A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003-2019
文献类型：期刊论文
| 作者 | Song, Peilin2,3,5; Zhang, Yongqiang2; Guo, Jianping4; Shi, Jiancheng1; Zhao, Tianjie3; Tong, Bing4 |
| 刊名 | EARTH SYSTEM SCIENCE DATA
 |
| 出版日期 | 2022-06-08 |
| 卷号 | 14期号:6页码:2613-2637 |
| ISSN号 | 1866-3508 |
| DOI | 10.5194/essd-14-2613-2022 |
| 英文摘要 | Surface soil moisture (SSM) is crucial for understanding the hydrological process of our earth surface. The passive microwave (PM) technique has long been the primary tool for estimating global SSM from the view of satellites, while the coarse resolution (usually >similar to 10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to the public that meet the qualification of 1 km resolution and daily revisit cycles under all-weather conditions. In this study, we developed one such SSM product in China with all these characteristics. The product was generated through downscaling the AMSR-E/AMSR-2-based (Advance Microwave Scanning Radiometer of the Earth Observing System and its successor) SSM at 36 km, covering all on-orbit times of the two radiometers during 2003-2019. MODIS optical reflectance data and daily thermalinfrared land surface temperature (LST) that had been gap-filled for cloudy conditions were the primary data inputs of the downscaling model so that the "all-weather" quality was achieved for the 1 km SSM. Daily images from this developed SSM product have quasi-complete coverage over the country during April-September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations through a specifically developed sub-model for filling the gap between seams of neighboring PM swaths during the downscaling procedure. The product compares well against in situ soil moisture measurements from 2000+ meteorological stations, indicated by station averages of the unbiased root mean square difference (RMSD) ranging from 0.052 to 0.059 vol vol(-1). Moreover, the evaluation results also show that the developed product outperforms the SMAP (Soil Moisture Active Passive) and Sentinel (active-passive microwave) combined SSM product at 1 km, with a correlation coefficient of 0.55 achieved against that of 0.40 for the latter product. This indicates the new product has great potential to be used by the hydrological community, by the agricultural industry, and for water resource and environment management. The new product is available for download at https://doi.org/10.11888/Hydro.tpdc.271762 (Song and Zhang, 2021b). |
| WOS关键词 | AMSR-E ; PERFORMANCE METRICS ; HIGH-RESOLUTION ; TIME-SERIES ; SMOS ; ASSIMILATION ; DISAGGREGATION ; VEGETATION ; MODEL ; EVAPOTRANSPIRATION |
| 资助项目 | National Natural Science Foundation of China[42001304] ; Open Fund of State Key Laboratory of Remote Sensing Science[OFSLRSS202117] ; CAS Pioneer Talents Program ; CAS-CSIRO International Cooperation Program ; International Partnership Program of Chinese Academy of Sciences[183311KYSB20200015] |
| WOS研究方向 | Geology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000807412800001 |
| 出版者 | COPERNICUS GESELLSCHAFT MBH |
| 资助机构 | National Natural Science Foundation of China ; Open Fund of State Key Laboratory of Remote Sensing Science ; CAS Pioneer Talents Program ; CAS-CSIRO International Cooperation Program ; International Partnership Program of Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/178984]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 4.Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China 5.Xi An Jiao Tong Univ, Sch Elect Sci & Engn, Xian 710049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Song, Peilin,Zhang, Yongqiang,Guo, Jianping,et al. A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003-2019[J]. EARTH SYSTEM SCIENCE DATA,2022,14(6):2613-2637. |
| APA | Song, Peilin,Zhang, Yongqiang,Guo, Jianping,Shi, Jiancheng,Zhao, Tianjie,&Tong, Bing.(2022).A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003-2019.EARTH SYSTEM SCIENCE DATA,14(6),2613-2637. |
| MLA | Song, Peilin,et al."A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003-2019".EARTH SYSTEM SCIENCE DATA 14.6(2022):2613-2637. |
入库方式： OAI收割
来源：地理科学与资源研究所
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