Accurate, timely, and fine-resolution crop phenology is essential for determining the optimal timing of agronomic management practices supporting precision agriculture and food security. Synthetic Aperture Radar (SAR) methods, unaffected by cloud occlusion, have been widely applied in monitoring maize phenology. Nonetheless, their reliance on manual threshold settings, which depend on the user’s expertise, limits their applicability. Furthermore, the neglect of SAR’s potential for monitoring other phenological periods (e.g., seven-leaves date (V7), jointing date (JD), tassel date (TD), and milky date (MID)) hinders their robustness, particularly for regional-scale applications. To address these issues, this study used an adaptive dynamic threshold to evaluate the ability of the Sentinel-1 cross-polarization ratio (CR) in detecting the three-leaves date (V3), V7, JD, TD, MID, and maturity date (MD) of maize. We analyzed the effect of incidence angle, precipitation, and wind speed on Sentinel-1 features to identify the optimal feature for time series fitting. Then, we employed linear regression to determine the optimal threshold and developed an adaptive dynamic threshold for phenology detection. This approach effectively mitigated the speckle noise of Sentinel-1 and minimized artificial interference caused by customary conventional thresholds. Finally, we mapped phenology across 8.3 million ha in Heilongjiang Province. The results indicated that the approach has a higher ability to detect JD (RMSE = 11.10 d), MID (RMSE = 10.31 d), and MD (RMSE = 9.41 d) than that of V3 (RMSE = 32.07 d), V7 (RMSE = 56.37 d), and TD (RMSE = 43.33 d) in Sentinel-1. Compared with Sentinel-2, the average RMSE of JD, MID, and MD decreased by 4.14%, 35.28%, and 26.48%. Moreover, when compared to different thresholds, the adaptive dynamic threshold can quickly determine the optimal threshold for detecting each phenological stage. CR is least affected by incident angle, precipitation, and wind speed, effectively suppressing noise to reflect phenological development better. This approach supports the rapid and feasible mapping of maize phenology across broad spatial regions with a few samples.

With the rapid socioeconomic development in China, the increasing soil erosion caused by anthropogenic production and construction activities is taking place, which is characterized by short duration, high frequency, and great damage to its surrounding environment. Therefore, the regulation and control of soil erosion of anthropogenically disturbed parcels is an urgent task. This study proposes an improved model that combines the boundary constraint and jagged hybrid dilated convolution channel shuffling module (BCJHDC) and the polarized self-attention (PSA) module for extracting anthropogenically disturbed parcels with soil erosion (ADPSE) from high-resolution remote sensing images in Hubei Province. Firstly, the PSA module is added to the encoder to better extract the feature information of the target object. Secondly, BCJHDC module is used to extract multi-scale semantic information from images and improve the boundary segmentation quality. Precision, recall, intersection over union (IOU), and F1 score are calculated to evaluate the model accuracy. The results indicate that our improved model performs well on the human-perturbed parcel extraction task with an F1 of 87.92% and an IOU of 78.44%. Ablation experiments and application experiments suggest the validity the applicability, and the portability of our proposed improved model, respectively. Compared with the other 7 advanced semantic segmentation models, our improved model has significant advantages. Overall, this study provides valuable reference for policy formulation of water and soil conservation.

Amidst burgeoning socioeconomic development, anthropogenic activities have exacerbated soil erosion. This erosion, characterized by its brief duration, high frequency, and considerable environmental degradation, presents a major challenge to ecological systems. Therefore, it is imperative to regulate and remediate erosion–prone, anthropogenically disturbed parcels, with dynamic change detection (CD) playing a crucial role in enhancing management efficiency. Currently, traditional methods for change detection, such as field surveys and visual interpretation, suffer from time inefficiencies, complexity, and high resource consumption. Meanwhile, despite advancements in remote sensing technology that have improved the temporal and spatial resolution of images, the complexity and heterogeneity of terrestrial cover types continue to limit large–scale dynamic monitoring of anthropogenically disturbed soil erosion parcels (ADPSE) using remote sensing techniques. To address this, we propose a novel ISegFormer model, which integrates the SegFormer network with a pseudo–residual multilayer perceptron (PR–MLP), cross–scale boundary constraint module (CSBC), and multiscale feature fusion module (MSFF). The PR–MLP module improves feature extraction by capturing spatial contextual information, while the CSBC module enhances boundary prediction through high– and low–level semantic guidance. The MSFF module fuses multiscale features with attention mechanisms, boosting segmentation precision for diverse change types. Model performance is evaluated using metrics, such as precision, recall, F1–score, intersection over union (IOU), and mean intersection over union (mIOU). The results demonstrate that our improved model performs exceptionally well in dynamic monitoring tasks for ADPSE. Compared to five other models, our model achieved an mIOU of 72.34% and a Macro–F1 score of 83.55% across twelve types of ADPSE changes, surpassing the other models by 1.52–2.48% in mIOU and 2.25–3.64% in Macro–F1 score. This work provides a theoretical and methodological foundation for policy–making in soil and water conservation departments.

Terraced fields are difficult to delineate from high-resolution imagery because narrow and irregular boundaries, fragmented patches, and visual similarity to adjacent cropland can lead to omission errors and boundary confusion. This study adapts DeepLabV3+ for terraced field extraction from 2 m GF-1 RGB imagery through a task-oriented integration of spatial-detail preservation, contextual modeling, and boundary refinement. The resulting framework incorporates a dilated residual encoder, a gated global–local context module, and a boundary-supervised PointRend refinement head to address spatial-detail loss, background confusion, and boundary uncertainty in terrace mapping. On the re-evaluated test set, the framework achieved 86.68% precision, 87.77% recall, 87.22% F1 score, and 77.34% target-class IoU. Relative to the unmodified DeepLabV3+ baseline, the F1 score and IoU increased by 10.01 and 14.46 percentage points, respectively. These results indicate that task-oriented integration of spatial-detail preservation, contextual modeling, and boundary refinement can improve fine-scale terraced-field delineation from high-resolution optical imagery.