Identifying the Spatio-Temporal Change in Winter Wheat–Summer Maize Planting Structure in the North China Plain between 2001 and 2020

Abstract

Tracking winter wheat–summer maize distribution is crucial for the management of agricultural water resources in the water-scarce North China Plain (NCP). However, the spatio-temporal change in planting structure that has occurred during the last 20 years remains unclear. Therefore, winter wheat–summer maize distribution between 2001 and 2020 was determined via the maximum likelihood algorithm of supervised classification and a threshold method using the MODIS NDVI product MOD13Q1 and Landsat 5/7 images. The results reveal that dividing distributions into six sample categories—winter wheat–summer maize, winter wheat–rice, spring maize, cotton, other double-cropping systems, and fruit trees—proved to be an efficient way to discriminate winter wheat–summer maize distribution, with R2 and RMSE values ranging from 0.738 to 0.901 and from 179.05 to 215.72 km2, respectively. From 2001 to 2020, the planting area continually expanded, experiencing a significant growth of 3.32 × 104 km2 (23.44%). Specifically, the planting area decreased by 2982.13 km2 (10.06%) in the northern part of the NCP, including the Beijing–Tianjin–Hebei region, while it increased by 3.62 × 104 km2 (32.30%) in the middle and southern parts, encompassing Shandong, Henan, Anhui, and Jiangsu provinces. The stable growing region was primarily concentrated in the middle of the Hebei Plain, along the Yellow River irrigation areas and humid zones of the southwest, accounting for 75–85% of the total NCP planting area. Our results can provide references for adjusting agricultural planting structures, formulating food security strategies, and optimizing the management of water resources in the NCP.

Evolution of Crop Planting Structure in Traditional Agricultural Areas and Its Influence Factors: A Case Study in Alar Reclamation

“…Although low-spatial-resolution remote-sensing images such as MODIS can provide greater image coverage and higher temporal resolution, the accuracy of plant detection is low, and the precision of the results is difficult to guarantee [37,38]. At present, the relevant Landsat CPS studies mainly use methods such as dynamic simulation, analysis of changes in trends, the spatial autocorrelation model, and the centroid migration model to investigate and reveal the spatial and temporal sequence [39,40] changes in the regional CPS to record distribution patterns and summarize the characteristics of the spatial and temporal development of the CPS.…”