Rapid urban development in China has aggravated land subsidence, which poses a potential threat to sustainable urban development. It is imperative to monitor and predict land subsidence over large areas. To address these issues, we chose Henan Province as the study area and applied small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain land deformation information for monitoring land subsidence from November 2019 to February 2022 with 364 multitrack Sentinel-1A satellite images. The current traditional time-series deep learning models suffer from the problems of (1) poor results in extracting a sequence of information that is too long and (2) the inability to extract the feature information between the influence factor and the land subsidence well. Therefore, a long short-term memory-temporal convolutional network (LSTM-TCN) deep learning model was proposed in order to predict land subsidence and explore the influence of environmental factors, such as the volumetric soil water layer and monthly precipitation, on land subsidence in this study. We used leveling data to verify the effectiveness of SBAS-InSAR in land subsidence monitoring. The results of SBAS-InSAR showed that the land subsidence in Henan Province was obvious and uneven in spatial distribution. The maximum subsidence velocity was −94.54 mm/a, and the uplift velocity was 41.23 mm/a during the monitoring period. Simultaneously, the land subsidence in the study area presented seasonal changes. The rate of land subsidence in spring and summer was greater than that in autumn and winter. The prediction accuracy of the LSTM-TCN model was significantly better than that of the individual LSTM and TCN models because it fully combined their advantages. In addition, the prediction accuracies, with the addition of environmental factors, were improved compared with those using only time-series subsidence information.

Research on soil organic carbon (SOC) is crucial for improving soil carbon sinks and achieving the "double-carbon" goal. This study introduces ten auxiliary variables based on the data from a 2021 land quality survey in Zhengzhou and a multi-objective regional geochemical survey. It uses geostatistical ordinary kriging (OK) interpolation, as well as classical machine learning (ML) models, including random forest (RF) and support vector machine (SVM), to map soil organic carbon density (SOCD) in the topsoil layer (0 − 20 cm) of cultivated land. It partitions the sampling data to assess the generalization capability of the machine learning models, with Zhongmu County designated as an independent test set (dataset2) and the remaining data as the training set (dataset1). The three models are trained using dataset1, and the trained machine learning models are directly applied to dataset2 to evaluate and compare their generalization performance. The distribution of SOCD and SOCS in soils of various types and textures is analyzed using the optimal interpolation method. The results indicated that: (1) The average SOC densities predicted by OK interpolation, RF, and SVM are 3.70, 3.74, and 3.63 kg/m², with test set precisions (R²) of 0.34, 0.60, and 0.81, respectively. (2) ML achieves a significantly higher predictive precision than traditional OK interpolation. The RF model's precision is 0.21 higher than the SVM model and more precise in estimating carbon stock. (3) When applied to the dataset2, the RF model exhibited superior generalization capabilities (R² = 0.52, MSE = 0.32) over the SVM model (R² = 0.32, MSE = 0.45). (4) The spatial distribution of surface SOCD in the study area exhibits a decreasing gradient from west to east and from south to north. The total carbon stock in the study area is estimated at approximately 10.76 × 10⁶t. (5) The integration of soil attribute variables, climatic variables, remote sensing data, and machine learning techniques holds significant promise for the high-precision and high-quality mapping of soil organic carbon density (SOCD) in agricultural soils.

Accurate spatial information of cropland is crucial for precision agricultural management and ensuring national food security. High-resolution remote sensing imagery combined with deep learning algorithms provides a promising approach for extracting detailed cropland information. However, due to the diverse morphological characteristics of croplands across different agricultural landscapes, existing deep learning methods encounter challenges in precise boundary localization. The advancement of large-scale vision models has led to the emergence of the Segment Anything Model (SAM), which has demonstrated remarkable performance on natural images and attracted considerable attention in the field of remote sensing image segmentation. However, when applied to high-resolution cropland extraction, SAM faces limitations in semantic expressiveness and cross-domain adaptability. To address these issues, this study proposes a dual-branch framework integrating SAM and a semantically aware network (SAM-SANet) for high-resolution cropland extraction. Specifically, a semantically aware branch based on a semantic segmentation network is applied to identify cropland areas, complemented by a boundary-constrained SAM branch that directs the model’s attention to boundary information and enhances cropland extraction performance. Additionally, a boundary-aware feature fusion module and a prompt generation and selection module are incorporated into the SAM branch for precise cropland boundary localization. The former aggregates multi-scale edge information to enhance boundary representation, while the latter generates prompts with high relevance to the boundary. To evaluate the effectiveness of the proposed approach, we construct three cropland datasets named GID-CD, JY-CD and QX-CD. Experimental results on these datasets demonstrated that SAM-SANet achieved mIoU scores of 87.58%, 91.17% and 71.39%, along with mF1 scores of 93.54%, 95.35% and 82.21%, respectively. Comparative experiments with mainstream semantic segmentation models further confirmed the superior performance of SAM-SANet in high-resolution cropland extraction.

