Abstract
ABSTRACT Monitoring of large agricultural lands is often hampered by data collection logistics at field level. To solve such a problem, remote sensing techniques have been used to estimate vegetation indices, which can subsidize crop management decision-making. Therefore, this study aimed to select vegetation indices to detect variability in irrigated corn crops. Data were collected in Sao Desiderio, Bahia State (Brazil), using an OLI sensor (Operational Land Imager) embedded to a Landsat-8 satellite platform. Five corn growing plots under central pivot irrigation were assessed. The following vegetation indices were tested: NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio), NDWI (Normalized Difference Water Index), and MSI (Moisture Stress Index). Among the tested indices, SR was more sensitive to high corn biomass, while GNDVI, NDVI, EVI, and SAVI were more sensitive to low values. Overall, all indices were found to be concordant with each other, with high correlations among them. Despite this, the use of a set of these indices is advisable since some respond better to certain peculiarities than others.
Highlights
Conventional crop monitoring, mainly of large areas, is costly and ineffective
Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI) showed better results during corn crop development, even though NDVI was less sensitive to high biomass amounts and SAVI was subjective in terms of adjustment factor weight
MSI and NDWI can be used as further information regarding leaf water content in corn plants
Summary
Conventional crop monitoring, mainly of large areas, is costly and ineffective It is often done by surveying, at site, the entire area to find trouble spots, that is, with biotic and abiotic stresses. It comprises a set of tools to obtain information on targets within the Earth's surface using distant or remote sensors, without physical contact, recording their interactions with electromagnetic radiation (Jensen & Epiphanio, 2011; Formaggio & Sanches, 2017). This favors identification of problems in the field, especially in large farming areas (Bernardi et al, 2017)
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