GLC_FCS30D: The first global 30-m land-cover dynamic monitoring product with a fine classification system from 1985 to 2022 using dense time-series Landsat imagery and continuous change-detection method

Abstract. Land cover change has been identified as an important cause or driving force of global climate change and is a significant research topic. Over the past few decades, global land-cover mapping has progressed, however, long time-series global land-cover change monitoring data are still sparse, especially at 30-m resolution. In this study, GLC_FCS30D is described as the first global 30-m land-cover dynamic monitoring dataset, containing 35 land-cover subcategories and covering the period of 1985–2022 with 26 time-steps (maps updated every five years before 2000 and annually after 2000). GLC_FCS30D has been developed using continuous change detection and all available Landsat imagery based on the Google Earth Engine platform. In specific, we first take advantage of the continuous change-detection model and full time-series Landsat observations to capture the time-points of changed pixels and identify the temporally stable areas. Then, we apply a spatiotemporal refinement method to derive the globally distributed and high-confidence training samples from these temporally stable areas. Next, locally adaptive classification models are used to update the land-cover information for the changed pixels, and a temporal-consistency optimization algorithm is adopted to improve their temporal stability and suppress some false changes. Further, the GLC_FCS30D product is validated using 84,526 globally distributed validation samples in 2020 and achieves an overall accuracy of 80.88 % (±0.27 %) for the basic classification system (10 major land-cover types) and 73.24 % (±0.30 %) for the LCCS level-1 validation system (17 LCCS land-cover types). Meanwhile, two third-party time-series validation datasets in the United States and Europe Union are also collected for analyzing accuracy variations, and the results show that the GLC_FCS30D offers significant stability for time-series accuracy variation and achieves the mean accuracies of 79.50 % (±0.50 %) and 81.91 % (±0.09 %) over the two regions. Last, we conclude the global land-cover change information from GLC_FCS30D dataset, namely, the forest and cropland variations dominate global land cover change over past 37 years, and net loss of forests reaches about 2.5 million km2 and net gain in cropland area is approximately 1.3 million km2. Therefore, the novel GLC_FCS30D is an accurate time-series land-cover dynamic monitoring product benefiting from its diverse classification system, high spatial resolution and the long time span of 1985–2022, thus, it will effectively support global climate change research and promote sustainable development analysis. The GLC_FCS30D datasets are available via https://doi.org/10.5281/zenodo.8239305 (Liu et al, 2023).