Research Article The Study on the Relationship between Normalized Difference Vegetation Index and Fractional Green Canopy Cover in Five Selected Crops Pavlo V. Lykhovyd , 1 Raisa A. Vozhehova , 2 Sergiy O. Lavrenko , 3 and Nataliya M. Lavrenko 4 1 Department of Marketing, Transfer of Innovations and Economic Studies, Institute of Irrigated Agriculture of NAAS, Kherson 73483, Ukraine 2 Institute of Irrigated Agriculture of NAAS, Kherson 73483, Ukraine 3 Department of Agriculture, Kherson State Agrarian and Economic University, Kherson 73006, Ukraine 4 Department of Land Management, Geodesy, and Cadaster, Kherson State Agrarian and Economic University, Kherson 73006, Ukraine Correspondence should be addressed to Pavlo V. Lykhovyd; pavel.likhovid@gmail.com Received 24 January 2022; Accepted 5 March 2022; Published 21 March 2022 Academic Editor: Stefano Bellucci Copyright © 2022 Pavlo V. Lykhovyd et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Crop models are of great use and importance in modern agriculture. Most models imply spatial vegetation indices, such as NDVI, or canopy cover characteristics, such as FGCC, to provide estimation of crops conditions and forecast productivity. e purpose of the study was to (1) determine the possibility of mutual conversion between spatial NDVI and Canopeo-derived FGCC in five crops (grain corn, sunflower, tomato, millet, and winter wheat) and (2) estimate the precision of such a conversion. e data set of thestudywasformedbytheOneSoilAIderivedsatelliteimageryonNDVIforthestudiedcropsindifferentstagesoftheirgrowing season combined with Canopeo-processed photographs of vegetating crops in the field with FGCC percentage calculation. e sets of NDVI and FGCC values were paired up and then statistically processed to obtain polynomial equations of NDVI into FGCC and inverse conversion for each crop. e results of the study revealed that mutual conversion between spatial NDVI and Canopeo-derived FGCC is possible. ere is a strong direct correlation (R 2 within 0.6779–0.9000 depending on the crop) between the studied indices for all crops. Close-growing crops, especially winter wheat, showed the highest correlation, while row crops and especially tomatoes had a less strong relationship between vegetation indices. e models for mutual conversion between FGCC and NDVI could be incorporated into the yield simulation models to improve the forecasting capacities. 1.Introduction Normalized difference vegetation index (NDVI), developed and introduced by Rouse et al. [1], is the most used one to assess the conditions of vegetation cover both in agricultural and environmental monitoring purposes [2]. Even not- withstanding the fact that it is highly susceptible to atmo- spheric effects and soil background related distortions, it has become the most popular vegetation index in agricultural crop monitoring, which is mainly due to its simplicity and availability in “ready-to-use” state from most satellite and remote sensing data providers [3]. Applications of NDVI in precision agriculture systems embrace crop mapping, crop health monitoring, crop growth and development control, crop productivity estimation, etc. [2]. For example, crop producers can easily predict their yields in advance to harvesting period just using the average field NDVI values and simple gradual scales or models that is of great im- portance for crop production sector of the economy [4]. erefore, most farmers are longing to have access to NDVI data. However, until now there is a great number of crop producers in Ukraine, who cannot afford paid services Hindawi e Scientific World Journal Volume 2022, Article ID 8479424, 6 pages https://doi.org/10.1155/2022/8479424