THE USE OF C-BAND SYNTHETIC APERTURE RADAR SATELLITE DATA FOR RICE PLANT GROWTH PHASE IDENTIFICATION

Anugrah Indah Lestari, Dony Kushardono

Abstract

Identification of the rice plant growth phase is an important step in estimating the harvest season and predicting rice production. It is undertaken to support the provision of information on national food availability. Indonesia’s high cloud coverage throughout the year means it is not possible to make optimal use of optical remote sensing satellite systems. However, the Synthetic Aperture Radar (SAR) remote sensing satellite system is a promising alternative technology for identifying the rice plant growth phase since it is not influenced by cloud cover and the weather. This study uses multi-temporal C-Band SAR satellite data for the period May–September 2016. VH and VV polarisation were observed to identify the rice plant growth phase of the Ciherang variety, which is commonly planted by farmers in West Java. Development of the rice plant growth phase model was optimized by obtaining samples spatially from a rice paddy block in PT Sang Hyang Seri, Subang, in order to acquire representative radar backscatter values from the SAR data on the age of certain rice plants. The Normalised Difference Polarisation Index (NDPI) and texture features, namely entropy, homogeneity and the Grey-Level Co-occurrence Matrix (GLCM) mean, were included as the samples. The results show that the radar backscatter value (σ0) of VH polarisation without the texture feature, with the entropy texture feature and GLCM mean texture feature respectively exhibit similar trends and demonstrate potential for use in identifying and monitoring the rice plant growth phase. The rice plant growth phase model without texture feature on VH polarisation is revealed as the most suitable model since it has the smallest average error.

Keywords

remote sensing satellite; SAR; C-band; texture feature; rice plant growth phase

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References

Badan Litbang Pertanian (2012). Varietas Padi Unggulan Maj. Agroinovasi Sinartani 2–7.

Badan Pengkajian dan Pengembangan Perdagangan (2016). Potret Perdagangan Beras 1–2.

Badan Pusat Statistik Indonesia (2013). Proyeksi Penduduk Indonesia (Indonesia Population Projection) 2010-2035. Jakarta: Badan Pusat Statistik.

Bousbih, S., Zribi, M., Lili-Chabaane, Z., Baghdadi, N., El Hajj, M., Gao Q., & Mougenot, B. (2017). Potential of Sentinel-1 radar data for the assessment of soil and cereal cover parameters, Sensors (Switzerland) 17, doi: 10.3390/s17112617

Broto, P. E., Saputro, A. H., & Kushardono, D. (2017). Prediction of paddy field area base on aerial photography using multispectral camera. IEEE Conference on Sustainable Information Engineering and Technology (SIET), 1, 425–9, doi: 10.1109/SIET.2017.8304176

Choo, A. L., Chan, Y. K., Koo, V. C., & Lim, T. S. (2014). Study on geometric correction algorithms for SAR images. Int. J. Microw. Opt. Technol., 9, 68–72.

Chulafak, G. A., Kushardono, D., & Zylshal (2017). Optimasi parameter dalam klasifikasi spasial penutup penggunaan lahan menggunakan data Sentinel SAR. J. Penginderaan Jauh, 111–30, doi: 10.30536/j.pjpdcd.1017.v14.a2746

Domiri, D. D. (2017). The method for detecting biological parameter of rice growth and early planting of paddy crop by using multi temporal remote sensing data. IOP Conf. Series: Earth and Environmental Science.

ESA (2013). Sentinel-1 User Handbook.

Fageria, N. K. (2007). Yield physiology of rice. J. Plant Nutr., 30, 843–79, doi: 10.1080/15226510701374831

Ferencz, C., Bognár, P., Lichtenberger, J., Hamar, D., Tarcsai, G., Timár, G., Molnár, G., Pásztor, S., Steinbach, P., Székely, B., Ferencz, O. E., & Ferencz-Árkos, I. (2004). Crop yield estimation by satellite remote sensing. Int. J. Remote Sens, 25, 4113–49, doi: 10.1080/01431160410001698870

Fung, A. K., & Ulaby, F. T. (1983), Manual of Remote Sensing (Virginia: American Society of Photogrammetry).

Gadkari, D. (2004). Image Quality Analysis Using GLCM. Thesis, The University of Central Florida.

Grady, D. O., Leblanc, M., & Gillieson, D. (2013). Relationship of local incidence angle with satellite radar backscatter for different surface conditions, Int. J. Appl. Earth Obs. Geoinf, 24, 42–53, doi: 10.1016/j.jag.2013.02.005

Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3, 610–21.

Huang, Q., Zhang, L., Wu, W., & Li, D. (2010) MODIS-NDVI-based crop growth monitoring in China Agriculture Remote Sensing Monitoring System. Second ITA International Conference on Geoscience and Remote Sensing, 287–90, doi: 10.1109/IITA-GRS.2010.5603948

Karthikeyan, S., & Rengarajan, N. (2013). Performance analysis of gray level cooccurrence matrix texture features for glaucoma diagnosis. Am. J. Appl. Sci., 11, 248–57, doi: 10.3844/ajassp.2014.248.257

Kham, D Van (2012). Using MODIS data for the monitoring growth and development of rice plants in Red River Delta, VNU J. Sci. Earth Sci., 28, 106–14.

