水力发电学报2026，Vol.45Issue(8)：13-28,16.DOI:10.11660/slfdxb.20260802
融合机理解析与遥感反演的水浇地精准识别研究
Study on accurate identification of irrigated farmland by integrating mechanism analysis and remote sensing inversion
摘要
Abstract
Accurate identification of irrigated farmland is crucial for optimizing water resource allocation,improving irrigation efficiency,monitoring agricultural drought,guiding hydraulic engineering operations,and promoting food security and sustainable water use.This study focuses on the entire region of Uxin Banner,Inner Mongolia,an arid-semiarid agro-pastoral ecotone.Differences between irrigated and non-irrigated farmland are compared and examined from four dimensions-moisture dynamics,energy dynamics,vegetation physiology,and vegetation phenology-leading to 10 key factors selected across four categories.We retrieve these factors from multi-source remote sensing data,and construct a comprehensive feature index library comprising 43 indicators.Then,we select the optimal feature subset through feature-importance evaluation,and use it to train a Random Forest(RF)model for irrigated farmland identification.Application to the 2019-2024 Uxin Banner data for analysis of the spatiotemporal variations shows that the model achieves an overall accuracy of 95.38%and a Kappa coefficient of 0.9128,demonstrating its strong capability of irrigated farmland extraction.In Uxin Banner,the irrigated area was 812.2733 km2 in 2024,mainly distributed in its southern and southwestern regions.Over the five years of 2019-2024,the irrigated area increased first and then decline with a turning point occurring in 2022.The new framework in this study—mechanism analysis,factor identification,remote sensing inversion,and model identification—would help identify irrigated farmland and promote interpretable machine learning applications.关键词
水浇地/多源遥感数据/机理解析/随机森林/精准识别Key words
irrigated farmland/multi-source remote sensing/mechanism-based analysis/random forest/accurate identification分类
天文与地球科学引用本文复制引用
樊福全,翁白莎,高海波,孙营伟..融合机理解析与遥感反演的水浇地精准识别研究[J].水力发电学报,2026,45(8):13-28,16.基金项目
鄂尔多斯市国家可持续发展议程创新示范区建设科技支撑项目(KCX2024013) （KCX2024013）
中国水科院基本科研业务费项目(MK0145B022021) （MK0145B022021）
