Yailymova, HannaYailymov, BohdanKussul, NataliiaShelestov, AndriiShumilo, Leonid2023-11-082023-11-082023-07Geospatial Monitoring of Sustainable and Degraded Agricultural Land / Yailymova H., Yailymov B., Kussul N., Shelestov A., Shumilo L. // In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium. - 2023. - P. 2446-2449.https://ela.kpi.ua/handle/123456789/62073In this study, the assessment of sustainable development goal (SDG) indicator 2.4.1 for Ukraine and Germany is conducted using geospatial and satellite data. The traditional methodology for the SDG indicator 2.4.1 calculation cannot be directly applied to the Ukrainian territory due to the lack of systematic data collection of the essential indicators. Therefore, the authors have developed an integrated approach to estimate land degradation, that uses different schemes for various land cover and crop types at the national scale, utilizing satellite data and employing the WOFOST model for crop growing simulation. The research describes the information sources used for creation crop type classification maps and the necessary data for modeling leaf area index (LAI) based on the WOFOST model. The calculated indicators are determined for each Ukrainian region from 2018 to 2022. Observations in 2022 show a decline in the indicator 2.4.1 across nearly all regions of Ukraine, directly attributed to the military conflicts within the Ukraine. To assess the possibility of applying the developed technology to a large area, the indicator was calculated for a European country (Germany).enGeospatial Data AnalysisMachine LearningLand DegradationRemote SensingLand CoverDG 2.4.1ArticleP. 2446 – 2449