Using normalised difference vegetation index in classification and agroecological zoning of spring row crops

Remote sensing is an important branch of modern science and technology with various applications in different branches of life sciences. Its application in agriculture is focused mainly on crop monitoring and yield prediction. However, the value of remote sensing in the systems of automated crop mapping and agroecological zoning of plant species is increasing. The main purpose of this study is to establish the possibility of using normalised difference vegetation index in the main spring row crops, namely maize, soybeans, sunflower, to precisely classify the fields with each crop, and to evaluate the best agroecological zones for their cultivation in rainfed conditions in Ukraine. The study was carried out using the data on the normalised difference vegetation index for the period May - November 2018 from 750 fields and experimental plots, randomly scattered over the territory of Ukraine with equal representation by every administrative district of the country. The index values were calculated using combined Landsat-8 and Sentinel-2 images, with further generalisation for every crop and region. Multiclass linear discriminant analysis and canonical discriminant analysis were applied to determine whether it is possible to distinguish between the studied crops using the values of the normalised difference vegetation index as the only input. As a result, it was established that the best zone for crop cultivation is the west of the country: NDVI values for the growing season averaged to 0.34 for sunflower, 0.36 for soybeans, and 0.36 for maize, respectively. The worst growing conditions, based on the lowest NDVI values, were observed in the east for sunflower (0.26) and maize (0.25), but the minimum NDVI for soybeans (0.27) was observed in the south. Regarding the classification problem, it was found that the highest importance for the classification of crops is attributed to the values of the normalised difference vegetation index, recorded in August. The supervised learning using canonical discriminant function resulted in mediocre predictive performance of the multiple linear function with general classification accuracy of 56.5%. The best accuracy of classification was achieved for sunflower (70.4%), while it is difficult to distinguish between maize and soybeans because these crops have quite similar intra-seasonal dynamics of the vegetation index (classification accuracy was 46.8% and 52.4%, respectively; the total number of incorrectly predicted samples in the "maize-soybeans" group was 134 or 26.8%). The main limitation of this study is its single year basis, notwithstanding the fact that the year of the study was characterized as a typical one for most territory of Ukraine in terms of meteorological conditions. Therefore, more studies are required to clarify the possibility of a classification between maize and soybeans based on remote sensing data.