determination (R2) of 0.89–0.91 with a root mean square error (RMSE) of 1.11–1.39 °C during the daytime, and an R2 of 0.96–0.97 with an RMSE of 0.59–0.60 °C at nighttime. In addition, the reconstructed LST under the cloud was evaluated using leave-one-station-out cross-validation (LOSOCV) using 22 weather stations. From the LOSOCV results under cloudy conditions, the proposed LightGBM model had an R2 of 0.55–0.63 with an RMSE of 2.41–3.00 °C during the daytime, and an R2 of 0.70–0.74 with an RMSE of 1.31–1.36 °C at nighttime. These results indicated that the reconstructed LST has higher accuracy than the LDAPS model. This study also demonstrated that cloud cover information improved the cloudy-sky LST estimation accuracy by adequately reflecting the heterogeneity of the relationship between LST and input variables under clear and cloudy skies. The reconstructed all-sky LST can be used in a variety of research applications including weather monitoring and forecasting.

Abstract Land surface temperature (LST) is an important parameter that supplies information about the skin temperature of the Earth surface. Remote sensing satellite systems with thermal bands can be used to obtain LST information. One of these satellite systems, namely, Moderate Resolution Imaging Spectroradiometer (MODIS) is mostly utilized in LST studies. One of the problems of obtaining LST from the MODIS data is missing pixels because of the effects such as cloud coverage. This drawback can be encountered by applying Long Short-Term Memory (LSTM) network with one-step-ahead prediction of MODIS data to reconstruct daily LST through the previous data. In this study, LSTM network was applied to the daytime and nighttime MODIS time-series, separately. MODIS LST data (MYD11A1) have the spatial resolution of 1 km × 1 km with 1-day temporal resolution. The selected data range from Day of Year (DOY) 1 in 2017 (01 January 2017) to DOY 59 in 2019 (28 February 2019). MODIS images were processed for the reconstruction of daily LST images concerning an agricultural region in Ceyhan, Adana, Turkey. 82% of data were chosen as the training data while the remaining data were used for testing purposes. The data were reconstructed by feeding the network adding the new data in a moving window in each prediction step. The produced Root Mean Square Error (RMSE) map regarding all reconstruction errors from daytime and nighttime images varied between 2 K to 9 K and 1 K–5 K, respectively. Besides, the coefficients of determination (R2) at a selected pixel of time-series analysis were obtained as 0.894 and 0.905 for daytime and nighttime LST image, respectively. The results revealed that the LSTM network could be used to fix the missing pixels in LST images.

Land surface temperature (LST) is one of the most important parameters of the interface between the earth surface and the atmosphere, and it plays a significant role in many research fields, such as agriculture, climate, hydrology, and the environment. However, the thermal infrared band of remote sensors is easily affected by clouds and aerosols, leading to many data gaps in LST products, which restricts the subsequent application of these products. In this paper, Beijing, China, is selected as the study area, and the LST data retrieved from Fengyun 4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) are reconstructed based on the two-point machine learning method. Firstly, the two-point machine learning model is built to reconstruct the theoretical clear-sky LST from simulated and actual images, and the accuracy of the reconstruction results is evaluated compared with the random forest algorithm and the inverse distance weighted method. Secondly, the actual LST under the influence of clouds is reconstructed by using the ERA5 reanalysis LST data as the auxiliary data, and the reconstruction accuracy is then evaluated by the field measurement LST data. The experimental results show that (1) the prediction accuracy of the two-point machine learning method is higher than that of the random forest method in both simulated data and actual data experiments; (2) the R2 of reconstructed LST under theoretical clear-sky conditions is 0.6860 and the root mean square error (RMSE) is 2.9 K, while the R2 of the reconstructed accuracy of actual LST under clouds is 0.7275 and the RMSE is 2.6 K, i.e., the RMSE decreases by 10.34%; (3) the two-point machine method combined with the auxiliary ERA5 LST data can well reconstruct LST under cloudy conditions and present a reasonable LST distribution.

