MONITORING FASE PERTUMBUHAN PADI DENGAN SENTINEL-2 DAN PENGARUH FAKTOR FISIK LINGKUNGANNYA
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Abstract
Rice is a staple food for the majority of Indonesia's population. Therefore, monitoring of rice growth phase and rice phenology is important. Remote sensing technology with Sentinel-2 imagery is applied to quickly obtain data over a large area at PT. Sang Hyang Seri. The NDVI algorithm is used to monitor plant growth and is correlated with plant age to determine the growth phase of rice. The effect of rainfall, soil type and slope were correlated to the NDVI value and the differences in NDVI values were analyzed for the differences in these physical factors. The results showed that the trend of the NDVI value of the rice growth phase was in the form of a parabolic curve of the order-2. The age of rice is generally 110 days old. Land units are divided into 3, according to these physical factors (OATRAL, DFTRL, DFTTL). The factors of rainfall, soil type and slope have an effect on plant fertility, but have no significant effect on the age of rice plants. The duration of the nursery affects the age of rice planting.
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References
Agricultural and Forest Meteorology. 151 (1): 101-115.
Badan Standarisasi Nasional. 2016. PT Sang Hyang Seri (Persero) Subang bersiap menerapkan SNI dari hulu sampai ke hilir. (online), (https://bsn.go.id/main/berita/berita_det/7663), diakses pada 9 Maret 2023.
Boschetti, M., Nutini, F., Manfron, G., Brivio, P.A., Nelson, A., 2014. Comparative analysis of normalised difference spectral indices derived from MODIS for detecting surface water in flooded rice cropping systems. PLOS ONE 9, e88741.
Bouvet, A., Le Toan, T., 2011. Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta. Remote Sens. Environ. 115, 1090–1101.
Ceglar, A., Z. Črepinšek and L. Kajfež-Bogataj. 2011. The simulation of phenological development in dynamic crop model: The Bayesian comparison of different methods.
Chen, C.; Quilang, E.J.P.; Alosnos, E.D.; Finnigan, J. Rice area mapping, yield, and production forecast for the province of Nueva Ecija using RADARSAT imagery. Can. J. Remote Sens. 2011, 37, 1–16.
Dirgahayu D., Nr L., Adhyani, Nugraheni S., 2005. Model Pertumbuhan Tanaman Padi Menggunakan Citra MODIS untuk Pendugaan Umur Padi. Prosiding Pertemuan Ilmiah Tahunan MAPIN XIV, Surabaya
Dirgahayu D., H Noviar, S Anwar, 2014. Model Pertumbuhan Tanaman Padi di Pulau Sumatera Menggunakan Data EVI MODIS Multitemporal. Prosiding Seminar Nasional Penginderaan Jauh: 333-343.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Bargellini, P. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, pp 25–36.
Escola, A., Badia, N., Arnó, J., & Martínez-Casasnovas, J. A. (2017). Using Sentinel-2 images to implement Precision Agriculture techniques in large arable fields: First results of a case study. Advances in Animal Biosciences, 8(2), 377–382.
FAO. (2016). Land suitability for rice production: A guide for using GIS and remote sensing. Rome: FAO.
Farrar, T. J., Nicholson, S. E., & Lare, A. R. (1994). The Influence of Soil Type on the Relationships between NDVI, Rainfall, and Soil Moisture in Semiarid Botswana. II. NDVI Response to Soil Moisture (Vol. 50).
Gujarati, D. N. (2003). Basic econometrics. Tata McGraw-Hill Education.
Gumma, M.K., Thenkabail, P.S., Maunahan, A., Islam, S., Nelson, A., 2014.Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010. ISPRS J. Photogramm. Remote Sens. 91, 98–113.
Hair Jr, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Hisyam, N. H. B. et al. 2022. Monitoring of Rice Growth Phases Using Multi Temporal Sentinel-2 Satellite Image. doi:10.1088/1755-1315/1051/1/012021.
Huang J, Wang X, Li X, Tian H, Pan Z (2013) Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA’s-AVHRR. PloS ONE 8(8): e70816. doi:10.1371/journal.pone.0070816
Hung, T. (2000). MODIS Application in Monitoring Surface Parameters. Institute of Industrial Science. University of Tokyo. Tokyo. Japan.
IRRI. (2017). Soil and water management for rice production. Los Baños, Philippines: IRRI.
Kriegler, F., Malila, W., Nalepka, R., & Richardson, W. (1969). Preprocessing transformations and their effect on multispectral recognition. Proceedings of the 6th International Symposium on Remote Sensing of Environment. Ann Arbor, MI: University of Michigan, 97-131.
