Dataset List

Vol.|Area

No.10 Vol.11,2024
No.09 Vol.11,2024
No.08 Vol.11,2024
No.07 Vol.11,2024
No.06 Vol.11,2024
No.05 Vol.11,2024
No.04 Vol.11,2024
No.03 Vol.11,2024
No.02 Vol.11,2024
No.01 Vol.11,2024
No.12 Vol.10,2023
No.11 Vol.10,2023
No.10 Vol.10,2023
No.09 Vol.10,2023
No.08 Vol.10,2023
No.07 Vol.10,2023
No.06 Vol.10,2023
No.05 Vol.10,2023
No.04 Vol.10,2023
No.03 Vol.10,2023
No.02 Vol.10,2023
No.01 Vol.10,2023
No.12 Vol.9,2022
No.11 Vol.9,2022
No.10 Vol.9,2022
No.09 Vol.9,2022
No.08 Vol.9,2022
No.07 Vol.9,2022
No.06 Vol.9,2022
No.05 Vol.9,2022
No.04 Vol.9,2022
No.03 Vol.9,2022
No.02 Vol.9,2022
No.01 Vol.9,2022
No.12 Vol.8,2021
No.11 Vol.8,2021
No.10 Vol.8,2021
No.09 Vol.8,2021
No.08 Vol.8,2021
No.07 Vol.8,2021
No.06 Vol.8,2021
No.05 Vol.8,2021
No.04 Vol.8,2021
No.03 Vol.8,2021
No.03 Vol.8,2021
No.02 Vol.8,2021
No.01 Vol.8,2021
No.09 Vol.7,2020
No.08 Vol.7,2020
No.07 Vol.7,2020
No.06 Vol.7,2020
No.05 Vol.7,2020
No.04 Vol.7,2020
No.03 Vol.7,2020
No.02 Vol.7,2020
No.01 Vol.7,2020
No.06 Vol.6,2019
No.05 Vol.6,2019
No.04 Vol.6,2019
No.03 Vol.6,2019
No.02 Vol.6,2019
No.01 Vol.6,2019
No.08 Vol.5,2018
No.07 Vol.5,2018
No.06 Vol.5,2018
No.05 Vol.5,2018
No.04 Vol.5,2018
No.03 Vol.5,2018
No.02 Vol.5,2018
No.01 Vol.5,2018
No.04 Vol.4,2017
No.03 Vol.4,2017
No.02 Vol.4,2017
No.01 Vol.4,2017
No.09 Vol.3,2016
No.08 Vol.3,2016
No.07 Vol.3,2016
No.06 Vol.3,2016
No.05 Vol.3,2016
No.04 Vol.3,2016
No.03 Vol.3,2016
No.02 Vol.3,2016
No.01 Vol.3,2016
No.02 Vol.2,2015
No.01 Vol.2,2015
No.02 Vol.1,2014
No.01 Vol.1,2014
China
Asia
Oceanic region
African
European
North American
South American
Arctic
Global Scale
Data Details

Global 10-day Surface Soil Moisture Dataset (RSSSM, 2003-2018)


CHEN Yongzhe1FENG Xiaoming1FU Bojie1
1 Research Center for Eco-Environmental Sciences，Chinese Academy of Sciences，Beijing 100085，China

DOI:10.3974/geodb.2021.02.01.V1

Published:Feb. 2021

Visitors：8194       Data Files Downloaded：1433      
Data Downloaded：638159.21 MB      Citations：

Key Words：

global,microwave remote sensing,surface soil moisture,10-day resolution,2003-2018

