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Estimating the contents of Chlorophyll, Nitrogen, and Yields on Rice through Sentinel-2 Vegetation Indices in Heterogeneous Land Management

Indonesian Journal of Geography · Vol. 56 No. 3 (2024) · hlm. 357–367

DOI
10.22146/ijg.87159
ISSN
2354-9114, 0024-9521
Terbit
2024-10-03
Penerbit
Faculty of Geography, Universitas Gadjah Mada
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc/4.0

Abstrak

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

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  • Universitas Jember
  • Universitas Pembangunan Nasional Veteran Yogyakarta
  • National Research and Innovation Agency
  • Sriwijaya University
  • Universitas Muhammadiyah Jember
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Rekan penulisOrganisasiArtikel bersama
Ummi SholikhahUniversitas Jember5
Basuki BasukiUniversitas Jember4
Bambang HermiyantoUniversitas Jember4
Rika NurjannahUniversitas Jember4
Maya Wenlow SaragihUniversitas Jember4
Rachmat Abdul GaniNational Research and Innovation Agency4
Tri Candra SetiawatiUniversitas Jember2
Fariz Kustiawan AlfarisyUniversitas Jember2
Wahyu Indra Duwi FanataUniversitas Jember1
Intan Hadiatun MaghfirahUniversitas Jember1
Anggara Dwi PamungkasUniversitas Jember1
Edy PrastyantoAfiliasi tidak diketahui1
Laily Ilman WiduriSriwijaya University1
Evita Soliha HaniUniversitas Jember1
Sigit SoeparjonoUniversitas Jember1
Roni YuliantoUniversitas Jember1
Wildan MuhlisonUniversitas Jember1
Mohammad NurcholisUniversitas Pembangunan Nasional Veteran Yogyakarta1
Dwi Fitri YudiantoroUniversitas Pembangunan Nasional Veteran Yogyakarta1
Sukma Agustin Dyan TikaUniversitas Jember1
Marga MandalaUniversitas Jember1
Vivi FitrianiUniversitas Jember1
Vega Kartika SariUniversitas Jember1
Syamsul HadiUniversitas Muhammadiyah Jember1
Basuki BasukiUniversitas Jember1
Muhammad HafizhUniversitas Jember1
Marcellia MaharaniUniversitas Jember1

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Sumber metadata: OAI-PMH (metadata jurnal) http://jurnal.ugm.ac.id/ijg/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Estimating the contents of Chlorophyll, Nitrogen, and Yields on Rice through Sentinel-2 Vegetation Indices in Heterogeneous Land Management</dc:title>
	<dc:creator>Wijayanto, Yagus</dc:creator>
	<dc:creator>Safitri, Mahardika</dc:creator>
	<dc:creator>Purnamasari, Ika</dc:creator>
	<dc:creator>Budiman, Subhan Arif</dc:creator>
	<dc:creator>Saputra, Tri Wahyu</dc:creator>
	<dc:creator>Regar, Arthur FC</dc:creator>
	<dc:creator>Ristiyana, Suci</dc:creator>
	<dc:subject xml:lang="en-US">Geography</dc:subject>
	<dc:subject xml:lang="en-US">Vegetation indices; Leaf Nitrogen; Chlorophyll; yields; NDVI</dc:subject>
	<dc:subject xml:lang="en-US">Remote Sensing</dc:subject>
	<dc:description xml:lang="en-US">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   </dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Geography, Universitas Gadjah Mada</dc:publisher>
	<dc:contributor xml:lang="en-US">None</dc:contributor>
	<dc:date>2024-10-03</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:type xml:lang="en-US">original research</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijg/article/view/87159</dc:identifier>
	<dc:identifier>10.22146/ijg.87159</dc:identifier>
	<dc:source xml:lang="en-US">Indonesian Journal of Geography; Vol 56, No 3 (2024): Indonesian Journal of Geography; 357-367</dc:source>
	<dc:source>2354-9114</dc:source>
	<dc:source>0024-9521</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/view/87159/pdf</dc:relation>
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	<dc:relation>https://journal.ugm.ac.id/ijg/article/downloadSuppFile/87159/29791</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/downloadSuppFile/87159/29792</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/downloadSuppFile/87159/29793</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/downloadSuppFile/87159/29796</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/downloadSuppFile/87159/29797</dc:relation>
	<dc:coverage xml:lang="en-US">Applied GIS</dc:coverage>
	<dc:coverage xml:lang="en-US">-</dc:coverage>
	<dc:coverage xml:lang="en-US">-</dc:coverage>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Authors</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc/4.0</dc:rights>
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