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Soil Moisture Retrieval over a Vegetation-Covered Area Using ALOS-2 L-Band Synthetic Aperture Radar Data
Article

Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees

1
Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
2
SMART Farming Technology Research Center, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
3
Laboratory of Plantation System Technology and Mechanization (PSTM), Institute of Plantation Studies (IKP), Universiti Putra Malaysia, Serdang 43400, Malaysia
4
FGV R&D Sdn Bhd, Level 9, Wisma FGV, Jalan Raja Laut, Kuala Lumpur 50350, Malaysia
5
Department of Human and Social Systems, Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Takeo Tadono and Masato Ohki
Remote Sens. 2021, 13(20), 4023; https://doi.org/10.3390/rs13204023
Received: 31 August 2021 / Revised: 2 October 2021 / Accepted: 5 October 2021 / Published: 9 October 2021
(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)
In oil palm crop, soil fertility is less important than the physical soil characteristics. It is important to have a balance and sufficient soil moisture to sustain high yields in oil palm plantations. However, conventional methods of soil moisture determination are laborious and time-consuming with limited coverage and accuracy. In this research, we evaluated synthetic aperture radar (SAR) and in-situ observations at an oil palm plantation to determine SAR signal sensitivity to oil palm crop by means of water cloud model (WCM) inversion for retrieving soil moisture from L-band HH and HV polarized data. The effects of vegetation on backscattering coefficients were evaluated by comparing Leaf Area Index (LAI), Leaf Water Area Index (LWAI) and Normalized Plant Water Content (NPWC). The results showed that HV polarization effectively simulated backscatter coefficient as compared to HH polarization where the best fit was obtained by taking the LAI as a vegetation descriptor. The HV polarization with the LAI indicator was able to retrieve soil moisture content with an accuracy of at least 80%. View Full-Text
Keywords: SAR; backscattering; soil moisture content; LAI; HH and HV polarization SAR; backscattering; soil moisture content; LAI; HH and HV polarization
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MDPI and ACS Style

Shashikant, V.; Mohamed Shariff, A.R.; Wayayok, A.; Kamal, M.R.; Lee, Y.P.; Takeuchi, W. Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees. Remote Sens. 2021, 13, 4023. https://doi.org/10.3390/rs13204023

AMA Style

Shashikant V, Mohamed Shariff AR, Wayayok A, Kamal MR, Lee YP, Takeuchi W. Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees. Remote Sensing. 2021; 13(20):4023. https://doi.org/10.3390/rs13204023

Chicago/Turabian Style

Shashikant, Veena, Abdul R. Mohamed Shariff, Aimrun Wayayok, Md R. Kamal, Yang P. Lee, and Wataru Takeuchi. 2021. "Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees" Remote Sensing 13, no. 20: 4023. https://doi.org/10.3390/rs13204023

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