LAUSR

As a key input variable to many global climates, land surfaces and crop models, cropping intensity (CI) accurately assesses and predicts crops’ output, in view of the global decline in food production in recent years due to declining natural resources, urban expansion and declining quality of arable land. Hence, research on CI mapping can have a contribution to solve this problem. Unfortunately, existing remote sensing data for CI mapping research, including Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images, are not adequate for obtaining CI information at higher spatial and temporal resolution. In this regard, we develop an algorithm to extract CI based on per-pixel physiognomy. To be specific, the algorithm is based on the Google Earth Engine (GEE) platform and constructs a high temporal (10 days) spatial (30 m) resolution dataset with the fusion of Landsat 7/8 and Sentinel-2 A/B image data and extracts CI information using a time series of peak discovery method, threshold method and phenological period feature extraction to obtain the 2018 Chinese Huai River Basin (HRB) CI map. Our results suggest that the overall accuracy (OA) of CI extraction in the HRB is 92.72%, with a kappa coefficient of 0.864. The single-season crop, double-season crop and three-season crop account for 41.6%, 57.7% and 0.7% of the total farmland area, respectively. Compared to existing CI identification and extraction methods, this approach achieves higher accuracy in the identification and extraction of CI information over a larger area.