[1] Arino O, Gross D, Ranera F et al., 2007. GlobCover:ESA service for global land cover from MERIS. In 2007 IEEE International Geoscience and Remote Sensing Symposium, 2412-2415.
[2] Breiman L, 2001. Random Forests. Machine Learning, 45(1):5-32. doi: 10.1023/A:1010933404324
[3] Chen B, Xiao X, Li X et al., 2017. A mangrove forest map of China in 2015:analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 131:104-120. doi: https://doi.org/10.1016/j.isprsjprs.2017.07.011
[4] Chen J, Chen J, Liao A et al., 2015. Global land cover mapping at 30m resolution:a POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103:7-27. doi: https://doi.org/10.1016/j.isprsjprs.2014.09.002
[5] Dong J, Xiao X, Menarguez M A et al., 2016. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing of Environment, 185:142-154. doi: https://doi.org/10.1016/j.rse.2016.02.016
[6] Friedl M A, McIver D K, Hodges J C F et al., 2002. Global land cover mapping from MODIS:algorithms and early results. Remote Sensing of Environment, 83(1):287-302. doi: https://doi.org/10.1016/S0034-4257(02)00078-0
[7] Funk C, Peterson P, Landsfeld M et al., 2015. The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes. Scientific Data, 2(1):150066. doi: 10.1038/sdata.2015.66
[8] Gong P, Liu H, Zhang M et al., 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(2095-9273):370-373. doi: https://doi.org/10.1016/j.scib.2019.03.002
[9] Gong P, Wang J, Yu L et al., 2013. Finer resolution observation and monitoring of global land cover:first mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing, 34(7):2607-2654. doi:10.1080/01431161.2012. 748992
[10] Gorelick N, Hancher M, Dixon M et al., 2017. Google Earth Engine:planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202:18-27. doi: https://doi.org/10.1016/j.rse.2017.06.031
[11] Hansen M C, Potapov P V, Moore R et al., 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160):850. doi: 10.1126/science.1244693
[12] Huete A, Didan K, Miura T et al., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1):195-213. doi: https://doi.org/10.1016/S0034-4257(02)00096-2
[13] Huete A R, 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3):295-309. doi: https://doi.org/10.1016/0034-4257(88)90106-X
[14] Huete A R, Liu H Q, Batchily K et al., 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59(3):440-451. doi: https://doi.org/10.1016/S0034-4257(96)00112-5
[15] Li W, Fu H, Yu L et al., 2016. Stacked Autoencoder-based deep learning for remote-sensing image classification:a case study of African land-cover mapping. International Journal of Remote Sensing, 37(23):5632-5646. doi:10.1080/01431161. 2016.1246775
[16] Liu H Q, Huete A, 1995. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33(2):457-465. doi: 10.1109/TGRS.1995.8746027
[17] Makungo R, Odiyo J O, Ndiritu J G et al., 2010. Rainfall-runoff modelling approach for ungauged catchments:a case study of Nzhelele River sub-quaternary catchment. Physics and Chemistry of the Earth, Parts A/B/C, 35(13):596-607. doi: https://doi.org/10.1016/j.pce.2010.08.001
[18] McFeeters S K, 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7):1425-1432. doi: 10.1080/01431169608948714
[19] Miller J D, Thode A E, 2007. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 109(1):66-80. doi: https://doi.org/10.1016/j.rse.2006.12.006
[20] Pekel J F, Cottam A, Gorelick N et al., 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540:418. doi: 10.1038/nature20584
[21] Pesaresi M, Ehrlich D, Ferri S et al., 2016. Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014. Publications Office of the European Union, 1-62.
[22] Savitzky A, Golay M J E, 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8):1627-1639. doi: 10.1021/ac60214a047
[23] Singha M, Dong J, Zhang G et al., 2019. High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data. Scientific data, 6(1):1-10. doi: 10.1038/s41597-019-0036-3
[24] Tian F, Wu B, Zeng H et al., 2019. Efficientidentification of corn cultivation area with multitemporal Synthetic Aperture Radar and optical images in the Google Earth Engine Cloud Platform. Remote Sensing, 11(6). doi: 10.3390/rs11060629
[25] Tucker C J, 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2):127-150. doi: https://doi.org/10.1016/0034-4257(79)90013-0
[26] Wang J, Zhao Y, Li C et al., 2015. Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250m resolution. ISPRS Journal of Photogrammetry and Remote Sensing, 103:38-47. doi: https://doi.org/10.1016/j.isprsjprs.2014.03.007
[27] Xiong J, Thenkabail P S, Gumma M K et al., 2017. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 126:225-244. doi: https://doi.org/10.1016/j.isprsjprs.2017.01.019
[28] Xu H, 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14):3025-3033. doi: 10.1080/01431160600589179
[29] Xu H, 2008. A new index for delineating built-up land features in satellite imagery. International Journal of Remote Sensing, 29(14):4269-4276. doi: 10.1080/01431160802039957
[30] Yu L, Wang J, Li X et al., 2014. A multi-resolution global land cover dataset through multisource data aggregation. Science China Earth Sciences, 57(10):2317-2329. doi: 10.1007/s11430-014-4919-z
[31] Zha Y, Gao J, Ni S, 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3):583-594. doi: 10.1080/01431160304987
[32] Zhang X, Wu B, Ponce-Campos E G et al., 2018. Mapping up-to-date paddy rice extent at 10 m resolution in China through the Integration of optical and Synthetic Aperture Radar Images. Remote Sensing, 10(8):1200. doi: 10.3390/rs10081200