In the context of growing urbanization and persistent cloud contamination in optical remote sensing, reliable large-scale land surface temperature (LST) monitoring in subtropical regions remains a significant challenge. To address this issue, this study develops an innovative SAR–optical collaborative framework that integrates Sentinel-1 dual-polarization features with Landsat-8 observations for improving land use and land cover (LULC) classification, restoring cloud-covered areas, and enabling high-quality reconstruction of cloud-free LST. Building on this foundation, future LULC dynamics were projected using the PLUS model, and LST variations were predicted with the XGBoost algorithm, which enabled quantification of LULC-specific contributions to urban thermal change. The prediction model achieved high accuracy (RMSE = 0.9940 °C, MAE = 0.4714 °C, R = 0.9819), underscoring the robustness of SAR–optical integration for LST reconstruction. The results further revealed a strong synchrony between built-up expansion and the increase in LST. Between 2024 and 2030, built-up land is projected to expand by 10.8%, accompanied by a 0.18% increase in extreme high-temperature areas. Overall, the proposed cloud-resilient LST retrieval and prediction framework offers practical value for urban climate adaptation, providing quantitative evidence to support heat mitigation planning, the optimization of green-blue infrastructure, and resilience-oriented spatial development.

Land surface temperature (LST) is a crucial input parameter in the study of land surface water and energy budgets at local and global scales. Because of cloud obstruction, there are many gaps in thermal infrared remote sensing LST products. To fill these gaps, an improved LST reconstruction method for cloud-covered pixels was proposed by building a linking model for the moderate resolution imaging spectroradiometer (MODIS) LST with other surface variables with a random forest regression method. The accumulated solar radiation from sunrise to satellite overpass collected from the surface solar irradiance product of the Feng Yun-4A geostationary satellite was used to represent the impact of cloud cover on LST. With the proposed method, time-series gap-free LST products were generated for Chongqing City as an example. The visual assessment indicated that the reconstructed gap-free LST images can sufficiently capture the LST spatial pattern associated with surface topography and land cover conditions. Additionally, the validation with in situ observations revealed that the reconstructed cloud-covered LSTs have similar performance as the LSTs on clear-sky days, with the correlation coefficients of 0.92 and 0.89, respectively. The unbiased root mean squared error was 2.63 K. In general, the validation work confirmed the good performance of this approach and its good potential for regional application.

Accurate, seamless, and long-term land surface temperature (LST) data sets are crucial for investigating climate change and agriculture production. However, factors like cloud contamination have led to invalid values in the LST product, which has restricted the application of the LST dataset. Therefore, the reconstruction of LST products is challenging, and it is attracting widespread attention. This study compared the performance of different algorithms (XGBoost, GBDT, RF, POLY, MLR) and different training sets (using only good-quality pixels or using both good-quality and other-quality pixels) in the estimation of missing pixels in the LST data, obtaining a seamless daily 1 km LST dataset of MODIS Terra-day, Aqua-day, Terra-night, and Aqua-night data for Zhejiang Province and its surrounding areas from 2000 to 2022. The results demonstrated that the performance of machine-learning models is significantly better than that of linear models, and among the five models, XGBoost performed the best, with an RMSE of less than 1 °C. The Wilcoxon test between the reconstructed LST and the true LST values revealed that including both good-quality and other-quality pixels for reconstruction resulted in a 33% increase in the number of days with non-significant differences compared with using only good-quality pixels. Moreover, the reconstructed nighttime LST has a lower RMSE compared with the reconstructed daytime LST, and the RMSE of the reconstructed LST on the Terra satellite is lower than the RMSE of the reconstructed LST on the Aqua satellite. The RMSEs for the reconstructed LSTs are 0.50 °C, 0.61 °C, 0.36 °C, and 0.39 °C, corresponding to Terra-day, Aqua-day, Terra-night, and Aqua-night for images with coverage reaching 70%, 0.65 °C, 0.83 °C, 0.49 °C, respectively, and 0.52 °C for images with coverage less than 70%. The accuracy of the reconstructed LSTs using our proposed framework outperforms the existing reconstruction methods. The 1 km daily seamless LST products can be applied in various fields, such as air temperature estimation, climate change, urban heat island, and crop temperature stress monitoring.