Kuenzer, C., Knauer, K., 2013. Remote sensing of rice crop areas. Int. J. Remote Sens. 34, 2101–2139
Lai, Joon Ket & Lin, Wen Shin. 2021. Assessment of the Rice Panicle Initiation by Using NDVI-Based Vegetation Indexes. Appl. Sci. 2021, 11, 10076. https://doi.org/10.3390/app112110076
Lillesand Th.M. and Ralp W. Keifer. 1994. Remote Sensing and Image Interpretation. John Willey and Sons. New York.
Liyantono, et al. 2020. Analysis of Paddy Productivity Using Normalized Difference Vegetation Index Value of Sentinel-2 and UAV Multispectral Imagery in The Rainy Season. doi:10.1088/1755-1315/542/1/012059.
Made Parsa, I., Dirgahayu, D., Manalu, J., Carolita, I., Pusat Pemanfaatan Penginderaan Jauh, W. K., &; Jln Kalisari, L. 2017. Uji Model Fase Pertumbuhan Padi Berbasis Citra Modis Multiwaktu Di Pulau Lombok (The Testing Of Phase Growth Rice Model Based On Multitemporal Modis In Lombok Island). http://MODIS.gsfc.nasa.
Malingreau J.P. 1981. Remote Sensing for Monitoring Rice Production in the Wet Tropics: Approach and Implication. Symposium on Application of Remote Sensing for Rice Production. Hyderabad, India.
Mosleh, M., Hassan, Q., 2014. Development of a remote sensing-based boro rice mapping system. Remote Sens. 6, 1938–1953.
Nicholson, S. E., & Farrar, T. J. (1994). The Influence of Soil Type on the Relationships between NDVI, Rainfall, and Soil Moisture in Semiarid Botswana. I. NDVI Response to Rainfall (Vol. 50).
Pemerintah Daerah Kabupaten Subang. 2020. Kabupaten Subang Produsen Beras Tertinggi Ketiga di Indonesia. (online), (https://www.subang.go.id/berita/kabupaten-subangprodusen-beras-tertinggi-ketiga-di-indonesia) diakses pada 10 Februari 2023.
Prakoso, Slamet Sigit & Safitri, Rizki Dwi. (2020). Analisis Perbandingan Metode NDVI dan Maximum Likelihood Classification untuk RTH Analisis Perbandingan Metode Normalized Vegetation Index Dan Maximum Likelihood Classification Untuk Analisis Ruang Terbuka Hijau.
Prasad, A.K.; Chai, L.; Singh, R.P.; Kafatos, M. Crop yield estimation model for Iowa using remote sensing and surface parameters. Int. J. Appl. Earth Obs. Geoinf. 2006, 8, 26–33. [CrossRef].
Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W. (1973) Monitoring Vegetation Systems in the Great Plains with ERTS (Earth Resources Technology Satellite). Proceedings of 3rd Earth Resources Technology Satellite Symposium, Greenbelt, 10-14 December, SP-351, 309-317.
Rudiana, E., Rustiadi, E., Firdaus, M., & Dirgahayu, D. (2017). Pengembangan Penggunaan Penginderaan Jauh untuk Estimasi Produksi Padi (Studi Kasus Kabupaten Bekasi). Jurnal Ilmu Tanah Dan Lingkungan, 19(1), 6–12. https://doi.org/10.29244/jitl.19.1.6-12.
Segarra, J., Buchaillot, Maria, L., Araus, Jose, L., Kefauver, Shawn, C. (2020). Remote sensing for precision agriculture: Sentinel-2 improved features and applications. Agronomy, 10(641), pp 1-18.
Suspidayanti, L., & Aries Rokhmana, C. (2021). Identifikasi Fase Pertumbuhan Padi Menggunakan Citra SAR (Synthetic Aperture Radar) Sentinel-1 (Issue 2).
Syafriyyin, M. A. R., & Sukojo, B. M. 2014. Optimalisasi Pemetaan Fase Pertumbuhan Padi Berdasarkan Analisa Pola Reflektan Dengan Data Hiperspektral Studi Kasus: Kabupaten Karawang. GEOID Vol. 09, No. 02, Februari 2014 (121-127).
Tornos, L., Huesca, M., Dominguez, J.A., Moyano, M.C., Cicuendez, V., Recuero, L., Palacios-Orueta, A., 2015. Assessment of MODIS spectral indices for determining rice paddy agricultural practices and hydroperiod. ISPRS J. Photogramm. Remote Sens. 101, 110–124.
USDA. (2018). Rice production handbook. Washington, D.C.: USDA.
Van Niel, TG., McVicar,_ TR. 2001. Rem.5ensofrice-based irrigated agric. Rice CRC,p. 52.
