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.

Investigating spatiotemporal dynamics and varying relationships between water table depth (WTD) and land use changes is critical for efficient groundwater management and land use planning in arid zones. The primary objective of the present study is to combine satellite and field measured data to quantitatively analyze variations in WTD and its relationship with land use change in the Ogan–Kucha River Oasis in the northwest arid zones of China, and reveal the spatial heterogeneity and variations in the abovementioned relationship at spatiotemporal scales. The spatiotemporal variations in WTD and land use change at different time intervals (1997–2007, 2007–2017, and 1997–2017) were analyzed based on geostatistical analysis methods and grid cell approaches. The relationships between land use types and changes in WTD were assessed using correlation and ordinary least square analyses. The relationships between spatiotemporal variations in WTD and land use change were explored using local level geographically weighted regression. The results indicated that influences of human activity on the variation of WTD have gradually increased, and that WTD has declined rapidly in most parts of the study area. The spatial distribution of WTD exhibited significant spatiotemporal heterogeneity, and WTD was lower in the inner parts of the oasis and shallower in the oasis-desert ecotone. The WTD decline rates in the irrigation area were much higher than those in the oasis-desert ecotone. The cultivated land area has expanded markedly, whereas the shrub covered area has shrunk rapidly. Changes in WTD were positively correlated with changes in cultivated land area, and the expansion center of cultivated land has gradually moved from upstream of the alluvial fan to downstream of the alluvial fan and to the oasis-desert ecotone. The relationships between changes in cultivated land and WTD in the ecotone were more prominent than those in the inner parts of the oasis. Therefore, agricultural land expansion and planning in the study area should be integrated based on spatiotemporal changes in the groundwater depth to maintain the stability of groundwater systems and sustainable groundwater exploitation.

Hyperspectral remote sensing technology can provide a rapid and nondestructive method for soil nickel (Ni) content detection. In order to select a high-effective method for estimating the soil Ni content using a hyperspectral remote sensing technique, 88 soil samples were collected in Urumqi, northwest China, to obtain Ni contents and related hyperspectral data. At first, 12 spectral transformations were used for the original spectral data. Then, Pearson’s correlation coefficient analysis (PCC) and the CARS method were used for selecting important wavelengths. Finally, partial least squares regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) models were used to establish the hyperspectral inversion models of the Ni content in the soil using the important wavelengths. The coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and residual prediction deviation (RPD) were selected to evaluate the inversion effects of the models. The results indicated that using the PCC and CARS method for the original and transformed wavebands can effectively improve the correlations between the spectral data and Ni content of the soil in the study area. The random forest regression model, based on the first-order differentiation of the reciprocal (RTFD–RFR), was more stable and had the best inversion effects, with the highest predictive ability (R² = 0.866, RMSE = 1.321, MAE = 0.986, RPD = 2.210) for determining the Ni content in the soil. The RTFD–RFR methods can be used as a means of the inversion of the Ni content in urban soil. The results of the study can provide a technical support for the hyperspectral estimation of the Ni content of urban soil.