Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR023354 |
Downscaling SMAP Radiometer Soil Moisture Over the CONUS Using an Ensemble Learning Method | |
Abbaszadeh, Peyman1; Moradkhani, Hamid1; Zhan, Xiwu2 | |
2019 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:1页码:324-344 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Soil moisture plays a critical role in improving the weather and climate forecast and understanding terrestrial ecosystem processes. It is a key hydrologic variable in agricultural drought monitoring, flood forecasting, and irrigation management as well. Satellite retrievals can provide unprecedented soil moisture information at the global scale; however, the products are generally provided at coarse resolutions (25-50km(2)). This often hampers their use in regional or local studies. The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) satellite mission was launched in January 2015 aiming to acquire soil moisture and freeze-thaw states over the globe with 2 to 3days revisit frequency. This work presents a new framework based on an ensemble learning method while using atmospheric and geophysical information derived from remote-sensing and ground-based observations to downscale the level 3 daily composite version (L3_SM_P) of SMAP radiometer soil moisture over the Continental United States at 1-km spatial resolution. In the proposed method, a suite of remotely sensed and in situ data sets are used, including soil texture and topography data among other information. The downscaled product was validated against in situ soil moisture measurements collected from two high density validation sites and 300 sparse soil moisture networks throughout the Continental United States. On average, the unbiased Root Mean Square Error between the downscaled SMAP soil moisture data and in-situ soil moisture observations adequately met the SMAP soil moisture retrieval accuracy requirement of 0.04m(3)/m(3). In addition, other statistical measures, that is, Pearson correlation coefficient and bias, showed satisfactory results. |
英文关键词 | soil moisture downscaling SMAP ensemble learning CONUS |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000459536500018 |
WOS关键词 | AMSR-E ; BRIGHTNESS TEMPERATURE ; DISAGGREGATION SCHEME ; PERFORMANCE METRICS ; INITIAL ASSESSMENT ; RESOLUTION ; SMOS ; RETRIEVALS ; PRECIPITATION ; ASSIMILATION |
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/20069 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Alabama, Dept Civil Construct & Environm Engn, Ctr Complex Hydrosyst Res, Tuscaloosa, AL 35487 USA; 2.NOAA NESDIS STAR, College Pk, MD USA |
推荐引用方式 GB/T 7714 | Abbaszadeh, Peyman,Moradkhani, Hamid,Zhan, Xiwu. Downscaling SMAP Radiometer Soil Moisture Over the CONUS Using an Ensemble Learning Method[J]. WATER RESOURCES RESEARCH,2019,55(1):324-344. |
APA | Abbaszadeh, Peyman,Moradkhani, Hamid,&Zhan, Xiwu.(2019).Downscaling SMAP Radiometer Soil Moisture Over the CONUS Using an Ensemble Learning Method.WATER RESOURCES RESEARCH,55(1),324-344. |
MLA | Abbaszadeh, Peyman,et al."Downscaling SMAP Radiometer Soil Moisture Over the CONUS Using an Ensemble Learning Method".WATER RESOURCES RESEARCH 55.1(2019):324-344. |
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