Land Surface Temperature (LST) is a critical environmental parameter for describing biophysical processes at the Earth's surface, applicable at regional or global scales. High-resolution LST downscaling is of paramount importance for research in areas such as urban heat islands, crop health, natural disasters, and climate change. However, sensors with a high revisit time are limited in their ability to provide detailed spatial information. Therefore, downscaling of LST products with coarse spatial resolution is considered a necessary and inevitable process to address this challenge and offer valuable data solutions. In this study, we have developed a LST downscaling and reconstruction method based on machine learning. By introducing predictor variables to characterize the spatial distribution of LST, we have successfully downscaled and reconstructed LST data from the Moderate Resolution Imaging Spectroradiometer (MODIS) with a resolution of 990m to 90m. Our analysis was carried out on various land surface cover types, including urban areas, croplands, mountainous regions, and water bodies. The comprehensive comparison and analysis of model performance have shown that in different seasons, the downscaling and reconstructed performance in mountainous areas is the best, with R-squared (R2) values reaching 0.71 and 0.80, and Root Mean Square Error (RMSE) values of 1.72 and 1.64, respectively. The proposed model was subsequently applied for downscaling and reconstructing surface temperatures in cloud-covered areas, and the results were confirmed through both visual assessments and statistical measurements of reconstruction accuracies.

… machine learning that use gapless LST maps is limited. With this motivation, a hybrid reconstruction method has been proposed in this study to practically obtain continuous LST maps, …

… the spatial distributions of LST. It also can provide a reference for future machine learning methods to select appropriate training samples and reconstruct the LST with high accuracy. …

Abstract Most algorithms for land surface temperature (LST) retrieval depend on acquiring prior knowledge. To overcome this drawback, we propose a novel LST retrieval method based on model-data-knowledge-driven and deep learning, called the MDK-DL method. Based on the expert knowledge and radiation transfer model, we deduce LST retrieval mechanism and determine the best combination of the thermal infrared (TIR) bands of the sensor. Then, we use the radiation transfer model simulation and reliable satellite-ground data to establish a training and test database, and finally use the deep learning neural network for optimal computation. Three typical high-, medium- and low-spatial-resolution TIR remote sensing datasets (from Gaofen, the Moderate Resolution Imaging Spectroradiometer (MODIS), and Fengyun) are used for theoretical simulation and application analysis. The simulation shows that the minimum mean absolute error (MAE) is less than 0.1 K (standard deviation: 0.04 K; correlation coefficient: 1.000) at a small viewing direction (

The land surface temperature (LST), defined as the radiative skin temperature of the ground, plays a critical role in land surface systems, from the regional to the global scale. The commonly utilized daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product at a resolution of one kilometer often contains missing values attributable to atmospheric influences. Reconstructing these missing values and obtaining a spatially complete LST is of great research significance. However, most existing methods are tailored for reconstructing clear-sky LST rather than the more realistic cloudy-sky LST, and their computational processes are relatively complex. Therefore, this paper proposes a simple and effective real LST reconstruction method combining Thermal Infrared and Microwave Remote Sensing Based on Temperature Conservation (TMTC). TMTC first fills the microwave data gaps and then downscales the microwave data by using MODIS LST and auxiliary data. This method maintains the temperature of the resulting LST and microwave LST on the microwave pixel scale. The average Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2 of TMTC were 3.14 K, 4.10 K, and 0.88 for the daytime and 2.34 K, 3.20 K, and 0.90 for the nighttime, respectively. The ideal MAE of the TMTC method exhibits less than 1.5 K during daylight hours and less than 1 K at night, but the accuracy of the method is currently limited by the inversion accuracy of microwave LST and whether different LST products have undergone time normalization. Additionally, the TMTC method has spatial generality. This article establishes the groundwork for future investigations in diverse disciplines that necessitate real LSTs.

