浏览全部资源
扫码关注微信
Published： 07 January 2021 ，
扫 描 看 全 文
张兵，李俊生，申茜，吴艳红，张方方，王胜蕾，姚月，郭立男，殷子瑶.2021.长时序大范围内陆水体光学遥感研究进展.遥感学报，25（1）： 37-52
Zhang B，Li J S，Shen Q，Wu Y H，Zhang F F，Wang S L，Yao Y，Guo L N and Yin Z Y. 2021. Recent research progress on long time series and large scale optical remote sensing of inland water. National Remote Sensing Bulletin， 25（1）：37-52
地球表面的江河、湖泊和水库等内陆水体是水资源的主要组成部分，由气候变化和人类活动所引起的内陆水体分布和水质时空变化等问题已成为各国科学家和政府关注的热点。相比常规实地采样监测手段，卫星遥感在长时序、大范围内陆水体监测方面具有重要优势。受到海洋水色遥感理论和方法的推动，同时也得益于内陆水体光学特性数据的不断积累，近年来内陆水体光学遥感研究取得了很大的进展，已经从典型研究区的典型算法实验性研究，拓展到长时序、大范围水体产品生产；从水色遥感算法的科学研究，发展到内陆水体参量时空变化分析和水环境监管决策支持方面。尤其是在水体分布提取、水体遥感数据大气校正、叶绿素a浓度反演、水体颜色监测、浑浊程度监测、营养状态评价、黑臭水体监测、湖冰监测等方面都已经取得了重要进展，形成了一些面向长时序、大范围内陆水体的光学遥感产品。未来，为了进一步提高内陆水体光学遥感的应用效果，需要进一步加强不同类型内陆水体光学特性数据获取和分析工作，完善面向长时序、大范围内陆水体的水色遥感算法。此外，有必要发射面向内陆水体监测的卫星星座，或者在通用陆地卫星星座遥感器设计中兼顾内陆水体的应用需求，从数据源上解决内陆水体光学遥感面临的问题。
Inland water
including rivers
lakes and reservoirs on the earth’s surface
is the main component of water resources. It is related to human life
ecological environment construction and protection
and social and economic sustainable development. The temporal and spatial distribution and variation of inland water body and water quality triggered by climate change and human activities have caused attention of scientists and governments all around the world. Compared with conventional field sampling monitoring methods
remote sensing monitoring has the advantages of long time series and large-scale coverage. There are series of optical remote sensing satellites which mainly based on visible/near-infrared bands and have a long history. In addition to monitor water surface information
they can also obtain water substances information within water. Therefore
optical remote sensing plays a particularly important role in inland water environment monitoring. Compared with the ocean water with simple optical properties
the optical properties of inland water body are more complex and vary greatly with region and season. Moreover
there is a lack of satellite specially designed for inland water
so the water color remote sensing of inland water is more difficult. However
due to the promotion of ocean color remote sensing theory and methods
as well as the continuous accumulation of inland water optical property data
the research of optical remote sensing of inland water has made great progress in recent years. It has developed from the experimental research of typical algorithms in typical study areas to the production of long time series and large-scale water products
and from the scientific research of water color remote sensing algorithm to the geological discovery of spatio-temporal variation of inland water body parameters and finally to provide enhanced decision support to water environment supervision sectors. In particular
important progress has been made in water distribution extraction
atmospheric correction for water body
chlorophyll-a concentration inversion
water color monitoring
water clarity inversion
trophic state evaluation
black and odorous water monitoring
lake ice monitoring
etc.
and some water color remote sensing products for long time series and large-scale inland water bodies have been produced. In the future
in order to further improve the application of optical remote sensing of inland water
it is necessary to further strengthen the acquisition and analysis of optical property data from different types of inland water
and also improve the water color remote sensing algorithms for long time series and large-scale inland water. In addition
to solve the problem of the lack of useful satellite data source
it is necessary to launch satellite constellation specially designed for inland water monitoring
or to consider the needs for inland water monitoring in the sensor design in general land satellite constellation.
内陆水体水色遥感长时序大范围光学遥感
inland waterwater color remote sensinglong time serieslarge-scaleoptical remote sensing
Arati P, Devarati T and Dibyendu D. 2018. Application and comparison of advanced supervised classifiers in extraction of water bodies from remote sensing images. Sustainable Water Resources Management, 4(4): 905-919 [DOI: 10.1007/s40899-017-0184-6http://dx.doi.org/10.1007/s40899-017-0184-6]
Aung E M M and Tint T. 2018. Ayeyarwady River Regions detection and extraction system from Google Earth imagery. 2018 IEEE International Conference on Information Communication and Signal Processing (ICICSP). IEEE
Baker C, Lawrence R, Montagne C and Patten D T. 2006. Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models. Wetlands, 26(2): 465-474 [DOI: 10.167 2/0277-5212(2006)26http://dx.doi.org/10.1672/0277-5212(2006)26[465:MWARAU]2.0.CO;2]
Bao Y, Tian Q J and Chen M. 2015. A weighted algorithm based on normalized mutual information for estimating the chlorophyll-a concentration in inland waters using Geostationary Ocean Color Imager (GOCI) data. Remote Sensing, 7(9): 11731-11752 [DOI: 10.3390/rs70911731http://dx.doi.org/10.3390/rs70911731]