Wahidah, N., Bambang, S. ;, Arwan, S. ;, & Wijaya, P. (2016). Analisis Fase Tumbuh Padi Menggunakan Algoritma Ndvi, Evi, Savi, Dan Lswi Pada Citra LANDSAT 8. In Jurnal Geodesi Undip Januari (Vol. 5, Issue 1).
Wahyunto, Widagdo, Heryanto, B. 2006. Pendugaan Produktivitas Tanaman Padi Sawah Melalui Analisis Citra Satelit.
Badan Standarisasi Nasional. 2016. PT Sang Hyang Seri (Persero) Subang bersiap menerapkan SNI dari hulu sampai ke hilir. (online), (https://bsn.go.id/main/berita/berita_det/7663), diakses pada 9 Maret 2023.
Boschetti, M., Nutini, F., Manfron, G., Brivio, P.A., Nelson, A., 2014. Comparative analysis of normalised difference spectral indices derived from MODIS for detecting surface water in flooded rice cropping systems. PLOS ONE 9, e88741.
Bouvet, A., Le Toan, T., 2011. Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta. Remote Sens. Environ. 115, 1090–1101.
Ceglar, A., Z. Črepinšek and L. Kajfež-Bogataj. 2011. The simulation of phenological development in dynamic crop model: The Bayesian comparison of different methods.
Chen, C.; Quilang, E.J.P.; Alosnos, E.D.; Finnigan, J. Rice area mapping, yield, and production forecast for the province of Nueva Ecija using RADARSAT imagery. Can. J. Remote Sens. 2011, 37, 1–16.
Dirgahayu D., Nr L., Adhyani, Nugraheni S., 2005. Model Pertumbuhan Tanaman Padi Menggunakan Citra MODIS untuk Pendugaan Umur Padi. Prosiding Pertemuan Ilmiah Tahunan MAPIN XIV, Surabaya
Dirgahayu D., H Noviar, S Anwar, 2014. Model Pertumbuhan Tanaman Padi di Pulau Sumatera Menggunakan Data EVI MODIS Multitemporal. Prosiding Seminar Nasional Penginderaan Jauh: 333-343.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Bargellini, P. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, pp 25–36.
Escola, A., Badia, N., Arnó, J., & Martínez-Casasnovas, J. A. (2017). Using Sentinel-2 images to implement Precision Agriculture techniques in large arable fields: First results of a case study. Advances in Animal Biosciences, 8(2), 377–382.
FAO. (2016). Land suitability for rice production: A guide for using GIS and remote sensing. Rome: FAO.
Farrar, T. J., Nicholson, S. E., & Lare, A. R. (1994). The Influence of Soil Type on the Relationships between NDVI, Rainfall, and Soil Moisture in Semiarid Botswana. II. NDVI Response to Soil Moisture (Vol. 50).
Gujarati, D. N. (2003). Basic econometrics. Tata McGraw-Hill Education.
Gumma, M.K., Thenkabail, P.S., Maunahan, A., Islam, S., Nelson, A., 2014.Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010. ISPRS J. Photogramm. Remote Sens. 91, 98–113.
Hair Jr, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Hisyam, N. H. B. et al. 2022. Monitoring of Rice Growth Phases Using Multi Temporal Sentinel-2 Satellite Image. doi:10.1088/1755-1315/1051/1/012021.
Huang J, Wang X, Li X, Tian H, Pan Z (2013) Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA’s-AVHRR. PloS ONE 8(8): e70816. doi:10.1371/journal.pone.0070816
Hung, T. (2000). MODIS Application in Monitoring Surface Parameters. Institute of Industrial Science. University of Tokyo. Tokyo. Japan.
IRRI. (2017). Soil and water management for rice production. Los Baños, Philippines: IRRI.
Kriegler, F., Malila, W., Nalepka, R., & Richardson, W. (1969). Preprocessing transformations and their effect on multispectral recognition. Proceedings of the 6th International Symposium on Remote Sensing of Environment. Ann Arbor, MI: University of Michigan, 97-131.
Kuenzer, C., Knauer, K., 2013. Remote sensing of rice crop areas. Int. J. Remote Sens. 34, 2101–2139
Lai, Joon Ket & Lin, Wen Shin. 2021. Assessment of the Rice Panicle Initiation by Using NDVI-Based Vegetation Indexes. Appl. Sci. 2021, 11, 10076. https://doi.org/10.3390/app112110076
Lillesand Th.M. and Ralp W. Keifer. 1994. Remote Sensing and Image Interpretation. John Willey and Sons. New York.
