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Article

Comparison of Field and SAR-Derived Descriptors in the Retrieval of Soil Moisture from Oil Palm Crops Using PALSAR-2

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, Institute of Plantation Studies, 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(23), 4729; https://doi.org/10.3390/rs13234729
Received: 14 September 2021 / Revised: 1 November 2021 / Accepted: 5 November 2021 / Published: 23 November 2021
(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)
Synthetic-aperture radar’s (SAR’s) capacity to resolve the cloud cover concerns encountered while gathering optical data has tremendous potential for soil moisture data retrieval using SAR data. It is possible to use SAR data to recover soil moisture because the backscatter coefficient is sensitive to both soil and vegetation by penetrating through the vegetation layer. This study investigated the feasibility of employing a SAR-derived radar vegetation index (RVI), the ratios of the backscatter coefficients using polarizations of HH/HV (RHH/HV) and HV/HH (RHH/HV) to an oil palm crops as vegetation indicators in the water cloud model (WCM) using phased-array L-band SAR-2 (PALSAR-2). These data were compared to the manual leaf area index (LAI) and a physical soil sampling method for computing soil moisture. The field data included the LAI input parameters and, more importantly, physical soil samples from which to calculate the soil moisture. The fieldwork was carried out in Chuping District, Perlis State, Malaysia. Corresponding PALSAR-2 data were collected on three observation dates in 2019: 17 January, 16 April, and 9 July. The results showed that the WCM modeled using the LAI under HV polarization demonstrated promising accuracy, with the root mean square error recorded as 0.033 m3/m3. This was comparable to the RVI and RHH/HV under HV polarization, which had accuracies of 0.031 and 0.049 m3/m3, respectively. The findings of this study suggest that SAR-based indicators, RHH/HV and RVI using PALSAR-2, can be used to reduce field-related input in the retrieval of soil moisture data using the WCM for oil palm crop. View Full-Text
Keywords: leaf area index; leave-one-out cross-validation; oil palm; radar vegetation index; synthetic aperture radar; soil moisture; vegetation descriptors; water cloud model leaf area index; leave-one-out cross-validation; oil palm; radar vegetation index; synthetic aperture radar; soil moisture; vegetation descriptors; water cloud model
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MDPI and ACS Style

Shashikant, V.; Mohamed Shariff, A.R.; Wayayok, A.; Kamal, M.R.; Lee, Y.P.; Takeuchi, W. Comparison of Field and SAR-Derived Descriptors in the Retrieval of Soil Moisture from Oil Palm Crops Using PALSAR-2. Remote Sens. 2021, 13, 4729. https://doi.org/10.3390/rs13234729

AMA Style

Shashikant V, Mohamed Shariff AR, Wayayok A, Kamal MR, Lee YP, Takeuchi W. Comparison of Field and SAR-Derived Descriptors in the Retrieval of Soil Moisture from Oil Palm Crops Using PALSAR-2. Remote Sensing. 2021; 13(23):4729. https://doi.org/10.3390/rs13234729

Chicago/Turabian Style

Shashikant, Veena, Abdul R. Mohamed Shariff, Aimrun Wayayok, Md R. Kamal, Yang P. Lee, and Wataru Takeuchi. 2021. "Comparison of Field and SAR-Derived Descriptors in the Retrieval of Soil Moisture from Oil Palm Crops Using PALSAR-2" Remote Sensing 13, no. 23: 4729. https://doi.org/10.3390/rs13234729

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