Brown L C and Duguay C R. 2010. The response and role of ice cover in lake-climate interactions. Progress in Physical Geography, 34(5): 671-704 [DOI: 10.1177/0309133310375653http://dx.doi.org/10.1177/0309133310375653]
Cai Y, Ke C Q, Li X G, Zhang G Q, Duan Z and Lee H. 2019. Variations of lake ice phenology on the Tibetan Plateau from 2001 to 2017 based on MODIS data. Journal of Geophysical Research-atmospheres, 124(2): 825-843 [DOI: 10.1029/2018JD028993http://dx.doi.org/10.1029/2018JD028993]
Cao H Y. 2017. Study on analysis of optical properties and remote sensing identifiable models of black and malodorous water in typical cities in China. Chengdu: Southwest Jiaotong University
曹红业. 2017. 中国典型城市黑臭水体光学特性分析及遥感识别模型研究. 成都: 西南交通大学
Cao Z G, Ma R H, Duan H T, Pahlevan N, Melack J, Shen M and Xue K. 2020. A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes. Remote Sensing of Environment, 248:111974 [DOI: 10.1016/j.rse.2020.111974http://dx.doi.org/10.1016/j.rse.2020.111974]
Carlson R E. 1977. A trophic state index for lakes. Limnology and Oceanography, 22(2): 361-369 [DOI: 10.4319/lo.1977.22.2.0361http://dx.doi.org/10.4319/lo.1977.22.2.0361]
Chen J, Wang Y F, Cao L G and Zheng J J. 2018. Variations in the ice phenology and water level of Ayakekumu Lake, Tibetan Plateau, derived from MODIS and satellite altimetry data. Journal of the Indian Society of Remote Sensing, 46(10): 1689-99 [DOI: 10.100 7/s12524-018-0824-9http://dx.doi.org/10.1007/s12524-018-0824-9]
Chen X Z, Wang G Y, Li W J, Zeng Q Z, Jin D H and Wang L H. 1995. Lake ice and its remote sensing monitoring in the Tibetan Plateau. Journal of Glaciology and Geocryology, 017(003): 241-246
陈贤章，王光宇，李文君，曾群柱，金德洪，王丽红. 1995. 青藏高原湖冰及其遥感监测. 冰川冻土, 017(003): 241-246
Doerffer R and Schiller H. 2007. The MERIS Case 2 water algorithm. International Journal of Remote Sensing, 28(3-4): 517-535 [DOI: 10.1080/01431160600821127http://dx.doi.org/10.1080/01431160600821127]
Dogliotti A I, Ruddick K G, Nechad B, Doxaran D and Knaeps E. 2015. A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters. Remote Sensing of Environment, 156: 157-168 [DOI: 10.1016/j.rse.2014.09.020http://dx.doi.org/10.1016/j.rse.2014.09.020]
Drnhfer K and Oppelt N. 2016. Remote sensing for lake research and monitoring-recent advances. Ecological Indicators, 64: 105-122 [DOI: 10.1016/j.ecolind.2015.12.009http://dx.doi.org/10.1016/j.ecolind.2015.12.009]
Du J K, Huang Y S, Feng X Z and Wang Z L. 2001. Study on water bodies extraction and classification from SPOT image. Journal of Remote Sensing, 5(3): 214-219
都金康，黄永胜，冯学智，王周龙. 2001. SPOT卫星影像的水体提取方法及分类研究. 遥感学报, 5(3): 214-219 [DOI: 10.11834/jrs.20010309http://dx.doi.org/10.11834/jrs.20010309]
Du J Y, Watts J D, Jiang L M, Lu H, Cheng X, Duguay C, Farina M, Qiu Y B, Kim Y, Kimball J S and Tarolli P. 2019. Remote sensing of environmental changes in cold regions: methods, achievements and challenges. Remote Sensing, 11(16): 1952 [DOI: 10.3390/rs1 1161952http://dx.doi.org/10.3390/rs11161952]
Duguay C R, Bernier M, Gauthier Y and Kouraev A. 2015a. Remote sensing of lake and river ice. Remote Sensing of the Cryosphere, 273-306 [DOI: 10.1002/9781118368909.ch12http://dx.doi.org/10.1002/9781118368909.ch12]
Duguay C, Brown L, Kang K K and Pour H K. 2015b. [The Arctic] Lake ice [In “State of the Climate in 2014”]. Bulletin of the American Meteorological Society, 96(7): S144-S5
Duntley S Q and Preisendorfer R W. 1952. The visibility of submerged objects. Final Report to Office of Naval Research
Feng L, Hou X J and Zheng Y. 2019. Monitoring and understanding the water transparency changes of fifty large lakes on the Yangtze plain based on long-term MODIS observations. Remote Sensing of Environment, 221: 675-686 [DOI: 10.1016/j.rse.2018.12.007http://dx.doi.org/10.1016/j.rse.2018.12.007]
Feng L, Hou X J, Li J S and Zheng Y. 2018. Exploring the potential of Rayleigh-corrected reflectance in coastal and inland water applications: A simple aerosol correction method and its merits. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 52-64 [DOI: 10.1016/j.isprsjprs.2018.08.020http://dx.doi.org/10.1016/j.isprsjprs.2018.08.020]
Feng M, Sexton J O, Channan S and Townshend J R.2016. A global, high-resolution (30 m) inland water body dataset for 2000: first results of a topographic-spectral classification algorithm. International Journal of Digital Earth, 9(2): 113-133 [DOI: 10.1080/1753 8947.2015.1026420http://dx.doi.org/10.1080/17538947.2015.1026420]
Feyisa G L, Meilby H, Fensholt R and Proud S R. 2014. Automated water extraction index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140: 23-35 [DOI: 10.1016/j.rse.2013.08.029http://dx.doi.org/10.1016/j.rse.2013.08.029]
Frazier P S and Page K J. 2000. Water body detection and delineation with Landsat TM data. Photogrammetric Engineering and Remote Sensing, 66(12): 1461-1467 [DOI: 10.1016/S1361-8415(00)000 23-2http://dx.doi.org/10.1016/S1361-8415(00)00023-2]
