Rice growth dynamics: NDVI modeling with sentinel-2 and environmental influences
DOI:
https://doi.org/10.58524/jgsa.v1i2.24Keywords:
NDVI, Rainfall, Rice Growth Phase, Soil Type, SlopeAbstract
Accurate monitoring of rice growth phase and phenology is crucial for food security in Indonesia, where rice is a staple crop. This study utilizes Sentinel-2 imagery and remote sensing techniques to efficiently assess rice growth dynamics over a large agricultural area at PT. Sang Hyang Seri, Subang, West Java. The Normalized Difference Vegetation Index (NDVI) was derived from Sentinel-2 data and correlated with plant age to determine rice growth stages. Furthermore, the influence of rainfall, soil type, and slope on NDVI values was analyzed to quantify the impact of these physical factors on rice development. Results indicate that the temporal trend of NDVI during the rice growth cycle can be effectively modeled using a second-order parabolic curve. While the overall rice growth duration was approximately 110 days, land units delineated based on physical factors (OATRAL, DFTRL, DFTTL) exhibited variations in NDVI values, suggesting differential plant fertility. Correlation analysis revealed that rainfall, soil type, and slope significantly affect plant fertility, though not the overall growth duration. Nursery duration, however, was found to influence the age of rice planting. These findings demonstrate the utility of Sentinel-2 NDVI for high-resolution monitoring of rice phenology and highlight the importance of considering environmental factors for optimizing rice production in the region.
Downloads
References
Abbas, S., & Mayo, Z. A. (2021). Impact of temperature and rainfall on rice production in Punjab, Pakistan. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-020-00647-8
Badrul Hisham, N. H., Hashim, N., Saraf, N. M., & Talib, N. (2022). Monitoring of Rice Growth Phases Using Multi-Temporal Sentinel-2 Satellite Image. IOP Conference Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/1051/1/012021
Chen, W., & Liu, G. (2024). A Novel Method for Identifying Crops in Parcels Constrained by Environmental Factors Through the Integration of a Gaofen-2 High-Resolution Remote Sensing Image and Sentinel-2 Time Series. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2023.3329987
Devkota, K. P., Yadav, S., Humphreys, E., Kumar, A., Kumar, P., Kumar, V., Malik, R. K., & Srivastava, A. K. (2021). Land gradient and configuration effects on yield, irrigation amount and irrigation water productivity in rice-wheat and maize-wheat cropping systems in Eastern India. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2021.107036
Dirgahayu, D. 2005. Model Pertumbuhan Tanaman Padi Menggunakan Data MODIS Untuk Pendugaan Umur Padi Sawah. Prosiding Pertemuan Ilmiah Tahunan MAPIN XIV 2005.
Dirgahayu, D., Noviar, H., & Anwar, S. (2014). Model pertumbuhan tanaman padi di pulau Sumatera menggunakan data EVI MODIS multitemporal. Proceedings, Seminar Nasional Penginderaan Jauh 2014.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., & Bargellini, P. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2011.11.026
Endiviana, O. A., Impron, Setiawan, Y., Imantho, H., Sugiarto, S. W., & Yuliawan, T. (2022). Selecting The Most Optimum Sentinel-2A Based Vegetation Index to Estimate Leaf Area Index of Three Rice Cultivars. Jurnal Keteknikan Pertanian. https://doi.org/10.19028/jtep.010.3.200-214
Girish R. S., and Hittalmani, K. L. 2004. Influence of climatological factors on rice under different water management practices. Field Crop Abst. 26: 1664.
Gujarati, D. N., & Porter, D. C. (2003). Basic econometrics (ed.). New York: McGraw-HiII.
Hair Jr, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2018). Multivariate data analysis (8th ed.). Cengage Learning.
Haque, M. A., Reza, M. N., Ali, M., Karim, M. R., Ahmed., S., Lee, K. D., Khang, Y. H., Chung, S. O. (2024). Effects of Environmental Conditions on Vegetation Indices from Multispectral Images: A Review. Korean Journal of Remote Sensing. 40(4), 319-341. https://doi.org/10.7780/kjrs.2024.40.4.1.
Hung, T. (2000). MODIS Application in Monitoring Surface Parameters. Institute of Industrial Science. University of Tokyo. Tokyo. Japan.
IUSS Working Group WRB. 2007. World Reference Base for Soil Resources 2006, first update 2007. World Soil Resources Reports No. 103. FAO, Rome.
Kriegler, F., Malila, W., Nalepka, R., & Richardson, W. (1969). Pre-processing 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.
Lai, J. K., & Lin, W. S. (2021). Assessment of the rice panicle initiation by using ndvi-based vegetation indexes. Applied Sciences (Switzerland). https://doi.org/10.3390/app112110076
Li, G., Cui, J., Han, W., Zhang, H., Huang, S., Chen, H., & Ao, J. (2022). Crop type mapping using time-series Sentinel-2 imagery and U-Net in early growth periods in the Hetao irrigation district in China. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.107478
Lillesand, T. M., & Kiefer, R. W. (1994). Remote sensing and image interpretation. 3rd edition. Remote Sensing and Image Interpretation. 3rd Edition.
