Accurately estimating sensible heat (H) and latent heat (LE) in mountainous areas is a significant challenge due to the influence of complex topography factors. Currently, most models have been developed to estimate surface heat flux for flat surfaces without considering the effect of complex geometric terrain structures. In this study, the Surface Energy Balance Algorithm for Land coupled with a mountainous net surface radiation (Rn) calculation method (MSEBAL) was proposed to accurately estimate H and LE in the upstream catchment regions of the Heihe River Basin (HRB). The Rnwas estimated by correcting the solar incoming radiation components using topographic factors, including slope, aspect, sky view factor (SVF), and terrain configuration factor (TCF). The SEBAL and MSEBAL models were applied to satellite remote sensing data from Landsat 8 images and ground-observed datasets. In situ measurements from the eddy covariance (EC) system of the A’rou superstation were used to validate the estimation accuracy of Rn, LE, and H by MSEBAL. The results show that Rn, LE, and H estimated by the MSEBAL exhibit good consistency with the validation of in situ measurements. The Rnestimated by MSEBAL showed a decrease in RMSE from 164.32 to 51.31 W/m2and a reduction in absolute bias from 154.50 to 10.50 W/m2compared to SEBAL. The H and LE estimated by MSEBAL exhibit low RMSE and bias, with values of 31.96 and -17.93 W/m2for H, and 35.67 and -6.18 W/m2 for LE, respectively, compared to SEBAL. The spatial pattern of surface heat fluxes exhibited variations with complex terrain changes. H and LE were found to be higher at mountain peaks, while lower values were observed in valleys. Additionally, H and LE were greater on east and south-facing slopes that receive more solar radiation compared to west and north-facing slopes. This study provides an effective tool for estimating surface heat fluxes over mountainous regions.

Accurate spatial quantification of cropland soil organic carbon (SOC) in plateau lake basins is crucial for assessing the carbon sequestration potential in ecologically fragile regions. This study developed a machine learning (ML) framework that integrates multi-source satellite-derived environmental covariates (topography, climate, vegetation, soil properties, and parent materials) to estimate SOC distribution in the Erhai Lake basin. Using 432 topsoil samples (0–20 cm), we systematically compared 15 models, including conventional ML approaches (e.g., random forest, support vector machine, and light gradient boosting machine) and deep learning (DL) models (e.g., long short-term memory, recurrent neural network, and multilayer perceptron). The results showed that DL models achieved higher predictive accuracy than conventional ML models, reducing RMSE by 0.1680 g kg⁻¹ and increasing R², RPIQ, and CCC by averages of 0.0225, 0.1143, and 0.0253, respectively, although conventional ML models exhibited greater robustness. Elevation and temperature were identified as dominant factors controlling SOC spatial patterns, with higher concentrations clustered in the western and northern subbasins. Greater prediction uncertainty in the northwestern and eastern margins was associated with complex terrain heterogeneity. Spatially explicit SOC mapping derived from the integration of multi-source satellite data and ML models offers innovative approaches for carbon management in ecologically fragile lacustrine agroecosystems.

Land surface temperature (LST) is a key factor in monitoring and improving thermal environments. However, conventional LST retrieval algorithms have not sufficiently accounted for the cavity and adjacency effects caused by the three-dimensional (3D) structures in urban settings. In this study, we propose an urban multiple scattering radiative transfer model (UMS-RTM). Based on this model, we develop an urban nonlinear split-window (UNSW) algorithm to retrieve urban land surface temperature (ULST) from Landsat-9 satellite data. The UMS-RTM optimizes the thermal radiation transfer process by correcting the cavity and adjacency effects. Analysis shows that land surface emissivity (LSE) and sky view factor (SVF) are the primary factors influencing these effects. The cavity effect increases the effective LSE by 0.01 to 0.08, while the adjacency effect raises the ground-leaving brightness temperature (BT) by 0.82 K to 3.51 K. The UNSW algorithm’s coefficients were calibrated across various LST, water vapor content (WVC), and SVF groupings to eliminate atmospheric effects and correct for cavity and adjacency effects. Sensitivity analyses of instrument noise, WVC, effective LSE, and SVF uncertainties demonstrated the reliability of the UNSW algorithm. Validation using simulated data showed that the ULST retrieved by the UNSW algorithm had a root-mean-square error (RMSE) of 0.35 K. When applied to Landsat-9 satellite data, the UNSW algorithm revealed that conventional algorithms and LST products overestimate ULST by 0 K to 2 K, with overestimations exceeding 1 K in areas with low SVF. The UNSW algorithm provides more accurate ULST retrieval and finer spatial distribution details.

