[1] Alsdorf D E, Rodríguez E, Lettenmaier D P, 2007. Measuring surface water from space. Reviews of Geophysics, 45(2):1-24. doi: 10.1029/2006RG000197
[2] Brown G, 2013. The relationship between social values for eco-system services and global land cover:an empirical analy-sis. Ecosystem Services, 5:58-68. doi:10.1016/j.ecoser.2013. 06. 004
[3] Chander G, Markham B L, Helder D L, 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environ-ment, 113(5):893-903. doi: 10.1016/j.rse.2009.01.007
[4] Danaher T, Collett L, 2006. Development, optimization and mul-ti-temporal application of a simple Landsat based water index. Proceeding of the 13th Australasian Remote Sensing and Photogrammetry Conference, Canberra, ACT, Australia.
[5] Fisher A, Flood N, Danaher T, 2016. Comparing Landsat water index methods for automated water classification in eastern Australia. Remote Sensing of Environment, 175:167-182. doi: 10.1016/j.rse.2015.12.055
[6] Friess D A, Kudavidanage E P, Webb E L, 2011. The digital globe is our oyster. Frontiers in Ecology & the Environment, 9 (10):542-542. doi: 10.1890/11.WB.029
[7] Gorelick N, Hancher M, Dixon M et al., 2017. Google Earth En-gine:planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202:18-27. doi:10.1016/j.rse.2017. 06.031
[8] Hansen MC, Potapov PV, Moore R et al., 2013. High-resolution global maps of 21st-Century forest cover change. Science, 342(6160):850. doi: 10.1126/science.1244693
[9] Guerschman J, Scarth P, Mcvicar T et al.., 2015. Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from landsat and modis data. Remote Sensing of Environment, 161:12-26. doi:10.1016/j.rse.2015. 01.021
[10] Hermosilla T, Wulder M A, White J C et al., 2015. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment, 158(158):220-234. doi: 10.1016/j.rse.2014.11.005
[11] Jay S, Jones C, Slinn P, Wood C, 2007. Environmental impact assessment:retrospect and prospect. Environmental Impact As-sessment Review, 27(4):287-300. doi:10.1016/j.eiar.2006. 12.001
[12] Jenkins K M, Boulton A J, Ryder D S, 2005. A common parched future? Research and management of Australian arid-zone floodplain wetlands. Hydrobiologia, 552(1):57-73. doi: 10.1007/s10750-005-1505-6
[13] Jones J A, Grant G E, 2001. Comment on ‘peak flow responses to clear:cutting and roads in small and large basins, western cascades, oregon’. Water Resources Research, 32(4):959- 974. doi: 10.1029/2000WR900277
[14] Kong D, Zhang Q, Singh V P, 2016. Seasonal vegetation response to climate change in the northern hemisphere (1982-2013). Global & Planetary Change, 148:1-8. doi: 10.1016/j.gloplacha.2016.10.020
[15] Lee J S H, Wich S, Widayati A, 2016. Detecting industrial oil palm plantations on landsat images with google earth engine. Remote Sensing Applications Society & Environment, 4:219-224. doi: 10.1016/j.rsase.2016.11.003
[16] Lewis A, Oliver S, Lymburner L et al., 2017. The Australian Ge-oscience DataCube:foundations and lessons learned. Remote Sensing of Environment, 202:276-292. doi: 10.1016/j.rse.2017.03.015
[17] Lobell D B, Thau D, Seifert C et al., 2015. A scalable satel-lite-based crop yield mapper. Remote Sensing of Environment, 164:324-333. doi: 10.1016/j.rse.2015.04.021
[18] Lymburner L, Botha E, Hestir E et al., 2016. Landsat 8:providing continuity and increased precision for measuring multi-decadal time series of total suspended matter. Remote Sensing of Envi-ronment, 185:108-118. doi: 10.1016/j.rse.2016.04.011
[19] Macpherson A J, Principe P P, Shao Y, 2013. Controlling for ex-ogenous environmental variables when using data envelopment analysis for regional environmental assessments. Journal of Environmental Management, 119:220-229. doi: 10.1016/j.jenvman.2012.12.044
[20] Mueller N, Lewis A, Roberts D et al., 2016 Water observations from space:mapping surface water from 25 years of Landsat imagery across Australia. Remote Sensing of Environment, 174:341-352. doi: 10.1016/j.rse.2015.11.003
[21] Olagunju A O, Blakley J A E, 2017. Towards an environmental governance agenda in regional environmental assessment:a case study of the crown managers partnership. Journal of En-vironmental Assessment Policy & Management, 19(6):1750009. doi: 10.1142/S1464333217500090
[22] Padarian J, Minasny B, Mcbratney A B, 2015. Using google's cloud-based platform for digital soil mapping. Computers & Geosciences, 83:80-88. doi: 10.1016/j.cageo.2015.06.023
[23] Pekel J F, Cottam A, Gorelick N et al., 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633):418-422. doi: 10.1038/nature20584
[24] Rachael F T, Richard T K, Yi L et al., 2011. Landsat mapping of annual inundation (1979-2006) of the Macquarie Marshes in semi-arid Australia. International Journal of Remote Sensing, 32(16):4545-4569. doi: 10.1080/01431161.2010.489064
[25] Rhemtulla J M, Mladenoff D J, Clayton M K, 2007. Regional land-cover conversion in the u.s. upper midwest:magnitude of change and limited recovery (1850-1935-1993). Landscape Ecology, 22(1):57-75. doi: 10.1007/s10980-007-9117-3
[26] Tang Z, Yao L, Yue G et al., 2016. Assessing nebraska playa wet-land inundation status during 1985-2015 using landsat data and google earth engine. Environmental Monitoring & Assessment, 188(12):654. doi: 10.1007/s10661-016-5664-x
[27] Xiang S, Shu X, Zhu X J et al., 2015. A new indices system for evaluating ecological-economic-social performances of wetland restorations and its application to Taihu Lake Basin, China. Ecological Modelling, 295:216-226. doi: 10.1016/j.ecolmodel.2014.10.008
[28] 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 & Remote Sensing, 126:225-244. doi:10.1016/j.isprsjprs. 2017.01.019