College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China.
National Demonstration Center for Experimental Surveying and Mapping Education (Shandong University of Science and Technology), Qingdao, China.
PLoS One. 2024 May 8;19(5):e0302882. doi: 10.1371/journal.pone.0302882. eCollection 2024.
Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.
冬小麦是世界上最重要的作物之一。及时、准确地获取冬小麦的种植面积，对制定农业政策具有重要意义。由于单 SAR 数据的分辨率有限，以及单光数据对天气条件的敏感性，仅使用 SAR 或光学数据很难准确获取冬小麦的种植面积。为了解决仅使用光学或 SAR 图像提取冬小麦精度低的问题，本文构建了一种结合时间序列 SAR 后向散射特征和 NDVI（归一化差异植被指数）的决策树分类方法。通过协同使用 SAR 和光学数据，可以弥补各自的不足。首先，利用 NDVI 在成熟阶段将冬小麦与其他植被区分开来，然后利用 SAR 后向散射特征进行提取。这种方法有助于实现冬小麦的半自动提取。以山东省禹城市为研究区，选取 9 景 Sentinel-1 图像和 1 景 Sentinel-2 图像作为数据源，获取 2022 年冬小麦的空间分布。结果表明，该方法的总体精度（OA）和kappa 系数（Kappa）分别为 96.10%和 0.94。与使用支持向量机（SVM）分类器对多时相合成伪彩色图像和单张 Sentinel-2 图像进行监督分类相比，OA 分别提高了 10.69%和 5.66%。与仅使用 SAR 特征进行决策树分类相比，提取冬小麦的生产者精度（PA）和用户精度（UA）分别提高了 3.08%和 8.25%。本文提出的方法快速准确，为提取冬小麦提供了一种新的技术方法。
