Liu Zhe, Zhang Lin, Yu Yaoqi, Xi Xiaojie, Ren Tianwei, Zhao Yuanyuan, Zhu Dehai, Zhu A-Xing
College of Land Science and Technology, China Agricultural University, Beijing, China.
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China.
Front Plant Sci. 2022 Mar 4;12:761148. doi: 10.3389/fpls.2021.761148. eCollection 2021.
Crop classification maps are fundamental data for global change research, regional agricultural regulation, fine production, and insurance services. The key to crop classification is samples, but it is very time-consuming in annual field sampling. Therefore, how to use historical samples in crop classification for future years at a lower cost is a research hotspot. By constructing the spectral feature vector of each historical sample in the historical year and its neighboring pixels in the target year, we produced new samples and classified them in the target year. Specifically, based on environmental similarity, we first calculated the similarities of every two pixels between each historical year and target year and took neighboring pixels with the highest local similarity as potential samples. Then, cluster analysis was performed on those potential samples of the same crop, and the class with more pixels is selected as newly generated samples for classification of the target year. The experiment in Heilongjiang province, China showed that this method can generate new samples with the uniform spatial distribution and that the proportion of various crops is consistent with field data in historical years. The overall accuracy of the target year by the newly generated sample and the real sample is 61.57 and 80.58%, respectively. The spatial pattern of maps obtained by two models is basically the same, and the classification based on the newly generated samples identified rice better. For areas with majority fields having no rotation, this method overcomes the problem of insufficient samples caused by difficulties in visual interpretation and high cost on field sampling, effectively improves the utilization rate of historical samples, and provides a new idea for crop mapping in areas lacking field samples of the target year.
作物分类地图是全球变化研究、区域农业调控、精准生产和保险服务的基础数据。作物分类的关键在于样本，但年度实地采样非常耗时。因此，如何以较低成本在未来年份的作物分类中使用历史样本是一个研究热点。通过构建历史年份中每个历史样本及其在目标年份的相邻像素的光谱特征向量，我们生成了新样本并在目标年份进行分类。具体来说，基于环境相似性，我们首先计算每个历史年份与目标年份之间每两个像素的相似度，并将局部相似度最高的相邻像素作为潜在样本。然后，对同一作物的那些潜在样本进行聚类分析，并选择像素较多的类别作为新生成的样本用于目标年份的分类。在中国黑龙江省进行的实验表明，该方法可以生成空间分布均匀的新样本，并且各种作物的比例与历史年份的田间数据一致。新生成样本和真实样本对目标年份的总体准确率分别为61.57%和80.58%。两种模型获得的地图空间模式基本相同，基于新生成样本的分类对水稻的识别效果更好。对于大多数田地没有轮作的地区，该方法克服了因目视解译困难和实地采样成本高导致样本不足的问题，有效提高了历史样本的利用率，为缺乏目标年份实地样本的地区的作物制图提供了新思路。
