References
- 1.Bontemps S, Defournya P, Van Bogaert E, Weber J L and Arino O. 2009. GlobCorine—A joint EEA-ESA project for operational land dynamics monitoring at pan-European scale // Proceedings of the 33rd International Symposium on Remote Sensing of Environment. Stresa, Italy.
- 2.Brown C F, Brumby S P, Guzder-Williams B, Birch T, Hyde S B, Mazzariello J, Czerwinski W, Pasquarella V J, Haertel R, Ilyushchenko S, Schwehr K, Weisse M, Stolle F, Hanson C, Guinan O, Moore R and Tait A M. 2022. Dynamic world, near real-time global 10 m land use land cover mapping. Scientific Data, 9(1): 251
- 3.Buchhorn M, Lesiv M, Tsendbazar N E, Herold M, Bertels L and Smets B. 2020. Copernicus global land cover layers—collection 2. Remote Sensing, 12(6): 1044
- 4.Chen J, Chen J, Liao A P, Cao X, Chen L J, Chen X H, He C Y, Han G, Peng S, Lu M, Zhang W W, Tong X H and Mills J. 2015. Global land cover mapping at 30m resolution: a POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103: 7-27
- 5.Chen X Y, Lin Y, Zhang M, Yu L, Li H C and Bai Y Q. 2017. Assessment of the cropland classifications in four global land cover datasets: A case study of Shaanxi Province, China. Journal of Integrative Agriculture, 16(2): 298-311
- 6.Chen Y C, Shao H, Li Y. 2021. Consistency analysis and accuracy assessment of multi-source land cover products in the Yangtze River Delta. Transactions of the Chinese Society of Agricultural Engineering, 37(6): 142-150
- 7.Gong P, Liu H, Zhang M N, Li C C, Wang J, Huang H B, Clinton N E, Ji L Y, Li W Y, Bai Y Q, Chen B, Xu B, Zhu Z L, Yuan C, Suen H P, Guo J, Xu N, Li W J, Zhao Y Y, Yang J, Yu C Q, Wang X, Fu H H, Yu L, Dronova I, Hui F M, Cheng X, Shi X L, Xiao F J, Liu Q F and Song L C. 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64(6): 370-373
- 8.Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D and Moore R. 2017. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18-27
- 9.Hou W and Hou X Y. 2019. Data fusion and accuracy analysis of multi-source land use/land cover datasets along coastal areas of the maritime silk road. ISPRS International Journal of Geo-Information, 8(12): 557
- 10.Jung M, Henkel K, Herold M and Churkina G. 2006. Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing of Environment, 101(4): 534-553
- 11.Karra K, Kontgis C, Statman-Weil Z, Mazzariello J C, Mathis M M and Brumby S P. 2021. Global land use / land cover with sentinel 2 and deep learning//2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. Brussels: IEEE, 4704-4707
- 12.Li Z J, Wang Z Y, Liu X T, Zhu Y D, Wang K and Zhang T G. 2022. Classification and evolutionary analysis of yellow river delta wetlands using decision tree based on time series SAR backscattering coefficient and coherence. Frontiers in Marine Science, 9: 940342.
- 13.Liu J, Li P, Tu C R, Wang H J, Zhou Z W, Feng Z X, Shen F and Li Z H. 2022a. Spatiotemporal change detection of coastal wetlands using multi-band SAR coherence and synergetic classification. Remote Sensing, 14(11): 2610.
- 14.Liu P Y, Pei J, Guo H, Tian H F, Fang H J and Wang L, 2022b. Evaluating the accuracy and spatial agreement of five global land cover datasets in the ecologically vulnerable south china karst. Remote Sensing, 14(13): 3090
- 15.Sulla-Menashe D, Gray J M, Abercrombie S P and Friedl M A. 2019. Hierarchical mapping of annual global land cover 2001 to present: the MODIS collection 6 land cover product. Remote Sensing of Environment, 222: 183-194
- 16.Venter Z S, Barton D N, Chakraborty T, Simensen T and Singh G. 2022. Global 10 m land use land cover datasets: a comparison of dynamic world, world cover and Esri land cover. Remote Sensing, 14(16): 4101
- 17.Wang J, Yang X M, Wang Z H, Cheng H B, Kang J M, Tang H T, Li Y, Bian Z P and Bai Z L. 2022a. Consistency analysis and accuracy assessment of three global ten-meter land cover products in rocky desertification region—A case study of southwest China. ISPRS International Journal of Geo-Information, 11(3): 202
- 18.Wang X Y, Jiang W G, Peng K F, Li Z and Rao P Z. 2022b. A framework for fine classification of urban wetlands based on random forest and knowledge rules: taking the wetland cities of Haikou and Yinchuan as examples. GIScience and Remote Sensing, 59(1): 2144-2163
- 19.Yang Y K, Xiao P F, Feng X Z and Li H X. 2017. Accuracy assessment of seven global land cover datasets over China. ISPRS Journal of Photogrammetry and Remote Sensing, 125: 156-173
- 20.Yang Y K, Xiao P F, Feng X Z, Li H X, Chang X and Feng W D. 2014. Comparison and assessment of large-scale land cover datasets in China and adjacent regions. Journal of Remote Sensing, 18(2): 453-475
- 21.Zanaga D, Van De Kerchove R, Daems D, De Keersmaecker W, Brockmann C, Kirches G, Wevers J, Cartus O, Santoro M, Fritz S, Lesiv M, Herold M, Tsendbazar N E, Xu P P, Ramoino F and Arino O. 2022. ESA WorldCover 10 m 2021 v200 (Version v200) [Data set]. Zenodo[EB/OL]. ,2022-10-28
- 22.Zanaga D, Van De Kerchove R, De Keersmaecker W, Souverijns N, Brockmann C, Quast R, Wevers J, Grosu A, Paccini A, Vergnaud S, Cartus O, Santoro M, Fritz S, Georgieva I, Lesiv M, Carter S, Herold M, Li L L, Tsendbazar N E, Ramoino F and Arino O. 2021. ESA WorldCover 10 m 2020 v100 (Version v100) [Data set]. Zenodo[EB/OL]. ,2021-10-20
- 23.Zhang B Y, Wdowinski S, Gann D, Hong S H and Sah J. 2022. Spatiotemporal variations of wetland backscatter: The role of water depth and vegetation characteristics in Sentinel-1 dual-polarization SAR observations. Remote Sensing of Environment, 270: 112864
- 24.Zhou Q, Xian G, Horton J, Wellington D, Domke G, Auch R, Li C C and Zhu Z. 2022. Tree regrowth duration map from LCMAP collection 1.0 land cover products in the conterminous United States, 1985-2017. GIScience and Remote Sensing, 59(1): 959-974


