Estimation of Rice Chlorophyll Content in Salt-Affected Soils Using UAV-Based Multispectral Sensing
DOI:
https://doi.org/10.23917/forgeo.16868Keywords:
Chlorophyll, Rice, Salinity, Multispectral UAV, Vegetation indicesAbstract
The decline in rice productivity in coastal areas is often associated with agricultural land salinization. Chlorophyll, as an indicator of plant health, can be monitored using remote sensing technology. This study aims to evaluate the capability of multispectral UAV imagery to estimate rice chlorophyll content and compare the effectiveness of several vegetation indices in saline coastal paddy fields. Data were collected over ±30 hectares of rice fields in Kendal Regency using a DJI Phantom 4 Multispectral UAV during the vegetative stage (30–45 days after planting). Field measurements included chlorophyll content (SPAD) and soil electrical conductivity (EC). The results showed a weak and statistically insignificant correlation between soil salinity and rice chlorophyll content (r = 0.119; p = 0.571). These findings suggest that under the specific conditions of this study, characterized by moderate salinity levels (±4.47 dS/m) and the use of rice varieties with varying degrees of tolerance (Ciherang as tolerant and IR32 as moderately tolerant), the spatial variability of chlorophyll was more strongly influenced by phenological stages and micro-environmental factors than by salinity stress. Among the evaluated indices, red-edge-based indices showed the best performance, with CIre (R² = 0.831) and NDRE (R² = 0.822) yielding the lowest estimation errors, while NDVI (R²= 0.297) was limited by spectral saturation and CIg (R²=0.415) was affected by its sensitivity to plant canopy structure. These results indicate that red-edge-based indices are highly effective for mapping chlorophyll variability. The limited impact of salinity observed in this study is likely due to the tolerance of the rice varieties and to salinity levels remaining below critical thresholds. Thus, CIre and NDRE are recommended as the most effective indices for estimating rice chlorophyll in coastal paddy fields using multispectral UAV imagery.
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Adzima, A. F., Neswati, R., Laban, S., Jayadi, M., Anshori, M. F., Mubarak, H., Noviyanti, E., Zaesar Muharram, N. Q., & Mallarangen, A. D. (2022). Drone-Based Vegetation Index Analysis to Estimated Nitrogen Content on The Rice Plantations. Jurnal Agritechno, 15(02), 132–140. doi: 10.20956/at.vi.928
Agustina, H., Jaelani, L. M., & Sanjaya, H. (2024). Estimation of Nitrogen Content of Rice Crops Using Sentinel-2 Data. Indonesian Journal of Geography, 56(3), 387–395. doi: 10.22146/ijg.88571
Al-Shammari, D., Whelan, B. M., Wang, C., Bramley, R. G. V., & Bishop, T. F. A. (2025). Assessment of red-edge based vegetation indices for crop yield prediction at the field scale across large regions in Australia. European Journal of Agronomy, 164, 127479. doi: 10.1016/j.eja.2024.127479
An, G., Xing, M., Liao, C., & He, B. (2020). Estimating chlorophyll content of rice based on UAV-based hyperspectral imagery and continuous wavelet transform. IEEE International Geoscience and Remote Sensing Symposium, 5270–5273. doi: 10.1109/IGARSS39084.2020.9323597
Annisa, M., Zulkifli, & Handayani, T. T. (2015). Pengaruh Asam Askorbat terhadap Ketahanan Stress Garam Padi Sawah (Oryza Sativa L.) Varietas Ciherang Effect ofAscorbic Acid on Salt Stress Resistance of Lowland Rice (Oryza Sativa L.) Ciherang Varieties Mirna. Prosiding Seminar Nasional Swasembada Pangan, April, 33–39.