Toward fully exploring multidisciplinary research topics under global warming, the spatiotemporal discontinuities of land surface temperature (LST) products due to cloud contamination still challenge such research topics. Data fusion (DF) that seeks the best compromise between multiple data sources plays a key role in providing gapless LST data. However, most models only use information about the variation of LST at different times or the difference between different LST products without effectively combining the two pieces of information. With the rapid development of deep-learning methods, powerful modeling capabilities can solve this problem. This article proposes a novel multiinformation fusion network (MIFN) based on convolutional neural networks (CNNs) and attention mechanisms (AMs) to map gapless all-sky LST, taking temporal-changing (TC) and data-differentiated (DD) information into consideration. Temporal normalization (TN) is used as a preprocessing step to match the time of moderate resolution imaging spectroradiometer (MODIS) and European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis Fifth-Generation (ERA5) LSTs. The MIFN extracts multiscale multitemporal TC and DD features by network constraints. Then, the weights of the target LST features reconstructed by TC and DD features are assigned through an AM to fuse them reasonably. Finally, we design a loss function that combines TC, DD, and LST reconstruction to improve the accuracy of LST prediction further. We performed comprehensive data experiments to validate the performance of the new method. Our technique is more advantageous in generating MODIS-like LSTs than four state-of-the-art methods and two CNN models using a single piece of information.

Land surface temperature (LST) serves as a key indicator for studying the thermal characteristics of the land–atmosphere interface. Nevertheless, cloud cover and atmospheric particulates frequently obstruct the thermal infrared (TIR) spectral range in satellite observations, leading to significant data missing in derived LST products. Multisource data fusion methods are the most effective means for all-weather TIR LST reconstruction. However, most of the existing multisource data fusion reconstruction methods only consider the spatial and temporal differences individually, which makes it challenging to ensure spatiotemporal consistency in LST gap-filling. In this study, a novel framework for LST reconstruction is proposed by constructing a spatio-temporal consistency-guided global–local fusion network (STCGL-Net) to fuse TIR and reanalysis data. Using a deep convolutional network architecture, the STCGL-Net efficiently captures the local spatial features and texture details of multisource observations. At the same time, spatiotemporal consistency transformer and coordinate attention mechanisms are cleverly embedded to capture the global spatiotemporal dependencies that drive changes in LST. Using spatiotemporal consistency as the core physical constraint to guide the STCGL-Net’s training process ensures that the reconstructed LST is not only numerically accurate but also evolves in accordance with physical laws. As a key input parameter, ERA5 surface net solar radiation (SSR) data quantitatively characterize solar forcing on land surface thermal dynamics and enhance LST reconstruction accuracy. Evaluation demonstrates that STCGL-Net achieves cloud-contaminated LST reconstruction with $R^{2} =0.87$ and mean absolute error (MAE) = 0.54 K. Comparative evaluations with three established classic methods confirm the STCGL-Net’s consistent performance across various spatial and temporal scales. Validation against surface radiation budget station LST confirms reconstruction robustness ( $R^{2}$ : 0.8–0.9, MAE: 3–5 K), establishing reliable all-weather LST generation capability.

… . To calculate the coefficients, we applied sliding windows to the second-stage fusion results and reanalysis LST. It searches for valid pixels and utilizes the restricted least squares …

