In semi-arid pasture areas, drought may directly influence livestock production, cause economic losses, and accelerate the processes of desertification along with destructive human activities (i.e., overgrazing). The aim of this article is to analyze the disadvantages of several drought indices derived from remote sensing data and develop a new vegetation drought index (VDI) for monitoring of grassland drought with high temporal frequency (dekad) and fine spatial resolution (1 km). The site-based soil moisture data from the field campaign in 2014 and the fenced biomass values at nine sites from 2000 to 2015 were adopted for validation. The results indicate that the proposed VDI would better reflect the extent, severity, and changes of drought compared with single drought indices or the vegetation health index (VHI); specifically, the VDI is more closely related to site-based soil moisture, with R human increasing to approximately 0.07 compared with the VHI; and with normalized fenced biomass (NFB) values, with average R human increasing to approximately 0.11 compared with the VHI. However, the correlations between VHI and VDI with NFB values are relatively lower in desert steppe regions. Furthermore, regional drought-affected data (RDA) are used to ensure spatial consistency of the evaluation; the VDI map is in good agreement with the RDA map based on field measurements. The presented VDI shows reliable and stable drought monitoring ability, which will play an important role in the future drought monitoring of inland grassland.

Land surface temperature (LST) plays a crucial role in the physical and chemical processes of the land-atmosphere system. Remote sensing technology has greatly advanced the measurement of thermal infrared land surface temperature (TIR LST), which is the most widely utilized surface temperature product. However, cloud cover and mist often cause significant data loss in TIR LST. To address this issue and reconstruct the MYD11A1 LST under cloudy conditions, this study proposes an all-weather LST generation method based on the eXtreme Gradient Boosting (XGBoost) model. This method incorporates spatial-seamless passive microwave land surface temperature (PMW LST) to capture the nonlinear relationship between TIR LST and other variables. Compared to the MYD11A1 LST, the generated all-weather LST provides continuous spatial texture information without a significant boundary reconstruction effect, improving the accuracy of spatiotemporal variations in LST in China. In situ validation demonstrated the high accuracy of the generated all-weather LST, with mean R ² , Bias and unbiased root mean square error (ubRMSE) of 0.96 (0.91), 1.08 K (3.61 K) and 2.92 K (4.54 K) under clear (cloudy) daytime conditions, and 0.92 (0.95), -0.93 K (-2.96 K) and 3.09 K (3.04 K) under clear (cloudy) nighttime conditions. These results indicate the feasibility and reasonableness of the all-weather LST generation method developed in this study and affirm its ability to generate highly accurate all-weather LST.

As a clean energy resource, coalbed methane (CBM) is an important industry in China’s dual-carbon strategic planning. Despite the immense potential of CBM resources in China, the current exploration level remains low due to outdated survey technology, impeding large-scale exploration and development. This study investigates the application of hyperspectral data in CBM enrichment areas, specifically focusing on the extraction of alteration minerals in the Hudi coal mine area of the Qinshui Basin using ZY-1 02D and Hyperion hyperspectral data. The hyperspectral alteration mineral identification methods are summarized and analyzed. A method that combines spectral feature matching and diagnostic characteristic parameters is proposed for mineral extraction based on the spectral characteristics of different minerals. The extraction results are verified through field samples using X-ray diffraction analysis. Results show that (1) both ZY-1 02D and Hyperion hyperspectral data yield favorable extraction results for clay and carbonate minerals; (2) the overall accuracy of clay and carbonate minerals extraction is higher using ZY-1 02D data compared with Hyperion data, with accuracies of 81.67% and 79.03%, respectively; (3) the proposed method effectively extracts alteration minerals in CBM enrichment areas using hyperspectral data, thereby providing valuable technical support for the application of hyperspectral data.

Drought is a perilous agrometeorological phenomenon that often causes crop damage in arid and semi-arid regions vulnerable to climate variability. However, accurate drought monitoring remains deficient in many countries, including Kyrgyzstan, and the interconnections between several types of drought and contributions to crop yield are still unclear. Hence, we aimed to determine the propagation time in three types of drought (meteorological drought, soil drought and vegetation drought) for understanding interconnections of them. Moreover, we focused on comprehensively evaluation the performance of multiple drought indices for each type over the complex terrain of Kyrgyzstan, especially for drought index of synergistic land surface temperature and vegetation conditions information. The results demonstrated that Standard Precipitation Index (SPI) effectively detected meteorological drought, while the vegetation health index (VHI) coupled with temperature data was optimal for vegetation drought monitoring in Kyrgyzstan. Furthermore, our findings indicated a one-month response time for soil drought at a 10 cm depth to SPI, and a four-month response time at a 40 cm depth to meteorological drought (SPI). The response time of VHI to soil drought condition index (SMCI) was approximately one month, regardless of whether the soil drought occurred at a depth of 10 cm or 40 cm. In general, the response time of VHI to SPI was three months. Finally, by analyzing the correlation between crop yield productivity and drought indices, we discovered that the crop yield predictions by the three types of drought were differential and complex, but VHI was the most effective index. At the same time, VHIacc(May-Sep.), SMCIr(0-40cm)_May-Sep. and SPI5_Aug. have different contributions to crop yield variations, and these are also differences in their impacts on different crops and provinces.The synergistic effect of the three types of drought may significantly improve crop yield prediction in Kyrgyzstan in future studies. These findings may significantly contribute to drought prevention and mitigation in drought-prone Central Asian countries.

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.