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Longer time high-resolution, high-frequency, consistent, and more detailed land cover data are urgently needed in order to achieve sustainable development goals on food security, high-quality habitat construction, biodiversity conservation and planetary health, and for the understanding, simulation and management of the Earth system. However, due to technological constraints, it is difficult to provide simultaneously high spatial resolution, high temporal frequency, and high quality observation data. Existing mapping solutions are limited by traditional remotely sensed data, that have shorter observation periods, poor spatio-temporal consistency and comparability. Therefore, a new mapping paradigm is needed. This paper develops a framework for intelligent mapping (iMap) of land cover based on state-of-the-art technologies such as cloud computing, artificial intelligence, virtual constellations, and spatio-temporal reconstruction and fusion. Under this framework, we built an automated, serverless, end-to-end data production chain and parallel mapping system based on Amazon Web Services (AWS) and produced the first 30 m global daily seamless data cubes (SDC), and annual to seasonal land cover maps for 1985–2020. The SDC was produced through a multi-source spatio-temporal data reconstruction and fusion workflow based on Landsat, MODIS, and AVHRR virtual constellations. Independent validation results show that the relative mean error of the SDC is less than 2.14%. As analysis ready data (ARD), it can lay a foundation for high-precision quantitative remote sensing information extraction. From this SDC, we produced 36-year long, 30 m resolution global land cover map data set by combining strategies of sample migration, machine learning, and spatio-temporal adjustment. The average overall accuracy of our annual land cover maps over multiple periods of time is 80% for level 1 classification and over 73% for level 2 classification (29 and 33 classes). Based on an objective validation sample consisting of FLUXNET sites, our map accuracy is 10% higher than that of existing global land cover datasets including Globeland30. Our results show that the average global land cover change rate is 0.36%/yr. Global forest decreased by 1.47 million km2 from 38.44 million km2, cropland increased by 0.84 million km2 from 12.49 million km2 and impervious surface increased by 0.48 million km2 from 0.57 million km2 during 1985– 2020.
Liu et al. (2021) studied this question.