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Analysis of L-Band SAR Data for Soil Moisture Estimations over Agricultural Areas in the Tropics

1
CESBIO, Université de Toulouse, CNRS/UPS/IRD/CNES, 18 av. Edouard Belin, bpi 2801, CEDEX 9, 31401 Toulouse, France
2
Department of Civil Engineering & Indo-French Cell for Water Sciences, Indian Institute of Science, Bangalore 560012, India
3
Université de Carthage/INAT/LR GREEN-TEAM, 43 Avenue Charles Nicolle, Tunis 1082, Tunisia
4
Satyukt Analytics Pvt Ltd, Bangalore 560094, India
5
IRSTEA, TETIS, University of Montpellier, 500 rue François Breton, CEDEX 5, 34093 Montpellier, France
6
RRSC-East, NRSC, Indian Space Research Organisation (ISRO), Kolkata 700156, India
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1122; https://doi.org/10.3390/rs11091122
Received: 26 February 2019 / Revised: 5 May 2019 / Accepted: 7 May 2019 / Published: 11 May 2019
(This article belongs to the Special Issue Microwave Remote Sensing for Hydrology)
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Abstract

The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture over tropical agricultural areas under dense vegetation cover conditions. Ten radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the Berambadi watershed (south India), between June and October of 2018. Simultaneous ground measurements of soil moisture, soil roughness, and leaf area index (LAI) were also recorded. The sensitivity of PALSAR observations to variations in soil moisture has been reported by several authors, and is confirmed in the present study, even for the case of very dense crops. The radar signals are simulated using five different radar backscattering models (physical and semi-empirical), over bare soil, and over areas with various types of crop cover (turmeric, marigold, and sorghum). When the semi-empirical water cloud model (WCM) is parameterized as a function of the LAI, to account for the vegetation’s contribution to the backscattered signal, it can provide relatively accurate estimations of soil moisture in turmeric and marigold fields, but has certain limitations when applied to sorghum fields. Observed limitations highlight the need to expand the analysis beyond the LAI by including additional vegetation parameters in order to take into account volume scattering in the L-band backscattered radar signal for accurate soil moisture estimation. View Full-Text
Keywords: PALSAR/ALOS-2; SAR; L-band; soil; moisture; roughness; vegetation; water cloud model; backscattering model PALSAR/ALOS-2; SAR; L-band; soil; moisture; roughness; vegetation; water cloud model; backscattering model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zribi, M.; Muddu, S.; Bousbih, S.; Al Bitar, A.; Tomer, S.K.; Baghdadi, N.; Bandyopadhyay, S. Analysis of L-Band SAR Data for Soil Moisture Estimations over Agricultural Areas in the Tropics. Remote Sens. 2019, 11, 1122.

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