Ardiansyah, M., Nugroho, B., & Sa’diyah, K. (2022). Estimasi Kadar Klorofil Dan Kadar N Daun Jagung Menggunakan Chlorophyll Content Index. Jurnal Ilmu Tanah Dan Lingkungan, 24(2), 53–61. doi: 10.29244/jitl.24.2.53-61
Ardiyanti, L., Ahidiyah, S., Mardani, M., Danuari Fitrio, B., & Nuryaman, A. (2024). Model Matematika Optimalisasi Keuntungan Penjualan: Regresi Linear Berganda. MATHunesa: Jurnal Ilmiah Matematika, 12(3), 662–670. doi: 10.26740/mathunesa.v12n3.p662-670
Badan Pusat Statistik Kabupaten Kendal. (2024). Luas Panen, Produktivitas, dan Produksi Padi Sawah, 2018-2023. Badan Pusat Statistik Kabupaten Kendal. Retrieved From https://kendalkab.bps.go.id/id/statistics-table/2/NDA2IzI=/luas-panen-produktivitas-dan-produksi-padi-sawah.html
BEHERA, S. D., GARNAYAK, L. M., BEHERA, B., & PANDA, R. K. (2022). Chlorophyll Content and SPAD Value Relationships Between Varying Nitrogen Application and Cultivar in Rice. Journal of the Indian Society of Coastal Agricultural Research, 40(1). doi: 10.54894/jiscar.40.1.2022.116784
Boiarskii, B. (2019). Comparison of NDVI and NDRE Indices to Detect Differences in Vegetation and Chlorophyll Content. Journal of Mechanics of Continua and Mathematical Sciences, 4, 20–29. doi: 10.26782/jmcms.spl.4/2019.11.00003
Buschmann, C., & Nagel, E. (1993). In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing, 14(4), 711–722. doi: 10.1080/01431169308904370
Cao, Y., Jiang, K., Wu, J., Yu, F., Du, W., & Id, T. X. (2020). Inversion modeling of japonica rice canopy chlorophyll content with UAV hyperspectral remote sensing. 1–15. doi: 10.1371/journal.pone.0238530
Chen, Y., Wang, X., Zhang, X., Wang, D., & Xu, X. (2025). A band selection method combining spectral color characteristics for estimating chlorophyll content of rice in different backgrounds. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 330, 125681. doi: 10.1016/j.saa.2024.125681
Clevers, J. G. P. W., & Gitelson, A. A. (2012). Using the red-edge bands on Sentinel-2 for retrieving canopy chlorophyll and nitrogen content. European Space Agency, (Special Publication) ESA SP, 707 SP(July).
Dharmaratne, P. P., Salgadoe, A. S. A., Rathnayake, W. M. U. K., Weerasinghe, A. D. A. J. K., Sirisena, D. N., & Wanninayaka, W. M. N. (2023). Remote Measurement of Nitrogen and Leaf Chlorophyll Concentration Using UAV-Based Multispectral Imagery from Rice Crop Field in Sri Lanka. In Lecture Notes in Electrical Engineering: Vol., 980. doi: 10.1007/978-981-19-8032-9_46
Dwi Putri, W. B., Latifah, S., & Aji, I. M. L. (2025). Model Penduga Biomassa Ruang Terbuka Hijau Kota Mataram dengan Menggunakan Citra Sentinel-2A. ULIN: Jurnal Hutan Tropis, 9(1), 191. doi: 10.32522/ujht.v9i1.16668
Fajri, Z. A., Juhadi, J., Sanjoto, T. B., & Sidiq, W. A. B. N. (2022). Model Estimasi Produktivitas Padi Menggunakan Ndvi Di Wilayah Kabupaten Demak Tahun 2021. Jurnal Geosaintek, 8(3), 279. doi: 10.12962/j25023659.v8i3.14900
FAO. (2023). FAOSTAT: Crops and livestock products – Rice production by country, 2023. FAO. Retrieved From https://www.fao.org/faostat/en/#data/QCL
Gitelson, A. A., Gritz, Y., & Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3), 271–282. doi: 10.1078/0176-1617-00887
Gitelson, A. A., Vina, A., Ciganda, V., Rundquist, D. C., & Arkebauer, T. J. (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32, 1–4. doi: 10.1029/2005GL022688
