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Remote Sens. 2018, 10(9), 1394; https://doi.org/10.3390/rs10091394

Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France

1
UMR TETIS, IRSTEA, University of Montpellier, 34093 Montpellier, France
2
Department of Space and Aeronautics, University of Science and technology of Hanoi, Vietnam Academy of Science and Technology, 122100 Hanoi, Vietnam
3
UMR 1114 EMMAH, INRA, University of Avignon, 84914 Avignon, France
4
UMR 0951 INNOVATION, INRA, University of Montpellier, 34060 Montpellier, France
*
Author to whom correspondence should be addressed.
Received: 12 July 2018 / Revised: 16 August 2018 / Accepted: 29 August 2018 / Published: 1 September 2018
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Abstract

The research and improvement of methods to be used for crop monitoring are currently major challenges, especially for radar images due to their speckle noise nature. The European Space Agency’s (ESA) Sentinel-1 constellation provides synthetic aperture radar (SAR) images coverage with a 6-day revisit period at a high spatial resolution of pixel spacing of 20 m. Sentinel-1 data are considerably useful, as they provide valuable information of the vegetation cover. The objective of this work is to study the capabilities of multitemporal radar images for rice height and dry biomass retrievals using Sentinel-1 data. To do this, we train Sentinel-1 data against ground measurements with classical machine learning techniques (Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Random Forest (RF)) to estimate rice height and dry biomass. The study is carried out on a multitemporal Sentinel-1 dataset acquired from May 2017 to September 2017 over the Camargue region, southern France. The ground in-situ measurements were made in the same period to collect rice height and dry biomass over 11 rice fields. The images were processed in order to produce a radar stack in C-band including dual-polarization VV (Vertical receive and Vertical transmit) and VH (Vertical receive and Horizontal transmit) data. We found that non-parametric methods (SVR and RF) had a better performance over the parametric MLR method for rice biophysical parameter retrievals. The accuracy of rice height estimation showed that rice height retrieval was strongly correlated to the in-situ rice height from dual-polarization, in which Random Forest yielded the best performance with correlation coefficient R 2 = 0.92 and the root mean square error (RMSE) 16% (7.9 cm). In addition, we demonstrated that the correlation of Sentinel-1 signal to the biomass was also very high in VH polarization with R 2 = 0.9 and RMSE = 18% (162 g·m 2 ) (with Random Forest method). Such results indicate that the highly qualified Sentinel-1 radar data could be well exploited for rice biomass and height retrieval and they could be used for operational tasks. View Full-Text
Keywords: rice dry biomass; rice height; Multiple Linear Regression; Support Vector Regression; Random Forest; Sentinel-1, TomoSAR platform; Camargue; souther France rice dry biomass; rice height; Multiple Linear Regression; Support Vector Regression; Random Forest; Sentinel-1, TomoSAR platform; Camargue; souther France
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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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Ndikumana, E.; Ho Tong Minh, D.; Dang Nguyen, H.T.; Baghdadi, N.; Courault, D.; Hossard, L.; El Moussawi, I. Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France. Remote Sens. 2018, 10, 1394.

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