Extraction of grassland irrigation information in arid regions based on multi-source remote sensing data

Irrigation is a vital measure for maintaining grassland productivity in arid and semi-arid regions. Grasslands typically have characteristics such as unclear boundaries, complex vegetation types, and relatively small irrigation amounts, making it challenging to extract irrigation information. Currently, research on extracting grassland irrigation information is scarce. This study proposes a method for extracting grassland irrigation information using high spatiotemporal resolution (30 m, 1 day) downscaled surface soil moisture data, combined with Landsat 8/9 and Sentinel 1/2 data. This method was applied to extract the irrigation area, timing, and frequency of grasslands in the Delingha Piedmont, northwestern China. The results showed that the overall classification accuracy of irrigated grassland was 93.43 %, and the kappa coefficient was 0.91, indicating high extraction accuracy. The average values of recall, precision, and F-score for irrigation timing and frequency were 82.54 %, 72.25 %, and 77.03 %, respectively, with most irrigation events accurately identified, indicating commendable overall efficacy. The combined use of multi-source remote sensing data is crucial for the extraction of grassland irrigation information. Among these, The high spatiotemporal resolution downscaled surface soil moisture data, by providing detailed spatiotemporal surface soil moisture dynamics, demonstrate a potent capacity for capturing irrigation events, thus effectively enhancing the accuracy of grassland irrigation data extraction.