biostudies-literatureParente LDeutsche Forschungsgemeinschaft (German Research Foundation)1303https://www.ebi.ac.uk/biostudies/studies/S-EPMC11634896biostudies-literatureUnknown11(1)The paper describes the production and evaluation of global grassland extent mapped annually for 2000-2022 at 30 m spatial resolution. The dataset showing the spatiotemporal distribution of cultivated and natural/semi-natural grassland classes was produced by using GLAD Landsat ARD-2 image archive, accompanied by climatic, landform and proximity covariates, spatiotemporal machine learning (per-class Random Forest) and over 2.3 M reference samples (visually interpreted in Very High Resolution imagery). Custom probability thresholds (based on five-fold spatial cross-validation) were used to derive dominant class maps with balanced user's and producer's accuracy, resulting in f1 score of 0.64 and 0.75 for cultivated and natural/semi-natural grassland, respectively. The produced maps (about 4 Scientific dataAnnual 30-m maps of global grassland class and extent (2000-2022) based on spatiotemporal Machine Learning.PMC11634896202548816Bonannella CMeyer COliveira BFerreira LMattos APEhrmann SFritz SSloat LMesquita VWitjes MTeles NHengl THunter MConsoli DSahin MStanimirova RWheeler IParente LMalek ZStolle FfalseAnnual 30-m maps of global grassland class and extent (2000-2022) based on spatiotemporal Machine Learning.The paper describes the production and evaluation of global grassland extent mapped annually for 2000-2022 at 30 m spatial resolution. The dataset showing the spatiotemporal distribution of cultivated and natural/semi-natural grassland classes was produced by using GLAD Landsat ARD-2 image archive, accompanied by climatic, landform and proximity covariates, spatiotemporal machine learning (per-class Random Forest) and over 2.3 M reference samples (visually interpreted in Very High Resolution imagery). Custom probability thresholds (based on five-fold spatial cross-validation) were used to derive dominant class maps with balanced user's and producer's accuracy, resulting in f1 score of 0.64 and 0.75 for cultivated and natural/semi-natural grassland, respectively. The produced maps (about 4 2024-01-01T00:00:00Z2024 Dec2026-05-29T16:37:54.642Z2025-04-04T09:12:45.886ZS-EPMC116348963966338610.1038/s41597-024-04139-6