Abstract：

Global 10-day surface soil moisture dataset (RSSSM, 2003-2018) was developed using a complicated neural network approach integrating 11 global-scale microwave-based surface soil moisture products and 9 main quality impact factors of microwave-based soil moisture retrieval. The spatial resolution of RSSSM is 0.1°, and the temporal resolution is approximately 10 days in 16 years. The dataset is archived in .MDD data format in 16 data files for 16 years (compressed into 4 data files). Because the dataset is the first case published in .MDD data format by the Digital Journal of Global Change Data Repository, the dataset is also published in .tif data format for convenient, 576 data files in .tif data format (compressed into 4 data files). In addition, the data validation file and the information list of 11 references datasets are included in the dataset. The total data size of the dataset is 21.7 GB (compressed into 10 data files with 4.04 GB). The pre-print version of the dataset is openly available at Pangaea, the data paper of the dataset was published at Earth System Science Data (ESSD), and the additional data paper in .MDD data format will published at the Journal of Global Change Data & Discovery.

Foundation Item：

Ministry of Science and Technology of P. R. China (2017YFA0604700); National Natural Science Foundation of China (41722104); Chinese Academy of Sciences (QYZDY-SSW-DQC025)

Data Citation：

CHEN Yongzhe, FENG Xiaoming, FU Bojie. Global 10-day Surface Soil Moisture Dataset (RSSSM, 2003-2018)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2021. https://doi.org/10.3974/geodb.2021.02.01.V1.

References：

[1] Albergel, C., Rüdiger, C., Pellarin, T., et al. From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations [J]. Hydrology and Earth System Sciences, 2008, 12(6): 1323-1337. DOI: 10.5194/hess-12-1323-2008.
     [2] Fujii, H., Koike, T., Imaoka, K. Improvement of the AMSR-E algorithm for soil moisture estimation by introducing a fractional vegetation coverage dataset derived from MODIS data [J]. Journal of the Remote Sensing Society of Japan, 2009, 29(1):282-292. DOI: 10.11440/rssj.29.282.
     [3] Fernandez-Moran, R., Al-Yaari, A., Mialon, A., et al. SMOS-IC: An alternative SMOS soil moisture and vegetation optical depth product [J]. Remote Sensing, 2017, 9(5): 457. DOI: 10.3390/rs9050457.
     [4] Yang, H., Weng, F., Lv, L., et al. The FengYun-3 microwave radiation imager on-orbit verification [J]. IEEE Transactions on Geoscience & Remote Sensing, 2011, 49(11): 4552-4560. DOI: 10.1109/TGRS.2011.2148200.
     [5] Baret, F., Weiss, M., Lacaze, R., et al. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production [J]. Remote Sensing of Environment, 2013, 137: 299-309. DOI: 10.1016/j.rse.2012.12.027.
     [6] Hoffmann, L., Günther, G., Li, D., et al. From ERA-Interim to ERA5: the considerable impact of ECMWF's next-generation reanalysis on Lagrangian transport simulations [J]. Atmospheric Chemistry and Physics, 2019, 19: 3097-3124. DOI: 10.5194/acp-19-3097-2019.
     [7] Gruber, A., Scanlon, T., Schalie, R. V. D., et al. Evolution of the ESA CCI soil moisture climate data records and their underlying merging methodology [J]. Earth System Science Data, 2019, 11(2): 717-739. DOI: 10.5194/essd-11-717-2019.
     [8] Dorigo, W. A., Wagner, W., Hohensinn, R., et al. The international soil moisture network: a data hosting facility for global in situ soil moisture measurements [J]. Hydrology and Earth System Sciences, 2011, 15(5): 1675-1698. DOI: 10.5194/hess-15-1675-2011.
     

Data Product:

ID Data Name Data Size Operation
1 MDD_2003-2006.rar 483431.34KB
2 MDD_2007-2010.rar 493008.46KB
3 MDD_2011-2014.rar 495986.24KB
4 MDD_2015-2018.rar 493761.97KB
5 TIF_2003-2006.rar 559061.56KB
6 TIF_2007-2010.rar 568695.35KB
7 TIF_2011-2014.rar 571303.75KB
8 TIF_2015-2018.rar 571321.01KB
9 _Filelist_ReferencedData.xlsx 33.12KB
10 _Table_S16_ISMN_sites.rar 19.21KB
Co-Sponsors
Superintend