重庆金佛山喀斯特生态系统国家野外科学观测研究站数据中心

Soil moisture data of 25KM-SMOS in the upper reaches of Yangtze River in China (2010-2016)

The SMOS INRA-CESBIO (SMOS-IC) algorithm was designed by INRA (National Institute of Agronomy) and CESBIO (Biospace Research Center) to perform global retrieval of SM and L-VOD. SMOS-IC is based on the two parameter inversion of the L-MEB model defined by Wigneron et al. (2017), and treats pixels as homogeneous. Therefore, the design basis of SMOS-IC is the same as that of Level 2 SM algorithm, but some simplifications are used. Specifically, SMOS-IC does not consider corrections related to processing retrieval with heterogeneous land cover areas (forest cover areas), antenna patterns, and complex SMOS view geometry. Therefore, one of the main objectives of the SMOS-IC product is to be independent of the auxiliary data as much as possible, so as to be more robust and less affected by the potential uncertainties in the above corrections. The SMOS-IC algorithm and data set are described in Fernandez Moran et al. (2017). The available soil moisture product is the second edition, which is provided in the 25km EASEv2 grid and is in the netcdf format. This product cuts data on the basis of metadata. The cut area is the upper reaches of the Yangtze River in TIFF format.

File naming and required software

File name: soil moisture data is stored in TIF format, and the file name is SMOS_ Yyyymmdd.tif, where yyyy represents the year, mm represents the month, and dd represents the day.

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Cite as:

NASA EOSDIS LP DAAC. (2022). Soil moisture data of 25KM-SMOS in the upper reaches of Yangtze River in China (2010-2016). Upper Yangtze River Scientific Data Center, (Download the reference：

Related Literatures:

1. Wigneron J-P, Y. Kerr, P. Waldteufel, K. Saleh, M.-J. Escorihuela, P. Richaume, P. Ferrazzoli, P. de Rosnay, R. Gurney, J.-C. Calvet, J.P. Grant, M. Guglielmetti, B. Hornbuckle, C. Mätzler, T. Pellarin, M. Schwank, 'L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and calibration against experimental data sets over crop fields', Remote Sens. Env., 107, p. 639-655, 2007( View Details | Bibtex)

2. Fernandez-Moran, R., Wigneron, J.-P., De Lannoy, G., Lopez-Baeza, E., Parrens, M., Mialon, A., Mahmoodi, A., Al-Yaari, A., Bircher, S., Al Bitar, A., Richaume, P., Kerr, Y., A new calibration of the effective scattering albedo and soil roughness parameters in the SMOS SM retrieval algorithm, Int J Appl Earth Obs Geoinformation, 62, 27–38, 2017. https://doi.org/10.1016/j.jag.2017.05.013( View Details | Bibtex)

3. Fernandez-Moran R., A. Al-Yaari, A. Mialon, A. Mahmoodi, A. Al Bitar, G. De Lannoy, N. Rodriguez-Fernandez, E. Lopez-Baeza, Y. Kerr and J.-P. Wigneron, "SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product", Remote Sensing, 9, 457; doi:10.3390/rs9050457, 2017( View Details | Bibtex)

4. Wigneron, J.-P., Li, X., Frappart F., Fan L., Al-Yaari A., De Lannoy G., Liu X., Wang M., Le Masson E., Moisy C., SMOS-IC data record of soil moisture and L-VOD: historical development, applications and perspectives, Remote Sens. Env., 254, 112238, https://doi.org/10.1016/j.rse.2020.112238, 202( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

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