Identification and Monitoring of Irrigated Areas in Arid Areas Based on Sentinel-2 Time-Series Data and a Machine Learning Algorithm

Accurate monitoring of irrigation areas is of great significance to ensure national food security and rational utilization of water resources. The low resolution of the Moderate Resolution Imaging Spectroradiometer and Landsat data makes the monitoring accuracy insufficient for actual demand. Thus, this paper proposes a method of extracting the irrigated area in arid regions based on Sentinel-2 long time-series imagery to realize the accurate monitoring of irrigation areas. In this paper, a typical irrigation area in the arid region of Northwest China–Xinjiang Santun River is selected as the study area. The long time series Sentinel-2 remote sensing data are used to classify the land use of the irrigation area. The random forest, CART decision tree, and support vector machine algorithms are used to combine the field collection of the typical irrigation point and non-irrigated sample points. The irrigation area is extracted by calculating the Normalized Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Optimized Soil-Adjusted Vegetation Index (OSAVI) time series data as the classification parameters. The results show that (1) the irrigated area of the dryland irrigation region can be effectively extracted using the SAVI time-series data through an object-oriented approach combined with the random forest algorithm. (2) The extracted irrigated areas were 44,417, 42,915, 43,411, 48,908, and 47,900 hm2 from 2019 to 2023, and the overall accuracies of the confusion matrix validation were 94.34%, 90.22%, 92.03%, 93.23%, and 94.63%, with kappa coefficients of 0.9011, 0.8887, 0.8967, 0.9009, and 0.9265, respectively. The errors of the irrigated area compared with the statistical data were all within 5%, which demonstrated the effectiveness of the method in extracting the irrigated area. This method provides a reference for extracting irrigated areas in arid zones.