Estimating Corn Productivity Using Sentinel-2 Imagery and Spectrometer
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Corn is a staple food for the Indonesian population due to its high carbohydrate content, second only to rice. Estimating corn production before the harvest period is crucial for predicting total production output from a given location. This study aims to develop a production model for corn using Sentinel-2 satellite imagery combined with spectral data from a spectrometer and field measurements. The research involved collecting field data on corn production, downloading Sentinel-2 imagery for the period from December 10, 2022, to February 28, 2023, performing atmospheric correction and image cropping, transforming the data into NDVI and EVI vegetation indices, and analyzing the data using simple linear regression to determine the relationship between the NDVI and EVI indices and corn plant parameters, specifically biomass. The results show a strong correlation between productivity estimates using Sentinel-2 and spectrometer data with field observations. For the Sentinel-2 Vegetation Index, EVI has the highest correlation with productivity at approximately 88%, compared to other vegetation indices at around 80%. For the Spectrometer Vegetation Index, NDVI has the highest correlation at around 83%, while other indices are below 80%. Therefore, Sentinel-2 and spectrometer data can effectively estimate productivity in corn plantations.
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Yeli Oktaviana Liku Liku
Faculty of Agriculture, Hasanuddin University
Indonesia
Nur Faridah Sitti
Faculty of Agriculture, Hasanuddin University
Indonesia
Copyright (c) 2024 Yeli Oktaviana Liku Liku, Daniel Daniel, Nur Faridah Sitti
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