Garaba S P, Friedrichs A, Voss D and Zielinski O. 2015. Classifying natural waters with the Forel-Ule Colour Index system: results, applications, correlations and crowdsourcing. International Journal of Environmental Research and Public Health. 12(12): 16096-16109 [DOI: 10.3390/ijerph121215044http://dx.doi.org/10.3390/ijerph121215044]
Gordon H R and Morel A Y. 1983. Remote assessment of ocean color for interpretation of satellite visible imagery: a review. Physics of the Earth and Planetary Interiors, 37(4):292-292 [DOI: 10.1029/LN004http://dx.doi.org/10.1029/LN004]
Gordon H R and Wang M. 1994. Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm. Applied Optics, 33(3): 443-452 [DOI: 10.1364/AO.33.000443http://dx.doi.org/10.1364/AO.33.000443]
Gou P, Ye Q H, Che T, Feng Q, Ding B H, Lin C G and Zong J B. 2017. Lake ice phenology of Nam Co, Central Tibetan Plateau, China, derived from multiple MODIS data products. Journal of Great Lakes Research, 43(6): 989-998 [DOI: 10.1016/j.jglr.2017.08.011http://dx.doi.org/10.1016/j.jglr.2017.08.011]
Guan Q, Feng L, Hou X J, Schurgers G, Zheng Y, and Tang J. 2020. Eutrophication changes in fifty large lakes on the Yangtze Plain of China derived from MERIS and OLCI observations. Remote Sensing of Environment, 246: 111890. [DOI: 10.1016/j.rse.2020.111890http://dx.doi.org/10.1016/j.rse.2020.111890]
Guo L N, Wu Y H, Zheng H X, Zhang B, Li J S, Zhang F F and Shen Q. 2018. Uncertainty and variation of remotely sensed lake ice phenology across the Tibetan Plateau. Remote Sensing, 10(10) [DOI: 10.3390/rs10101534]
Guo L N, Zheng H X, Wu Y H, Zhang T Q, Wen M X, Fan L X and Zhang B. 2020. Responses of lake ice phenology to climate change at Tibetan Plateau. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 99 [DOI: 10.1109/JSTARS.2020.3006270http://dx.doi.org/10.1109/JSTARS.2020.3006270]
Gurlin D, Gitelson A A and Moses W J. 2011. Remote estimation of chl-a concentration in turbid productive waters—return to a simple two-band NIR-red model? Remote Sensing of Environment, 115(12): 3479-3490 [DOI: 10.1016/j.rse.2011.08.011http://dx.doi.org/10.1016/j.rse.2011.08.011]
Hou X J, Feng L, Duan H T, Chen X L, Sun D Y and Shi K. 2017. Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China. Remote Sensing of Environment, 190: 107-121 [DOI: 10.1016/j.rse.2016.12.006http://dx.doi.org/10.1016/j.rse.2016.12.006]
Hu C M, Carder K L and Muller-Karger F E. 2000. Atmospheric correction of SeaWiFS imagery over turbid coastal waters: a practical method. Remote Sensing of Environment, 74: 195-206 [DOI: 10.1016/S0034-4257(00)00080-8http://dx.doi.org/10.1016/S0034-4257(00)00080-8]
Hu C M, Lee Z P, Ma R H, Yu K, Li D Q and Shang S L. 2010. Moderate resolution imaging spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China. Journal of Geophysical Research [DOI: 10.1029/2009JC005511http://dx.doi.org/10.1029/2009JC005511]
Hu C M. 2009. A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environment, 113(10): 2118-2129 [DOI: 10.1016/j.rse.2009.05.012http://dx.doi.org/10.1016/j.rse.2009.05.012]
Jackson T, Sathyendranath S and Mélin F. 2017. An improved optical classification scheme for the ocean colour essential climate variable and its applications. Remote Sensing of Environment, 203: 152-161 [DOI: 10.1016/j.rse.2017.03.036http://dx.doi.org/10.1016/j.rse.2017.03.036]
Jain S K, Singh R D, Jain M K and Lohani A K. 2005. Delineation of flood-prone areas using remote sensing techniques. Water Resources Management, 19(4): 333-347 [DOI: 10.1007/s11269-005-3281-5http://dx.doi.org/10.1007/s11269-005-3281-5]
Ji L, Zhang L and Wylie B. 2009. Analysis of dynamic thresholds for the normalized difference water index. Photogrammetric Engineering and Remote Sensing, 75(11): 1307-1317 [DOI:10.14358/PERS.75.11.1307http://dx.doi.org/10.14358/PERS.75.11.1307]
Jiang G J, Loiselle S A, Yang D T, Ma R H, Su W and Gao C J. 2020. Remote estimation of chlorophyll a concentrations over a wide range of optical conditions based on water classification from VIIRS observations. Remote Sensing of Environment, 241: 111735 [DOI: 10.1016/j.rse.2020.111735http://dx.doi.org/10.1016/j.rse.2020.111735]
Jin X C and Tu Q Y. 1990. The specifications of investigation on the eutrophication of lakes (2nd Edition). Beijing: China Environmental Science Press: 138-142, 239-259
金相灿, 屠清瑛. 1990. 湖泊富营养化调查规范（第二版）. 北京: 中国环境科学出版社: 138-142, 239-259
Kritikos H, Yorinks L and Smith H. 1974. Suspended solids analysis using ERTS-A data. Remote Sensing of Environment, 3: 69-80 [DOI: 10.1016/0034-4257(74)90039-Xhttp://dx.doi.org/10.1016/0034-4257(74)90039-X]
Kropáček J, Maussion F, Chen F and Hoerz S. 2013. Analysis of ice phenology of lakes on the Tibetan Plateau from MODIS data. Cryosphere, 7(1): 287-301 [DOI: 10.5194/tc-7-287-2013http://dx.doi.org/10.5194/tc-7-287-2013]
Kuhn C, Aline D M V, Ward N, Loken L, Sawakuchi H O, Kampel M, Richey J, Stadler P, Crawford J, Strieg R, Vermote E, Pahlevan N and Butman D. 2019. Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity. Remote Sensing of Environment, 224: 104-118 [DOI: 10.1016/j.rse.2019.01.023http://dx.doi.org/10.1016/j.rse.2019.01.023]
