The effects of Landsat image acquisition date on winter wheat classification in the North China Plain

文献类型： 外文期刊

第一作者： Fan, Lingling

作者： Fan, Lingling;Yang, Jing;Sun, Xiao;Zhao, Fen;Liang, Shefang;Duan, Dingding;Chen, Hao;Xia, Lang;Sun, Jing;Yang, Peng;Yang, Peng

作者机构：

关键词： Multi-temporal; Acquisition dates; Effects evaluation; Winter wheat; Landsat

期刊名称：ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING （ 影响因子：11.774； 五年影响因子：12.123 ）

ISSN： 0924-2716

年卷期： 2022 年 187 卷

页码：

收录情况： SCI

摘要： Accurate winter wheat distribution provides critical information for yield estimation and agricultural resources monitoring, which is associated with food security and agricultural ecosystem sustainability. The increasingly high spatiotemporal resolutions of globally available satellite images (e.g., Landsat) create new possibilities for generating accurate datasets. Although winter wheat classification using Landsat time-series data has been increasingly acknowledged in the literature, the contributing effects of a single acquisition date on classification remain poorly explored. Accordingly, this study aimed to evaluate the effects of a single acquisition date (nine acquisition dates were obtained throughout the growing season) on winter wheat classification using multi temporal Landsat 8 imagery. We adopted Dynamic time warping (DTW) to discriminate winter wheat and determined the most relevant acquisition dates for classification accuracy with different numbers of images. The most significant advantage of DTW from other similarity measurement methods is that DTW can identify the optimal alignment of two time-series datasets with an unequal number of images. Then, we calculated the importance of each acquisition date using the random forest algorithm and quantified their contributions to the classification using variance analysis. The conditional combination of our best accuracy was 84.51% for overall accuracy (OA) and 84.62% for F-score when all nine images were used for the classification. While the lowest accuracy was 46.2% for OA and 51.3% for F-score through the classification of two image combinations from the wintering and heading images. It was shown that the initial and late growth images were the most discriminating dates for time series classification of winter wheat in ~ 91% of the total combinations explored (i.e., 456 combinations). The main effects of the late growth images and their interactions significantly affected model accuracy, while the interactions between the initial growth images also played an important role, particularly for greater numbers of acquisition dates. The relative contributions explained by the main effects of late growth images gradually decreased with the increasing image acquisition dates, while those explained by the internal interactions of both initial and late growth images significantly increased. The wide distribution of winter wheat in many parts of the world and its strong consequences for global food security suggest that better monitoring of wheat is necessary, and the results indicated that appropriate selection of image dates was significant for accomplishing this task.

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