TY - JOUR
T1 - A Spatially Constrained Multichannel Algorithm for Inversion of a First-Order Microwave Emission Model at L-Band
AU - Gao, Lun
AU - Sadeghi, Morteza
AU - Feldman, Andrew F.
AU - Ebtehaj, Ardeshir
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Understanding and reducing the uncertainties in the inversion of the first-order radiative transfer models at the L-band are important for the improved spaceborne retrievals of soil moisture (SM) and vegetation optical depth (VOD) over dense canopy. This article quantifies and compares the sensitivity of dual-channel inversion of the two-stream (2S) and - models and proposes a new inversion approach for simultaneous retrievals of SM, VOD, and vegetation-scattering albedo from a single satellite overpass. In particular, the inversion algorithm incorporates the information of the nearby spatial observations, assuming that the values of VOD and remain locally invariant, and constrains its solutions to high-resolution a priori physical/climatological knowledge of the retrieval variables. The results demonstrate that the uncertainty in the inversion of 2S model is slightly higher than the - model under noisy observations and remains homoscedastic for SM and , while grows heteroscedastically for higher VOD values due to the shape of the cost function. The results are validated using the SMAP data, the dense Mesonet SM network, the in situ measurements from the International SM Network (ISMN), and the derived VOD from the Moderate Resolution Imaging Spectroradiometer (MODIS)-normalized difference vegetation index (NDVI) over the state of Oklahoma in the United States. It is shown that the new approach can recover simultaneously high-resolution features of SM, VOD, and only from a single Soil Moisture Active Passive (SMAP) overpass, where the unbiased root-mean-squared error (ubRMSE) of SM and VOD is reduced by 30% and 70%, respectively, when compared with an unconstrained time-windowed inversion approach.
AB - Understanding and reducing the uncertainties in the inversion of the first-order radiative transfer models at the L-band are important for the improved spaceborne retrievals of soil moisture (SM) and vegetation optical depth (VOD) over dense canopy. This article quantifies and compares the sensitivity of dual-channel inversion of the two-stream (2S) and - models and proposes a new inversion approach for simultaneous retrievals of SM, VOD, and vegetation-scattering albedo from a single satellite overpass. In particular, the inversion algorithm incorporates the information of the nearby spatial observations, assuming that the values of VOD and remain locally invariant, and constrains its solutions to high-resolution a priori physical/climatological knowledge of the retrieval variables. The results demonstrate that the uncertainty in the inversion of 2S model is slightly higher than the - model under noisy observations and remains homoscedastic for SM and , while grows heteroscedastically for higher VOD values due to the shape of the cost function. The results are validated using the SMAP data, the dense Mesonet SM network, the in situ measurements from the International SM Network (ISMN), and the derived VOD from the Moderate Resolution Imaging Spectroradiometer (MODIS)-normalized difference vegetation index (NDVI) over the state of Oklahoma in the United States. It is shown that the new approach can recover simultaneously high-resolution features of SM, VOD, and only from a single Soil Moisture Active Passive (SMAP) overpass, where the unbiased root-mean-squared error (ubRMSE) of SM and VOD is reduced by 30% and 70%, respectively, when compared with an unconstrained time-windowed inversion approach.
KW - L-band radiometry
KW - Soil Moisture Active Passive (SMAP)
KW - soil moisture (SM)
KW - vegetation optical depth (VOD)
KW - vegetation-scattering albedo
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U2 - 10.1109/TGRS.2020.2987490
DO - 10.1109/TGRS.2020.2987490
M3 - Article
AN - SCOPUS:85088897165
SN - 0196-2892
VL - 58
SP - 8134
EP - 8146
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 11
M1 - 9079480
ER -