Surface soil water content (SWC) is a major determinant of crop production, and accurately retrieving SWC plays a crucial role in effective water management. Unmanned aerial systems (UAS) can acquire images with high temporal and spatial resolutions for SWC monitoring at the field scale. The objective of this study was to develop an algorithm to retrieve SWC by integrating soil texture into a vegetation index derived from UAS multispectral and thermal images. The normalized difference vegetation index (NDVI) and surface temperature (T_s) derived from the UAS multispectral and thermal images were employed to construct the temperature vegetation dryness index (TVDI) using the trapezoid model. Soil texture was incorporated into the trapezoid model based on the relationship between soil texture and the lower and upper limits of SWC to form the texture temperature vegetation dryness index (TTVDI). For validation, 128 surface soil samples, 84 in 2019 and 44 in 2020, were collected to determine soil texture and gravimetric SWC. Based on the linear regression models, the TTVDI had better performance in estimating SWC compared to the TVDI, with an increase in R² (coefficient of determination) by 14.5% and 14.9%, and a decrease in RMSE (root mean square error) by 46.1% and 10.8%, for the 2019 and 2020 samples, respectively. The application of the TTVDI model based on high-resolution multispectral and thermal UAS images has the potential to accurately and timely retrieve SWC at the field scale.

Efficiently monitoring and quantifying the response of genotypes to water stress is critical in developing resilient crop cultivars in water-limited environments. The objective of this study was to assess water stress in cotton (Gossypium hirsutum L.) using high-resolution unmanned aerial system (UAS) images and identify water-stress-resistant cultivars in plant breeding. Various vegetation indices (VIs) and the crop water stress index (CWSI) derived from UAS images were applied to assess water stress in eight cotton cultivars under four irrigation treatments (90%, 60%, 30%, and 0% ET). The enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), normalized difference vegetation index (NDVI), and crop water stress index (CWSI) were effective in detecting the effects of the irrigation treatments during the growing season. These VIs effectively differentiated cultivars in the middle and late seasons, while the CWSI detected cultivar differences in the mid–late growing season. The NDVI, GNDVI, NDRE, and EVI had a strong positive relationship with cotton yield starting from the mid-growing season in two years (R² ranged from 0.90 to 0.95). Cultivars under each irrigation treatment were clustered into high-, medium-, and low-yielding groups based on the VIs at the mid–late growing seasons using hierarchical cluster analysis (HCA). The EVI derived from UAS images with high temporal and spatial resolutions can effectively screen drought-resistant cotton varieties under 30% and 60% irrigation treatments. The successful classification of cultivars based on UAS images provides critical information for selecting suitable varieties in plant breeding to optimize irrigation management based on water availability scenarios. This technology enables the targeted selection of water-stress-resistant cotton cultivars and facilitates site-specific crop management and yield prediction, ultimately contributing to precision irrigation and sustainable agriculture in water-limited environments.

Soil salinization in arid regions has drawn considerable attention due to its constraints on agricultural productivity and ecological security. Climate and soil texture, as key drivers at the macroscale, still lack systematic quantitative assessments regarding their mechanisms in shaping the long-term dynamics of salinity, and comparative evaluations of their relative contributions remain insufficient. Therefore, there is an urgent need to explore the spatiotemporal variations in soil salinization in arid regions and its responses to climate and soil texture. This study was based on salinity sampling sites collected in southern Xinjiang in 2023. A Random Forest (RF)-based inversion model was constructed using spectral indices derived from Landsat-9 and Sentinel-2 as environmental predictors. The predictive performance of models using all variables was compared with those using RF-based feature selection. The optimal model was then applied to retrieve soil salinity concentrations for 2008, 2013, 2018, and 2023 at four equidistant time points, enabling the spatiotemporal evolution of soil salinization across the study area to be assessed. Finally, a Boosted Regression Tree (BRT) model was employed to quantify the driving contributions of climate and soil texture. Results showed that the feature-selected Landsat-9 model performed best, with an R² of 0.747, significantly outperforming the Sentinel-2 model. The mean soil salinity concentration declined rapidly from 2008 to 2013, followed by a relatively slower but sustained decrease thereafter. The proportion of non-salinized land increased from 3.08% to 30.81%. The Sen’s slope−Mann−Kendall test indicated that 78.6% of salinity levels exhibited a significant downward trend, while 18.8% showed a slight increase. The relative contribution analysis indicated that climatic factors consistently exerted a stronger influence on the evolution of soil salinization than soil texture. Specifically, the contribution of climatic variables increased from 65.2% in 2008 to 66.8% in 2023, whereas that of soil texture decreased slightly from 34.8% to 33.2%. Among the climatic variables, the effect of potential evapotranspiration gradually weakened, while the impacts of temperature and precipitation continued to intensify. In contrast, soil texture variables played a comparatively minor yet stable role throughout the study period. These findings provide an effective framework for long-term monitoring of soil salinization and offer critical insights for adaptive management in arid regions under climate change.

