Soil texture is an important physical property of soil. Understanding the spatial distribution of cultivated soil texture in black soil areas is crucial for precise agricultural management and cultivated land protection in these zones. This study utilizes the random forest algorithm, Landsat-8 satellite remote sensing data, and climate- and terrain-related environmental covariates to map the spatial distribution of soil texture and analyze its impact on crop growth. The results show that (1) the order of prediction accuracy differs for different soil texture types; April is determined to have the highest prediction accuracy for silt and sand, while May exhibits the greatest accuracy for clay. (2) Adding environmental variables can significantly improve the accuracy of soil texture predictions; the root mean square error (RMSE) has decreased to varying degrees (silt: 0.84; clay: 0.04; sand: 0.85). (3) Soybean growth has the strongest response to soil texture; clay grain is the key factor affecting crop growth in drought scenarios, and sand grain is the dominant factor influencing flooding. This study improves the accuracy of the remote sensing mapping of soil texture through the combination of remote sensing images and environmental variables and quantitatively evaluates the impact of soil texture on crop growth.

High sampling density in digital soil mapping improves accuracy but increases costs. This study optimized sampling density for soil organic matter (SOM) mapping in Longjiang County using an XGBoost model, integrating Sentinel-2 imagery (April–May) and environmental variables. We evaluated the impact of reducing a dataset of 2059 surface soil samples using a soil-type-based stratified random (SR) sampling strategy. The optimal scale was determined using a cost–performance index restricting RMSE increases to under 10%. Results showed that integrating April–May environmental variables significantly improved model accuracy. Crucially, the SR strategy allowed training samples to be reduced to just 434 while maintaining stable predictive performance (R² = 0.499, RMSE = 5.319 g/kg). Furthermore, SOM spatial prediction patterns at this optimized, lower density aligned closely with high-density results, showing no systematic bias. In conclusion, combining environmental constraints with stratified thinning allows regional SOM mapping to maintain reliable accuracy at significantly reduced sampling densities. This offers a cost-effective approach for optimizing soil monitoring networks in black-soil regions.

Long-term monitoring of wetland degradation, recovery, and spatial resilience requires methods that can jointly exploit multi-temporal remote sensing observations and spatially heterogeneous environmental information. This study develops an intelligent remote sensing framework for monitoring the disturbance–recovery dynamics and spatial resilience of typical wetland landscapes in Northeast China by coupling Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) with geographically weighted random forest (GWRF). Annual wetland landscape state sequences for 2000–2022 are constructed from multi-temporal Landsat observations and associated geospatial variables. LandTrendr time-series segmentation is used to identify disturbance timing, disturbance magnitude, recovery rate, and recovery duration, thereby converting long-term wetland change trajectories into quantitative indicators of degradation and recovery. At the GIS grid scale, remotely sensed vegetation and wetness indicators are integrated with wetland patch structure, hydrological conditions, topography, land-cover change, and anthropogenic pressure. GWRF then models local nonlinear relationships under spatial-distance weighting to generate a spatially explicit wetland resilience index and reveal geographically varying dominant controls. The LandTrendr–GWRF model achieved an R² of 0.872, an RMSE of 0.057, and an MAE of 0.039, outperforming RF, GWR, XGBoost, and standalone GWRF benchmarks. The mean wetland resilience index was 0.641, with low-, medium-, and high-resilience areas accounting for 27.86%, 44.37%, and 27.77% of the study area, respectively. Significant spatial clustering was detected (Moran’s I = 0.421, Z = 6.92, p = 0.001). Hydrological regulation, landscape connectivity, fragmentation constraint, and anthropogenic disturbance pressure emerged as the four dominant spatial response types. High-resilience cells formed contiguous clusters where hydrological recharge and landscape connectivity were strong, while low-resilience cells clustered where fragmentation and construction pressure were concentrated. Disturbance magnitude and recovery duration differed markedly among the four response types, with connectivity-dominated cells returning to the pre-disturbance state within 4.4 years and anthropogenic pressure-dominated cells requiring 11.7 years. The proposed framework extends wetland remote sensing from static condition mapping toward process-oriented monitoring of degradation, recovery, connectivity, and resilience, providing a practical approach for long-term wetland restoration assessment and spatial management.