Deep Learning Method Based on Spectral Characteristic Rein-Forcement for the Extraction of Winter Wheat Planting Area in Complex Agricultural Landscapes

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-03, Vol.15 (5), p.1301
• Main Authors: Sun, Hanlu, Wang, Biao, Wu, Yanlan, Yang, Hui
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural land; Agricultural management; Agriculture; Artificial neural networks; Classification; complex agricultural landscapes; Crops; Deep learning; Farms; Growth conditions; Land area; Landscape; Machine learning; Methods; Neural networks; Phenology; Plant extracts; Planting; Remote sensing; Spatial distribution; spectral characteristics; Triticum aestivum; Vegetation index; Wheat; Wind; Winter wheat
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15051301

Winter wheat is one of the most important food crops in the world. Remote sensing technology can be used to obtain the spatial distribution and planting area of winter wheat in a timely and accurate manner, which is of great significance for agricultural management. Influenced by the growth conditions of winter wheat, the planting structures of the northern and southern regions differ significantly. Therefore, in this study, the spectral and phenological characteristics of winter wheat were analyzed in detail, and four red-edge vegetation indices (NDVI, NDRE, SRre, and CIred-edge) were included after band analysis to enhance the ability of the characteristics to extract winter wheat. These indices were combined with a deep convolutional neural network (CNN) model to achieve intelligent extraction of the winter wheat planting area in a countable number of complex agricultural landscapes. Using this method, GF-6 WFV and Sentinel-2A remote sensing data were used to obtain full coverage of the region to evaluate the geographical environment differences. This spectral characteristic enhancement method combined with a CNN could extract the winter wheat data well for both data sources, with average overall accuracies of 94.01 and 93.03%, respectively. This study proposes a method for fast and accurate extraction of winter wheat in complex agricultural landscapes that can provide decision support for national and local intelligent agricultural construction. Thus, our study has important application value and practical significance.