深层土壤水分反演算法

… In summary, the reflected wave method can obtain SWC at relatively deep soil depths in a … Forward modeling methods based on various inversion schemes are also introduced into …

… function is not perturbed by the soil and where volumetric moisture content at depth (θ v (… moisture content without recourse to mathematically complicated, and unstable profile inversion …

Abstract. Dual permeability models are widely used to simulate water fluxes and solute transport in structured soils. However, so far obtaining necessary data for model calibration is a problem due to the large set of unconstrained parameters. Therefore, this study presents a simplified 1D dual permeability model whose structure is similar to the MACRO model together with a calibration scheme that allows constraining the parameters using time series of soil water content. The inversion scheme consists of four consecutive steps: First, the parameters of three different water retention functions were assessed using vertical soil water content profiles assuming hydraulic equilibrium. Second, the soil sorptivity and diffusivity functions were estimated from Boltzmann-transformed soil water content data of a drying period. Third, the parameters governing macropore flow were determined using the most dynamic part of the soil water content time series during the first 12 h after a precipitation event. The model was calibrated using data of artificial, homogeneous and shallow soils from mesocosms. The resulting retention functions predicted similar values as pedotransfer functions apart from for very dry conditions. The predicted soil water content time series were in good agreement with measurements at 5 and 12 cm soil depth. Predicted macropore seepage fluxes exhibited high uncertainty and differed between water retention functions, but average predictions were close to measurements for two of the three water retention functions. The study demonstrates the feasibility of calibrating a 1D dual permeability model with soil water content time series.

In this paper we have tested the feasibility of the inverse modeling approach to derive root water uptake parameters (RWUP) from soil water content data using numerical experiments for three differently textured soils and for an optimal drying period. The RWUP of interest are the rooting depth and the bottom root length density. In a first step, a thorough sensitivity analysis was performed. This showed that soil water content dynamics is relatively insensitive to RWUP and that the sensitivity depends on the texture of the considered soil. For medium‐fine textured soil, the sensitivity is particularly low due to relatively high unsaturated hydraulic conductivity values. These ones allow a “compensating effect” to occur, i.e., vertical unsaturated water fluxes overshadowing in some way the root water uptake. In a second step, we analyzed the well‐posedness of the solution (stability and nonuniqueness) when only RWUP are optimized. For this case, the inverse problem is clearly ill‐posed except for the estimation of the rooting depth parameter for coarse and the very fine textured soils. In a third step, we addressed the case where RWUP are estimated simultaneously with additional parameters of the system (i.e., with soil hydraulic parameters). For this case, our study showed that the inverse problem is well‐posed for the coarse and very fine textured soils, allowing for the estimation of both RWUP of interest provided that a powerful global optimization algorithm is used. On the contrary, the estimation of RWUP is unfeasible for medium‐fine textured soil due to the “compensating effect” of the vertical unsaturated water flows. In conclusion, we can state that the inverse modeling approach can be applied to derive RWUP for some soils (coarse and very fine textured) and that the feasibility is strongly improved if the RWUP are simultaneously optimized with additional parameters. Nevertheless, more detailed research is needed to apply the inverse modeling approach to real cases for which additional issues are likely to be encountered such as soil heterogeneity and root dynamics.

… that soil hydraulic property estimates obtained from inverse … of soil moisture contents with time by “scaling” the predicted … the deep layer, except for 4 d when the moisture content was …

… The results show that the estimate of parameters related to soil water content and soil depth … for conditions were water stress effects are important, that is for shallow soils, dry weathers …

Abstract. Land surface models (LSM) are widely used as scientific and operational tools to simulate mass and energy fluxes within the soil vegetation atmosphere continuum for numerous applications in meteorology, hydrology or for geobiochemistry studies. A reliable parameterization of these models is important to improve the simulation skills. Soil moisture is a key variable, linking the water and energy fluxes at the land surface. An appropriate parameterisation of soil hydraulic properties is crucial to obtain reliable simulation of soil water content from a LSM scheme. Parameter inversion techniques have been developed for that purpose to infer model parameters from soil moisture measurements at the local scale. On the other hand, remote sensing methods provide a unique opportunity to estimate surface soil moisture content at different spatial scales and with different temporal frequencies and accuracies. The present paper investigates the potential to use surface soil moisture information to infer soil hydraulic characteristics using uncertain observations. Different approaches to retrieve soil characteristics from surface soil moisture observations is evaluated and the impact on the accuracy of the model predictions is quantified. The results indicate that there is in general potential to improve land surface model parameterisations by assimilating surface soil moisture observations. However, a high accuracy in surface soil moisture estimates is required to obtain reliable estimates of soil characteristics.

… and evaluate three approaches for the fusion of active and passive microwave records for an enhanced representation of the soil moisture status [18]: 1) estimation of soil moisture by …

… multi‐angular microwave brightness temperatures TB observations at L‐band since 2010 (Kerr et al., 2012). The ESA established a passive microwave SSM fusion study to investigate …

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture.

