The 10-meter Winter Wheat Mapping in Shandong Province Using Sentinel-2 Data and Coarse Resolution Maps

Abstract

Timely and accurate large-scale mapping of the spread of winter wheat (Triticum aestivum) is crucial to guarantee food security, study climate change, and monitor operational agriculture. Traditional winter wheat mapping frameworks are constrained by insufficient spatial resolution and heavy dependence on field surveys, while traditional machine learning models excessively rely on subjective judgment. Furthermore, collecting sufficient field samples covering a large area is expensive and time-consuming. In this context, an automatic label update deep learning solution is developed to produce 10-m resolution winter wheat maps using Sentinel-2 data and existing coarse-resolution (30 m) winter wheat mapping products. In particular, a label update module considering the unique phenological (seasonal) characteristics of winter wheat is designed to update labels in the training phase. The results indicate that our method yields a satisfactory classification result with an overall accuracy exceeding 92% and an ${F}_{1}$ score greater than 0.85 for all validation samples, even when no field survey data were used for training. In addition, a 10-m spatial resolution winter wheat map for the entire Shandong province is generated, showing a significant correlation between the computed winter wheat map and the agricultural statistical land, with correlation coefficients of 0.95 and 0.78 at the municipal and county levels, respectively. The proposed methodology can serve as a viable and promising method for high-resolution, operational agricultural monitoring over large areas.

Keywords

• Deep learning
• mapping
• noise correction
• sentinel-2
• winter wheat