The customized algorithms inferred ten distinct ecotopes in the wetland based on unique homogenous hydrological, anthropogenic, and vegetation characteristics. This study pertains to the characterization and change detection of ecotopes for Harike wetland (Ramsar site) of India using Machine Learning (ML) tools adopting a Random Forest (RF) classifier upon Surface Reflectance Tier 1 data products of Landsat images (1990–2020) in Google Earth Engine (GEE). Monitoring of ecotope-based diversity in inaccessible wetlands at a spatio-temporal scale is facilitated by remote sensing and GIS technique, cost-effectively. All raster products are freely available for academic and non-commercial research. The data are provided in 21-class (all configurations) and aggregated 10-class (1 km raster products only) versions. The UK Land Cover Map product suite includes a set of raster products in various projections, thematic and spatial resolutions (10 m, 25 m and 1 km), and land-parcel or vector products. Compiled results were validated against 35 182 reference samples, with correspondence tables indicating variable class accuracy and an overall accuracy of 82.6 % for the 21-class data and 86.5 % at a 10-aggregated-classes level. The training data were used to construct a random forest classifier, which yielded classified images. A set of training areas was created based on data harvested from historic UKCEH land cover maps. Google Earth Engine scripts were used to create an input data stack of satellite and context data. The production of LCM2021 involved three highly automated key stages: pre-processing of input data, image classification and production of the final data products. LCM2021 is based on the classification of satellite and spatial context data into 21 land cover or habitat classes, from which a product suite is derived. LCM2021 methods replicate those of LCM2017 to LCM2020 with minor deviations in cloud-masking processes and training data sourcing to enhance accuracy. Here, we introduce the latest, annual UK Land Cover Map representing 2021 (LCM2021), and we describe its production and validation. Recent advances in computation and satellite data availability have enabled annual UKCEH land cover maps since 2017. The UK Centre for Ecology and Hydrology (UKCEH) has provided reliable land cover information since the early 1990s this supports multiple scientific, government and commercial objectives. Land cover is a key environmental variable, underpinning widespread environmental research and decision making.

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