Gu, Q., Huang, F., Lou, W., Zhu, Y., Hu, H., Zhao, Y., Zhou, H., & Zhang, X. (2024). Unmanned aerial vehicle-based assessment of rice leaf chlorophyll content dynamics across genotypes. Computers and Electronics in Agriculture, 221, 108939. doi: 10.1016/j.compag.2024.108939
Hamidah, N. M., Sasongko, P. E., & Mindari, W. (2025). Identifikasi Salinitas Tanah dan Produktivitas Lahan Sawah di Hilir Kabupaten Lamongan dan Tuban. Agroteknika, 8(3), 578–593. doi: 10.55043/agroteknika.v8i3.538
Hasibuan, W. A., Ginting, J. S., & Wardani, D. K. (2026). The Effect of Salinity Stress Levels on the Growth of Various Rice Varieties (Oryza sativa L). Jurnal Ilmiah Pertanian (JIPERTA), 8(1), 49–57. doi: 10.31289/jiperta.v8i1.6589
Ismail, S. (2022). Pengaruh Penggunaan Model Pembelajaran Berbasis Proyek “Project Based Learning” Terhadap Hasil Belajar Fisika Peserta Didik Kelas X IPA SMA Negeri 35 Halmahera Selatan Pada Konsep Gerak Lurus”. Jurnal Ilmiah Wahana Pendidikan, 8(5), 256–269. doi: 10.5281/zenodo.6466594
Kanawapee, N., Sanitchon, J., Lontom, W., & Threerakulpisut, P. (2012). Evaluation of salt tolerance at the seedling stage in rice genotypes by growth performance, ion accumulation, proline and chlorophyll content. Plant and Soil, 358(1–2), 235–249. doi: 10.1007/s11104-012-1179-6
Kanke, Y., Tubaña, B., Dalen, M., & Harrell, D. (2016). Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields. Precision Agriculture, 17(5), 507–530. doi: 10.1007/s11119-016-9433-1
Kautsar, V., Faizah, K., Uktoro, A. I., Khasanah, L., & Filiphus, F. (2024). Estimating SPAD, Nitrogen Concentration, and Chlorophyll Content in Rice Leaves using Calibrated Smartphone Digital Image. Planta Tropika, 12(2), 127–138. doi: 10.18196/pt.v12i2.20553
Lasaiba, M. A., & Tetelepta, E. G. (2023). Analisis Spasial Kerapatan Vegetasi Kota Ambon Berbasis Normalized Difference Vegetation Index (Ndvi). Jurnal Pengembangan Kota, 11(2), 124–139. doi: 10.14710/jpk.11.2.124-139
Liu, L., Xie, Y., Zhu, B., & Song, K. (2024). Rice leaf chlorophyll content estimation with different crop coverages based on Sentinel-2. Ecological Informatics, 81(April), 102622. doi: 10.1016/j.ecoinf.2024.102622
Lu, Y., & Fricke, W. (2023). Salt Stress—Regulation of Root Water Uptake in a Whole-Plant and Diurnal Context. International Journal of Molecular Sciences, 24(9). doi: 10.3390/Ijms24098070
Mardiatmoko, G. (2020). Pentingnya Uji Asumsi Klasik pada Analisis Regresi Linier Berganda (Studi Kasus Penyusunan Persamaan Allometrik Kenari Muda [Canarium Indicum L.]). Barekeng: Jurnal Ilmu Matematika Dan Terapan, 14(3), 333–342. doi: 10.30598/barekengvol14iss3pp333-342
Marwan, M., Wirandha, F. S., Husna, M., Saria, C. O., Nizzamuddin, N., & Yanis, M. (2021). Analisis Normalized Difference Vegetation Index pada Unmanned Aerial Vehicle untuk Identifikasi Pertumbuhan Tanaman Padi di Kabupaten Aceh Besar Provinsi Aceh. Positron, 11(2), 121–125. doi: 10.26418/positron.v11i2.48102
Mergono Adi Ningrat, Carolina Diana Mual, & Yohanis Yan Makabori. (2021). Rice Plant Growth and Yield (Oryza sativa L.) on Various Planting Systems in Village Desay District Prafi, Manokwari Regency. Prosiding Seminar Nasional Pembangunan Dan Pendidikan Vokasi Pertanian, 2(1), 325–332.
