SMAP soil moisture and vegetation optical depth product using MCCA (2015-2022)- A Big Earth Data Platform for Three Poles

Soil moisture is an important boundary condition of earth-atmosphere exchanges, and it has been defined as an essential climate variable by GCOS. Vegetation optical depth is a physical variable to measure the attenuation of vegetation in microwave radiative transfer model, and it has been proved to be a good indicator of vegetation water content and biomass.

This dataset uses the multi-channel collaborative algorithm (MCCA) to retrieve both soil moisture and polarized vegetation optical depth with SMAP brightness temperature. The algorithm uses a self-constraint relationship between land parameters and an analytical relationship between brightness temperature at different channels to perform the retrieval process. The MCCA does not depend on other auxiliary data on vegetation properties and can be applied to a variety of satellites. The soil moisture product from this dataset includes the soil moisture content in the unfrozen period and the liquid water content in the frozen period. Both horizontal- and vertical-polarization vegetation optical depth are retrieved. So far as we know, it is the first polarization-dependent vegetation optical depth product at L-band.

This dataset was validated by 19 dense soil moisture observation networks (9 core validation sites used by SMAP team and 13 sites not used by them), and the widely used soil climate analysis network (SCAN). It was found that ubRMSE (unbiased root mean square error) of MCCA retrieved soil moisture is generally smaller than that of other SMAP products.

File naming and required software

Data file naming method:
<MCCA_SMAP_xxKM_CCFP_VSM_VOD_YYYY_V0.nc>, MCCA represents the multi-channel collaborative algorithm, SMAP represents the SMAP satellite, <xxKM> represents the grid size of ease grid 2.0 projection, <CCFP> indicates F-band (F is frequency) brightness temperature of P polarization was used as the core channel in MCCA, VSM_VOD is the product of soil moisture and vegetation optical thickness, <YYYY> is the year of the data, and V1 represents the data version, which will be updated according to your valuable suggestions.
usage method:
Data can be read in various programming languages such as MATLAB, Python and IDL, and can also be visualized in hdfview and panoply. Please refer to the description document for details. We provide Python and Matlab code to read and plot data.

Data Citations Data citation guideline What's data citation?

Cite as:

Zhao, T., Peng, Z., Yao, P., Shi, J. (2022). SMAP soil moisture and vegetation optical depth product using MCCA (2015-2022). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Terre.tpdc.272088. CSTR: 18406.11.Terre.tpdc.272088. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Zhao, T.J., Shi, J.C., Entekhabi, D., Jackson, T.J., Hu, L., Peng, Z.Q., Yao, P.P., Li, S.N., & Kang, C.S. (2021). Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm. Remote Sensing of Environment, 257, 112321.( View Details | Bibtex)

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

Support Program

Second Tibetan Plateau Scientific Expedition Program

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License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)