Assessment of land quality is important for sustainable land management, particularly for the industrial regions, recognizing the most deteriorated areas and enabling the implementation of good practices in soil use and recovery. The most suitable platform for this task is Google Earth Engine (GEE), offering scalable cloud processing of multi-temporal data with high efficiency and reproducibility. The aim of this study is to develop an approach for land quality assessment using a geospatial datacube built entirely GEE data catalogs. For this, the Random Forest model is used for classification and feature importance ranking of the input geospatial datacube. Also, the applied classification method is Random Forest, since it is available in GEE. In order to test an effectiveness of the developed approach, the experiment was conducted for the Kryvyi Rih region, using twelve geospatial layers representing topographic parameters, climate data, vegetation indices, and land cover classification. The model achieved the OA of 93.1%, confirming its reliability for land quality assessment. Also, the feature importance analysis identified the slope layer as the most influential, while land cover classification was found to be the least influential.
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