A twenty-year dataset of soil moisture and vegetation optical depth from AMSR-E/2 measurements using the multichannel collaborative algorithm

Authors: HU, L. - Nanjing University; ZHAO, T. - Chinese Academy Of Sciences; JU, W. - Nanjing University; PENG, Z. - Chinese Academy Of Sciences; SHI, J. - Chinese Academy Of Sciences; WIGNERON, J. - National Agricultural Research Foundation (NAGREF); RODRIGUES-FERNANDEZ, N. - Collaborator; Cosh, Michael; YANG, K. - Tsinghua University; LU, H. - Tsinghua University; YAO, P. - Chinese Academy Of Sciences

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2022
Publication Date: 7/1/2023
Citation: Hu, L., Zhao, T., Ju, W., Peng, Z., Shi, J., Wigneron, J., Rodrigues-Fernandez, N., Cosh, M.H., Yang, K., Lu, H., Yao, P. 2023. A twenty-year dataset of soil moisture and vegetation optical depth from AMSR-E/2 measurements using the multichannel collaborative algorithm. Remote Sensing of Environment. 292:113595. https://doi.org/10.1016/j.rse.2023.113595.
DOI: https://doi.org/10.1016/j.rse.2023.113595

Interpretive Summary: Long term satellite data records of soil moisture and vegetation are important to the study of climate and ecology, but there are discontinuous data sources for these products. For large scale models and studies, a global scale harmonized product is needed. Using data from two C-band microwave satellites, 20 years of data were created and calibrated against a set of in situ data series for soil moisture. For vegetation, a comparison to other available satellite vegetation products that are based on visible-near infrared bands was conducted, though these other products do not have the temporal resolution of the C-band satellites. These new products will benefit climate and hydrologic modeling as well as ecologic modeling and forecasting.

Technical Abstract: Soil moisture (SM) and vegetation optical depth (VOD) are essential variables in the terrestrial ecosystem. The multi-frequency radiometers AMSR-E and AMSR2 provide more than 20 years of data records, enabling the development of long-term SM and VOD products. Most of the current retrieval algorithms either only focus on SM or VOD, and generally ignore the polarization and frequency dependence of vegetation effects for reducing the unknowns and facilitating the retrieval process, limiting the synergic applicability of VOD and SM products in soil-plant-atmosphere continuum. In this study, a new global SM and frequency- and polarization-dependent VOD product from 2002 to 2021 was developed using the multi-channel collaborative algorithm (MCCA), based on the inter-calibrated AMSR-E/2 multi-frequency passive microwave measurements. The MCCA algorithm comprehensively considers the physical relationship between multiple microwave channels and could retrieve frequency- and polarization-dependent VOD while considering the accuracy of the SM retrievals. In the overall comparison with other SM products (AMSR-ANN, CCI-passive v07.1, LPRM-C/X, JAXA) over 25 dense SM networks, although the R-value of MCCA (0.709) was slightly lower than that of LPRM-X (0.735), MCCA achieved the best scores in terms of root mean square error (RMSE=0.074 cm3/cm3), unbiased root mean square error (ubRMSE=0.073 cm3/cm3) and bias (0.007 cm3/cm3). For the indirect evaluation of VOD with aboveground biomass (AGB) and MODIS NDVI, the MCCA product showed the performance comparable to other products (LPRM-C/X, VODCA-C/X/Ku). MCCA-derived VODs exhibited smooth non-linear density distribution with AGB and high temporal correlations with MODIS NDVI over most regions, especially for the H-polarized VOD. In particular, MCCA-derived VODs can physically present reasonable variations across the microwave spectrum (values of VOD increase with microwave frequency), which is superior to the LPRM and VODCA. It is expected that the MCCA algorithm can be extended to the observations of the ongoing AMSR2 or other similar satellite missions with multi-frequency capability, such as FY-3B/C/D or the upcoming AMSR3 and CMIR missions.