Estimation of Nitrogen Content of Rice Crops Using Sentinel-2 Data

https://doi.org/10.22146/ijg.88571

Heni Agustina(1), Lalu Muhamad Jaelani(2*), Hartanto Sanjaya(3)

(1) Department of Geomatics Engineering, Faculty of Civil, Planning, and Geo-Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya 60111, Indonesia
(2) Department of Geomatics Engineering, Faculty of Civil, Planning, and Geo-Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya 60111, Indonesia
(3) Department of Civil Engineering, Faculty of Civil, Planning and Geo-Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya 60111, Indonesia and National Research and Innovation Agency, Gedung B.J. Habibie, Jl. M.H. Thamrin No. 8, Jakarta Pusat 10340,Indonesia
(*) Corresponding Author

Abstract


Nitrogen (N) is one of the most essential nutrients for rice crops. Farmers generally provide Nitrogen requirements in rice through fertilization, but the fertilization process is only based on an estimation without calculating the amount needed first. However, neither insufficient nor excessive nitrogen content is good for rice crops, and the nitrogen needs of rice crops are different at each growth stage. The nitrogen requirement in the generative phase is relatively high because the process of panicle formation and grain filling occurs at this stage. Several methods can be used to monitor nitrogen content in rice, one of which is using remote sensing methods. With the vegetation index approach, the nitrogen content of rice plants is estimated through data analysis of the light spectrum reflected by the leaf. Sentinel-2 satellite imagery was used in this research, and several vegetation indexes such as OSAVI, GNDVI, and SRRE were applied to form an estimation model using the regression method. From the results, three vegetation indexes positively correlate with nitrogen content in rice crops. The SRRE index gives the highest correlation coefficient value of 0.692, while the correlation coefficient value for GNDVI is 0.498, and OSAVI is only 0.470. The estimation map of the nitrogen content of rice crops was obtained based on the estimation model made by linear regression between SPAD-based nitrogen content data and the best vegetation index using the SRRE index. The analysis shows that the nitrogen content of rice plants estimated in the paddy fields of Karangjati Subdistrict is dominated by nitrogen values with optimum classification.


Keywords


Generative; Nitrogen; Sentinel-2; SPAD; Vegetation Index



References

Balai Penelitian dan Pengembangan Pertanian. (2020). Rekomendasi Pupuk N, P dan K Spesifik Lokasi untuk Tanaman Padi, Jagung, dan Kedelai pada Lahan Sawah (Per Kecamatan). Kementerian Pertanian.

Bhupenchandra, I., Devi, S. H., Basumatary, A., Dutta, S., Singh, L. K., Kalita, P., Bora, S. S., Devi, S. R., Saikia, A., Sharma, P., Bhagowati, S., Tamuli, B., Dutta, N., & Borah, K. (2020). Biostimulants: Potential and Prospects in Agriculture. International Research Journal of Pure and Applied Chemistry, 21(14), 20–35. https://doi.org/10.9734/irjpac/2020/v21i1430244

BPS Jawa Timur. (2022). Produksi Padi Jawa Timur Selama Tahun 2021 Sebesar 9,789 Juta Ton Gabah Kering Giling. https://jatim.bps.go.id/pressrelease/2022/03/01/1312/produksi-padi-jawa-timur-selama-tahun-2021-sebesar-9-789-juta-ton-gabah-kering-giling.html

Coste, S., Baraloto, C., Leroy, C., Marcon, É., Renaud, A., Richardson, A. D., Roggy, J.-C., Schimann, H., Uddling, J., & Hérault, B. (2010). Assessing Foliar Chlorophyll Contents With the SPAD-502 Chlorophyll Meter: A Calibration Test With Thirteen Tree Species of Tropical Rainforest in French Guiana. Annals of Forest Science, 67(6), 607. https://doi.org/10.1051/forest/2010020

Darmawan, A., Hariyanto, T., Sukojo, B. M., & Sadly, M. (2011). Prediksi Parameter-parameter Biofisik Tanaman Padi Dari Data Groundspectrometer dan Hyperspectral Pesawat Terbang Dengan Menggunakan Teknik Partial Least Square Regression (PLSR) dan Normalized Difference Spectral Index (NDSI). J. Tek. Ling, 12(1), 93–101.

ESA. (2015). SENTINEL-2 User Handbook Sentinel-2 User Handbook SENTINEL-2 User Handbook (Issue 1, pp. 1–64).

