LAUSR

Irrigation is critical to agricultural production in arid and semiarid regions, and it is imperative to map high-resolution irrigated area to improve water productivity. This study proposes a field-scale (30-m resolution) irrigated area mapping method based on soil moisture change detection using remote sensing data only. First, normalized soil moisture is obtained using the optical trapezoid model (OPTRAM) and then converted to soil water content. Next, individual irrigation events are identified in the time series of soil water content using threshold detection. Finally, irrigation events are accumulated over the time series, and then, the irrigated area map can be obtained. This method was tested using Google Earth Engine (GEE) to analyze remote sensing images and map irrigated areas in a typical arid and semiarid region called Hexi Corridor in northwestern China in the past 30 years. In situ validation shows that this method has an accuracy close to 100%. The shortcoming of low recall is also overcome by long-term observations. An application of the proposed method shows that the irrigated cropland of Hexi Corridor has increased by 4840 km2 (42.2%) over a 31-year time period (1990–2020). This field-scale irrigated area mapping method can improve the management of water resources.