Land surface temperature (LST) is one of the crucial parameters in the physical processes of the Earth. Acquiring LST images with high spatial and temporal resolutions is currently difficult because of the technical restriction of satellite thermal infrared sensors. Downscaling LST from coarse to fine spatial resolution is an effective means to alleviate this problem. A spatial random forest downscaling LST method (SRFD) was proposed in this study. Abundant predictor variables—including land surface reflection data, remote sensing spectral indexes, terrain factors, and land cover type data—were considered and applied for feature selection in SRFD. Moreover, the shortcoming of only focusing on information from point-to-point in previous statistics-based downscaling methods was supplemented by adding the spatial feature of LST. SRFD was applied to three different heterogeneous regions and compared with the results from three classical or excellent methods, including thermal image sharpening algorithm, multifactor geographically weighted regression, and random forest downscaling method. Results show that SRFD outperforms other methods in vision and statistics due to the benefits from the supplement of the LST spatial feature. Specifically, compared with RFD, the second-best method, the downscaling results of SRFD are 10% to 24% lower in root-mean-square error, 5% to 20% higher in the coefficient of determination, 11% to 25% lower in mean absolute error, and 4% to 17% higher in structural similarity index measure. Hence, we conclude that SRFD will be a promising LST downscaling method.

Estimating forage yield and nutrient composition using hyperspectral remote sensing is a major challenge. However, there is still a lack of comprehensive research on the optimal wavelength for the analysis of various nutrients in pasture. In this research, conducted in Hailar District, Hulunber City, Inner Mongolia Autonomous Region, China, 126 sets of hyperspectral data were collected, covering a spectral range of 350 to 1800 nanometers. The primary objective was to identify key spectral bands for estimating forage dry matter yield (DMY), nitrogen content (NC), neutral detergent fiber (NDF), and acid detergent fiber (ADF) using principal component analysis (PCA), random forests (RF), and SHapley Additive exPlanations (SHAP) analysis methods, and then the RF and Extra-Trees algorithm (ERT) model was used to predict aboveground biomass (AGB) and nutrient parameters using the optimized spectral bands and vegetation indices. Our approach effectively minimizes redundancy in hyperspectral data by selectively employing crucial spectral bands, thus improving the accuracy of forage nutrient estimation. PCA identified the most variable bands at 400 nm, 520–550 nm, 670–720 nm, and 930–950 nm, reflecting their general spectral significance rather than a link to specific forage nutrients. Further analysis using RF feature importance pinpointed influential bands, predominantly within 930–940 nm and 700–730 nm. SHAP analysis confirmed critical bands for DMY (965 nm, 712 nm, and 1652 nm), NC (1390 nm and 713 nm), ADF (1390 nm and 715–725 nm), and NDF (400 nm, 983 nm, 1350 nm, and 1800 nm). The fitting accuracy for ADF estimated using RF was lower (R² = 0.58), while the fitting accuracy for other indicators was higher (R² ≥ 0.59). The performance and prediction accuracy of ERT (R² = 0.63) were noticeably superior to those of RF. In conclusion, our method effectively identifies influential bands, optimizing forage yield and quality estimation.

Medium resolution spectral imager II (MERSI-2) is a payload for the Chinese meteorological satellite Feng Yun 3 (FY-3). China's satellite remote sensing observation capabilities such as climate change research can be improved during MERSI-2's operation in orbit, and the sensor is the world's first imaging instrument that can obtain a global infrared split-window data with a spatial resolution of 250 m. We developed an operational spilt-window (SW) algorithm to retrieve land surface temperature (LST) accurately from the MERSI-2 data. The SW algorithm coefficients were derived from a simulation dataset that was established with the Moderate spectral resolution atmospheric Transmittance model version 5.2 and the thermodynamic initial guess retrieval dataset. In the practical retrieval, the precise algorithm coefficients were determined by view zenith angle and atmospheric water vapor content (WVC), the atmospheric WVC were obtained from the ERA5 dataset, and the land surface emissivity was dynamically estimated using the advanced spaceborne thermal emission and reflection radiometer global emissivity dataset, considering the fractional of vegetation cover and snow cover. The retrieved LST compared with in situ LST, which was highly consistent with the in situ LST and that the root-mean-square error of the two is within 3 K. The retrieved LST was compared with the MYD11_L2 and MYD21_L2 LST products, and the results indicated that MERSI-2 LST was more consistent with the MYD21 LST. The operational SW algorithm for FY-3D MERSI-2 developed in this study could retrieve LST accurately and has a wide range of popularization and application values.

<!-- *** Custom HTML *** --> Medium Resolution Spectral Imager II (MERSI-2) is a payload of the China meteorological satellite FY-3D. The sensor bands 24 (10.3 to 11.3 μm) and 25 (11.5 to 12.5 μm) images, which are most suitable for land surface temperature (LST) retrieval, have higher spatial resolution than that of similar sensors. However, bands 24/25 images with spatial resolution of 250 m (hereafter bands 24/25 250-m images) have considerable horizontal stripe noise caused by the limitation of the sensor imaging system’s multi-detector parallel scanning and the multiple interferences from space. Thus, the application value of LST retrieval using bands 24/25 250-m images is severely affected. At present, existing methods used to remove stripe noise often cannot retain the spectral information of the original image. Our research considers that MERSI-2 bands 24/25 images with spatial resolution of 1000 m (hereafter bands 24/25 1000-m images) and no stripe noise can provide reasonable reference statistics for bands 24/25 250-m images. Thus, the moment matching method considering synchronous reference image is applied to remove the stripe noise of bands 24/25 250-m images in the land area. Results shows that the moment matching method considering the synchronous reference image can effectively remove the stripe noise in the land area. In addition, quantitative evaluation criteria, such as information entropy H, mean-absolute-error (MAE), and peak signal-to-noise ratio, are better than those of the traditional moment matching method. After destriping, the LST values using MERSI-2 data are retrieved with better effect and decrease by 0.339 and 0.504 K using MAE and root-mean-square error, respectively, in contrast to the Moderate Resolution Imaging Spectroradiometer LST product.