High-resolution (10 m) dataset of multi-crop planting structure on the Loess Plateau during 2018–2022 - Xining Zhao; Jichao Wang; Yelu Ding; Xiaodong Gao; Changjian Li; Hongwei Huang; Xuerui Gao (Scientific Data)
Abstract Accurate and timely information on planting intensity and crop rotation is essential for guiding agricultural policies and ensuring food security. However, reliable and up-to-date maps for major crops—wheat, maize, rapeseed, soybean, and potatoes—are lacking in the Loess Plateau, a key grain-producing region in western China. To address this gap, this study aims to generate a high-resolution (10 m) crop planting pattern dataset for the Loess Plateau from 2018 to 2022. The research methodology involved four key steps: (1) Enhancing the sample dataset using phenological indices and the Dynamic Time Warping (DTW) algorithm; (2) Identifying crop planting intensity based on phenological growth curves; (3) Developing independent random forest classifiers tailored to agricultural climate zones; and (4) Constructing an optimal feature subset for crop classification. The resulting maps demonstrated high overall accuracies (OA) is greater than 0.81, with satellite-based estimates showing strong agreement with municipal statistical data (R2 ≥ 0.60). These results provide crucial insights for the management of agricultural ecosystems in the Loess Plateau and can support more informed decision-making in regional agriculture.