Abstract
Agricultural applications of remote sensing have recently been extended to attempt detailed identification and mapping of rice growth stages. In terms of agricultural insurance, the information plays a critical role in the damage assessment as rice plants of certain ages cannot survive flooding or drought events. In this research, Phased Array-type L-band Synthetic Aperture Radar (PALSAR-2) data were evaluated in combination with machine learning techniques. Two forms of PALSAR-2 images were investigated, i.e., backscatter coefficients and their combination of textural and decomposition properties. The datasets were ingested into seven machine learning processes so that the accuracy of each combination of tools and datasets for identifying rice growth stages could be evaluated. Additional SAR properties provided a benefit to all machine learning processes, with at least 4% improvement. Random Forest was the best performing algorithm with 83% overall accuracy, while competing processes such as C5.0 and Extreme Gradient Boosting, followed closely with a margin of about 5%.
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Acknowledgment
The authors would like to express their gratitude to Japan Aerospace Exploration Agency (JAXA) for data support through RA6-3040 to the second author. We thank the SATREPS team, led by Associate Professors Chiharu Hongo and Baba Barus and their team members (especially Rika Wijayanti, La Ode S. Iman, MSi and Drs. Khursatul Munibah, Muhammad Ardiansyah, and Boedi Tjahjono), during the field survey.
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Trisasongko, B.H., Panuju, D.R., Griffin, A.L., Paull, D.J., Shih, P.TY., Kanniah, K.D. (2022). Examining the Outcome of Coupling Machine Learning with Dual Polarimetric SAR for Rice Growth Mapping. In: Kumar, A., Kumar, P., Singh, S.S., Trisasongko, B.H., Rani, M. (eds) Agriculture, Livestock Production and Aquaculture. Springer, Cham. https://doi.org/10.1007/978-3-030-93262-6_8
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