High spatiotemporal resolution land surface temperature (LST) data are essential for monitoring thermal environments. However, existing data sources—meteorological station observations, satellite remote sensing, and reanalysis datasets—each have distinct limitations, including sparse spatial coverage, frequent data gaps, and insufficient resolution for localized studies. As a result, data fusion is needed to combine the strengths of these sources. Yet, existing methods typically necessitate extensive pre-tuning of model parameters or error characteristics, as well as the incorporation of complex auxiliary data, placing substantial demands on user expertise. Therefore, there is an urgent need for a simple, efficient, and user-friendly algorithm to meet diverse application requirements. To address these challenges, this study introduces a data-fusion method based on an Extended Kalman Filter dual-cycle assimilation framework (EKFDCA). This framework achieves computationally efficient integration of satellite and reanalysis LST by directly extracting dynamic evolution from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5-Land LST, eschewing both full model runs and ancillary data needs while maintaining accuracy comparable to satellite products. Applied to Geostationary Operational Environmental Satellite-16 (GOES-16) and ERA5-Land LST, it produces an hourly 2 km LST dataset for 2023, rigorously evaluated against seven Surface Radiation Budget Network (SURFRAD) stations across the United States. Results demonstrate strong agreement between the fused LST and ground observations, with coefficient of determination (R2) values exceeding 0.88 during periods of satellite availability and remaining above 0.87 during data gaps. Mean bias error (BIAS) ranged from -1.27 to 0.90 °C, with root mean square error (RMSE) values of approximately 2.70 °C and 2.90 °C for periods with and without satellite data, respectively. The fused data align well with station trends and effectively correct extreme biases present in the original datasets. At the regional scale, the fused product exhibits high consistency with GOES-16 data, capturing fine-grained spatial patterns while maintaining numerical stability. This method delivers stable, high-quality fused LST despite satellite data gaps, without requiring prior ground observations, offering an efficient and robust solution for continuous temperature monitoring and analysis.

Landsat surface temperature (LST) is an important physical quantity for global climate change monitoring. Over the past decades, several LST products have been produced by satellite thermal infrared (TIR) bands or land surface models (LSMs). Recent research has increased the spatio-temporal resolution of LST products to 2-km, hourly based on Geostationary Operational Environmental Satellites (GOES)-R Advanced Baseline Imager (ABI) LST data. The spatial resolution of 2 km, however, is insufficient for monitoring at the regional scale. This article investigates the feasibility of applying spatio-temporal fusion to generate reliable 100-m, hourly LST data based on fusion of the newly released 2-km, hourly GOES-16 ABI LST and 100-m Landsat LST data. The most accurate fusion method was identified through a comparison between several popular methods. Furthermore, a comprehensive comparison was performed between fusion (with Landsat LST) involving satellite-derived LST (i.e., GOES) and model-derived LSMs [i.e., European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v.5 (ERA5)-Land]. The spatial and temporal adaptive reflectance fusion model (STARFM) method was demonstrated to be an appropriate method to generate 100-m, hourly data, which produced an average root-mean-square error (RMSE) of 2.640 K, a mean absolute error (MAE) of 2.159 K, and an average coefficient of determination ( $R^{2}$ ) of 0.982 referring to the in situ time-series. Furthermore, inheriting the advantages of direct observation and the fusion of Landsat and GOES for the generation of 100-m, hourly LST produced greater accuracy compared to the fusion of Landsat and ERA5-Land LST in the experiments. The generated 100-m, hourly LST can provide important diurnal data with fine spatial resolution for various monitoring applications.

Land surface temperature (LST) is a critical state variable of land surface energy equilibrium and a key indicator of environmental change such as climate change, urban heat island, and freezing-thawing hazard. The high spatial and temporal resolution datasets are urgently needed for a variety of environmental change studies, especially in remote areas with few LST observation stations. MODIS and Landsat satellites have complementary characteristics in terms of spatial and temporal resolution for LST retrieval. To make full use of their respective advantages, this paper developed a pixel-based multi-spatial resolution adaptive fusion modeling framework (called pMSRAFM). As an instance of this framework, the data fusion model for joint retrieval of LST from Landsat-8 and MODIS data was implemented to generate the synthetic LST with Landsat-like spatial resolution and MODIS temporal information. The performance of pMSRAFM was tested and validated in the Heihe River Basin located in China. The results of six experiments showed that the fused LST was high similarity to the direct Landsat-derived LST with structural similarity index (SSIM) of 0.83 and the index of agreement (d) of 0.84. The range of SSIM was 0.65–0.88, the root mean square error (RMSE) yielded a range of 1.6–3.4 °C, and the averaged bias was 0.6 °C. Furthermore, the temporal information of MODIS LST was retained and optimized in the synthetic LST. The RMSE ranged from 0.7 °C to 1.5 °C with an average value of 1.1 °C. When compared with in situ LST observations, the mean absolute error and bias were reduced after fusion with the mean absolute bias of 1.3 °C. The validation results that fused LST possesses the spatial pattern of Landsat-derived LSTs and inherits most of the temporal properties of MODIS LSTs at the same time, so it can provide more accurate and credible information. Consequently, pMSRAFM can be served as a promising and practical fusion framework to prepare a high-quality LST spatiotemporal dataset for various applications in environment studies.