Mu, X., Jin, Z., Guo, Z., Bai, J., & Yu, F. (2026). Inversion modeling of rice chlorophyll content based on optimized UAV hyperspectral remote sensing image data. Computers and Electronics in Agriculture, 242, 111330. doi: 10.1016/j.compag.2025.111330
Muttaqien, M. I., & Rahmawati, D. (2019). Karakter Kualitatif dan Kuantitatif Beberapa Varietas Padi (Oryza sativa L.) terhadap Cekaman Salinitas (NaCl). Agriprima : Journal of Applied Agricultural Sciences, 3(1), 42–53. doi: 10.25047/agriprima.v3i1.94
Nasution, F. H., Santosa, S., & Putri, R. E. (2019). Model Prediksi Hasil Panen Berdasarkan Pengukuran Non-Destruktif Nilai Klorofil Tanaman Padi. AgriTECH, 39(4), 289. doi: 10.22146/agritech.34893
Oelviani, R., Adiyoga, W., Suhendrata, T., Bakti, I. G. M. Y., Sutanto, H. A., Fahmi, D. A., Chanifah, Jatuningtyas, R. K., Samijan, Malik, A., Sahara, D., Utomo, B., Wulanjari, M. E., Minarti, E., Yardha, & Aristya, V. E. (2024). Effects of soil salinity on rice production and technical efficiency : Evidence from the northern coastal region of Central Java , Indonesia. Case Studies in Chemical and Environmental Engineering, 10, 101010. doi: 10.1016/j.cscee.2024.101010
Oelviani, R., Susilowati, I., Dinar Iskandar, D., & Waridin, W. (2024). Karakteristik Sosial Ekonomi Petani Padi di Pesisir di Kabupaten Kendal: Kerentanan, Tantangan dan Peluang. Jurnal Pangan, 33(1), 17–30. doi: 10.33964/jp.v33i1.836
Pratiwi, G., & Lubis, T. (2021). Pengaruh Kualitas Produk dan Harga Terhadap Kepuasan Pelanggan UD Adli di Desa Sukajadi Kecamatan Perbaungan. All Fields of Science Journal Liaison Academia and Sosiety, 1(3), 27–41. doi: 10.58939/afosj-las.v1i3.83
Pratiwi, N., Daniel, & Suhardi. (2017). Pendugaan Produktivitas Padi Sawah Berdasarkan Reflektansi (Indeks Vegetasi), Warna dan Kerapatan Tanaman. Jurnal AgriTechno, 10(2), 99–108. doi: 10.70124/at.v10i2.64
Putra, G. S. A., Wijaya, I. M. A. S., & Sumiyati, S. (2024). Analisis Hubungan Nilai SPAD dengan Nilai Indeks Vegetasi pada Tanaman Padi yang Terserang Penyakit Blas Menggunakan Citra Multispektral. Jurnal BETA (Biosistem Dan Teknik Pertanian), 12(2), 264–272. doi: 10.24843/JBETA.2024.v12.i02.p08
Rehman, T. H., Lundy, M. E., & Linquist, B. A. (2022). Comparative Sensitivity of Vegetation Indices Measured via Proximal and Aerial Sensors for Assessing N Status and Predicting Grain Yield in Rice Cropping Systems. Remote Sensing, 14(12), 1–18. doi: 10.3390/rs14122770
Sah, S. S., Maulud, K. N. A., Sharil, S., Karim, O. A., & Nahar, N. F. A. (2021). Impact of Saltwater Intrusion on Paddy Growth in Kuala Kedah, Malaysia. Journal of Sustainability Science and Management, 16(6), 15–30. doi: 10.46754/jssm.2021.08.004
Saidi, B. B., Hendri, J., Suharyon, & Purnama, H. (2020). Adaptation of New Rice High Yielding Varieties on New Constructed Irrigated Rice Field Affected by Sea Water Intrusion in West Tanjung Jabung District , Jambi. Journal of Tropical Soils, 25(3), 119–126. doi: 10.5400/jts.2019.v25i3.119
Samsuddin Sah, S., Abdul Maulud, K. N., Sharil, S., A. Karim, O., & Pradhan, B. (2023). Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV images. Egyptian Journal of Remote Sensing and Space Science, 26(4), 989–998. doi: 10.1016/j.ejrs.2023.11.005
Santanoo, S., Lontom, W., Dongsansuk, A., Vongcharoen, K., & Theerakulpisut, P. (2023). Photosynthesis Performance at Different Growth Stages, Growth, and Yield of Rice in Saline Fields. Plants, 12(9), 1903. doi: 10.3390/plants12091903
Saputra, A., Listiana, I., & Saputri, D. A. (2025). Analisis Kandungan Klorofil pada Benih Padi Tua (Oryza Sativa L.) yang Terpapar Medan Magnet Extremely Low Frequency (ELF) pada Masa Pertumbuhan Vegetatif. Bioscientist : Jurnal Ilmiah Biologi, 13(1), 327. doi: 10.33394/bioscientist.v13i1.15057
Saputro, A. A., Armita, D., & Nihayati, E. (2022). Pengaruh Pemberian Berbagai Konsentrasi Garam dan Frekuensi Penyiraman terhadap Pertumbuhan, Hasil, dan Kadar Flavonoid pada Tanaman Sweet Basil (Ocimum basilicum). Agrotechnology Research Journal, 6(2), 110–117. doi: 10.20961/agrotechresj.v6i2.65083
Sihotang, S. F., Harahap, F. S. W., & Mazaly, M. R. (2024). Pendekatan Korelasi Pearson dan Spearman dalam Menganalisis Hubungan Penggunaan Bahan Ajar Berbasis Daring dan Prestasi Akademik Mahasiswa. MES (Journal of Mathematics Education and Science), 10(1), 270–276. doi: 10.30743/mes.v10i1.10204
Siregar, I. H., Jonharnas, & Khairiah. (2017). Kajian Aplikasi Pemupukan Urea pada Padi Sawah oleh Petani Kabupaten Asahan-Sumatera Utara. Prosiding Balai Besar Penelitian Tanaman Padi (BB Padi), 27, 249–256.
