A Normalised Difference Vegetation Index Model for Maize Crop Performance Monitoring and Cropland Area Mapping in Sudan Ecological Zone of Nigeria

The monitoring and mapping of crops remotely are critical for easy identification of stressed crop, prompt response to part of the crop field that requires immediate attention and the potential harvest as well as for agricultural field management. Optical remote sensing offers one of the most attractive options for vegetation indices evaluation and some optical remote sensing data are readily available free for this application, especially, Sentinel-2A, which is equipped with a multispectral sensor (MSI), which enables calculation of some vegetation indices and assessment of vegetation health and status. However, serious attention has not been given to the potential of vegetation indices calculated from MSI data in the developing countries, Nigeria inclusive. Thus, the study therefore calculated the time series NDVI for the length of the growing season for the selected crops (Maize) and geometrically calculated area of the farm plot size. In this study. The study used the Normalized Difference Vegetation Index and Supervised Image classification technique for the crop health assessment and cropland area mapping for maize. The result showed the mean, standard deviation, range, minimum and maximum NDVI values for all the farm plots over the growing season from planting period to the harvesting period for the selected crop. The average NDVI value in May which marks the onset of the growing season for maize in the study area ranges from 0.044 to 0.148. In July, which represents the period of the grain filing stage ranges from 0.136 to 0.348 and in August, which is the maturity stage for harvest ranges from 0.110 to 0.450. Also, it was observed that cropland area is 194.973269 Square Km. It is therefore evident that the results of our NDVI analysis and cropland area mapping are good insights into solving national agricultural planning problems and agricultural resources allocation for effective agricultural practices for national food security. Our results showed that vegetation indices had the greatest contributions in identifying specific crop types and crop conditions during the growing season.