Gascon, F., Cadau, E., Colin, O., Hoersch, B., Isola, C., López Fernández, B., & Martimort, P. (2014). Copernicus Sentinel-2 mission: products, algorithms and Cal/Val. In J. J. Butler, X. (Jack) Xiong, & X. Gu (Eds.), Proceedings Volume 9218, Earth Observing Systems XIX; 92181E (p. 92181E). https://doi.org/10.1117/12.2062260

Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289–298. https://doi.org/10.1016/S0034-4257(96)00072-7

Herdiyanti, T., , S., & Aswidinnoor, H. (2016). Tanggap Tiga Varietas Padi Sawah terhadap Kombinasi Pemupukan dengan Sistem Pembenaman Jerami. Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy), 43(3), 179. https://doi.org/10.24831/jai.v43i3.11242

Hirel, B., Le Gouis, J., Ney, B., & Gallais, A. (2007). The challenge of improving nitrogen use efficiency in crop plants: towards a more central role for genetic variability and quantitative genetics within integrated approaches. Journal of Experimental Botany, 58(9), 2369–2387. https://doi.org/10.1093/jxb/erm097

Isnaeni Fathonah, F., & Mashilal. (2021). Rice Production Analysis in Reflecting Rice Self-sufficiency in Indonesia. E3S Web of Conferences, 316, 02041. https://doi.org/10.1051/e3sconf/202131602041

Kjeldahl, J. (1883). Neue Methode zur Bestimmung des Stickstoffs in organischen Körpern. Fresenius’ Zeitschrift Für Analytische Chemie, 22(1), 366–382. https://doi.org/10.1007/BF01338151

Lailatus Sa’adah. (2021). Statistik Inferensial. LPPM Universitas KH. A. Wahab Hasbullah. https://books.google.co.id/books?id=o5kwEAAAQBAJ

Lathifah, R., & Sukojo, B. M. (2014). Hasil Analisa Kadar Nitrogen Vegetasi Padi Dengan Data Hyperspectral Menggunakan Index Vegetasi (Studi Kasus: Karawang). Geoid, 9(2), 158. https://doi.org/10.12962/j24423998.v9i2.760

Lu, B., & He, Y. (2021). Assessing the Impacts of Species Composition on the Accuracy of Mapping Chlorophyll Content in Heterogeneous Ecosystems. Remote Sensing, 13(22), 4671. https://doi.org/10.3390/rs13224671

OECD-FAO. (2023). OECD-FAO Agricultural Outlook 2023-2032. OECD. https://doi.org/10.1787/08801ab7-en

Patti, P. S., Kaya, E., & Silahooy, C. (2018). Analisis Status Nitrogen Tanah Dalam Kaitannya Dengan Serapan N Oleh Tanaman Padi Sawah Di Desa Waimital, Kecamatan Kairatu, Kabupaten Seram Bagian Barat. Agrologia, 2(1). https://doi.org/10.30598/a.v2i1.278

Peng, S., García, F. V., Laza, R. C., & Cassman, K. G. (1993). Adjustment for Specific Leaf Weight Improves Chlorophyll Meter’s Estimate of Rice Leaf Nitrogen Concentration. Agronomy Journal, 85(5), 987–990. https://doi.org/10.2134/agronj1993.00021962008500050005x

Pratiwi, D. (2022). ANALYSIS OF NATIONAL RICE AVAILABILITY TOWARDS SELF-SUPPORT WITH A DYNAMIC MODEL APPROACH. Economic Management and Social Sciences Journal, 1(3), 91–101. https://doi.org/10.56787/ecomans.v1i3.15

Purhartanto, L. N., Danoedoro, P., & Wicaksono, P. (2019). Kajian Transformasi Indeks Vegetasi Citra Satelit Sentinel-2a Untuk Estimasi Produksi Daun Kayu Putih Menggunakan Linear Spectral Mixture Analysis. Jurnal Nasional Teknologi Terapan, 3(1), 47–70.

Rhezali, A., & Aissaoui, A. El. (2021). Feasibility Study of Using Absolute SPAD Values for Standardized Evaluation of Corn Nitrogen Status. Nitrogen, 2(3), 298–307. https://doi.org/10.3390/nitrogen2030020

Roflin, E., & Riana, F. (2022). Analisis Korelasi dan Regresi. Penerbit NEM. https://books.google.co.id/books?id=evp7EAAAQBAJ

Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95–107. https://doi.org/10.1016/0034-4257(95)00186-7

Saberioon, M. M., & Gholizadeh, A. (2016). Novel approach for estimating nitrogen content in paddy fields using low altitude remote sensing system. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B1, 1011–1015. https://doi.org/10.5194/isprsarchives-XLI-B1-1011-2016

Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J.-P., Lewis, P., Barnsley, M., Hobson, P., Disney, M., Roberts, G., Dunderdale, M., Doll, C., D’Entremont, R. P., Hu, B., … Roy, D. (2002). First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1–2), 135–148. https://doi.org/10.1016/S0034-4257(02)00091-3

Secretariate General - Ministry of Agriculture Republic of Indonesia. (2023). Statistics of Food Consumption 2023 (Mas’ud & S. Wahyuningsih, Eds.). Center for agricultural data and information system. https://satudata.pertanian.go.id/assets/docs/publikasi/Buku_Statsitik_Konsumsi_Pangan_2023.pdf

