A twenty-year dataset of soil moisture and vegetation optical depth from AMSR-E/2 measurements using the multi-channel collaborative algorithm - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement
Article Dans Une Revue Remote Sensing of Environment Année : 2023

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

Résumé

Soil moisture (SM) and vegetation optical depth (VOD) are essential variables in the terrestrial ecosystem. The multi-frequency radiometers AMSR-E and AMSR2 provide >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 or simplify the frequency dependence of vegetation effects for reducing the unknowns and facilitating the retrieval process, limiting the synergic applicability of VOD and SM products in the 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, MCCA achieved the best scores in terms of root mean square error (RMSE = 0.074 m3/m3), unbiased root mean square error (ubRMSE = 0.073 m3/m3) and bias (0.007 m3/m3), and presented slightly lower value of Pearson's correlation coefficient (R = 0.709) than LPRM-X (R = 0.735). 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, especially for the H-polarized VODs, exhibited smooth non-linear density distribution with AGB and high temporal correlations with MODIS NDVI over most regions of the globe. 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 products. 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/F/G or the upcoming AMSR3 and CMIR missions.
Fichier principal
Vignette du fichier
2023_Hu.pdf (21.91 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte
Licence

Dates et versions

hal-04118657 , version 1 (06-06-2023)

Licence

Identifiants

Citer

Lu Hu, Tianjie Zhao, Weimin Ju, Zhiqing Peng, Jiancheng Shi, et al.. A twenty-year dataset of soil moisture and vegetation optical depth from AMSR-E/2 measurements using the multi-channel collaborative algorithm. Remote Sensing of Environment, 2023, 292, pp.113595. ⟨10.1016/j.rse.2023.113595⟩. ⟨hal-04118657⟩
52 Consultations
21 Téléchargements

Altmetric

Partager

More