Kyryliuk D and Kratzer S. 2019. Evaluation of Sentinel-3A OLCI products derived using the Case-2 regional coast colour processor over the Baltic Sea. Sensors, 19(16): 3609 [DOI: 10.3390/s1916 3609http://dx.doi.org/10.3390/s19163609]
Latifovic R and Pouliot D. 2007. Analysis of climate change impacts on lake ice phenology in Canada using the historical satellite data record. Remote Sensing of Environment, 106(4): 492-507 [DOI: 10.1016/j.rse.2006.09.015http://dx.doi.org/10.1016/j.rse.2006.09.015]
Le C F, Li Y M, Zha Y, Sun D Y, Huang C C and Zhang H. 2011. Remote estimation of chlorophyll-a in optically complex waters based on optical classification. Remote Sensing of Environment, 115: 725-737 [DOI: 10.1016/j.rse.2010.10.014http://dx.doi.org/10.1016/j.rse.2010.10.014]
Lee Z P, Carder K L and Arnone R A. 2002. Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Applied Optics, 41(27): 5755-5772 [DOI: 10.1364/AO.41.005755http://dx.doi.org/10.1364/AO.41.005755]
Lee Z P, Hu C M, Shang S L, Du K P, Lewis M, Arnone R and Brewin R. 2013. Penetration of UV-visible solar radiation in the global oceans: Insights from ocean color remote sensing. Journal of Geophysical Research: Oceans, 118(9): 4241-4255 [DOI: 10.1002/jgrc.20308http://dx.doi.org/10.1002/jgrc.20308]
Lee Z P, Shang S L, Hu C M, Du K P, Weidemann A, Hou W L, Lin J F and Lin G. 2015. Secchi disk depth: A new theory and mechanistic model for underwater visibility. Remote Sensing of Environment, 169: 139-149 [DOI: 10.1016/j.rse.2015.08.002http://dx.doi.org/10.1016/j.rse.2015.08.002]
Li J Q, Li J G, Zhu L, Shen Q, Dai H Y and Zhu Y F. 2019. Remote sensing identification and validation of urban black and odorous water in Taiyuan city. Journal of Remote Sensing, 23(4): 773-784
李佳琦, 李家国, 朱利, 申茜, 戴华阳, 朱云芳. 2019. 太原市黑臭水体遥感识别与地面验证. 遥感学报, 23(4): 773-784 [DOI:10.11834/jrs.20197292http://dx.doi.org/10.11834/jrs.20197292]
Li J S, Gao M, Feng L, Zhao H L, Shen Q, Zhang F F, Wang S L and Zhang B. 2019. Estimation of chlorophyll-a concentrations in a highly turbid eutrophic lake using a classification-based MODIS land-band algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 99: 1-15 [DOI: 10.1109/JSTARS.2019.2936403http://dx.doi.org/10.1109/JSTARS.2019.2936403]
Li J S, Hu C M, Shen Q, Barnes B B, Murch B, Feng L, Zhang M W and Zhang B. 2017. Recovering low quality MODIS-Terra data over highly turbid waters through noise reduction and regional vicarious calibration adjustment: a case study in Taihu Lake. Remote Sensing of Environment, 197: 72-84 [DOI: 10.1016/j.rse.20 17.05.027http://dx.doi.org/10.1016/j.rse.2017.05.027]
Li J S, Wang S L, Wu Y H, Zhang B, Chen X L, Zhang F F, Shen Q, Peng D L and Tian L Q. 2016. MODIS observations of water color of the largest 10 lakes in China between 2000 and 2012. International Journal of Digital Earth, 9(8): 788-805 [DOI: 10.1080/17538947.2016.1139637http://dx.doi.org/10.1080/17538947.2016.1139637]
Liu C, Zhu L P, Li J S, Wang J B, Ju J T, Qiao B J, Ma Q F and Wang S L. 2020b. The increasing water clarity of Tibetan lakes over last 20 years according to MODIS data. Remote Sensing of Environment, 112199 [DOI: 10.1016/j.rse.2020.112199http://dx.doi.org/10.1016/j.rse.2020.112199]
Liu G, Li L, Song K S, Li Y N, Lyu H, Wen Z D, Fang C, Bi S, Sun X P, Wang Z M, Cao Z G, Shang Y X, Yu G L, Zheng Z B, Huang C C, Xu Y F and Shi K. 2020a. An OLCI-based algorithm for semi-empirically partitioning absorption coefficient and estimating chlorophyll-a concentration in various turbid Case-2 waters. Remote Sensing of Environment, 239: 111648 [DOI: 10.1016/j.rse.2020.111648http://dx.doi.org/10.1016/j.rse.2020.111648]
Liu Y, Xiao C, Li J, Zhang F and Wang S. 2020b. Secchi disk depth estimation from China’s new generation of GF-5 hyperspectral observations using a semi-analytical scheme. Remote Sensing, 12(11): 1849. [DOI: 10.3390/rs12111849http://dx.doi.org/10.3390/rs12111849]
Liu D, Duan H T, Loiselle S, Hu C M, Zhang G Q, Li J L, Yang H, Thompson J R, Cao Z G, Shen M, Ma R H, Zhang M and Han W X. 2020c. Observations of water transparency in China’s lakes from space. International Journal of Applied Earth Observation and Geoinformation, 92: 102187 [DOI: 10.1016/j.jag.2020.10 2187http://dx.doi.org/10.1016/j.jag.2020.102187]
Livingstone D M. 1993. Lake oxygenation: Application of a one-box model with ice cover. Internationale Revue der gesamten Hydrobiologie und Hydrographie, 78(4): 465-480 [DOI: 10.1002/iroh.19930780402http://dx.doi.org/10.1002/iroh.19930780402]
Magnuson J J, Robertson D M, Benson B J, Wynne R H, Livingstone D M, Arai T, Assel R A, Barry R G, Card V, Kuusisto E, Granin N G, Prowse T D, Stewart K M and Vuglinski V S. 2000. Historical trends in lake and river ice cover in the Northern Hemisphere. Science 289(5485): 1743-1746 [DOI: 10.1126/science.289.5485.1743http://dx.doi.org/10.1126/science.289.5485.1743]
Matthews M W and Odermatt D. 2015. Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters. Remote Sensing of Environment, 156: 374-382 [DOI: 10.1016/j.rse.2014.10.010http://dx.doi.org/10.1016/j.rse.2014.10.010]
