An edge-effect-aware framework for uncertainty-resolved crop mapping and planting-structure monitoring in arid irrigation landscapes
Year: 2026
Type: article
Abstract: Accurate crop mapping is critical for agricultural water management in the Yellow River Basin under strict water constraints. To address mixed-pixel and noise issues in fragmented landscapes like the Yichang Irrigation Area, this study developed an edge-effect-aware framework. Using Sentinel-2 composites from 2023 to 2025, a 7-dimensional static spectral dataset was constructed. We utilized Optuna optimization to evaluate three deep learning models (SNN, 1D-CNN, LSTM) and Random Forest (RF), coupled with a spatial majority voting filter. Results indicated: (1) The structurally simple SNN demonstrated superior robustness, achieving a peak Overall Accuracy of 0.953 after filtering, outperforming 1D-CNN, RF, and LSTM. By directly mapping static features, SNN avoided the overfitting issues LSTM encountered with non-temporal mixed spectral signals. (2) Spatiotemporal analysis revealed a "sunflower matrix and maize aggregation" pattern. Driven by economic incentives, maize expanded its area proportion from 24% to 30.1%, reflecting trade-offs with water constraints. (3) Boundary-distance analysis confirmed that mixed pixels within the 0–10 m range constitute the primary physical bottleneck limiting accuracy. However, SNN exhibited significantly better stability in these transition zones compared to LSTM. This framework provides uncertainty-resolved crop mapping to support adaptive planting-structure regulation in arid regions.
Source: Agricultural Water Management
Institutions: Inner Mongolia Agricultural University, China Institute of Water Resources and Hydropower Research, Ministry of Water Resources of the People's Republic of China, Inner Mongolia Electric Power (China), Inner Mongolia Yili Industrial Group (China), Hefei University of Technology, Inner Mongolia Electric Power Survey & Design Institute (China), Inner Mongolia Autonomous Region Meteorological Bureau, Inner Mongolia Autonomous Region Hospital of Traditional Chinese Medicine
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,070
OpenAlex ID: W7222815460