Precise fertilizer application in agriculture requires accurate and dependable measurements of the soil total nitrogen (TN) concentration. Henan Province is one of the most important grain-producing areas in China. In order to promote the development of precision agriculture in Henan Province, this study took the high-standard basic farmland construction area in central Henan Province as the research area. Using single-phase images acquired from the ZY1-02D satellite hyperspectral sensor on 28 January 2021 (with a spatial resolution of 30 m × 30 m, a spectral range that covered 400–2500 nm, and a revisit period of 3 days) for spectral reflectance transformation and feature spectral band extraction. Based on multiple representation models, such as multiple linear regression, partial least squares regression, and support vector machine (SVM), all bands, feature bands, feature band combinations, and differential evolution (DE) algorithms were used to extract the secondary characteristic variables of the combination of characteristic bands, which were used as model inputs to estimate the content of TN in the study area. It was found that (1) the spectral reflectance transformation could help to improve the accuracy of prediction by reducing the interference from noise in the model, but the optimal spectral transformation method differed between different models and even between the training and test sets of the same model; (2) the estimation accuracy of the TN content model based on the minimum shrinkage and feature selection operator of the feature band was usually better than that of the full band, the feature combination band contained more effective information related to the TN content, and the combination of the DE algorithm and the SVM model achieved a better estimation accuracy for secondary feature extraction and TN content estimation of the feature combination band; and (3) ZY1-02D hyperspectral satellite data have the potential for the dynamic and non-destructive monitoring of regional TN content.

Henan Province, a major coal producing region in China, faces severe surface subsidence induced by extensive underground mining, which compromises regional ecological security and infrastructure stability. In this study, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) was applied to Sentinel-1A imagery acquired from March 2017 to February 2025 to characterize surface deformation in concentrated coal mining areas. A local validation was conducted within a representative mining area in Study Area 3 using measurements from 14 leveling benchmarks acquired between 5 May and 20 July 2023. The comparison yielded an $R^2$ of 0.816 and an RMSE of 9.22 mm, indicating good agreement between the SBAS-InSAR and leveling measurements during the validation interval. The subsidence in the study area exhibits significant spatial heterogeneity and continuous accumulation characteristics. The most negative approximate vertically projected deformation rate reached −371 mm/yr, and the maximum cumulative displacement reached −2101 mm. Scenario-based sensitivity analysis indicated potential projection errors of 6.76–8.34% for a horizontal-to-vertical displacement ratio of 0.10 and 20.28–25.01% for a ratio of 0.30, with larger uncertainty expected near subsidence trough margins. Given the difficulty of quantifying large-scale underground mining parameters, this study employs multisource environmental and topographic variables as auxiliary indicators and develops an XGBoost-SHAP model to evaluate their relative explanatory contributions to the spatial heterogeneity of mining-induced subsidence. Among the selected measurable environmental and topographic variables, groundwater table depth represents the most important measurable explanatory factor for the spatial heterogeneity of subsidence, with distinct response patterns between plain areas with thick unconsolidated layers and piedmont bedrock regions. Furthermore, wavelet coherence analysis identifies scale-dependent spatial associations between topography and subsidence. At the regional scale, elevation exhibits spatial correspondence with the geomorphological framework of contiguous subsidence basins. At the local scale, slope and aspect show localized associations with differential deformation gradients near the margins of subsidence troughs.

Ground deformation associated with rapid urbanization has emerged as a common global challenge facing cities. However, identifying its contributing mechanisms remains difficult because multiple factors, including groundwater management, engineering loads, and extreme weather events, are often superimposed in space and time. This study proposes a scale-aware analytical framework that integrates Small Baseline Subset InSAR (SBAS-InSAR), Stationary Wavelet Transform (SWT), and Independent Component Analysis (ICA) to separate deformation signals by spatial scale and statistical structure, and then support the interpretation of their potential drivers. The original deformation field is first decomposed into a large-scale background component and a small-scale local component using SWT. ICA is then applied to the large-scale background component to extract two statistically separated deformation modes with distinct spatiotemporal characteristics. These modes are further examined using Least-Squares Cross-Wavelet Analysis (LSCWA) and ancillary hydrological information to evaluate their associations with groundwater-related processes. We applied and validated this framework in Zhengzhou, China, as a representative case. Results show that Zhengzhou exhibits a clear “central uplift and peripheral subsidence” pattern. The large-scale component contains two statistically separated modes. The first mode shows a long-term uplift pattern that is spatially consistent with the groundwater no-take zone and temporally associated with confined-aquifer water-level recovery. The second mode shows an annual oscillation that is coherent with groundwater storage anomaly, with an estimated deformation lag of approximately 1–2 months. The small-scale local component highlights subsidence bowls that are spatially and temporally associated with engineering and land-use features, including newly constructed transportation corridors, high-density building areas, landfills, and waterfront park projects. The proposed framework provides a scale-aware pathway for interpreting complex urban deformation fields and may support more targeted geohazard monitoring and urban planning in areas affected by coupled hydrological and anthropogenic pressures.