National high-resolution cropland classification of Japan with agricultural census information and multi-temporal multi-modality datasets
National high-resolution cropland classification of Japan with agricultural census information and multi-temporal multi-modality datasets
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DOI：
10.1016/j.jag.2023.103193
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
J. Xia;N. Yokoya;B. Adriano;Keiichiro Kanemoto
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文献类型：
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作者：
J. Xia;N. Yokoya;B. Adriano;Keiichiro Kanemoto
多模式数据集为处理具有互补信息的框架提供了优势，特别是对于大规模耕地绘图。需要大量的训练数据集来训练机器学习算法，这可能具有挑战性。为了缓解这一局限性，我们从农业普查信息中提取训练样本。我们以日本为例，展示2015年的农业普查数据如何绘制全国不同作物类型的地图。由于缺乏2015年的Sentinel-2数据集，本研究利用了在日本各地收集的Sentinel-1和Landsat-8，并将不同都道府县时期（每月、每两个月、每季度）的观测结果合并为复合数据。最近的深度学习技术已经调查了农业普查信息样本的性能。最后，我们在全国范围内获得9种作物类型（约3100万个地块），并将我们的结果与农业普查测试样本以及日本最近的土地覆盖产品中获得的结果进行比较。生成的地图准确地反映了日本各地的作物类型分布，在47个县的9个类别中，总体准确率达到87%。我们的研究结果强调了使用多模态数据与农业普查信息来评估日本农业生产率的重要性。最终产品可在https://doi.org/10.5281/zenodo.7519274上获得。
Multi-modality datasets offer advantages for processing frameworks with complementary information, particularly for large-scale cropland mapping. Extensive training datasets are required to train machine learning algorithms, which can be challenging to obtain. To alleviate the limitations, we extract the training samples from the agricultural census information. We focus on Japan and demonstrate how agricultural census data in 2015 can map different crop types for the entire country. Due to the lack of Sentinel-2 datasets in 2015, this study utilized Sentinel-1 and Landsat-8 collected across Japan and combined observations into composites for different prefecture periods (monthly, bimonthly, seasonal). Recent deep learning techniques have been investigated the performance of the samples from agricultural census information. Finally, we obtain nine crop types on a countrywide scale (around 31 million parcels) and compare our results to those obtained from agricultural census testing samples as well as those obtained from recent land cover products in Japan. The generated map accurately represents the distribution of crop types across Japan and achieves an overall accuracy of 87% for nine classes in 47 prefectures. Our findings highlight the importance of using multi-modality data with agricultural census information to evaluate agricultural productivity in Japan. The final products are available at https://doi.org/10.5281/zenodo.7519274.