Soil salinization is one of the most common forms of land degradation. The detection and assessment of soil salinity is critical for the prevention of environmental deterioration especially in arid and semi-arid areas. This study introduced the fractional derivative in the pretreatment of visible and near infrared (VIS–NIR) spectroscopy. The soil samples (n = 400) collected from the Ebinur Lake Wetland, Xinjiang Uyghur Autonomous Region (XUAR), China, were used as the dataset. After measuring the spectral reflectance and salinity in the laboratory, the raw spectral reflectance was preprocessed by means of the absorbance and the fractional derivative order in the range of 0.0–2.0 order with an interval of 0.1. Two different modeling methods, namely, partial least squares regression (PLSR) and random forest (RF) with preprocessed reflectance were used for quantifying soil salinity. The results showed that more spectral characteristics were refined for the spectrum reflectance treated via fractional derivative. The validation accuracies showed that RF models performed better than those of PLSR. The most effective model was established based on RF with the 1.5 order derivative of absorbance with the optimal values of R² (0.93), RMSE (4.57 dS m⁻¹), and RPD (2.78 ≥ 2.50). The developed RF model was stable and accurate in the application of spectral reflectance for determining the soil salinity of the Ebinur Lake wetland. The pretreatment of fractional derivative could be useful for monitoring multiple soil parameters with higher accuracy, which could effectively help to analyze the soil salinity.

Although the net primary productivity (NPP) of arid/semiarid ecosystem is generally thought to be controlled by precipitation, other factors like CO₂ fertilization effect and temperature change may also have important impacts, especially in the cold temperate areas of the northern China, where significant warming was reported in the recent decades. However, the impacts of climate and atmospheric CO₂ changes to the NPP dynamics in the arid and semiarid areas of China (ASA-China) is still unclear, hindering the development of climate adaptation strategy. Based on numeric experiments and factorial analysis, this study isolated and quantified the effects of climate and CO₂ changes between 1980–2014 on ASA-China’s NPP, using the Arid Ecosystem Model (AEM) that performed well in predicting ecosystems’ responses to climate/CO₂ change according to our evaluation based on 21 field experiments. Our results showed that the annual variation in NPP was dominated by changes in precipitation, which reduced the regional NPP by 10.9 g·C/(m²·year). The precipitation-induced loss, however, has been compensated by the CO₂ fertilization effect that increased the regional NPP by 14.9 g·C/(m²·year). The CO₂ fertilization effect particularly benefited the extensive croplands in the Northern China Plain, but was weakened in the dry grassland of the central Tibetan Plateau due to suppressed plant activity as induced by a drier climate. Our study showed that the climate change in ASA-China and the ecosystem’s responses were highly heterogeneous in space and time. There were complex interactive effects among the climate factors, and different plant functional types (e.g., phreatophyte vs. non-phreatophyte) could have distinct responses to similar climate change. Therefore, effective climate-adaptive strategies should be based on careful analysis of local climate pattern and understanding of the characteristic responses of the dominant species. Particularly, China’s policy makers should pay close attention to climate change and ecosystem health in northeastern China, where significant loss in forest NPP has been triggered by drought, and carefully balance the ecological and agricultural water usage. For wildlife conservation, the drought-stressed grassland in the central Tibetan Plateau should be protected from overgrazing in the face of dramatic warming in the 21st century.

Soil moisture content (SMC) is a basic condition for crop growth, and a key parameter for crop yield prediction and drought monitoring. An advantage of large-scale synchronous observation using remote sensing technology is that it provides the possibility of dynamic monitoring of soil moisture content in a large area. This study aimed to explore the feasibility of accurately estimating soil moisture content at a regional scale by combining ground hyper-spectral data with multispectral remote sensing (Sentinel-2) data. The results showed that the different mathematical transformations increased the correlation between soil spectral reflectance and SMC to varying degrees. Hyper-spectral optimized index normalized difference index (NDI) ((B_769~797 – B_848~881/B_769~797 + B_848~881); (B₈₄₂ – B₇₄₀/B₈₄₂ + B₇₄₀)) derived from the transformed reflectance (the first-order derivate of reciprocal-logarithm (Log (1/R))′, second-order derivate of reciprocal-logarithm (Log (1/R)) ′′) showed significant correlation (correlation coefficient (r) = 0.61; r = 0.47) with SMC, and the correlation coefficient values higher than difference index (DI) and ratio index (RI). From the performance of 12 prediction models which were taken optimized indices as independent variables, the central wavelength reflectance model (Log (1/R))′′ and the average wavelength reflectance model ((Log (1/R)) ′ presented higher validation coefficients (coefficient of determination (R²) = 0.61, root mean square error (RMSE) = 4.09%, residual prediction deviation (RPD) = 1.82; R² = 0.69, RMSE = 3.48%, RPD = 1.91) compared with other models. When verifying the accuracy, the model yields R² values of 0.619 and 0.701. These results indicated that the two-band hyper-spectral optimized indices (NDI) as an optimal indicator for quickly and accurately soil moisture content estimation. Combining the ground hyper-spectral data and satellite remote sensing image regional scale soil moisture content prediction provides a scientific reference for land-space integrated soil moisture content remote sensing monitoring.

In this work, rain totals from 1960–2014, obtained during the warm season (May to October) from 52 meteorological stations, over Xinjiang, China were classified as either light, moderate, or heavy rain in two sub-regions (northern and southern). Spatial and temporal trends for rain amounts and days for the three rain classes were determined. All light, moderate, and heavy rain amounts displayed increasing trends over the two sub-regions. Furthermore, heavy rain amounts contributed the most to changes in total rain amounts. Light rain days in northern Xinjiang significantly decreased, in contrast to increasing light rain days in southern Xinjiang and moderate and heavy rain days within two sub-regions. Results obtained from correlation and relative weights analyses implied that lower-tropospheric specific humidity was the main factor responsible for light rain day trends in Xinjiang. Increasing temperatures were not found to have a significant effect.