Reconstruction of a Global 9 km, 8-Day SMAP Surface Soil Moisture Dataset during 2015–2020 by Spatiotemporal Fusion

遥感学报 Pub Date : 2022-07-25 DOI:10.34133/2022/9871246

Haoxuan Yang, Qunming Wang, Wei Zhao, X. Tong, P. Atkinson

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引用次数: 5

Abstract

Soil moisture, a crucial property for Earth surface research, has been focused widely in various studies. The Soil Moisture Active Passive (SMAP) global products at 36 km and 9 km (called P36 and AP9 in this research) have been published from April 2015. However, the 9 km AP9 product was retrieved from the active radar and L-band passive radiometer and the active radar failed in July 2015. In this research, the virtual image pair-based spatiotemporal fusion model was coupled with a spatial weighting scheme (VIPSTF-SW) to simulate the 9 km AP9 data after failure of the active radar. The method makes full use of all the historical AP9 and P36 data available between April and July 2015. As a result, 8-day composited 9 km SMAP data at the global scale were produced from 2015 to 2020, by downscaling the corresponding 8-day composited P36 data. The available AP9 data and in situ reference data were used to validate the predicted 9 km data. Generally, the predicted 9 km SMAP data can provide more spatial details than P36 and are more accurate than the existing EP9 product. The VIPSTF-SW-predicted 9 km SMAP data are an accurate substitute for AP9 and will be made freely available to support research and applications in hydrology, climatology, ecology, and many other fields at the global scale.

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全球重建9 km，通过时空融合的2015–2020年8天SMAP地表土壤水分数据集

土壤水分是地球表面研究的一个重要性质，在各种研究中得到了广泛关注。土壤水分主动-被动（SMAP）全球产品排名36 km和9 km（本研究中称为P36和AP9）已于2015年4月发表。然而从有源雷达和L波段无源辐射计中检索到km AP9产品，有源雷达于2015年7月出现故障。在本研究中，基于虚拟图像对的时空融合模型与空间加权方案（VIPSTF-SW）相结合，模拟了9 有源雷达故障后的km AP9数据。该方法充分利用了2015年4月至7月期间可用的所有AP9和P36历史数据。结果，8天合成了9 2015年至2020年，通过缩小相应的8天合成P36数据，产生了全球范围内的km SMAP数据。可用的AP9数据和现场参考数据用于验证预测的9 km数据。一般来说，预测的9 km SMAP数据可以提供比P36更多的空间细节，并且比现有的EP9产品更准确。VIPSTF SW预测9 km SMAP数据是AP9的准确替代品，将免费提供，以支持全球范围内水文、气候学、生态学和许多其他领域的研究和应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

遥感学报 Social Sciences-Geography, Planning and Development

CiteScore

3.60

自引率

0.00%

发文量

3200

期刊介绍： The predecessor of Journal of Remote Sensing is Remote Sensing of Environment, which was founded in 1986. It was born in the beginning of China's remote sensing career and is the first remote sensing journal that has grown up with the development of China's remote sensing career. Since its inception, the Journal of Remote Sensing has published a large number of the latest scientific research results in China and the results of nationally-supported research projects in the light of the priorities and needs of China's remote sensing endeavours at different times, playing a great role in the development of remote sensing science and technology and the cultivation of talents in China, and becoming the most influential academic journal in the field of remote sensing and geographic information science in China. As the only national comprehensive academic journal in the field of remote sensing in China, Journal of Remote Sensing is dedicated to reporting the research reports, stage-by-stage research briefs and high-level reviews in the field of remote sensing and its related disciplines with international and domestic advanced level. It focuses on new concepts, results and progress in this field. It covers the basic theories of remote sensing, the development of remote sensing technology and the application of remote sensing in the fields of agriculture, forestry, hydrology, geology, mining, oceanography, mapping and other resource and environmental fields as well as in disaster monitoring, research on geographic information systems (GIS), and the integration of remote sensing with GIS and the Global Navigation Satellite System (GNSS) and its applications.