Liyantono, et al. 2020. Analysis of Paddy Productivity Using Normalized Difference Vegetation Index Value of Sentinel-2 and UAV Multispectral Imagery in The Rainy Season. doi:10.1088/1755-1315/542/1/012059.
Made Parsa, I., Dirgahayu, D., Manalu, J., Carolita, I., Pusat Pemanfaatan Penginderaan Jauh, W. K., &; Jln Kalisari, L. 2017. Uji Model Fase Pertumbuhan Padi Berbasis Citra Modis Multiwaktu Di Pulau Lombok (The Testing Of Phase Growth Rice Model Based On Multitemporal Modis In Lombok Island). http://MODIS.gsfc.nasa.
Malingreau J.P. 1981. Remote Sensing for Monitoring Rice Production in the Wet Tropics: Approach and Implication. Symposium on Application of Remote Sensing for Rice Production. Hyderabad, India.
Mosleh, M., Hassan, Q., 2014. Development of a remote sensing-based boro rice mapping system. Remote Sens. 6, 1938–1953.
Nicholson, S. E., & Farrar, T. J. (1994). The Influence of Soil Type on the Relationships between NDVI, Rainfall, and Soil Moisture in Semiarid Botswana. I. NDVI Response to Rainfall (Vol. 50).
Pemerintah Daerah Kabupaten Subang. 2020. Kabupaten Subang Produsen Beras Tertinggi Ketiga di Indonesia. (online), (https://www.subang.go.id/berita/kabupaten-subangprodusen-beras-tertinggi-ketiga-di-indonesia) diakses pada 10 Februari 2023.
Prakoso, Slamet Sigit & Safitri, Rizki Dwi. (2020). Analisis Perbandingan Metode NDVI dan Maximum Likelihood Classification untuk RTH Analisis Perbandingan Metode Normalized Vegetation Index Dan Maximum Likelihood Classification Untuk Analisis Ruang Terbuka Hijau.
Prasad, A.K.; Chai, L.; Singh, R.P.; Kafatos, M. Crop yield estimation model for Iowa using remote sensing and surface parameters. Int. J. Appl. Earth Obs. Geoinf. 2006, 8, 26–33. [CrossRef].
Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W. (1973) Monitoring Vegetation Systems in the Great Plains with ERTS (Earth Resources Technology Satellite). Proceedings of 3rd Earth Resources Technology Satellite Symposium, Greenbelt, 10-14 December, SP-351, 309-317.
Rudiana, E., Rustiadi, E., Firdaus, M., & Dirgahayu, D. (2017). Pengembangan Penggunaan Penginderaan Jauh untuk Estimasi Produksi Padi (Studi Kasus Kabupaten Bekasi). Jurnal Ilmu Tanah Dan Lingkungan, 19(1), 6–12. https://doi.org/10.29244/jitl.19.1.6-12.
Segarra, J., Buchaillot, Maria, L., Araus, Jose, L., Kefauver, Shawn, C. (2020). Remote sensing for precision agriculture: Sentinel-2 improved features and applications. Agronomy, 10(641), pp 1-18.
Suspidayanti, L., & Aries Rokhmana, C. (2021). Identifikasi Fase Pertumbuhan Padi Menggunakan Citra SAR (Synthetic Aperture Radar) Sentinel-1 (Issue 2).
Syafriyyin, M. A. R., & Sukojo, B. M. 2014. Optimalisasi Pemetaan Fase Pertumbuhan Padi Berdasarkan Analisa Pola Reflektan Dengan Data Hiperspektral Studi Kasus: Kabupaten Karawang. GEOID Vol. 09, No. 02, Februari 2014 (121-127).
Tornos, L., Huesca, M., Dominguez, J.A., Moyano, M.C., Cicuendez, V., Recuero, L., Palacios-Orueta, A., 2015. Assessment of MODIS spectral indices for determining rice paddy agricultural practices and hydroperiod. ISPRS J. Photogramm. Remote Sens. 101, 110–124.
USDA. (2018). Rice production handbook. Washington, D.C.: USDA.
Van Niel, TG., McVicar,_ TR. 2001. Rem.5ensofrice-based irrigated agric. Rice CRC,p. 52.
Wahidah, N., Bambang, S. ;, Arwan, S. ;, & Wijaya, P. (2016). Analisis Fase Tumbuh Padi Menggunakan Algoritma Ndvi, Evi, Savi, Dan Lswi Pada Citra LANDSAT 8. In Jurnal Geodesi Undip Januari (Vol. 5, Issue 1).
Wahyunto, Widagdo, Heryanto, B. 2006. Pendugaan Produktivitas Tanaman Padi Sawah Melalui Analisis Citra Satelit.