Agroecological zoning of Ukraine using remote sensing and unsupervised clustering

Abstract

Current climate change, primarily driven by global warming, is significantly altering the suitability of agricultural lands for crop production. Given these dramatic climatic shifts in meteorological events and the advances in remote sensing and data science, providing an unbiased and scientifically sound agroecological zoning of Ukraine is a critical task for agricultural science. As a major food producer for Europe, developing an optimal agrarian policy in Ukraine is essential. This study used remote sensing vegetation indices, including NDVI, NDMI, and NRI, as primary features for clustering, as these indicators reflect vegetation vigor, water stress levels, and nitrogen availability. Index values were derived from the Ukrainian Crop Production Map web application for the period of 2005-2023. We implemented the K-means clustering algorithm to group agricultural lands into distinct zones. To ensure the study was unbiased, the optimal number of clusters (K) was determined using the Elbow method, and the quality of the clustering was confirmed with a silhouette score. As a result, four distinct agroecological zones were identified and mapped. The most prospective zone for crop production, given current climate conditions and soil nitrogen reserves, was established to be the western part of Ukraine. The southern region of Ukraine was identified as the riskiest for agriculture due to its drought-prone character. Meanwhile, the central and eastern parts of the country occupy an intermediate position, representing a balanced transitional zone