Matthews M W. 2014. Eutrophication and cyanobacterial blooms in South African inland waters: 10 years of MERIS observations. Remote Sensing of Environment, 155: 161-177 [DOI: 10.1016/j.rse.2014.08.010http://dx.doi.org/10.1016/j.rse.2014.08.010]
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: 10.1080/0 1431169608948714http://dx.doi.org/10.1080/01431169608948714]
Mi H, Fagherzzi S, Qiao G, Hong Y and Fichot C G. 2019. Climate change leads to a doubling of turbidity in a rapidly expanding Tibetan lake. Science of the Total Environment, 688: 952-959 [DOI: https:10.1016/j.scitotenv.2019.06.339http://dx.doi.org/https:10.1016/j.scitotenv.2019.06.339]
Ministry of Ecology and Environment of the People's Republic of China. 2019. Guidelines for treatment of black and odorous water bodies in rural areas (Trial). (中华人民共和国生态环境部. 2019. 农村黑臭水体治理工作指南（试行）. [2019-11-07]. http://www.h2o-china.com/news/299403.htmlhttp://www.h2o-china.com/news/299403.html)
Ministry of Housing and Urban-Rural Development. 2015. Urban black odor water remediation work guide. (住房和城乡建设部. 2015. 环境保护部关于印发城市黑臭水体整治工作指南的通知. [2017-04-25]. http://www.mohurd.gov.cn/wjfb/201509/t2015 0911_224828.htmlhttp://www.mohurd.gov.cn/wjfb/201509/t20150911_224828.html)
Mishra S and Mishra D R. 2012. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, 117: 394-406 [DOI: 10.1016/j.rse.2011.10.016http://dx.doi.org/10.1016/j.rse.2011.10.016]
Moore T S, Campbell J W and Dowell M D. 2009. A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product. Remote Sensing of Environment, 113: 2424-2430 [DOI: 10.1016/j.rse.2009.07.016http://dx.doi.org/10.1016/j.rse.2009.07.016]
Moore T S, Dowell M D, Bradt S and Verdu A R. 2014. An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters. Remote Sensing of Environment, 143: 97-111 [DOI: 10.1016/j.rse.2013.11.021http://dx.doi.org/10.1016/j.rse.2013.11.021]
Morel A and Prieur L. 1977. Analysis of variations in ocean color. Limnology and Oceanography, 22: 709-722 [DOI: 10.4319/lo.1977.22.4.0709http://dx.doi.org/10.4319/lo.1977.22.4.0709]
Nashait A F, Jasim O Z, Ismail M M and Saad F H. 2020. Integrating various satellite images for identification of the water bodies through using machine learning: A case study of Salah Adin, Iraq. IOP Conference Series: Materials Science and Engineering, 737(1): 012223 [DOI: 10.1088/1757-899X/737/1/012223http://dx.doi.org/10.1088/1757-899X/737/1/012223]
Nazeer M and Nichol J E. 2016. Improved water quality retrieval by identifying optically unique water classes. Journal of Hydrology, 541: 1119-1132 [DOI: 10.1016/j.jhydrol.2016.08.020http://dx.doi.org/10.1016/j.jhydrol.2016.08.020]
Nechad B, Ruddick K G and Neukermans G. 2009. Calibration and validation of a generic multisensor algorithm for mapping of turbidity in coastal waters. Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2009, SPIE 7473: 74730H [DOI: 10.111 7/12.830700http://dx.doi.org/10.1117/12.830700]
Neil C, Spyrakos E, Hunter P D and Tyler A N. 2019. A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types. Remote Sensing of Environment, 229: 159-178 [DOI: 10.1016/j.rse.2019.04.027http://dx.doi.org/10.1016/j.rse.2019.04.027]
Olmanson L G, Bauer M E and Brezonik P L. 2008. A 20-year Landsat water clarity census of Minnesota's 10,000 lakes. Remote Sensing of Environment, 112(11): 4086-4097 [DOI: 10.1016/j.rse.2007.12.013http://dx.doi.org/10.1016/j.rse.2007.12.013]
O'Reilly J E, Stéphane Maritorena, Mitchell G, Siegel D A and Mcclain C R. 1998. Ocean color algorithms for SeaWiFS. Journal of Geophysical Research Atmospheres, 103(C11): 24937-24950 [DOI: 10.1029/98JC02160http://dx.doi.org/10.1029/98JC02160]
Pahlevan N, Smith B, Schalles J, Binding C E and Stumpf R. 2020. Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: a machine-learning approach. Remote Sensing of Environment, 240: 111604 [DOI: 10.1016/j.rse.2019.111604http://dx.doi.org/10.1016/j.rse.2019.111604]
Pekel J F, Cottam A, Gorelick N and Belward A S. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540: 418-422. [DOI: 10.1038/nature20584http://dx.doi.org/10.1038/nature20584]
Pickens A H, Hansen M C, Hancher M, Stehman S V and Sherani Z. 2020. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sensing of Environment, 243: 111792 [DOI: 10.1016/j.rse.2 020.111792http://dx.doi.org/10.1016/j.rse.2020.111792]
Pitarch J, van der Woerd H J, Brewin R J W and Zielinski O. 2019. Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations. Remote Sensing of Environment, 231: 111249 [DOI: 10.1016/j.rse.2019.111249http://dx.doi.org/10.1016/j.rse.2019.111249]
Qi K K. 2019. Remote sensing classification and recognition of urban black and odorous water based on multi-source high-resolution images. Southwest Jiaotong University
七珂珂. 2019. 基于多源高分影像的城市黑臭水体遥感分级识别.西南交通大学
Qin X L, Yang J, Li P X and Sun W D. 2019. Research on water body extraction from GF-3 imagery based on polarimetric decomposition and machine learning. IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE.
