Estimation of Rice Age in Sentinel-2 Image with Gaussian Mixture Model Approach
Abstract
Monitoring the growth phase or age of rice is carried out to estimate the harvest area and production of rice plants. Remote sensing technology can monitor the age of rice plants, one of which is using Sentinel-2 imagery. This research aims to identify the age of rice plants, and to map/monitor the spatio-temporal distribution of rice age using GMM classification on multi-temporal Sentinel-2 images. The GMM classification is a simple method based on density function. The research was carried out in rice fields at the Agency of Agriculture, Plantation, Food and Horticulture, Cianjur Regency, West Java Province, with observation periods from May - August 2021. The results of the research showed that the temporal spectral response was different between the visible band (blue, green and red) and the near infrared band, where the 3 visible bands have a similar pattern with a lower value than the near infrared band. Result of GMM classification can show the continuity of rice age classes at each image acquisition from 0 - 130 days after planting, so it can be used to monitor the age or growth phase of rice.
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References
[BPS] Badan Pusat Statistik Kabupaten Cianjur. 2018. Luas Panen dan Produksi Padi di Kabupaten Cianjur. Cianjur (ID): Badan Pusat Statistik.
[BPS] Badan Pusat Statistik, [Kementan] Kementrian Pertanian. 2012. Buku Pedoman Pengumpulan Data Tanaman Pangan. Jakarta (ID): BPS.
[USDA] United States Department of Agriculture. 2022. https://apps.fas.usda.gov/psdonline/circulars/grain-rice.pdf (diakses 8 Oktober 2022).
Arifin S, Manalu J, Kartika T, Yulianto F, Jutzarika A, Mukhoriyah, Sukowati KAD, Nugroho G. 2020. Metode pemantauan eksploitasi dan reklamasi tambang batubara menggunakan data Sentinel-2. Jurnal Penginderaan Jauh, 17(2):123 – 133.
Bouman, B. 2022. Rice Knowledge Bank. http://www.knowledgebank.irri.org/ (diakses 8 pada 15 Oktober 2022).
Chen Y., L.Tang, X.Yang, M. Bilal and Q. Li 2019. Object-based multi-modal convolution neural networks for building extraction using panchromatic and multispectral imagery. Neurocomputing, 1-26. doi:10.1016/j.neucom.2019.12.098
Dirgahayu D., H. Noviar dan S. Anwar. 2014. Model Pertumbuhan Tanaman Padi di Pulau Sumatera Menggunakan Data EVI MODIS Multitemporal. Seminar Nasional Penginderaan Jauh.
ESA [European Space Agency]. 2015. Sentinel-2 User Handbook Revision 2. ESA Communication, Noordwijk: 64.
ESA [European Space Agency]. 2018. Sen2Cor. https://step.esa.int/main/snap-supported-plugins/sen2cor/ (diakses 10 Oktober 2022)
Frantz, D., E. Hab, A. Uhl, J. Stoffels and J. Hill 2018. Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects. Remote sensing of Environment, 215: 471 – 481.
Maspiyanti F., M.V. Fanany and M.A. Aniati. 2013. Klasifikasi fase pertumbuhan padi berdasarkann citra hiperspektral dengan modifikasi logika fuzzy. Jurnal Penginderaan Juah, 10(1): 41 – 48.
Meng, Q. and E.S. Runkle. 2020. Growth Responses of Red-Leaf Lettuce to Temporal Spectral Changes. Front. Plant Sci, 11 (2020): 1 – 14. doi.org/10.3389/fpls.2020.571788.
Nurtyawan, R., A. Saepuloh, A.B. Harto, K. Wikantika dan A. Kondoh. 2018. Satellite Imagery for Classification of Rice Growth Phase Using Freeman Decomposition in Indramayu, West Java, Indonesia. J. of Biosciences HAYATI, 25(3): 126 – 137.
Patel, E. and D.S. Kushwaha. 2020. Clustering cloud workloads: K-Means vs Gaussian Mixture Model. Procedia Computer Science, 171:158 – 167.
Patel, N.K., T.P. Singh, B. Sahai and M.S. Patel. 1984. Spectral response of rice crop and its relation to yield and yield attributes. International Journal of Remote Sensing, 6: 5: 657 – 664.
Putra, D.K., I.I. Triasmoro dan D.A. Ratri. 2017. Simulasi dan analisis speaker recognition menggunakan metode Mel Frequency Cepstrum Coeficient (MFCC) dan Gaussian Mixture Model (GMM). e-Proceeding of Engineering, 4(2): 1766 – 1772.
Septiani, R., I.P.A. Citra dan A.S.A. Nugraha. 2019. Perbandingan metode supervised classification dan unsupervised classification terhadap penutup lahan di Kabupaten Buleleng. Jurnal Geografi, 16(2): 90 – 96.
Sujinah, A. Hairmansis, P. Sasmita dan Y. Nug. 2020. Hubungan Fenologi Pertumbuhan Tanaman Padi dengan Hasil Gabah, Umur Panen, Biomasa, dan Pengaruh Pemupukan. Penelitian Pertanian Tanaman Pangan, 4 (2): 63 – 71
Triscowati, D.W. 2019. Klasifikasi fase pertumbuhan padi menggunakan random forest berdasarkan data multitemporal LANDSAT-8 [Tesis]. Bogor (ID): Institut Pertanian Bogor.
Wang, M., J. Wang, Y. Cui, J. Liu and L. Chen. 2022. Agricultural Field Boundary Delineation with Satellite Image Segmentation for High-Resolution Crop Mapping: A Case Study of Rice Paddy. Agronomy, 12. doi: 10.3390/agronomy12102342
Yang, K., H. Zhang, F. Wang and R. Lai. 2022. Extraction of Broad-Leaved Tree Crown Based on UAV Visible Images and OBIA-RF Model: A Case Study for Chinese Olive Trees. Remote Sen., 14 (2469) : 1 – 23. doi.org/ 10.3390
Zhou, W., Lv. Tengfei, Z. Yang, T. Wang, Y. Fu, Y. Chen, B. Hu and W. Ren. 2017. Morphophysiological mechanism of rice yield increase in response to optimized nitrogen management. Scientific Reports, 7(17226): 1 – 10.
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Department of Soil Science and Land Resources Departemen Ilmu Tanah dan Sumberdaya Lahan, Faculty of Agriculture Fakultas Pertanian, IPB University
