Automated In-Season Crop-Type Data Layer Mapping Without Ground Truth for the Conterminous United States Based on Multisource Satellite Imagery
Automated In-Season Crop-Type Data Layer Mapping Without Ground Truth for the Conterminous United States Based on Multisource Satellite Imagery
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DOI：
10.1109/tgrs.2024.3361895
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发表时间：
2024
影响因子：
8.2
通讯作者：
Hui Li;Liping Di;Chen Zhang;Li Lin;Liying Guo;E. Yu;Zhengwei Yang
中科院分区：
文献类型：
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作者：
Hui Li;Liping Di;Chen Zhang;Li Lin;Liying Guo;E. Yu;Zhengwei Yang
在农业遥感中，全国应季作物类型数据制图是一项重要而富有挑战性的任务。美国现有的作物种植数据产品，如耕地数据层（CDL），在促进近实时应用方面存在不足。本文设计了一个工作流，旨在为美国自动化生产季节性cdl产品。我们采用Sentinel-2、Landsat 8和Landsat-9作为光谱数据的来源，系统地从历史CDL数据集中提取可信像元作为土地覆盖标签，使用随机森林分类器进行全国范围内的作物类型分类。这些分类被整合到覆盖整个美国（CONUS）的当季作物数据层（ICDL）中。这种方法促进了2022年5月、6月和7月icdl的高效生成，并在7月达到了令人满意的精度。与内布拉斯加州和爱荷华州的地面真值数据相比，玉米和大豆的ICDL F1得分分别为（0.911,0.845）和（0.959,0.969）。此外，ICDL对主要作物（玉米、大豆、春小麦、棉花、冬小麦和水稻）的区域种植面积估计与美国农业部（USDA）国家农业统计局（NASS）的数据密切一致，差异最小（0.01%,- 0.68%,0.19%,- 4.39%,- 5.78%,- 1.28%）。值得注意的是，ICDL在大多数评估中优于CDL。这项研究从5月到7月持续产生年度icdl，公众可以在icdl系统中轻松访问。同时，它还提供了一种全国范围内季节性作物类型制图的替代技术。
Mapping nationwide in-season crop-type data is a significant and challenging task in agriculture remote sensing. The existing data product for U.S. crop-type planting, such as the Cropland Data Layer (CDL), falls short in facilitating near-real-time applications. This article designed a workflow aimed at automating the generation of in-season CDL-like products for USA. We methodically extracted trusted pixels as land cover labels from historical CDL datasets, employing Sentinel-2, Landsat 8, and Landsat-9 as sources for spectrum data, using the random forest classifier to conduct nationwide crop-type classifications. These classifications were integrated into the In-Season Crop Data Layer (ICDL) covering the entire Conterminous United States (CONUS). This approach facilitated the efficient generation of ICDLs for May, June, and July 2022, achieving satisfactory accuracy in July. Compared to Nebraska and Iowa ground truth data, ICDL achieved F1 scores of (0.911, 0.845) for corn and (0.959, 0.969) for soybean. Furthermore, ICDL’s regional acreage estimates for major crops (corn, soybean, spring wheat, cotton, winter wheat, and rice) closely align with the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) figures, showing minimal variances as low as (0.01%, −0.68%, 0.19%, −4.39%, −5.78%, −1.28%). Notably, ICDL outperforms CDL in most assessments. This research consistently produces annual ICDLs from May to July that are readily accessible to the public in the iCrop system. Simultaneously, it presents an alternative technique for nationwide, in-season mapping of crop types.