Rao P Z, Jiang W G, Wang X Y and Chen K. 2019. Flood disaster analysis based on MODIS data——taking the flood in Dongting Lake area in 2017 as an example. Journal of Catastrophology, 34(1), 203-207
饶品增，蒋卫国，王晓雅，陈坤. 2019. 基于MODIS数据的洪涝灾害分析研究——以2017年洞庭湖区洪水为例.灾害学, 34(1): 203-207 [DOI: 10.3969/j.issn.1000-811X.2019.01.037http://dx.doi.org/10.3969/j.issn.1000-811X.2019.01.037]
Ren J L, Zheng Z B, Li Y M, Lv G N, Wang Q, Lyu H, Huang C C, Liu G, Du C G, Mu M, Lei S H and Bi S. 2018. Remote observation of water clarity patterns in Three Gorges Reservoir and Dongting Lake of China and their probable linkage to the Three Gorges Dam based on Landsat 8 imagery. Science of the Total Environment, 625: 1554-1566 [DOI: 10.1016/j.scitotenv.2018.01.036http://dx.doi.org/10.1016/j.scitotenv.2018.01.036]
Ruddick K G, Ovidio F and Rijkeboer M. 2000. Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters. Applied Optics, 39(6): 897-912 [DOI: 10.1364/AO.39.000897http://dx.doi.org/10.1364/AO.39.000897]
Schiller H and Doerffer R. 1999. Neural network for emulation of an inverse model-operational derivation of Case II water properties from MERIS data. International Journal of Remote Sensing, 20: 1735-1746 [DOI: 10.1080/014311699212443http://dx.doi.org/10.1080/014311699212443]
Shang S L, Lee Z P, Shi L H, Lin G, Wei G M and Li X D. 2016. Changes in water clarity of the Bohai Sea: observations from MODIS. Remote Sensing of Environment, 186: 22-31 [DOI: 10.1016/j.rse.2016.08.020http://dx.doi.org/10.1016/j.rse.2016.08.020]
Sharma S, Blagrave K, Filazzola A, Imrit M A and Franssen H H. 2020. Forecasting the permanent loss of lake ice in the Northern Hemisphere within the 21st century. Geophysical Research Letters, 06 [DOI:10.1029/2020GL091108http://dx.doi.org/10.1029/2020GL091108]
Shen Q, Yao Y, Li J S, Zhang F F, Wang S L, Wu Y H, Ye H P and Zhang B. 2019. A CIE color purity algorithm to detect black and odorous water in urban rivers using high-resolution multispectral remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57(9): 6577-6590 [DOI:10.1109/TGRS.2019.290 7283http://dx.doi.org/10.1109/TGRS.2019.2907283]
Shi K, Zhang Y L, Song K S, Liu M L, Zhou Y Q, Zhang Y B, Li Y, Zhu G W and Qin B Q. 2019. A semi-analytical approach for remote sensing of trophic state in inland waters: bio-optical mechanism and application. Remote Sensing of Environment, 232: 111323 [DOI:10.1016/j.rse.2019.111323http://dx.doi.org/10.1016/j.rse.2019.111323]
Skakun S. 2012. A neural network approach to flood mapping using satellite imagery. Computing and Informatics, 29(6):1013-1024 [DOI: 10.1007/978-3-642-11840-1_26http://dx.doi.org/10.1007/978-3-642-11840-1_26]
Smith V H. 2003. Eutrophication of freshwater and coastal marine ecosystems-a global problem. Environmental Science and Pollution Research, 10(2): 126-139 [DOI: 10.1065/espr2002.12.142http://dx.doi.org/10.1065/espr2002.12.142]
Song K S, Liu G, Wang Q, Wen Z D, Lyu L L, Du Y X, Sha L W and Fang C. 2020. Quantification of lake clarity in China using Landsat OLI imagery data. Remote Sensing of Environment, 243: 111800 [DOI: 10.1016/j.rse.2020.111800http://dx.doi.org/10.1016/j.rse.2020.111800]
Soomets T, Uudeberg K, Jakovels D, Brauns A, Zagars M and Kutser T. 2020. Validation and comparison of water quality products in Baltic lakes using sentinel-2 MSI and sentinel-3 OLCI data. Sensors, 20(3): 742 [DOI: 10.3390/s20030742http://dx.doi.org/10.3390/s20030742]
Steinmetz F, Deschamps P Y and Ramon D. 2011. Atmospheric correction in presence of sun glint: application to MERIS. Optics Express 19: 9783-9800 [DOI: 10.1364/OE.19.009783http://dx.doi.org/10.1364/OE.19.009783]
The State Council. 2015. Action plan of water pollution prevention. (国务院. 2015. 水污染防治行动计划的通知. [2017-09-28]. http://www.gov.cn/zhengce/content/2015-04/16/content_9613.htmhttp://www.gov.cn/zhengce/content/2015-04/16/content_9613.htm)
Tulbure M G, Broich M, Stehman S V and Kommareddy A. 2016. Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment, 178:142-157 [DOI: 10.1016/j.rse.2016.02.034http://dx.doi.org/10.1016/j.rse.2016.02.034]
U.S. National Ice Center. 2008. IMS daily northern hemisphere snow and ice analysis at 1 km, 4 km, and 24 km resolutions, Version 1. USA; NSIDC: National Snow and Ice Data Center
