* Corresponding Author
Estimating the contents of Chlorophyll, Nitrogen, and Yields on Rice through Sentinel-2 Vegetation Indices in Heterogeneous Land Management
https://doi.org/10.22146/ijg.87159 Yagus Wijayanto(1*), Mahardika Safitri(2), Ika Purnamasari(3), Subhan Arif Budiman(4), Tri Wahyu Saputra(5), Arthur FC Regar(6), Suci Ristiyana(7)
(1) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(2) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(3) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(4) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(5) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(6) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(7) Faculty of Agriculture, University of Jember, Jl Kalimantan Kamus Tegalboto, Jember, 68121, Indonesia.
(*) Corresponding Author
Abstract
Addressing the global food demand is an urgent priority for governments worldwide. Efficient and effective methods for gauging crop production are crucial. Relying solely on ground-based measurements proves inefficient and expensive, prompting exploration of remote sensing using vegetation indices as a viable alternative. This study sought to achieve three objectives: estimating chlorophyll content in paddy fields, evaluating leaf nitrogen content, and predicting yields. The investigation utilized Sentinel-2A satellite imagery, Soil Plant Analysis Development (SPAD) for chlorophyll measurement, and employed statistical and accuracy analyses. Findings revealed an increase in chlorophyll and leaf nitrogen content from the vegetative to maturity phases, followed by a decline at maturity. NDVI and GNDVI emerged as superior to SAVI and VARI for chlorophyll estimation, attributed to their spectral sensitivity. Likewise, nitrogen prediction showed similar trends, with NDVI and GNDVI exhibiting better RMSE values compared to SAVI and VARI, albeit marginally. However, yield prediction accuracy varied, with NDVI proving most accurate, followed by SAVI, VARI, and GNDVI, indicating the latter's reduced predictive precision due to nitrogen sensitivity. In scenarios where nitrogen is not the predominant yield-limiting factor, NDVI could outperform GNDVI in forecasting yield.
Received: 2023-07-22 Revised: 2024-04-18 Accepted: 2024-08-24 Published: 2024-10-10
Keywords
References
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DOI: https://doi.org/10.22146/ijg.87159
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