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Physics-Based Modeling of Active and Passive Microwave Covariations Over Vegetated Surfaces.

Authors :
Jagdhuber, Thomas
Konings, Alexandra G.
McColl, Kaighin A.
Alemohammad, Seyed Hamed
Das, Narendra Narayan
Montzka, Carsten
Link, Moritz
Akbar, Ruzbeh
Entekhabi, Dara
Source :
IEEE Transactions on Geoscience & Remote Sensing. Feb2019, Vol. 57 Issue 2, p788-802. 15p.
Publication Year :
2019

Abstract

Active and passive low-frequency microwave measurements from a number of space- and airborne instruments are used to estimate soil moisture. Each of the sensing approaches has distinct advantages and disadvantages. There is increasing interest in combining active and passive measurements in order to realize the advantages and alleviate the disadvantages. In order to combine active and passive measurements, their covariations with respect to soil moisture need to be known. The covariation is dependent on how the active and passive microwaves interact with vegetation canopy and soil surface. In this paper, we introduce a physics-based model for the covariation of active and passive microwaves over soil surfaces with vegetation cover. The analytical form for a covariation function is derived which depends on the scattering and absorption of microwaves by soil and vegetation with different orientations, structures, and water contents. The main finding is that the covariation function $\beta $ is related to the roughness and vegetation losses in the two measurements. An increase in soil roughness or in vegetation cover leads to less negative values of $\beta $ , which is pronounced for dense and moist vegetation. Both the soil and vegetation components introduce a polarization dependence of $\beta $ that is caused by polarization-induced differences in soil scattering and oriented plant structures. The forward modeled covariations are plotted together with statistically derived covariation estimates from two months of global active and passive L-band observations of the Soil Moisture Active Passive mission. The physically modeled and statistically derived estimates of covariation are comparable in magnitude and scale. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01962892
Volume :
57
Issue :
2
Database :
Academic Search Index
Journal :
IEEE Transactions on Geoscience & Remote Sensing
Publication Type :
Academic Journal
Accession number :
134552067
Full Text :
https://doi.org/10.1109/TGRS.2018.2860630