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.

In regions with complex terrain, high-spatial-resolution downward surface shortwave radiation (DSSR) is critical for monitoring mountain ecological processes and for environmental management. However, currently available DSSR products are often too coarse (from a kilometer to tens of kilometers) to capture the spatial heterogeneity of DSSR in topographically complex regions. To address this issue, this study proposes a new downscaling method for all-sky instantaneous DSSR, employing a machine learning (ML) method, top-of-atmosphere reflectance, and topographic data. The method is used to downscale the 5-km Himawari-8 (H-8) DSSR product to the Sentinel 10 m scale. A region of Southwest China was chosen as a case study. Validated by field measurements from nine stations in 2020, the downscaled DSSR showed improvements in the mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE) of 32.74%, 9.31%, and 6.34%, respectively, when compared with the original product. The downscaled DSSR can be generated in all-sky conditions. In general, this method successfully captures high-resolution DSSR over complex terrain and should be helpful for related 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.

Downward shortwave radiation (DSR) is an essential parameter in land surface energy budget. However, current DSR products are mainly generated at coarse-resolution scales (more than 5 km) and fail to accurately depict DSR distribution over different topographic and land cover conditions. Meanwhile, the existence of frequent cloud cover constrains the high-resolution DSR estimation. To overcome the above issues, a novel spatial downscaling method for high-resolution DSR estimation was proposed in this study by incorporating coarse-resolution Meteosat Second Generation (MSG) DSR product and Landsat-8 observations. Through decomposing the downscaling scheme into three separate models: fully cloudy, partial cloudy, and cloud-free, the 3 km MSG DSR data was spatially downscaled to 30 m scale under all-sky conditions, based on the assumption of scale-invariant of the models established at 3 km scale. An empirical model for DSR estimation under cloud cover condition was constructed between the top of atmosphere radiance from Landsat-8 and MSG DSR. The downscaled results showed reasonable DSR values under different cloud cover conditions and the spatial heterogeneity of the downscaled DSR was also well depicted with the variation of surface topography. Meanwhile, the validation within-situmeasurements also revealed the significant improvement in terms of the coefficient of determination (R2) (from 0.53 to 0.79) and the root mean squared error (RMSE) (from 198.5 to 140.41 W/m2). In general, the proposed downscaling method in this study show good potential for high-resolution DSR estimation without regard to the atmospheric information required in traditional DSR estimation under all-sky condition.

Near surface air temperature (NSAT) products are required for environment-related researches and applications. Existing NSAT products vary in spatial-temporal resolution and data quality. Thus, it is necessary to evaluate and investigate the difference of different NSAT products to provide an overall assessment to help researchers and users to choose and use among the many NSAT products. In this study, Tibetan Plateau was selected as our study area, and six released NSAT products were collected for comparison and evaluation. The NSAT products were compared with in situ NSAT from China Meteorological Administration stations (CMA) respectively. The evaluation process was conducted from daily and monthly scale and gave out the accuracy ranking of the six NSAT products.

The complex terrain in mountainous areas distorts solar illumination, which brings a strong topographic effect on optical remote-sensing observations. Although many efforts have been done to correct this effect via normalizing solar illumination induced differences, there are still high uncertainty, especially for poor illuminated surfaces. In this study, a downwelling shortwave radiation (DSR)-based correction (DSRC) method was proposed. The topographic effects were accounted by normalizing DSR differences at different topographic conditions, and a stratified correction strategy was applied by separating the image into different groups according to normalized difference vegetation index (NDVI) to consider the spectral differences of different land-cover types. The DSRC method was applied to nine Landsat 8 scenes with high-resolution DSR data acquired by downscaling the Meteosat Second Generation (MSG) DSR product. The performance analysis indicates that the correlation coefficient between the corrected surface reflectance and illumination conditions notably decreased. Compared with SCS + C, empirical rotation, Statistical-Empirical, and Modified Minnaert methods, the DSRC method well retains inherent spectral pattern and provides good advantages in normalizing the aspect differences of surface reflectance. Furthermore, the comparison of NDVI values before and after correction indicated that DSRC preserved the original values and successfully corrected the overestimated NDVI values of poor illuminated surfaces. The corrected NDVI time series provide more reasonable cycle of the phenology of vegetated surfaces than the original series. In summary, the DSRC method has a strong potential for reducing topographic effects that currently limit the applications of remotely sensed data in mountainous areas.

To reduce the topographic influence on the high-resolution optical remote sensing data, a radiation based topographic correction method was developed in this study by normalizing the solar illumination differences in mountain areas with the use of high-resolution downward shortwave radiation data (DSR). The coarse-resolution Meteosat Second Generation (MSG) SEVIRI DSR product was downscaled to the same spatial scale as the Landsat 8/OLI data to obtain the high-resolution DSR. The correction results indicated that the corrected spectral reflectance is poorer correlated with solar illumination than the original one. In addition, the corrected images also maintain the spectral characteristics with few overcorrections. Therefore, the proposed method will be of good potential in mountain remote sensing data process to reduce the topographic impacts.

Downward shortwave radiation (DSR) plays a very important role in land surface radiation budget and land-surface processes modeling. Although there are several radiation products developed based on satellite observations, the coarse spatial resolution greatly limits their applications in regional or local scales. To get high-resolution and accuracy-reliable DSR data, a practical downscaling method for clear-sky condition was proposed by using the scale-invariant relationship of the radiative transfer process to decompose the global radiation into direct and diffuse components at horizontal level and conducting topographic correction finally. Based on this method, the time series of Meteosat Second Generation (MSG) DSR product covering part of Navarre province in the northern Spain was disaggregated into 30-m level with the use of the ALOS World 3D-30m digital elevation model (DEM) data. The downscaled results not only presented high spatial heterogeneity with respect to the changes in surface topography but also showed reasonable values at different times over different days in one year. The in situ validation indicated that the hourly downscaled DSR had quite high correlation with the surface measurements at each day with the coefficient of determination above 0.97 and the root-mean-squared error lower than 35 W/m2. Overall, the evaluation allows concluding on the proposed method that can be a good way to get reliable and high-resolution DSR data from coarse-resolution DSR product under clear-sky condition.

Soil moisture (SM) downscaling has become more and more crucial for assisting the application of the coarse-resolution SM product, such as climate change, sustainable development of agroforestry, efficient management of water resources, and monitoring of natural hazards. The main idea of downscaling methods lies on the help of fine-resolution auxiliary data, such as the widely used, land surface temperature (LST) and normalized difference vegetation index (NDVI). However, in the downscaling process, the ancillary data, especially for the daily LST, is strongly affected by cloud cover, resulting high frequency of blank areas in the final downscaled SM products. By contrast, the impact is usually omitted or paid less attention in current downscaling studies. To obtain the spatially continuous fine-resolution SM product, this study firstly introduced an annual temperature cycle (ATC) model to fill the gaps in daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product induced by cloud cover. Then the 36-km SM product from Soil Moisture Active Passive (SMAP) satellite mission was downscaled from 36-km to 1-km spatial resolution with the synergistic use of the filled LST and MODIS NDVI to estimate spatially continuous fine-resolution SM product.