Hongtao, Jiang; Huanfeng, Shen; Xinghua, Li; Lili, Li (2022): The 43-year (1978-2020) global 9km remotely sensed soil moisture product [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.940409
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Abstract:
The 43-year global 9km remotely sensed soil moisture product is estimated by the fusion of two kinds of microwave soil moisture products using the spatial temporal fusion model (STFM). One product is the Climate Change Initiative (CCI) 0.25° passive soil moisture product in version 6.1. European Space Agency (ESA) integrates multi-source passive microwave observation data from 1978 to 2020 for CCI 0.25° passive soil moisture estimation. Another product is the Soil moisture Active and Passive (SMAP) 9km soil moisture product in version 3. The SMAP 9km data is less than three months (from April 13–July 7) as the failure of SMAP radar. The soil moisture STFM takes the known CCI 0.25°data and SMAP 9km data at the same date as the reference, and then to fuse other date CCI soil moisture for the unknown 9km soil moisture estimation at the date. The estimated 9km soil moisture covers from 1978 to 2020 in global scale, one image per day, 15402 in total and the data volume up to 13.6 G. The estimated long time series 9km soil moisture will play an important role in the researches and applications at regional scale.
Related to:
Cheng, Qing; Liu, Halilong; Shen, Huanfeng; Wu, Penghai; Zhang, Liangpei (2017): A Spatial and Temporal Nonlocal Filter-Based Data Fusion Method. IEEE Transactions on Geoscience and Remote Sensing, 55(8), 4476-4488, https://doi.org/10.1109/TGRS.2017.2692802
Dorigo, Wouter; Wagner, Wolfgang; Albergel, Clement; Albrecht, Franziska; Balsamo, Gianpaolo; Brocca, Luca; Chung, Daniel; Ertl, Martin; Forkel, Matthias; Gruber, Alexander; Haas, Eva; Hamer, Paul D; Hirschi, Martin; Ikonen, Jaakko; de Jeu, Richard; Kidd, Richard A; Lahoz, William; Liu, Yi Y; Miralles, Diego; Mistelbauer, Thomas; Nicolai-Shaw, Nadine; Parinussa, Robert; Pratola, Chiara; Reimer, C; van der Schalie, Robin; Seneviratne, Sonia I; Smolander, Tuomo; Lecomte, Pascal (2017): ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sensing of Environment, 203, 185-215, https://doi.org/10.1016/j.rse.2017.07.001
Entekhabi, Dara; Das, Narendra; Njoku, Eni; Johnson, Joel; Shi, Jiancheng (accepted): SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture, Version 3. NASA National Snow and Ice Data Center DAAC, https://doi.org/10.5067/7KKNQ5UURM2W
Gruber, Alexander; Scanlon, Tracy; van der Schalie, Robin; Wagner, Wolfgang; Dorigo, Wouter (2019): Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology. Earth System Science Data, 11(2), 717-739, https://doi.org/10.5194/essd-11-717-2019
Hongtao, Jiang; Huanfeng, Shen; Xinghua, Li; Chao, Zeng; Huiqin, Liu; Fangni, Lei (2019): Extending the SMAP 9-km soil moisture product using a spatio-temporal fusion model. Remote Sensing of Environment, 231, 111224, https://doi.org/10.1016/j.rse.2019.111224
Comment:
1. The 9km soil moisture was estiamted by dwonscaling of the European Space Agency Climate Change Initiative (CCI) data with the assistance of the Soil Moisture Active and Passvie (SMAP) data,
2. The unit of the 9km soil moisture is in volumetric (m3/m3).
3. The data is in integer format and the valid range is from 1 to 10000, null or invalid values are represented by 0. To obatin the true soil moisture, the data should be multiplied by 0.0001.
4. This data inherits the spatial coverage of CCI data. Therefore, there is a certain null value region in the spatial distribution of the data.
5. Based on the theoretical basis of spatiotemporal fusion model,the data can be taken as the SMAP-like soil moisture as the similar spatial distribution.
6. By evaluation against in-situ data from ISMN(International Soil Moisture Network), it shows that the accuary of the estimated 9km soil moisture is comparable with the accucary of CCI and has slightly better temporal correlation and unbiased root mean square error.
Parameter(s):
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0)
Status:
Curation Level: Basic curation (CurationLevelB) * Processing Level: PANGAEA data processing level 3 (ProcLevel3)
Size:
15402 data points
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