Leaf water content (LWC) is a vital indicator of crop growth and development. While visible and near-infrared (VIS–NIR) spectroscopy makes it possible to estimate crop leaf moisture, spectral preprocessing and multiband spectral indices have important significance in the quantitative analysis of LWC. In this work, the fractional order derivative (FOD) was used for leaf spectral processing, and multiband spectral indices were constructed based on the band-optimization algorithm. Eventually, an integrated index, namely, the multiband spectral index (MBSI) and moisture index (MI), is proposed to estimate the LWC in spring wheat around Fu-Kang City, Xinjiang, China. The MBSIs for LWC were calculated from two types of spectral data: raw reflectance (RR) and the spectrum based on FOD. The LWC was estimated by combining machine learning (K-nearest neighbor, KNN; support vector machine, SVM; and artificial neural network, ANN). The results showed that the fractional derivative pretreatment of spectral data enhances the implied information of the spectrum (the maximum correlation coefficient appeared using a 0.8-order differential) and increases the number of sensitive bands, especially in the near-infrared bands (700–1100 nm). The correlations between LWC and the two-band index (RVI_{1156, 1628 nm}), three-band indices (3BI-3_{(766, 478, 1042 nm)}, 3BI-4_{(1129, 1175, 471 nm)}, 3BI-5_{(814, 929, 525 nm)}, 3BI-6_{(1156, 1214, 802 nm)}, 3BI-7_{(929, 851, 446 nm)}) based on FOD were higher than that of moisture indices and single-band spectrum, with r of − 0.71**, 0.74**, 0.73**, − 0.72**, 0.75** and − 0.76** for the correlation. The prediction accuracy of the two-band spectral indices (DVI_{(698, 1274 nm)} DVI_{(698, 1274 nm)} DVI_{(698, 1274 nm)}) was higher than that of the moisture spectral index, with R² of 0.81 and R² of 0.79 for the calibration and validation, respectively. Due to a large amount of spectral indices, the correlation coefficient method was used to select the characteristic spectral index from full three-band indices. Among twenty seven models, the FWBI-3BI_{− 0.8 order} model performed the best predictive ability (with an R² of 0.86, RMSE of 2.11%, and RPD of 2.65). These findings confirm that combining spectral index optimization with machine learning is a highly effective method for inverting the leaf water content in spring wheat.

Soil salinity is one of the major factors causing land degradation and desertification on earth, especially its important damage to farming activities and land-use management in arid and semiarid regions. The salt-affected land is predominant in the Keriya River area of Northwestern China. Then, there is an urgent need for rapid, accurate, and economical monitoring in the salt-affected land. In this study, we used the electrical conductivity (EC) of 353 ground-truth measurements and predictive capability parameters of WorldView-2 (WV-2), such as satellite band reflectance and newly optimum spectral indices (OSI) based on two dimensional and three-dimensional data. The features of spectral bands were extracted and tested, and different new OSI and soil salinity indices using reflectance of wavebands were built, in which spectral data was pre-processed (based on First Derivative (R-FD), Second Derivative (R-SD), Square data (R-SQ), Reciprocal inverse (1/R), and Reciprocal First Derivative (1/R-FD)), utilizing the partial least-squares regression (PLSR) method to construct estimation models and mapping the regional soil-affected land. The results of this study are the following: (a) the new OSI had a higher relevance to EC than one-dimensional data, and (b) the cross-validation of established PLSR models indicated that the β-PLSR model based on the optimal three-band index with different process algorithm performed the best result with R²_V = 0.79, Root Mean Square Errors (RMSE_V) = 1.51 dS·m⁻¹, and Relative Percent Deviation (RPD) = 2.01 and was used to map the soil salinity over the study site. The results of the study will be helpful for the study of salt-affected land monitoring and evaluation in similar environmental conditions.

Drought presents significant challenges in arid regions, influencing local climate and environmental dynamics. While the large-scale climatic phenomena in Xinjiang, northwest China, are well-documented, the finer-scale climatic variability in subregions such as the Ili River Valley (IRV) remains insufficiently studied. This knowledge gap impedes effective regional planning and environmental management in this ecologically sensitive area. In this study, we analyze the spatiotemporal evolution of drought in the IRV from 1961 to 2023, using data from ten meteorological stations. The SPEI drought index, along with Sen’s trend analysis, the Mann–Kendall test, the cumulative departure method, and wavelet analysis, were employed to assess drought patterns. Results show a significant drying trend in the IRV, starting in 1995, with frequent drought events from 2018 onwards, and no notable transition year observed from wet to dry conditions. The overall drought rate was −0.09 per decade, indicating milder drought severity in the IRV compared to broader Xinjiang. Seasonally, the IRV experiences drier summers and wetter winters compared to regional averages, with negligible changes in autumn and milder drought conditions in spring. Abrupt changes in the drying seasons occurred later in the IRV than in Xinjiang, with delays of 21 years for summer, and over 17 and 35 years for spring and autumn, respectively, indicating a lagged response. Spatially, the western plains are more prone to aridification than the central and eastern mountainous regions. The study also reveals significant differences in drought cycles, which are longer than those in Xinjiang, with distinct wet–dry phases observed across multiple time scales and seasons, emphasizing the complexity of drought variability in the IRV. In conclusion, the valley exhibits unique drought characteristics, including milder intensity, pronounced seasonal variation, spatial heterogeneity, and notable resilience to climate change. These findings underscore the need for region-specific drought management strategies, as broader approaches may not be effective at the subregional scale.

Drought presents significant challenges in arid regions, adversely impacting agriculture, water resources, and ecosystems. In Xinjiang, Northwest China, although large-scale climatic phenomena are well studied, finer-scale climatic variability in subregions such as the Ili River Valley (IRV) remains understudied. This knowledge gap impairs effective agricultural planning and environmental management in this ecologically fragile but agriculturally important region. In this study, we analyze the spatiotemporal evolution of drought in the IRV from 1961 to 2023, using data from ten meteorological stations. We applied the SPEI drought Index alongside Sen's trend analysis, the Mann-Kendall test, the cumulative departure method, and wavelet analysis to examine drought patterns. Results indicate significant drying trends in the IRV beginning in 2005, with frequent drought events from 2015 onwards, and 2019 marking a key transition from wet to dry conditions. The overall drought rate was -0.09/10a, suggesting a milder drought severity in the IRV compared to broader Xinjiang. Seasonally, the IRV experiences drier summers and wetter winters relative to the region's averages, with negligible changes in autumn and milder drought conditions in spring. Abrupt changes in drying seasons occurred later than in Xinjiang, with delays of 5, 21, and over 37 years for spring, summer, and autumn, respectively, indicating a lagging response. Spatially, the western plains are more prone to aridification than the central and eastern mountainous regions. significant differences in drought cycles, longer than those in Xinjiang, with distinct wet-dry phases observed over multiple time scales and across seasons highlighting the complexity of drought variability in IRV. In conclusion, the Vally exhibits unique drought characteristics, with milder intensities, pronounced seasonal variation, spatial heterogeneity, and notable resilience to climate change. These findings emphasize the necessity of region-specific drought management strategies, as those effective on a larger regional scale may not be suitable for subregions.