The wide range acquisition of all-weather land surface temperatures (LSTs) from passive microwave (PMW) remotely sensed data contributes to understanding the land-atmosphere interactions, surface energy balance, and the global water cycle. Although significant progress has been made in PMW-based LST retrieval using statistical models, physical models, and machine learning methods, there remains a need to propose a model with high accuracy alongside strong physical interpretability and good generalization ability. This article aims to develop a physics-constrained deep learning (DL) hybrid model to obtain accurate LSTs under nonprecipitation clouds and then compare it with the pure physical and DL models. The hybrid model is developed by incorporating the physical loss function into the convolutional neural network, inheriting the advantages of the physical and DL models. Results show that the constructed model achieved good performance with a root mean square error (RMSE) of 1.60 K and a mean absolute error (MAE) of 1.26 K in the simulated data. Sensitivity analysis revealed that the hybrid model is less sensitive to input parameters than the pure physical and pure DL models and exhibits robustness across varying land surface and atmospheric conditions. Furthermore, during the evaluation using U.S. Surface Radiation Budget (SURFRAD) site data, the hybrid model yielded RMSEs of 4.37 and 3.48 K for day and night, respectively, with Advanced Microwave Scanning Radiometer 2 (AMSR2) and ERA5 data from 2012 to 2024, while outperforming the other two models at each SURFRAD site. The spatiotemporal applicability of the hybrid model further highlighted its superior generalization ability in mapping LSTs. We believe that the evident strength of the developed model over the traditional pure physical and DL models is attributed to its good accuracy, robustness to uncertainties in input parameters, and physical interpretability, which will benefit the other parameter estimates.

Acquiring cloudy land surface temperature (LST) is crucial for terrestrial ecosystem monitoring and global climate observation. Although numerous methods have been proposed to retrieve cloudy-sky LST using microwave remote sensing, the physical significance of these methods is inadequate due to their oversimplification of the effects of atmospheric components and clouds. To obtain accurate cloudy-sky LST, by simultaneously considering the influences of water vapor and cloud properties on LST, a physics-based LST retrieval algorithm is developed for cloudy skies with adjacent three brightness temperatures (BTs) at 18.7, 23.8, and 36.5 GHz vertically polarization. We develop this algorithm by building a simulated database, which includes a large range of BTs, surface emissivities, air temperatures, and water vapor contents. Meanwhile, various cloudy atmospheric profiles are constructed to reveal multifarious weather conditions. The test results with the simulated database show that the algorithm has good accuracy with an RMSE of 1.75 K and MAE of 1.33 K under cloudy weather. Sensitivity analysis indicates that precipitable water vapor (PWV) and cloud liquid water (CLW) are indispensable for correcting cloudy LST. In particular, LST accuracy shows an evident sensitivity to PWV, and RMSEs are reduced with an increase in PWV. Meanwhile, the proposed algorithm was applied to AMSR-E BTs and ERA5 profile dataset over the China region in 2008 and validated with ground-based air temperatures. Results indicate that RMSEs between retrieved LSTs and true LSTs are 3.47 K for cloudy weather, and there is better performance at high water vapor status, with an RMSE of 2.05 K.

