Mr. Emile Ndikumana Profile

Emile Ndikumana

at IRSTEA

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Publications (2)

Proceedings Article | 10 October 2018 Presentation + Paper

Rice height and biomass estimations using multitemporal SAR Sentinel-1: Camargue case study

Emile Ndikumana, Dinh Ho Tong Minh, Dang Nguyen Hai Thu, Nicolas Baghdadi, Dominique Courault, Laure Hossard, Ibrahim El Moussawi

Proceedings Volume 10783, 107830U (2018) https://doi.org/10.1117/12.2325174

KEYWORDS: Synthetic aperture radar, Biological research, Backscatter, Polarization, Radar, Agriculture, Remote sensing, Machine learning, Data modeling, Data acquisition

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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) Sentinel1 constellation provides synthetic aperture radar (SAR) images coverage with a 6 days revisit period at a high spatial resolution of pixel spacing 20 m. Sentinel-1 data are considerable useful, as they provide valuable information of the vegetation cover. The objective of this paper is to provide a better understanding of the capabilities of Sentinel-1 radar images for rice height and dry biomass retrievals. To do this, we train Sentinel1 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 multi-temporal 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 an intensity radar data 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 R² = 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 R² = 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

Proceedings Article | 9 October 2018 Presentation + Paper

Applying deep learning for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France

Emile Ndikumana, Dinh Ho Tong Minh, Nicolas Baghdadi, Dominique Courault , Laure Hossard

Proceedings Volume 10789, 1078911 (2018) https://doi.org/10.1117/12.2325160

KEYWORDS: Agriculture, Synthetic aperture radar, Machine learning, Data modeling, Radar, Neural networks, Remote sensing, Polarization, Spatial resolution, Neurons

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