Van der Woerd H J and Wernand M R. 2015. True colour classification of natural waters with medium-spectral resolution satellites: SeaWiFS, MODIS, MERIS and OLCI. Sensors, 15(10): 25663-25680 [DOI: 10.3390/s151025663http://dx.doi.org/10.3390/s151025663]
Van der Woerd H J and Wernand M R. 2018. Hue-angle product for low to medium spatial resolution optical satellite sensors. Remote Sensing, 10(2): 180 [DOI: 10.3390/rs10020180http://dx.doi.org/10.3390/rs10020180]
Vanhellemont Q and Ruddick K. 2014. Turbid wakes associated with offshore wind turbines observed with Landsat 8. Remote Sensing of Environment, 145: 105-115 [DOI: 10.1016/j.rse.2014.01.009http://dx.doi.org/10.1016/j.rse.2014.01.009]
Vanhellemont Q and Ruddick K. 2015.Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8. Remote Sensing of Environment, 161: 89-106 [DOI: 10.1016/j.rse.2015.02.007http://dx.doi.org/10.1016/j.rse.2015.02.007]
Vanhellemont Q and Ruddick K. 2016. Acolite for Sentinel-2: aquatic applications of MSI imagery. Proceedings of the 2016 ESA Living Planet Symposium, Prague, Czech Republic, 9-13
Verpooter C, Kutser T, Seekell D A and Tranvik L J. 2014. A global inventory of lakes based on high-resolution satellite imagery, Geophysical Research Letters, 41(18): 6396-6402 [DOI: 10.1002/201 4GL060641http://dx.doi.org/10.1002/2014GL060641]
Vincent W F. 2009. Effects of climate change on lakes. Encyclopedia of Inland Waters, (14): 55-60 [DOI: 10.1016/B978-012370626-3.00233-7http://dx.doi.org/10.1016/B978-012370626-3.00233-7]
Wang M C, Liu X Q and Zhang J H. 2002. Evaluate method and classification standard on lake eutrophication. Environmental Monitoring in China, 18(5): 47-49
王明翠, 刘雪芹, 张建辉. 2002. 湖泊富营养化评价方法及分级标准. 中国环境监测, 18(5): 47-49 [DOI: 10.19316/j.issn.1002-6002.2002.05.023http://dx.doi.org/10.19316/j.issn.1002-6002.2002.05.023]
Wang M H and Shi W. 2007. The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing. Optics Express, 15: 15722-15733 [DOI: 10.1364/OE.15.015722http://dx.doi.org/10.1364/OE.15.015722]
Wang M H, Shi W and Tang, J W. 2011. Water property monitoring and assessment for china's inland Lake Taihu from MODIS-Aqua measurements. Remote Sensing of Environment, 115(3): 841-854 [DOI: 10.1016/j.rse.2010.11.012http://dx.doi.org/10.1016/j.rse.2010.11.012]
Wang M H. 2007. Remote sensing of the ocean contributions from ultraviolet to near-infrared using the shortwave infrared bands: simulations. Applied Optics, 46(9): 1535-1547 [DOI: 10.1364/AO.46.001535http://dx.doi.org/10.1364/AO.46.001535]
Wang M H. 2010. Atmospheric correction for remotely-sensed ocean-colour products. In: Reports and Monographs of the International Ocean-Colour Coordinating Group (IOCCG)
Wang S L, Li J S, Shen Q, Zhang B, Zhang F F and Lu Z Y. 2015. MODIS-based radiometric color extraction and classification of inland water with the Forel-Ule scale: A case study of Lake Taihu. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2): 907-918 [DOI: 10.1109/JSTARS.2014.2360564http://dx.doi.org/10.1109/JSTARS.2014.2360564]
Wang S L, Li J S, Zhang B, Lee Z P, Spyrakos E, Feng L, Liu C, Zhao H L, Wu Y H, Zhu L P, Jia L M, Wan W, Zhang F F, Shen Q, Tyler A N and Zhang X F. 2020. Changes of water clarity in large lakes and reservoirs across China observed from long-term MODIS. Remote Sensing of Environment, 247: 111949 [DOI: 10.1016/j.rse.2020.111949http://dx.doi.org/10.1016/j.rse.2020.111949]
Wang S L, Li J S, Zhang B, Shen Q, Zhang F F, Lu Z Y. 2016. A simple correction method for the MODIS surface reflectance product over typical inland waters in China. Internal Journal Remote Sensing, 37 (24): 6076-6096 [DOI: 10.1080/01431161.2016.1256508http://dx.doi.org/10.1080/01431161.2016.1256508]
Wang S L, Li J S, Zhang B, Spyrakos E, Tyler A N, Shen Q, Zhang F F, Kuster T, Lehmann M K, Wu Y H and Peng D L. 2018. Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index. Remote Sensing of Environment, 217: 444-460 [DOI: 10.1016/j.rse.2018.08.026http://dx.doi.org/10.1016/j.rse.2018.08.026]
Wang S, Li J, Zhang W, Cao C, Zhang F, Shen Q, Zhang X, and Zhang B. A dataset of remote-sensed Forel-Ule Index for global inland waters during 2000-2018. Scientific Data (In press).
