Spring Row Crops Productivity Prediction Using Normalized Difference Vegetation Index

Abstract

The results of statistical modelling for the yields prediction of spring row crops, namely, maize, sorghum and soybean, depending on the values of the remotely sensed normalized difference vegetation index (NDVI) at critical stages of the crops growth and development were presented. The spatial NDVI data obtained from the Sentinel-2 satellite were used to create the models. Quadratic regression analysis was applied to develop the yielding models based on true yield data of the crops obtained in the period of 2017 and 2018 at the experimental field of the Institute of Irrigated Agriculture of NAAS, Ukraine. The results of statistical modelling revealed that the method is suitable for precise yield prediction, and the best stages for NDVI screening and use in this purpose are different for the studied crops. The best accuracy of prediction could be obtained at the stage of tasselling (VT) or silking (R1) for maize (the mean absolute percentage error MAPE is 8.75%); at the stage of second trifoliate (V2) for soybean (MAPE is 3.75%), and at the stage of half bloom (S6) for sorghum (MAPE is 17.62%). The yield predictions by NDVI are reliable at a probability level of 95% (p < 0.05).

The Study on the Relationship between Normalized Difference Vegetation Index and Fractional Green Canopy Cover in Five Selected Crops

“…[ 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 importance for crop production sector of the economy [ 4 ]. Therefore, most farmers are longing to have access to NDVI data.…”

Monitoring impacts of soil bund on spatial variation of teff and finger millet yield with Sentinel-2 and spectroradiometric data in Ethiopia

“…However, as biomass density increases, NDVI becomes saturated [ 66 , 67 ]. NDVI data accurately explained the variation of sorghum yield [ 68 , 69 ] (maize GY [ 70 , 71 ] and teff GY [ 72 ] in previous researches.…”

Cereal yield forecasting in semi-arid region of Algeria using MODIS-NDVI

“…The reported yield values were regressed against the NDVI data, and they found that MODIS-NDVI data could effectively predict crop yield for the Tisza river catchment area 6-8 weeks before harvest. Similarly, Lykhovyd (2020) and Vozhehova et al (2020) applied NDVIbased regression models for forecasting yield of spring row crops at the field scale. The combination of crop models and remote sensing data has increasingly been used to forecast crop yield.…”