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Accurately mapping crop cultivation types is essential for the sustainable development for precision agriculture. Environmental restrictions on crop growth, such as soil salinization in arid zones, generally lead to spatial crop growth heterogeneity within cropland fields, which in turn generates differences in the spectral responses reflected in optical remote sensing images of the same croplands and leads to pixel-scale crop-mapping misclassifications. Thus, through this work, we proposed a method to solve this problem at the geoparcel scale by integrating geometric features from a Gaofen-2 high-resolution remote sensing image and the spectral-temporal features derived from Sentinel-2 time series. The results showed that cropland parcels could be accurately extracted from Gaofen-2 images by employing the U-Net semantic segmentation model with an overall accuracy (OA) reaching 97&#x0025; and a kappa coefficient of 0.95. Then, geoparcel-scale crop types were mapped based on prior crop phenology knowledge and the corresponding Sentinel-2 time series using the time-weighted dynamic time warping (TWDTW) classification algorithm. The parcel-based TWDTW algorithm had an OA of 99.64&#x0025;, a kappa coefficient of 0.99, and optimal spatial homogeneity in the results, thus outperforming the pixel-based TWDTW method. These results provide a potential solution for mapping crops under spatially heterogeneous cropland conditions affected by various environmental constraints.

ieee journal of selected topics in applied earth observations and remote sensing

A novel method for identifying crops in parcels constrained by environmental factors through the integration of a Gaofen-2 high-resolution remote sensing image and Sentinel-2 time series