Kushardono, D. (1996). Study on High Accuracy Land Cover Classification Methods in Remote Sensing. Disertation. Tokai University Research & Information Center

Kushardono, D. (2012). Klasifikasi spasial penutup lahan dengan data SAR dual- polarisasi menggunakan Normalised Difference Polarisation Index dan fitur keruangan dari matrik kookurensi. J. Penginderaan Jauh, 9, 12–24.

Kushardono, D., Anas, A., Maryanto, A., Utama, A. B., & Winanto (2015). Pemanfaatan data LSA (LAPAN Surveillance Aircraft) untuk mendukung pemetaan skala rinci. Prosiding Pertemuan Ilmiah Tahunan XX MAPIN 2015, doi: 10.13140/2.1.1504.3365

Kushardono, D., Fukue, K., Shimoda, H., & Sakata, T. (1994). Spatial land cover classification with the aid of neural network, Proc. SPIE 2315 Image and Signal Processing for Remote Sensing pp 702–10, doi: 10.1117/12.196770

Lam-Dao, N., Apan, A., Young, F., Le-Van, T., Le-Toan, T., & Bouvet, A. (2007). Rice monitoring using ENVISAT ASAR data: preliminary results of a case study in the Mekong River Delta, Vietnam. Asian Conference on Remote Sensing (Kuala Lumpur).

Mauludyani, A. V. R., Martianto, D., & Baliwati, Y. F. (2008). Pola konsumsi dan permintaan pangan pokok berdasarkan analisis data Susenas 2005. J. Gizi dan Pangan, 3, 101–17, doi: 10.25182/jgp.2008.3.2.101-117

Miranda, N., & Meadows, P. J. (2015). Radiometric calibration of S-1 Level-1 Products generated by the S-1 IPF (ESA).

Nguyen, D. B., Gruber, A., & Wagner, W. (2016). Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data. Remote Sens. Lett., 7, 1209–18, doi: 10.1080/2150704X.2016.1225172

Nguyen, D. B., & Wagner, W. (2017). European rice cropland mapping with Sentinel-1 data: The Mediterranean region case study. Water (Switzerland), 9, 1–21, doi: 10.3390/w9060392

Noor, M. (2015). Kebijakan pembangunan kependudukan dan bonus demografi. Serat Acitya-Jurnal Ilm. UNTAG, 121–8.

Nuarsa, I. W., & Nishio, F. (2007). Relationships between rice growth parameters and remote sensing data. Int. J. Remote Sens. Earth Sci., 4, pp102–12, doi: 10.30536/j.ijreses.2007.v4.a1221

Parsa, I. M., Dirgahayu, D., Manalu, J., Carolita, I., & Harsanugraha, K. W. (2017). Uji model fase pertumbuhan padi Berbasis citra MODIS multiwaktu di Pulau Lombok, J. Penginderaan Jauh, 14, 51–64.

Parsa, I. M., & Domiri, D. D. (2013). Multitemporal Landsat data to quick mapping of paddy field based on statistical parameters of vegetation index (case study : Tanggamus, Lampung), Int. J. Remote Sens. Earth Sci., 10, 19–24, doi: 10.30536/j.ijreses.2013.v10.a1838

Pathak, B., & Barooah, D. (2013). Texture analysis based on the gray-level co-occurrence matrix considering possible orientations. Int. J. Adv. Res. Electr. Electron. Instrum. Eng., 2, 4206–12.

Raviz, J., Laborte, A., Barbieri, M., Mabalay, M. R., Garcia, C., Elena, J., Bibar, A., Mabalot P., & Gonzaga, H. (2016). Mapping and monitoring rice areas in Central Luzon, Philippines using X and C-band SAR imagery, 37th Asian Conference on Remote Sensing (Colombo).

Ribbes, F., & Le Toan, T. (1996). Use of ERS-1 SAR data for ricefield mapping and rice crop parameters retrieval. International Geoscience and Remote Sensing Symposium, 4, 1983–5, doi: 10.1109/IGARSS.1996.516863

Soh, L., & Tsatsoulis, C. (1999). Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Transactions Geosci. Remote Sens., 37, 780–95, doi: 10.1109/36.752194

Son, N., Chen, C-F., Chen, C-R., & Minh, V. (2017). Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines. Geocarto Int., 33, 587–601, doi: 10.1080/10106049.2017.1289555

Smith, R. B. (2012). Introduction to interpreting digital radar images. MicroImages, Inc.

Stubenrauch, C. J., Chédin, A., Rädel, G., Scott, N. A., & Serrar, S. (2006). Cloud properties and their seasonal diurnal variability from TOVS Path-B. J. Clim., 19, 5531–53, doi: 10.1175/JCLI3929.1

Yoshida, S. (1981). Fundamentals of rice crop science. Manila: International Rice Research Institute.

Wijesingha, J. S. J., Deshapriya, N. L., & Samarakoon, L. (2015). Rice crop monitoring and yield assessment with MODIS 250m gridded vegetation product: A case study in Sa Kaeo Province, Thailand. 36th International Symposium on Remote Sensing of Environment, 40(Berlin), 121–7, doi: 10.5194/isprsarchives-XL-7-W3-121-2015

Zhang, X., Cui, J., Wang, W., & Lin, C. (2017). A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-

occurrence matrix fusion algorithm. Sensors (Switzerland), 17, doi: 10.3390/s17071474

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