Liu, J., Yang, K., Tariq, A., Lu, L., Soufan, W., & El Sabagh, A. (2023). Interaction of climate, topography and soil properties with cropland and cropping pattern using remote sensing data and machine learning methods. Egyptian Journal of Remote Sensing and Space Science. https://doi.org/10.1016/j.ejrs.2023.05.005
Liyantono, L., Sianjaya, A., & Sari, I. K. (2020). Analysis of paddy productivity using normalized difference vegetation index value of sentinel-2 and UAV multispectral imagery in the rainy season. IOP Conference Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/542/1/012059
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.
National Standardization Agency. (2016). PT Sang Hyang Seri (Persero) Subang prepares to implement SNI from upstream to downstream. (online), (https://bsn.go.id/main/berita/berita_det/7663), accessed on March 9, 2023.
Phung, H.-P., Nguyen, L.-D., Thong, N.-H., Thuy, L.-T., & Apan, A. A. (2020). Monitoring rice growth status in the Mekong Delta, Vietnam using multitemporal Sentinel-1 data. Journal of Applied Remote Sensing. https://doi.org/10.1117/1.jrs.14.014518
Qin, Z., Li, Y., Deng, S., Dou, X. (2025). Big data-driven insights into soil-plant-microbe interactions: Mechanisms, applications, and sustainable management in paddy fields. Advances in Resources Research. 5(2), 772-792. https://doi.org/10.50908/arr.5.2_772.
Ramadhani, H. A., Awaluddin, M., & Nugraha, A. L. (2016). Analisis Fase Tumbuh Padi Menggunakan Algoritma NDVI, EVI, SAVI dan LSWI pada Citra Landsat 8. Jurnal Geodesi Undip.
Rouse, J.; Hass, R.; Schell, J.; Deering, D. Monitoring vegetation systems in the great plains with ERTS. In Third ERTS Symposium; NASASP-351 I: Greenbelt, MD, USA, 1973; pp. 309–317.
Rudiana, E., Rustiadi, E., Firdaus, M., Dirgahayu, D., Ekonomi, F., Ipb, M., Pemanfaatan, P., & Jauh, P. (2016). Remote Sensing Application for Regional Rice Production Estimation (A Case Study in Bekasi District). J. Il. Tan. Lingk.
Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., & Ohno, H. (2005). A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2005.03.008
Segarra, J., Buchaillot, M. L., Araus, J. L., & Kefauver, S. C. (2020). Remote sensing for precision agriculture: Sentinel-2 improved features and applications. In Agronomy. https://doi.org/10.3390/agronomy10050641
Subang Regency Government. (2020). Subang Regency is the Third Highest Rice Producer in Indonesia. (online), (https://www.subang.go.id/berita/kabupaten-subangprodusen-beras-tertinggi-ketiga-di-indonesia) accessed on February 10, 2023.
Suspidayanti, L., & Rokhmana, C. A. (2021). Identifikasi Fase Pertumbuhan Padi Menggunakan Citra SAR (Synthetic Aperture Radar) SENTINEL-1. Elipsoida : Jurnal Geodesi Dan Geomatika. https://doi.org/10.14710/elipsoida.2021.10729
Syafriyyin, R., & Sukojo, B. M. (2014). Optimalisasi Pemetaan Fase Pertumbuhan Padi Berdasarkan Analisa Pola Reflektan Dengan Data Hiperspektral Studi Kasus: Kabupaten Karawang. Geoid. https://doi.org/10.12962/j24423998.v9i2.743
Triscowati, D. W., Sartono, B., Kurnia, A., Dirgahayu, D., & Wijayanto, A. W. (2020). Classification Of Rice-Plant Growth Phase Using Supervised Random Forest Method Based on Landsat-8 Multitemporal Data. International Journal of Remote Sensing and Earth Sciences (IJReSES). https://doi.org/10.30536/j.ijreses.2019.v16.a3217
Van Niel, T. G., & McVicar, T. R. (2001). Remote sensing of rice-based irrigated agriculture: a review. Production.
Wahyunto, Widagdo, & Heryanto, B. (2006). Pendugaan Produktivitas Tanaman Padi Sawah Melalui Analisis Citra Satelit. Informatika Pertanian.
Wang, J., Chen, C., Wang, J., Yao, Z., Wang, Y., Zhao, Y., Sun, Y., Wu, F., Han, D., Yang, G., Liu, X., Sun, C., & Liu, T. (2025). NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning. Agronomy, 15(1), 63. https://doi.org/10.3390/agronomy15010063.
Wicaksono, M. G. S., Suryani, E., & Hendrawan, R. A. (2021). Increasing productivity of rice plants based on IoT (Internet of Things) to realize Smart Agriculture using System Thinking approach. Procedia Computer Science. https://doi.org/10.1016/j.procs.2021.12.179
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Nur Afifah, Rarasati, Astuti, Hartono, Wiwoho (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