This work focuses on the problem of surface soil moisture estimation from multi-modal remote sensing observations. We focus on the scenario where both passive radiometer observations from NASA SMAP satellite, as well as active radar measurements from ESA Sentinel 1 are available. We formulate the problem as multi-source observation fusion and develop a deep learning model for SM estimation. To train and validate the performance of the proposed scheme, we consider observations from in-situ SM sensor networks over the continental USA. Experimental results demonstrate that the proposed model achieves high quality SM estimation, surpassing the performance of available products.

… sensors or fusion of these sensors, but microwave sensors … to estimate soil moisture from depth up to 0.7–1 m (microwave … roughness using active and passive microwave sensors. …

… Active and passive microwave systems are sensitive to changes in the dielectric properties of the soil … capabilities useful for the quantification of these soil parameters. In this context, a …

Accurate daily surface soil moisture (SSM) mapping at high spatial resolution (e.g., 30 m) remains challenging due to individual satellite sensor limitations. Although passive microwave sensors provide frequent coarse-resolution observations and synthetic aperture radar (SAR) offers high-resolution data intermittently, achieving both simultaneously requires sensor synergy. This paper introduces the microwave–optical multi-stage synergistic downscaling framework (MMSDF) to generate daily 30 m SSM products. The framework integrates SMAP L4 (9 km), MODIS data (500 m–1 km), harmonized Landsat Sentinel-2 (HLS, 30 m), radiometric terrain corrected Sentinel-1 (RTC-S1, 30 m), and auxiliary geographic data. It comprises three stages: (1) downscaling SMAP L4 to 1 km via random forest; (2) calibrating Sentinel-1 water cloud model (WCM) using intermediate 1 km SSM to retrieve 30 m SSM without in situ calibration; and (3) fusing daily 1 km SSM and intermittent 30 m WCM-derived retrievals using the spatial–temporal fusion model (ESTARFM) to generate seamless daily 30 m SSM maps. Validation against in situ measurements from 16 sites in Hunan Province, China (summer 2024) yielded R of 0.54 and RMSE of 0.045 cm3/cm3. Results demonstrate the framework’s capability to synergize multi-source data for high-resolution daily SSM estimates valuable for hydrological and agricultural applications.

Soil moisture (SM) with high precision and spatiotemporal resolution is crucial for crop yield estimation and water resource management, yet the spatial resolution of widely used passive microwave-based SM products remains low (tens of kilometers), making them inadequate for regional-scale applications. Spatial downscaling technique provides a viable solution to enhance the spatial resolution of passive microwave SM products. Despite extensive efforts made so far, surface heterogeneity which is an essential factor that causes differences in SM across coarse and fine scales by affecting processes such as water infiltration, evaporation, and storage has often been overlooked in previous algorithms, limiting the effectiveness of SM downscaling in heterogeneous regions. To address this knowledge gap, this study proposed a new SM downscaling method, termed heterogeneity-aware downscaling algorithm (HADA), which integrates surface heterogeneity including heterogeneity in land cover (LC), soil texture (ST), terrain, and vegetation coverage using the data-driven machine learning [i.e., random forest (RF)] approach. Moreover, a weighted scheme based on the importance ranking of heterogeneity parameters was developed to perform a more physically reasonable spatial correction of downscaling residuals. The proposed HADA was adopted to downscale the SM products generated by the newly developed microwave SM index (SMI) using SM active passive (SMAP) observations from 0.25° to 0.05°. Finally, the downscaling results were assessed using ground SM observations from 1260 sites across various regions worldwide and compared with existing methods and datasets. The results indicate the global distribution of the downscaled SM aligns well with that of the global aridity index (GAI), indicating a reasonable spatial behavior. Incorporating surface heterogeneity notably improves the fitting ability and estimation accuracy of the downscaling model. The downscaled products maintain accuracy comparable to the original data when validated by in situ SM but exhibits enhanced spatial details. Compared with the traditional DisPATCH approach, SMAP, ERA5-Land, and SiTHv2 SM datasets, HADA is superior in terms of both absolute accuracy and the capability to capture SM dynamics. This study not only provides an effective and feasible method to downscale passive microwave-based SM data but also introduces a potential approach to mitigate uncertainties caused by surface heterogeneity when downscaling other satellite derived products, thereby offering high-quality data support for diverse applications.