Sharifi, A. (2020). Using Sentinel-2 Data to Predict Nitrogen Uptake in Maize Crop. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2656–2662. https://doi.org/10.1109/JSTARS.2020.2998638

Shrestha, J., Karki, T. B., & Hossain, M. A. (2022a). Application of Nitrogenous Fertilizer in Rice Production: A Review. Journal of Nepal Agricultural Research Council, 8, 16–26. https://doi.org/10.3126/jnarc.v8i.44815

Shrestha, J., Karki, T. B., & Hossain, M. A. (2022b). Application of Nitrogenous Fertilizer in Rice Production: A Review. Journal of Nepal Agricultural Research Council, 8, 16–26. https://doi.org/10.3126/jnarc.v8i.44815

Singh, A., Sarkar, S., Jaswal, A., & Sahoo, S. (2022). On-farm Evaluation of Leaf Colour Chart and Chlorophyll Meter for Need-based Nitrogen Management in Kharif Maize (Zea mays L.). LEGUME RESEARCH - AN INTERNATIONAL JOURNAL, Of. https://doi.org/10.18805/LR-4972

Sukmono, A., Handayani, H. H., & Wibowo, A. (2012). ALGORITMA ESTIMASI KANDUNGAN KLOROFIL TANAMAN PADI DENGAN DATA AIRBORNE HYPERSPECTRAL. Geoid, 8(1), 47. https://doi.org/10.12962/j24423998.v8i1.707

Sun, H., Feng, M., Yang, W., Bi, R., Sun, J., Zhao, C., Xiao, L., Wang, C., & Kubar, M. S. (2022). Monitoring Leaf Nitrogen Accumulation With Optimized Spectral Index in Winter Wheat Under Different Irrigation Regimes. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.913240

Tegeder, M., & Masclaux‐Daubresse, C. (2018). Source and sink mechanisms of nitrogen transport and use. New Phytologist, 217(1), 35–53. https://doi.org/10.1111/nph.14876

Uddling, J., Gelang-Alfredsson, J., Piikki, K., & Pleijel, H. (2007). Evaluating the Relationship Between Leaf Chlorophyll Concentration and SPAD-502 Chlorophyll Meter Readings. Photosynthesis Research, 91(1), 37–46. https://doi.org/10.1007/s11120-006-9077-5

Vishwakarma, M., Kulhare, P. S., & Tagore, G. S. (2023). Estimation of Chlorophyll Using SPAD meter. International Journal of Environment and Climate Change, 13(11), 1901–1912. https://doi.org/10.9734/ijecc/2023/v13i113348

Wang, L., Chen, S., Li, D., Wang, C., Jiang, H., Zheng, Q., & Peng, Z. (2021). Estimation of Paddy Rice Nitrogen Content and Accumulation Both at Leaf and Plant Levels from UAV Hyperspectral Imagery. Remote Sensing, 13(15), 2956. https://doi.org/10.3390/rs13152956

Wang, W., & Lu, Y. (2018). Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. IOP Conference Series: Materials Science and Engineering, 324, 012049. https://doi.org/10.1088/1757-899X/324/1/012049

Wang, Y.-P., Chang, Y.-C., & Shen, Y. (2022). Estimation of nitrogen status of paddy rice at vegetative phase using unmanned aerial vehicle based multispectral imagery. Precision Agriculture, 23(1), 1–17. https://doi.org/10.1007/s11119-021-09823-w

Warr, P., & Yusuf, A. A. (2014). Fertilizer subsidies and food self-sufficiency in Indonesia. Agricultural Economics, 45(5), 571–588. https://doi.org/10.1111/agec.12107

Yadav, T. K., Singh, S. P., Singh, M. K., & Singh, M. K. (2022). Influence of Nitrogen Levels and Weed Management Practices on Soil Quality of Wetland Rice. International Journal of Plant & Soil Science, 34(20), 394–400. https://doi.org/10.9734/ijpss/2022/v34i2031167

Zhang, J., Tong, T., Potcho, P. M., Huang, S., Ma, L., & Tang, X. (2020a). Nitrogen Effects on Yield, Quality and Physiological Characteristics of Giant Rice. Agronomy, 10(11), 1816. https://doi.org/10.3390/agronomy10111816

Zhang, J., Tong, T., Potcho, P. M., Huang, S., Ma, L., & Tang, X. (2020b). Nitrogen Effects on Yield, Quality and Physiological Characteristics of Giant Rice. Agronomy, 10(11), 1816. https://doi.org/10.3390/agronomy10111816

Zheng, J., Song, X., Yang, G., Du, X., Mei, X., & Yang, X. (2022). Remote Sensing Monitoring of Rice and Wheat Canopy Nitrogen: A Review. Remote Sensing, 14(22), 5712. https://doi.org/10.3390/rs14225712



DOI: https://doi.org/10.22146/ijg.88571

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