Soil salinization is a critical and global environmental problem. Effectively mapping and monitoring the spatial distribution of soil salinity is essential. The main aim of this work was to map soil salinity in Shandong Province located on the Yellow River Delta of China using Sentinel-1/2 remote sensing data and digital elevation model (DEM) data, coupled with soil sampling data, and combined with four regression models: support vector regression (SVR), stepwise multi-regression (SMR), partial least squares regression (PLSR) and random forest regression (RFR). For these purposes, 60 soil samples were collected during the field survey conducted from 9 to 14 October 2019, corresponding to the Sentinel-1/2 and DEM data. Then we established a soil salinity and feature dataset based on the sampled data and the features extracted from Sentinel-1/2 and DEM data. This study adopted the feature importance of the RF model to screen all features. The results showed that the CRSI index made the greatest contribution in retrieving soil salinity in this region. In this paper, 18 sampling points were used to validate and compare the performance of the four models. The results reveal that, compared with the other regression models, the PLSR model has the best performance (R² = 0.66, and RMSE = 1.30). Finally, the PLSR method was used to predict the spatial distribution of soil salinity in the Yellow River Delta. We concluded that the model can be used effectively for the quantitative estimation of soil salinity and provides a useful tool for ecological construction.

In the early stages, uncontrolled groundwater extraction led to the Hangjiahu (HJH) Plain becoming one of the areas with the most severe land subsidence in China. Since the beginning of this century, comprehensive measures have been taken to control the continuous aggravation of large land subsidence patterns in some areas; however, urban land subsidence issues, influenced by various factors, still persist and exhibit complex geographical distribution characteristics. In this study, we utilized Sentinel-1A images and the SBAS-InSAR technique to capture surface deformation over the HJH Plain in Zhejiang from 16 March 2017 to 20 January 2023. Through a comparative analysis with geological conditions, changes in surface mass loading, rainfall and groundwater, and land use types, we discussed the contributions of natural and anthropogenic factors to land subsidence. Augmented with optical remote sensing images and field investigations, we conducted a correlation analysis of the land subsidence status. The preliminary findings suggest that changes in surface mass loading and short-term heavy rainfall under extreme weather conditions can lead to periodic land subsidence changes in the region. Additionally, extensive infrastructure construction triggered by urbanization has resulted in significant and sustained land subsidence deformation. The research findings play an important guiding role in formulating scientifically effective strategies for land subsidence prevention and control, as well as urban planning and construction.

Soil Moisture Content (SMC) is crucial for sustaining agricultural productivity, ecosystem health, and climate feedback processes. This study investigates the spatiotemporal variation of SMC during two key maize growth stages using a combined physically-based and data-driven approach, which synergizes Water Cloud vegetation correction, Dubois–Dobson dielectric retrieval. The developed SMC inversion method achieving high accuracy with determination coefficients (R²) of 0.75 in the maturity stage and 0.78 in the filling stage, yielding high-resolution SMC product. Machine learning methods, enhanced by Shapley Additive Explanations (SHAP), were employed to analyze the impacts of environmental factors on SMC based on the high-resolution SMC product. Land surface temperature (LST) was identified as the primary driver of spatial variation during maturity, while elevation dominated during the filling stage. The different levels of SMC in two stages were largely dictated by meteorological factors, but the role of maize was deemed inconsequential on SMC’s temporal variation. Furthermore, the relationships between SMC and environmental factors were quantified. SMC exhibits a gradual decrease trend with rising LST, yet this trend escalates sharply when LST surpasses 15 °C. An optimal range of Normalized difference vegetation index (NDVI) value, between 0.2 and 0.5, was discovered to be most effective for preserving SMC. This research offers a comprehensive perspective on the drivers of SMC variation, which is pivotal for informed agricultural practices and the enhancement of climate models.