Mapping Irrigated Areas Based on Remotely Sensed Crop Phenology and Soil Moisture

Artificial irrigation is critical for improving soil moisture conditions and ensuring crop growth. Its irrational deployment can lead to ecological environmental issues. Mapping understanding the changes in irrigated areas are vital effectively managing limited water. However, most researchers map with a single data resource, which makes it hard detect signals complex situations. The case study area this paper was China’s winter wheat region, an generated by analyzing effects of artificial on phenological characteristics time series. mapping process involved three steps: (1) generating basic employing ISODATA classification method Kolmogorov–Smirnov test from microwave remote sensing reanalysis data; (2) creating other maximum likelihood ratio zoning scheme parameters extracted NDVI series; (3) fusing these two maps at decision level obtain final higher spatial resolution 1 km. evaluated against existing highly compatible GMIA 5.0. overall accuracy (OA) 73.49%.