Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR024902 |
Gap Filling of High-Resolution Soil Moisture for SMAP/Sentinel-1: A Two-Layer Machine Learning-Based Framework | |
Mao, Hanzi1; Kathuria, Dhruva2; Duffield, Nick3; Mohanty, Binayak P.2 | |
2019-08-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:8页码:6986-7009 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | As the most recent 3-km soil moisture product from the Soil Moisture Active Passive (SMAP) mission, the SMAP/Sentinel-1 L2_SM_SP product has a unique capability to provide global-scale 3-km soil moisture estimates through the fusion of radar and radiometer microwave observations. The spatial and temporal availability of this high-resolution soil moisture product depends on concurrent radar and radiometer observations which is significantly restricted by the narrow swath and low revisit schedule of the Sentinel-1 radars. To address this issue, this paper presents a novel two-layer machine learning-based framework which predicts the brightness temperature and subsequently the soil moisture at gap areas. The proposed method is able to gap-fill soil moisture satisfactorily at areas where the radiometer observations are available while the radar observations are missing. We find that incorporating historical radar backscatter measurements (30-day average) into the machine learning framework boosts its predictive performance. The effectiveness of the two-layer framework is validated against regional holdout SMAP/Sentinel-1 3-km soil moisture estimates at four study areas with distinct climate regimes. Results indicate that our proposed method is able to reconstruct 3-km soil moisture at gap areas with high Pearson correlation coefficient (47%/35%/20%/80% improvement of mean R, at Arizona/Oklahoma/Iowa/Arkansas) and low unbiased Root Mean Square Error (20%/10%/7%/26% improvement of mean unbiased root mean square error) when compared to the SMAP 33-km soil moisture product. Additional validations against airborne data and in situ data from soil moisture networks are also satisfactory. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000490973700036 |
WOS关键词 | LAND-SURFACE TEMPERATURE ; DATA ASSIMILATION ; L-BAND ; SMAP ; CLIMATE ; DISAGGREGATION ; PRECIPITATION ; SENTINEL-1 ; RADAR ; ALGORITHM |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/185876 |
专题 | 资源环境科学 |
作者单位 | 1.Texas A&M Univ, Dept Comp Sci & Engn, College Stn, TX USA; 2.Texas A&M Univ, Biol & Agr Engn, College Stn, TX 77843 USA; 3.Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX USA |
推荐引用方式 GB/T 7714 | Mao, Hanzi,Kathuria, Dhruva,Duffield, Nick,et al. Gap Filling of High-Resolution Soil Moisture for SMAP/Sentinel-1: A Two-Layer Machine Learning-Based Framework[J]. WATER RESOURCES RESEARCH,2019,55(8):6986-7009. |
APA | Mao, Hanzi,Kathuria, Dhruva,Duffield, Nick,&Mohanty, Binayak P..(2019).Gap Filling of High-Resolution Soil Moisture for SMAP/Sentinel-1: A Two-Layer Machine Learning-Based Framework.WATER RESOURCES RESEARCH,55(8),6986-7009. |
MLA | Mao, Hanzi,et al."Gap Filling of High-Resolution Soil Moisture for SMAP/Sentinel-1: A Two-Layer Machine Learning-Based Framework".WATER RESOURCES RESEARCH 55.8(2019):6986-7009. |
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