Abstract
Timely and accurate estimation of the winter wheat planting area and its spatial distribution is essential for the implementation of crop growth monitoring and yield estimation, and hence for the development of national agricultural production and food security. In remotely sensed winter wheat mapping based on spectral similarity, the reference curve is obtained by averaging multiple standard curves, which limits mapping accuracy. We propose a spectral reconstruction method based on singular value decomposition (SR-SVD) for winter wheat mapping based on the unique growth characteristics of crops. Using Sentinel-2 A/B satellite data, we tested the SR-SVD method in Puyang County, and Shenzhou City, China. Performance was increased, with the optimal overall accuracy and the Kappa of Puyang County and Shenzhou City were 99.52% and 0.99, and 98.26% and 0.97, respectively. We selected the spectral angle mapper (SAM) and Euclidean Distance (ED) as the similarity measures. Compared to spectral similarity methods, the SR-SVD method significantly improves mapping accuracy, as it avoids excessive extraction, can identify more detailed information, and is advantageous in distinguishing non-winter wheat pixels. Three commonly used supervised classification methods, support vector machine (SVM), maximum likelihood (ML), and minimum distance (MD) were used for comparison. Results indicate that SR-SVD has the highest mapping accuracy and greatly reduces the number of misidentified pixels. Therefore, the SR-SVD method can achieve high-precision crop mapping and provide technical support for monitoring regional crop planting structure information.
Highlights
Wheat is one of the most important cereal crops worldwide, as well as a commercial and strategic reserve grain [1,2]
The use of the SR-singular value decomposition (SVD) method can significantly improve the mapping and extraction accuracy of methods that are based on the principle of spectral similarity
The accuracy verification shows that spectral angle mapper (SAM)-SR-SVD is the best among all the methods involved in this study; it suggests that the training set we selected is sufficiently representative of the study area
Summary
Wheat is one of the most important cereal crops worldwide, as well as a commercial and strategic reserve grain [1,2]. The global wheat planting area exceeds 200 million hectares, of which 80% is winter wheat [3,4]. In 2017, the wheat sowing area in China reached 24,510 thousand hectares, ranking third, globally, with 98% of it being winter wheat [5]. Information on wheat planting is essential for agricultural production and structure adjustments, and for monitoring the growth and yield of winter wheat and assessing food security [8,9,10,11]. Remote sensing provides an effective means for quick and accurate estimation of the spatial distribution of crops [12,13]. Many studies have elaborated on the use of remote sensing for assessing the spatial distribution of winter wheat [14,15,16,17].
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