Sivagnanam, S., Duraisami, V. P., & Ramasamy, J. (2015). Reflectance Measurement of Leaf Nitrogen Content and Reflectance Measurement of Leaf Nitrogen Content and Chlorophyll in Rice Crop Using Red Edge Indices and Regression. Plant and Soil, 17, 274–277.
Sukojo, B. M., & Kurniawan, R. H. (2021). Rice Growth Stages Mapping with Normalized Difference Vegetation Index (NDVI) Algorithm Using Sentinel-2 Time Series Satellite Imagery. International Journal on Advanced Science, Engineering and Information Technology, 11(4), 1594–1598. doi: 10.18517/ijaseit.11.4.12335
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), 1–21. doi: 10.3390/agronomy15010063
Wang, T., Yang, G., Xu, X., Sun, J., Meng, Y., Yang, X., Feng, H., Xue, H., Xu, X., & Song, Y. (2026). Artificial Intelligence in Geosciences Remote sensing estimation of rice chlorophyll content based on UAV image feature selection and PSO-optimized ensemble learning. Artificial Intelligence in Geosciences, 7(1), 100190. doi: 10.1016/j.aiig.2026.100190
Widyawati, N., Herawati, M. M., Kurnia, T. D., Murdono, D., Simanjuntak, B. H., & Setiawan, A. W. (2023). Kandungan Klorofil, Pertumbuhan dan Hasil Vertikultur Padi (Oryza sativa L.) Varietas Situ Bagendit. Vegetalika, 12(3), 256. doi: 10.22146/veg.83196
Wu, C., Niu, Z., & Gao, S. (2012). The potential of the satellite derived green chlorophyll index for estimating midday light use efficiency in maize , coniferous forest and grassland. Ecological Indicators, 14(1), 66–73. doi: 10.1016/j.ecolind.2011.08.018
Yanti, D., Putri, T. A., & Tjandra, M. A. (2023). Pemanfaatan Data Satelit Modis Untuk Menentukan Fase Tumbuh Tanaman Padi Di Kecamatan Harau. Rona Teknik Pertanian, 16(1), 57–68. doi: 10.17969/rtp.v16i1.31147
Zhang, H., Li, J., Liu, Q., Lin, S., Huete, A., Liu, L., Croft, H., Clevers, J. G. P. W., Zeng, Y., Wang, X., Gu, C., Zhang, Z., Zhao, J., Dong, Y., Mumtaz, F., & Yu, W. (2022). A novel red-edge spectral index for retrieving the leaf chlorophyll content. Methods in Ecology and Evolution, 13(12), 2771–2787. doi: 10.1111/2041-210X.13994
Zhou, Y., Ata-Ul-Karim, S. T., Yin, J., Cnicatti, M., Toth, L., Wang, S., Ma, Y., Wu, C., Andersen, M. N., & Cammarano, D. (2026). Assessing in-season crop nitrogen status based on UAV multispectral imaging and AI-driven model optimization. Computers and Electronics in Agriculture, 246, 111638. doi: 10.1016/j.compag.2026.111638
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