Vegetation diversity is a crucial indicator for evaluating grassland ecosystems. Remote sensing technology has great potential in assessing grassland vegetation diversity. In this study, the relationship between remote sensing indices and species diversity was investigated at varying spatial and temporal scales in Bayanbulak Grassland National Nature Reserve, China. Spectral variation, defined as the coefficient of variation in vegetation indices, was used as a proxy for species diversity, which was quantified using species diversity indices. The “spectral diversity-species diversity” relationship was validated across diverse spatial scales and between different years using Sentinel-2 images and ground investigation data. This study found that Kendall’s τ coefficients showed the best performance in evaluating the relationship between the coefficient of variation in VIs (CV_VIs) and species diversity index. The highest τ value was observed for CV_NDVI in 2017 (τ = 0.660, p < 0.01), followed by the Shannon index in 2018 (τ = 0.451, p < 0.01). In addition, CV_EVI demonstrated a significant positive correlation with the Shannon-Wiener Index at the 50 m scale (τ = 0.542), and the highest relationship τ between CV_NDVI and the Shannon-Wiener Index was observed at the 100 m scale (τ = 0.660). The Shannon-Wiener Index in relation to CV_VIs performs better in representing changes in grassland vegetation. Spatial scales and vegetation indices influence the assessment of grassland vegetation diversity. These findings underscore the critical role of remote sensing technology in assessing grassland vegetation diversity across various scales, offering valuable support tools for measuring regional grassland vegetation diversity.

Climate anomalies and human disturbances exert complex effects on regional carbon sequestration (CS), causing ecosystem CS to either increase or decrease due to factors such as vegetation greening, climate extremes, land use/land cover (LULC) changes, and farming systems. This study employs the patch-level land use simulation model to forecast changes in various land-use types in the Xinjiang Uyghur Autonomous Region, China, between 2020 and 2060 under different climatic conditions. The Intergovernmental Panel on Climate Change has proposed shared socioeconomic pathways (SSPs) as potential socioeconomic and environmental trajectories that might influence the region’s future land-use patterns. Three salient findings emerged: (1) LULC displayed considerable variation across future climate scenarios, with a notable rise in forest and grassland cover and a marked decrease in cropland areas under the SSP126 scenario. Conversely, the SSP585 scenario witnessed a substantial cropland expansion, paralleled by a decrease in forest and grassland areas; (2) CS exhibited significant disparities across different future climate scenarios. Xinjiang achieved a peak CS of 9.81 Pg between 2020 and 2060 under the SSP126 scenario, marking an increase of 335.22 Tg in 2060 compared to that in 2020. The SSP585 scenario registered the lowest CS at 9.41 Pg, indicating a decline of 50.07 Tg in 2060 relative to 2020; (3) The impacts of longitude and latitude on CS in Xinjiang predominantly arose from shifts in forest area and vegetation cover based on latitude, which positively influenced CS. In contrast, a decrease in vegetation cover and a subsequent decline in CS were evident with increasing longitude, particularly in the Altai, Tianshan, and Kunlun Mountains. These findings hold significant implications for formulating CS management strategies for terrestrial ecosystems and enhancing ecological preservation in Xinjiang.

Cotton (Gossypium hirsutum L.) is a primary global commercial crop, and accurate monitoring of its growth and yield prediction are essential for optimizing water management. This study integrates leaf area index (LAI) data derived from unmanned aerial system (UAS) imagery into the Decision Support System for Agrotechnology Transfer (DSSAT) model to improve cotton growth simulation and yield estimation. The results show that the normalized difference vegetation index (NDVI) exhibited higher estimation accuracy for the cotton LAI during the squaring stage (R² = 0.56, p < 0.05), whereas the modified triangle vegetation index (MTVI) and enhanced vegetation index (EVI) demonstrated higher and more stable accuracy in the flowering and boll-setting stages (R² = 0.64 and R² = 0.76, p < 0.05). After assimilating LAI data, the optimized DSSAT model accurately represented canopy development and yield variation under different irrigation levels. Compared with the DSSAT, the assimilated model reduced yield prediction error from 40–52% to 3.6–6.3% under 30%, 60%, and 90% irrigation. These findings demonstrate that integrating UAS-derived LAI data with the DSSAT substantially enhances model accuracy and robustness, providing an effective approach for precision irrigation and sustainable cotton management.