This study presents a novel multimodel fusion approach for enhancing land surface temperature (LST) recovery and spatial resolution improvement. We compare and integrate linear Ridge regression and nonlinear random forest (RF) ensemble models to retrieve high-resolution LST from thermal infrared (TIR) remote sensing data. Initially, LST is computed using a single-channel (SC) algorithm at 30 m (SCLST-30 m) and 90 m (SCLST-90 m). The SCLST-90 m is then enhanced through a multimodel fusion framework that combines both ensemble models with high-resolution predictor features, including surface reflectance, multiple indices (soil adjusted vegetation index, normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and urban index), and digital elevation model. The fusion approach integrates the complementary strengths of both models, with RF capturing nonlinear relationships and Ridge regression providing stability in linear patterns. Cross-validation between SCLST-30 m and the fused LST demonstrates superior performance, with the integrated approach achieving higher accuracy than individual models. Validation against in-situ LST confirms the effectiveness of the fusion framework, yielding R2 values of 0.89 and 0.84 for RF and Ridge models respectively. The RMSE values for SCLST-30 m, RF-DLST, and Ridge-DLST are 0.37, 0.38, and 0.45 K, respectively. These results demonstrate the effectiveness of our multimodel fusion approach in achieving accurate recovery and resolution enhancement of LST, offering valuable insights for environmental monitoring and urban planning applications.

Land Surface Temperature (LST) is a critical variable for understanding land–atmosphere interactions and is widely applied in urban heat monitoring, evapotranspiration estimation, near-surface air temperature modeling, soil moisture assessment, and climate studies. MODIS LST products, with their global coverage, long-term consistency, and radiometric calibration, are a major source of LST data. However, frequent data gaps caused by cloud contamination and atmospheric interference severely limit their applicability in analyses requiring high spatiotemporal continuity. This study presents a seamless MODIS LST reconstruction framework that integrates multi-source data fusion and a multi-stage optimization strategy. The method consists of three key components: (1) topography- and land cover-constrained spatial interpolation, which preliminarily fills orbit-induced gaps using elevation and land cover similarity criteria; (2) pixel-level LST reconstruction via random forest (RF) modeling with multi-source predictors (e.g., NDVI, NDWI, surface reflectance, DEM, land cover), coupled with HANTS-based temporal smoothing to enhance temporal consistency and seasonal fidelity; and (3) Poisson-based image fusion, which ensures spatial continuity and smooth transitions without compromising temperature gradients. Experiments conducted over two representative regions—Huainan and Jining—demonstrate the superior performance of the proposed method under both daytime and nighttime scenarios. The integrated approach (Step 3) achieves high accuracy, with correlation coefficients (CCs) exceeding 0.95 and root mean square errors (RMSEs) below 2K, outperforming conventional HANTS and standalone interpolation methods. Cross-validation with high-resolution Landsat LST further confirms the method’s ability to retain spatial detail and cross-scale consistency. Overall, this study offers a robust and generalizable solution for reconstructing MODIS LST with high spatial and temporal fidelity. The framework holds strong potential for broad applications in land surface process modeling, regional climate studies, and urban thermal environment analysis.

