AMSR-E/AMSR2 soil moisture and vegetation optical depth product using MCCA (2002-2021)- A Big Earth Data Platform for Three Poles

Soil moisture (SM) plays a vital role in regulating the water and energy exchange between land surfaces and the atmosphere and is declared an essential climate variable by the Global Climate Observing System (GCOS). Vegetation optical depth (VOD) is a crucial parameter describing vegetation attenuation properties in microwave radiative transfer equation, and it has been proven to be a promising ecological indicator for studying plant hydraulics, carbon stocks, and vegetation phenology.

A long-term SM and polarization-, frequency-dependent VODs (C/X/Ku) product was derived from the inter-calibrated AMSR-E/2 multi-frequency brightness temperature, using the multi-channel collaborative algorithm (MCCA). The MCCA comprehensively considers the physical relationship between multiple microwave channels and could simultaneously retrieve frequency- and polarization-dependent VODs and SM.

The new MCCA AMSR-E/2 SM dataset was validated over 25 dense soil moisture networks from the International Soil Moisture Network (ISMN) and United States Department of Agriculture (USDA) watersheds. The results showed that MCCA performs best in terms of ubRMSE among the current publicly available SM datasets related to AMSR-E/2. In addition, polarization-, frequency-dependent VODs from MCCA may provide new insights for better understanding the water fluxes in plant physiology.

File naming and required software

<MCCA_AMSRE/2_025D_CCXP_VSM_VOD_Des/Asc_YYYYMMDD_V0.nc4>
<MCCA> represents the multi-channel collaborative algorithm, <AMSRE/2> represents the AMSR-E sensor or AMSR2 sensor, <025D> represents the spatial resolution of data, <CCXP> indicates X-band brightness temperature at P polarization (P denotes H or V) was used as the core channel in MCCA, VSM_VOD is the product of soil moisture and vegetation optical depth, <Des/Asc> represents the descending orbit or ascending orbit,<YYYYMMDD> is the date, and V0 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 MATLAB code to read and plot the data.

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Cite as:

Hu, L., Zhao, T., Ju, W., Peng, Z., Yao, P., Shi, J. (2022). AMSR-E/AMSR2 soil moisture and vegetation optical depth product using MCCA (2002-2021). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Terre.tpdc.272907. CSTR: 18406.11.Terre.tpdc.272907. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Zhao, T., Shi, J., Entekhabi, D., Jackson, T. J., Hu, L., Peng, Z., Yao, P., Li, S., and Kang, C. S. (2021). Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm, Remote Sensing of Environment, 257, 112321, https://doi.org/10.1016/j.rse.2021.112321.( 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

National Key Research and Development Program of China (No:2016YFA0600202)

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