Using Remote Sensing Normalised Difference Vegetation Index to Rec-ognise Irrigated Croplands via Agroland Classifier Application

Formulation of the problem. Recognition between irrigated and non-irrigated croplands is an important task of modern agricultural science in order to ensure efficient management of water resources in agriculture and control the usage of irrigation systems. Remote sensing data could be utilized as a means for the automation of this task through the implementation of machine classification algorithms. The normalised difference vegetation index, calculated based on aerospace images, could be of great usefulness in this regard to determine the patterns of vegetation cover in different humidification conditions and provide a key to distinguish between rainfed and irrigated crops. The purpose of this study was to assess the accuracy of cropland meliorative status recognition using remote sensing normalised difference vegetation index through different mathematical algorithms within Agroland Classifier application and to find out whether this application could be applied for automated cropland recognition. Methods. The study was conducted for the Southern Steppe zone of Ukraine, and included 100 randomly selected fields (50 irrigated, and 50 non-irrigated) within the boundaries of Kherson and Mykolaiv regions. The data on the values of the field normalised difference vegetation index were obtained through the calculation of the average monthly index value using free of distortion cloudless aerospace imagery with a resolution of 250 m from OneSoil remote sensing platform, and then fetched to the application Agroland Classifier to get a decision on the meliorative status of the field (irrigated or non-irrigated). Agroland Classifier utilises linear canonical discriminant function and logistic regression algorithms to distinguish between the irrigated and rainfed fields. The accuracy of the application recognition was evaluated through the calculation of general correctness rate, as well as correctness rates for each recognition algorithm separately. Results. The study revealed that Agroland Classifier provides high general correctness rate (92% for the combined algorithms) for the recognition between the irrigated and non-irrigated croplands. Each algorithm of the application was established to have its unique advantages and disadvantages. The linear canonical discriminant function provides more stable results both for the irrigated (88% of correct assumptions) and non-irrigated lands (84% of correct assumptions). At the same time, logistic regression failed to recognize the irrigated crops (just 78% of correct assumptions), while the accuracy of the non-irrigated lands recognition was significantly higher (96% of correct assumptions). Scientific novelty and practical significance. The article provides novel insights on the implementation of remote sensing data in the classification between irrigated and non-irrigated crops in the Southern Steppe zone of Ukraine via Agroland Classifier application. The application could be recommended for scientific and practical purposes to improve cropland mapping and monitoring of the use of water resources in agriculture.