Acquisition of Crop Spatial Patterns Based on Remote Sensing Data from Sentinel-2 Satellite

The timely and accurate acquisition of spatial distribution information for crops holds significant scientific significance for crop yield estimation, management, and timely adjustments to crop planting structures. This study revolves around Henan and Shaanxi provinces, employing a spatiotemporal image data fusion approach. Utilizing the characteristic representation of the Normalized difference vegetation index (NDVI) temporal data from Sentinel-2 satellite imagery, a multi-scale segmentation of patches is conducted based on spatiotemporal fusion images. Decision tree classification rules are constructed through the analysis of crop phenological differences, facilitating the extraction of the crop spatial patterns (CSPs) in the two provinces. The classification accuracy is assessed, yielding overall accuracies of 91.11% and 90.12%, with Kappa coefficients of 0.897 and 0.887 for Henan and Shaanxi provinces, respectively. The results indicate the following: (1) the proposed method enhances crop identification capabilities; (2) an accuracy evaluation against the data from the Third National Land Resource Survey and provincial statistical yearbook data for 2022 demonstrates extraction accuracy exceeding 90%; and (3) an analysis of the crop spatial patterns in 2022 reveals that wheat and corn are the predominant crops in Henan and Shaanxi provinces, covering 74.42% and 62.32% of the total crop area, respectively. The research outcomes can serve as a scientific basis for adjusting the crop planting structures in these two provinces.