… [44] PE O’Neill, NS Chauhan, and TJ Jackson, “Use of active and passive microwave remote sensing for soil moisture estimation through corn,” Int. J. Remote Sens., vol. …

This study evaluates the potential of L-band passive microwave data for monitoring soil moisture (SM) in boreal and temperate forests using SMAP and SMOS AM and PM overpasses. SMAP and SMOS Level 3 SM products were first assessed for spring and summer seasons. SMOS showed lower accuracy (r2 = 0.04–0.24, ubRMSE = 0.09–0.13 m3/m3), while SMAP performed better (r2 = 0.18–0.62, ubRMSE = 0.05–0.07 m3/m3) across sites and overpasses. Given the larger number of SMAP TB observations at a fixed incidence angle and greater temporal coverage over the study area, SMAP was selected for SM estimation using ML models. Feature importance analysis identified brightness temperature (TB) as the most influential variable, followed by vegetation water content (VWC), air and soil temperatures, and the microwave polarization difference index (MPDI). Soil and air temperatures were interchangeable during AM overpasses, whereas PM overpasses showed distinct differences, likely due to thermal absorption by dense vegetation. Using optimal features, SM was estimated with CatBoost, Gradient Boosting (GB), Random Forest (RF), and Principal Component Regression (PCR), using stratified shuffle split (SSS) and leave-one-year-out cross-validation (LOYOCV). In SSS, CatBoost achieved slightly higher accuracy than the other ensemble models (AM: r2 = 0.73; PM: R2 = 0.74), while PCR yielded substantially lower accuracy across both overpasses. LOYOCV showed closer rankings among models, with CatBoost ranking highest overall (r2 = 0.58 for AM and 0.54 for PM). Results highlight the feasibility of improved SM estimation in forests using L-band TB, VWC, temperature variables, and MPDI.

… soil moisture profile throughout the top 100 cm layer of soil by integrating three widely used RZSM products; Soil Moisture Active Passive … Unlike traditional fusion methods requiring …

… CCI SM products is a fusion of active and passive microwave observation datasets (Preimesberger et al., 2021). It integrates active and passive microwave remote sensing data with in …

… : satellite-based approaches, such as the fusion of active-passive microwave data or the fusion of optical/thermal infrared and microwave data; geography-based methods that utilize …

… In this study, a radiative transfer model is used to model the emissivity and transmissivity of … To facilitate the soil moisture inversion from radiometry data, the unknown variables need to …

The study presents a first-order Radiative Transfer model for monostatic scattering by integrating a modified Oh model for soil surface scattering and radiative transfer model with higher order interaction terms for volumetric scattering. The approach aims to improve crop signature analysis for monitoring and retrieval purposes of biophysical parameters, particularly soil moisture (SM) and leaf area index (LAI) in our case, with the use of Sentinel-1 satellite dual-polarized SAR data. The SAR data (GRD and SLC), along with the in situ LAI and SM data from February to April 2024, has been used for training and testing purposes of the model. The Oh model shows the surface scattering part, and it has been modified by the incorporation of attenuation factor and one soil-scaling factor fsoil. Similarly, the volumetric scattering part has been described for the vegetation components, along with the 1st order interaction terms from the soil-veg, veg-soil double bounce scattering has been used in this model. Least-square optimization technique has been used to calibrate the scattering albedo and the loss factor present in the attenuation term. Finally, the inversion algorithm to retrieve the soil moisture data, has been done again with the Look-up Table (LUT) approach and the least square technique again for the sake of simplicity. The backscattering coefficient retrieval performance metric for VV polarization ($\mathrm{R}=0.8922$; $\mathrm{RMSE}=$ 0.6126) shows a better correlation with RMSE slightly higher when compared with VH polarization ($\mathrm{R}=0.8745$; RMSE = 0.4150). However, for soil moisture, the VH ($\mathrm{R}=\mathbf{0. 6 0 7 4}$; RMSE =0.1065) polarization shows better results than $\mathrm{VV}(\mathrm{R}=0.4169$; RMSE = 0.1576).

… the retrieval of soil moisture from multitemporal SAR images simulated by the discrete radiative transfer model (… To learn the inverse theoretical mapping between ASAR signals and the …

This letter presents a new approach to incorporate topographic relief effect in land data assimilation system over mountainous terrain. The conventional radiative transfer model (RTM) used for assimilation of microwave brightness temperature (Tb) is subjected to systematic bias owing to its flat earth model assumption. This is theoretically important for direct Tb assimilation system since a difference between simulated and observed Tb may disarray the assimilation system. Here, we consider three crucial relief effects, namely: 1) change in local incidence angle; 2) rotation of the plane of polarization; and 3) effect of pixels in shadow not visible to radiometer. Results indicate that the RTM formulation with relief effects (Topo) significantly reduce the bias as compared to the conventional RTM (Flat). The simulated Tb for the Topo case shows improved sensitivity toward soil moisture as compared to the Flat case. The proposed land surface model (LSM)–RTM assimilation framework has an immense potential source for initialization of LSM for seasonal climate prediction over mountainous terrain.

… A solution for the radiative transfer equation, which is … ) and land surface properties (eg soil moisture), is derived based on … For the inverse problem, the radiation field is known and it is …

This paper presents a new method to retrieve soil moisture and roughness from ERS-1. … through a first-order radiative transfer model. The soil moisture and roughness are subsequently …

… SOIL moisture has a critical role in the terrestrial water cycle … soil moisture observation owing to its high sensitivity to soil … moisture inversion algorithms. The SMAPVEX12 campaign was …

… L-band (1.4 GHz) from the Soil Moisture and Ocean Salinity (SMOS) satellite, integrating it with glaciological thermodynamic and radiative transfer models to infer the ice sheet's internal …