Acquiring the satellite land surface temperature (LST) with high spatiotemporal resolutions is pressing in the land surface biophysical process. However, most current LST products hardly satisfy this requirement. LST Downscaling provides an effective way to solve this issue by introducing driving factors, but existing methods usually ignore the weights of descriptors. In this letter, based on the Geographically Weighted Regression (GWR) and Random Forest (RF), a new downscaling method (i.e., WGWR) considering the weights of LST descriptors is proposed. To examine the performance of WGWR, the 100-m Landsat-8 TIRS and Terra ASTER LSTs are aggregated to 1000 m as the simulated coarse LSTs, and then the coarse LSTs are downscaled to 100 m using WGWR, RF, and GWR. Meanwhile, the original 100-m LSTs are used as validation references. Results indicate that the proposed WGWR outperforms RF and GWR: for RF (GWR), the RMSEs can be reduced by 0.34 K (0.26 K) in Zhangye and 0.22 K (0.1 K) in Beijing. Compared to RF and GWR, WGWR also yields better image quality: the downscaled LST images have neither obvious smoothing effect nor boundary effect and maintain the details of the image at high spatial resolution. Validation based onin-situLST indicates that the downscaled LST based on WGWR has better agreement with thein-situLST, and the RMSE is reduced by 0.57 K. The proposed WGWR contributes to obtain high spatio-temporal resolution LSTs and promote hydrological, meteorological, and ecological studies.

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 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. STEF method can produce NRT-AW LST without relying on the data after the target moment. STEF is tested in the Tibetan Plateau. Validation results on DOY 215-366 of 2020 indicate that STEF has good accuracy: RMSEs (MBEs) under clear-sky, cloudy-sky, and all-weather 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. STEF method can improve the accuracies of FY-4A LST, and RMSEs are reduced by about 0.77 K to 1.82 K. The NRT-AW LSTs estimated by STEF have better accuracies than CLDAS LSTs under all-weather 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 contributes to improving the timeliness of region monitoring and related parameter estimations.

Land surface temperature (LST) products with high spatial resolution and short revisiting cycles are crucial for environmental studies. However, due to the tradeoff between spatial and temporal resolutions of satellite observations, such data are not directly available. Spatial downscaling and spatiotemporal fusion methods are existing solutions for this problem, but their robustness is limited under different surface conditions. Here, we propose a Robust Framework for Combining Downscaling and spatiotemporal Fusion methods (RFCDF) to generate the synthesized daily high-resolution LST with high accuracy in different landscapes. RFCDF introduces a novel weighting strategy that determines pixel-level weights using an empirical function under the constraint of the image-level weights of two predictions. We implement the framework using the thermal sharpening algorithm (TsHARP) and Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) data in Beijing and Baotou, as well as Landsat 8 and simulated coarse resolution imagery in nine sub-regions with different surface landscapes in Beijing. Our results demonstrate that RFCDF can generate more accurate estimations and preserve more spatial details than either individual or combination methods, improving accuracy by 0.1–0.6 K and 0.4–1.3 K in the two study areas, respectively. Moreover, the proposed framework is robust, reducing the root mean square error of estimations by 8-24% under different surface conditions. RFCDF can also generate dense high-resolution LST time series, which is crucial for studying the surface thermal environment at a finer scale.

Microwave land surface emissivity (MLSE) at the K band plays a key role in driving geophysical parameters, such as land surface temperature (LST). However, satellite-based MLSE currently is hard to be quickly retrieved in clear skies since the time cost is high in removing atmospheric contributions. In this letter, one clear-sky atmospheric profile dataset, including a wide range of precipitable water vapor (PWV) values, was constructed using the Thermodynamic Initial Guess Retrieval database for analyzing numerical relationships between PWV and atmospheric parameters. Then, a simplified algorithm was developed for accurately retrieving instantaneous K-bandMLSEs (18.7 and 23.8 GHz) under clear skies, which can significantly save the time of atmospheric correction. The sensitivity analysis shows that PWV is a key factor affecting MLSE estimation at 23.8 GHz, and the brightness temperature (BT) uncertainty has a greater impact on MLSE estimation than LST. Additionally, with LST derived from the Moderate-resolution Imaging Spectroradiometer, BT from the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), and the ERA5 reanalysis PWV in 2008, the proposed algorithm was respectively applied in Europe and the United States for presenting its applicability at a station scale and regional scale. The actual sounding profile and global AMSR-E MLSE product were used as validation datasets. Results indicate the simplified approach has a good performance with RMSEs less than 0.02 in the site and regional validations. Whereas, there are some apparent overestimations in estimating clear-skies MLSEs, especially for 23.8 GHz. We believe the proposed approach is promising for retrieving other parameters.

