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, 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 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.

The surface air temperature (Ta) dataset of the Tibetan Plateau is obtained by downscaling the China regional surface meteorological feature dataset (CRSMFD). It contains the daily mean Ta and 3-hourly instantaneous Ta. This dataset has a spatial resolution of 0.01°. Its time range for surface air temperature dataset is from 2000 to 2015. Spatial dimension of data: 73°E-106°E, 40°N-23°N. The Ta with a 0.01° can serve as an important input for the modeling of land surface processes, such as surface evapotranspiration estimation, agricultural monitoring, and climate change analysis.

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.

Crops are greatly affected by the temperature of farmland surface during their growing period. It is feasible to investigate the growth status of crops based on temperature information. For serving the research of crop growth status, the component temperature (e.g. temperature of vegetation and temperature of soil) are in need to be obtained. In this study, an unmanned aerial vehicle (UAV) temperature measurement system with a thermal infrared (TIR) imager and a charge-coupled device (CCD) camera is assembled and applied to measure the brightness temperatures of farmland surface. The target areas were photographed by the UAV temperature measurement system according to a pre-set route, and obtain TIR and visible images. The component temperatures are obtained from the TIR image as following processes: (1) When shaded components are negligible at noon, two components, i.e. vegetation and soil, are divided by the OTSU algorithm; and (2) When shaded components cannot be ignored in the morning and afternoon, various components, i.e. vegetation, soil and concrete, the TIR image is divided into soil, vegetation and concrete by the corresponding classified visible images; Then, each of the components is divided into light and shaded components by the OTSU algorithm; thus, four components are obtained, including sunlit vegetation, shaded vegetation, sunlit soil, and shaded soil. The derived component temperatures can serve as inputs to agricultural and water resource models.

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., weighted GWR (WGWR)] considering the weights of LST descriptors is proposed. To examine the performance of WGWR, the 100-m Landsat−8 thermal infrared sensor (TIRS) and Terra advanced spaceborne thermal emission and reflection radiometer (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 root mean square error (RMSE) can be reduced by 0.34 K (0.26 K) in Zhangye (ZY) and 0.22 K (0.1 K) in Beijing (BJ). 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 on in situ LST indicates that the downscaled LST based on WGWR has better agreement with the in situ LST, 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.

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 with in-situ measurements also revealed the significant improvement in terms of the coefficient of determination (R ² ) (from 0.53 to 0.79) and the root mean squared error (RMSE) (from 198.5 to 140.41 W/m ² ). 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.