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结合使用陆地卫星8号和哨兵2A号影像进行区域尺度的冬季作物制图和冬小麦产量评估。

Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale.

作者信息

Skakun Sergii, Vermote Eric, Roger Jean-Claude, Franch Belen

机构信息

Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA.

NASA Goddard Space Flight Center Code 619, 8800 Greenbelt Road, Greenbelt, MD 20771, USA.

出版信息

AIMS Geosci. 2017;3(2):163-186. doi: 10.3934/geosci.2017.2.163. Epub 2017 May 23.

DOI:10.3934/geosci.2017.2.163
PMID:29888751
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5992624/
Abstract

Timely and accurate information on crop yield is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to enable temporal resolution of an image every 3-5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat assessment at regional scale. For the former, we adapt a previously developed approach for Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m resolution that allows automatic mapping of winter crops taking into account knowledge on crop calendar and without ground truth data. For the latter, we use a generalized winter wheat yield model that is based on NDVI-peak estimation and MODIS data, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A has a positive impact both for winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times comparing to the single satellite usage.

摘要

及时、准确的作物产量信息对于农业监测中的许多应用至关重要。由于其覆盖范围和时间分辨率,低空间分辨率卫星图像一直是国家和区域尺度上产量预测和评估的宝贵信息来源。随着Landsat - 8和哨兵 - 2遥感卫星获取的免费图像的可用性,每3 - 5天获取一次图像的时间分辨率成为可能,因此,可以开发更高空间分辨率(30米)的下一代农业产品。本文探讨了Landsat - 8和哨兵 - 2A在区域尺度上用于冬作物制图和冬小麦评估的联合使用。对于前者,我们采用了一种先前为250米分辨率的中分辨率成像光谱仪(MODIS)开发的方法,该方法允许在考虑作物物候知识且无需地面真值数据的情况下自动绘制冬作物。对于后者,我们使用了一个基于归一化植被指数(NDVI)峰值估计和MODIS数据的广义冬小麦产量模型,并进一步将其尺度缩小以适用于30米分辨率。我们表明,Landsat - 8和哨兵 - 2A的整合对冬作物制图和冬小麦产量评估都有积极影响。特别是,与单颗卫星使用相比,冬小麦产量估计误差可降低达1.8倍。

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本文引用的文献

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Automatic sub-pixel co-registration of Landsat-8 OLI and Sentinel-2A MSI images using phase correlation and machine learning based mapping.基于相位相关和机器学习映射的陆地卫星8号OLI和哨兵2A号MSI图像自动亚像素配准
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