Soil salinity plays a key role in influencing the soil dielectric constant and soil backscatter coefficient. However, soil moisture (SM) retrieval models constructed based on active microwave data hardly consider soil salinity. Thus, obtaining the SM datasets with various salinity on regional and local scales is difficult. This study aimed to employ theoretical model simulation to investigate the errors of SM retrieval due to not considering the impact of soil salinity. Then, three typical saline soil dielectric constant models were validated and compared based on the experimental measurement datasets. Results show that the WYR saline soil dielectric constant model has excellent performance. The soil salinity mainly affects the imaginary part of the dielectric constant and the effect of salinity on the soil dielectric constant is more significant when the SM has larger values. In addition, in retrieving SM with soil salinity more than 10 g/kg, the retrieval result of SM has an absolute error of 0.04$\text{m}^{3}/\text{m}^{3}$and a relative error of 5% when not considering the soil salinity impact. In retrieving SM with soil salinity less than 10 g/kg, the retrieved SM error increased by 2%, and the absolute error increased by 0.01$\text{m}^{3}/\text{m}^{3}$as soil salinity increased by 3 g/kg. We believe that The study will give a theoretical reference for establishing the SM retrieval model in saline soil areas using microwave data.

The derivation of microwave land surface emissivity (MLSE) under various weather conditions from the microwave radiometer plays a crucial role in acquiring land surface and atmospheric parameters. Nevertheless, currently, most existing studies mainly focus on the clear-sky scenarios owing to a lack of cloudy-sky land surface temperature (LST) and uncertainties in simulating the scattering and emission properties of atmospheric hydrometeors. Under this background, with satellite observations and the random forest (RF) model, this study proposes a method to estimate the MLSE under cloudy skies. First, clear-sky MLSEs with satisfactory accuracy are retrieved by using the brightness temperatures (BTs) from the Advanced Microwave Scanning Radiometer-Earth sensor, LSTs from the Moderate Resolution Imaging Spectroradiometer, and atmospheric profiles from the ERA5 reanalysis. Then, the relation among the clear-sky MLSE and related impact factors is built with the RF and extended to the cloudy-sky environment for generating all-weather MLSEs with a 0.25°. The results show that the input datasets present a considerable impact on the calculation of instantaneous MLSE, and a 5.73 K bias of ERA5 LST may generate a 0.014-0.021 error in the MLSE from 6.9 to 89 GHz horizontal polarization, while the impacts of BT and profile uncertainties on the MLSE are smaller. The retrieved clear-sky MLSE is coincident with the existing MLSE for the spatiotemporal variations, and there is an average difference range from -0.035 to 0.035 in January 2008. Meanwhile, the constructed RF model can successfully apply to cloudy-sky status and recover the MLSE image gaps affected by cloud contamination.

Investigating the relations between land surface temperature (LST) and biophysical compositions can help the understanding of the surface biophysical process. However, there are still uncertainties in determining the impacts of biophysical compositions on LST due to the atmospheric effects. In this article, four atmospheric correction algorithms were used to correct 12 Landsat 8 images in Xi'an, Beijing, Wuhan, and Guangzhou, China, including the Atmospheric Correction for Flat Terrain (ATCOR2), Quick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and Second Simulation of Satellite Signal in the Solar Spectrum (6S). Then, geodetector was used to investigate the atmospheric correction differences in the spatial heterogeneity relationships between LST and normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and bare soil index (BSI). Results indicate that the selected composition factors were greatly improved after atmospheric correction, and the relations between LST and three factors were characterized by obvious atmospheric correction differences in four study areas. On the whole, the 6S algorithm performed the best in improving the factor values and impacting the spatial heterogeneity relations between LST and biophysical compositions, followed by FLAASH, QUAC, and ATCOR2 algorithms. Except for Wuhan, 6S, FLAASH, and QUAC algorithms significantly enhanced the correlation between LST and NDVI. However, all algorithms weakened the correlations between LST, NDVI, and BSI, except Guangzhou. These findings have been verified using the regression analysis. In addition, with geodetector, combinations of any two composition factors all had strongly enhanced impacts on LST, and a combination between NDVI and NDBI performed the strongest in most cases.