Spatiotemporal fusion technology effectively improves the spatial and temporal resolution of remote sensing data by fusing data from different sources. Based on the strong time-series correlation of pixels at different scales (average Pearson correlation coefficients > 0.95), a new long time-series spatiotemporal fusion model (LOTSFM) is proposed for land surface temperature data. The model is distinguished by the following attributes: it employs an extended input framework to sidestep selection biases and enhance result stability while also integrating Julian Day for estimating sensor difference term variations at each pixel location. From 2013 to 2022, 79 pairs of Landsat8/9 and MODIS images were collected as extended inputs. Multiple rounds of cross-validation were conducted in Beijing, Shanghai, and Guangzhou with an all-round performance assessment (APA), and the average root-mean-square error (RMSE) was 1.60 °C, 2.16 °C and 1.71 °C, respectively, which proved the regional versatility of LOTSFM. The validity of the sensor difference estimation based on Julian days was verified, and the RMSE accuracy significantly improved (p < 0.05). The accuracy and time consumption of five different fusion models were compared, which proved that LOTSFM has stable accuracy performance and a fast fusion process. Therefore, LOTSFM can provide higher spatiotemporal resolution (30 m) land surface temperature research data for the evolution of urban thermal environments and has great application potential in monitoring anthropogenic heat pollution and extreme thermal phenomena.

… LST, based on the fusion of remote sensing and reanalysis … , compared to other LST products, the GOES LST data are … of Landsat LST data, as the GOES LST data are also satellite-…

Abstract. Surface net radiation (SNR) is a vital input for many land surface and hydrological models. However, most of the current remote sensing datasets of SNR come mostly at coarse resolutions or have large gaps due to cloud cover that hinder their use as input in models. Here, we present a downscaled and continuous daily SNR product across Europe for 2018–2019. Long-wave outgoing radiation is computed from a merged land surface temperature (LST) product in combination with Meteosat Second Generation emissivity data. The merged LST product is based on all-sky LST retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG) satellite and clear-sky LST retrievals from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the polar-orbiting Sentinel-3A satellite. This approach makes use of the medium spatial (approx. 5–7 km) but high temporal (30 min) resolution, gap-free data from MSG along with the low temporal (2–3 d) but high spatial (1 km) resolution of the Sentinel-3 LST retrievals. The resulting 1 km and daily LST dataset is based on an hourly merging of both datasets through bias correction and Kalman filter assimilation. Short-wave outgoing radiation is computed from the incoming short-wave radiation from MSG and the downscaled albedo using 1 km PROBA-V data. MSG incoming short-wave and long-wave radiation and the outgoing radiation components at 1 km spatial resolution are used together to compute the final daily SNR dataset in a consistent manner. Validation results indicate an improvement of the mean squared error by ca. 7 % with an increase in spatial detail compared to the original MSG product. The resulting pan-European SNR dataset, as well as the merged LST product, can be used for hydrological modelling and as input to models dedicated to estimating evaporation and surface turbulent heat fluxes and will be regularly updated in the future. The datasets can be downloaded from https://doi.org/10.5281/zenodo.8332222 (Rains, 2023a) and https://doi.org/10.5281/zenodo.8332128 (Rains, 2023b).

High temporal resolution and spatially complete (seamless) land surface temperature (LST) play a crucial role in numerous geoscientific aspects. This paper proposes a data fusion method for producing hourly seamless LST from Himawari-8 Advanced Himawari Imager (AHI) data. First, the high-quality hourly clear-sky LST was retrieved from AHI data by an improved temperature and emissivity separation algorithm; then, the hourly spatially complete China Land Data Assimilation System (CLDAS) LST was calibrated by a bias correction method. Finally, the strengths of the retrieved AHI LST and bias-corrected CLDAS LST were combined by the multiresolution Kalman filter (MKF) algorithm to generate hourly seamless LST at different spatial scales. Validation results showed the bias and root mean square error (RMSE) of the fused LST at a finer scale (0.02°) were −0.65 K and 3.38 K under cloudy sky conditions, the values were −0.55 K and 3.03 K for all sky conditions, respectively. The bias and RMSE of the fused LST at the coarse scale (0.06°) are -0.46 K and 3.11 K, respectively. This accuracy is comparable to the accuracy of all-weather LST derived by various methods reported in the published literature. In addition, we obtained the consistent LST images across different scales. The seamless finer LST data over East Asia can not only reflect the spatial distribution characteristics of LST during different seasons, but also exactly present the diurnal variation of the LST. With the proposed method, we have produced a 0.02° seamless LST dataset from 2016 through 2021 that is freely available at the National Tibetan Plateau Data Center. It is the first time that we can obtain the hourly seamless LST data from AHI.