Wang Z Y, Wu Y H, Chang J, Zhang X, Peng D L. 2017. Temporal and spatial variation of lake ice phenology and its influencing factors in the Tibetan Plateau. Journal of Beijing University of Technology, 43(5):701-709
王智颖，吴艳红，常军，张鑫，彭代亮. 2017. 青藏高原湖冰物候的时空变化及其影响因素. 北京工业大学学报, 43(5): 701-709 [DOI: 10.11936/bjutxb2016110002http://dx.doi.org/10.11936/bjutxb2016110002]
Warren M A, Simis S G H, Martinez-Vicente V, Poser K, Bresciani M, Alikas K, Spyrakos E, Giardino C and Ansper A. 2019. Assessment of atmospheric correction algorithms for the sentinel-2a multispectral imager over coastal and inland waters. Remote Sensing of Environment, 225: 267-289 [DOI: 10.1016/j.rse.2019.03.018http://dx.doi.org/10.1016/j.rse.2019.03.018]
Weber H, Riffler M, Nõges T and Wunderle S. 2016. Lake ice phenology from AVHRR data for European lakes: An automated two-step extraction method. Remote Sensing of Environment, 174: 329-340 [DOI: 10.1016/j.rse.2015.12.014http://dx.doi.org/10.1016/j.rse.2015.12.014]
Wei Q F and Ye Q H. 2010. Review of lake ice monitoring by remote sensing. Progress in geography, 29(7): 803-810
魏秋方，叶庆华. 2010. 湖冰遥感监测方法综述. 地理科学进展, 29(7): 803-810[DOI: CNKI:SUN:DLKJ.0.2010-07-005http://dx.doi.org/CNKI:SUN:DLKJ.0.2010-07-005]
Wen S, Wang Q, Li Y M, Zhu L, Lü H, Lei S H, Ding X L and Miao S.2018. Remote sensing identification of urban black-odor water bodies based on high-resolution images: a case study in Nanjing. Environmental Science, 39(1): 57-67
温爽, 王桥, 李云梅, 朱利,吕恒, 雷少华, 丁潇蕾, 苗松. 2018. 基于高分影像的城市黑臭水体遥感识别: 以南京为例. 环境科学, 39(1): 57-67 [DOI: 10.13227/j.hjkx.201703264http://dx.doi.org/10.13227/j.hjkx.201703264]
Wernand M R, van der Woerd H J and Gieskes W W. 2013a. Trends in ocean colour and chlorophyll concentration from 1889 to 2000, worldwide. Plos One, 8(6): e63766 [DOI: 10.1371/journal.pone.0063766http://dx.doi.org/10.1371/journal.pone.0063766]
Wernand M R, Hommersom A and van der Woerd H J. 2013b. MERIS-based ocean colour classification with the discrete Forel-Ule scale. Ocean Science, 9(3): 477-487 [DOI: 10.5194/os-9-477-2013http://dx.doi.org/10.5194/os-9-477-2013]
Xu H. 2006. Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27 (14): 3025-3033 [DOI: 10.1080/01431160600589179http://dx.doi.org/10.1080/01431160600589179]
Xue K, Ma R H, Shen M, Li Y, Duan H T, Cao Z G, Wang D and Xiong J F. 2020. Variations of suspended particulate concentration and composition in Chinese lakes observed from Sentinel-3A OLCI images. Science of the Total Environment, 137774 [DOI: 10.1016/j.scitotenv.2020.137774http://dx.doi.org/10.1016/j.scitotenv.2020.137774]
Yamazaki D, Trigg M A and Ikeshima D. 2015. Development of a global ~90 m water body map using multi-temporal Landsat images. Remote Sensing of Environment, 171: 337-351 [DOI: 10.1016/j.rse.2015.10.014http://dx.doi.org/10.1016/j.rse.2015.10.014]
Yang Y P, Wang Q and Xiao Q. 2007. Eutrophication evaluation of Taihu Lake based on quantitative remote sensing inversion. Geography and Geo-Information Science, 23(3): 33-37
杨一鹏, 王桥, 肖青. 2007. 太湖富营养化遥感评价研究. 地理与地理信息科学, 23(3): 33-37 [DOI: 10.3969/j.issn.1672-0504.2007.03.008http://dx.doi.org/10.3969/j.issn.1672-0504.2007.03.008]
Yao H M, Lu Y N and Gong Z Q. 2019. Remote sensing identification of urban black and odorous water body based on PlanetScope images: A case study in Qingzhou, Guangxi province. Environmental Engineering, 37(10), 35-43
姚焕玫, 卢燕南, 龚祝清. 2019. 基于PlanetScope影像的广西钦州市黑臭水体识别方法研究.环境工程,37(10):35-43 [DOI:10.13205/j.hjgc.201910006http://dx.doi.org/10.13205/j.hjgc.201910006]
Yao X J, Li L, Zhao J, Sun M P, Li J, Gong P and An L. 2016. Spatial-temporal variations of lake ice phenology in the Hoh Xil region from 2000 to 2011. Journal of Geographical Sciences, 26(1): 70-82 [DOI: 10.1007/s11442-016-1255-6http://dx.doi.org/10.1007/s11442-016-1255-6]
Yao Y, Shen Q, Zhu L, Gao H J, Cao H Y, Han H, Sun J G and Li J S. 2019. Remote sensing identification of urban black-odor water bodies in Shenyang city based on GF-2 image. Journal of Remote Sensing, 23(2): 230-242
姚月, 申茜, 朱利, 高红杰, 曹红业, 韩惠, 孙建国, 李俊生. 2019. 高分二号的沈阳市黑臭水体遥感识别. 遥感学报,23(2): 230-242 [DOI:10.11834/jrs.20197482http://dx.doi.org/10.11834/jrs.20197482]
Yao Y. 2018. Study on urban black-odor water identification model based on Gaofen multispectral images. Lanzhou Jiaotong University
姚月. 2018. 基于GF多光谱影像的城市黑臭水体识别模型研究.兰州交通大学
Zhan L H. 2019. Study on recognition models of urban black and odorous water bodies based on optical characteristics. East China Normal University
占玲骅. 2019. 基于光学特性的城市黑臭水体识别模型研究. 华东师范大学
Zhang F F, Li J S, Shen Q, Zhang B, Tian L Q, Ye H P, Wang S L and Lu Z Y. 2019. A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake. International Journal of Applied Earth Observation and Geoinformation, 74: 138-149 [DOI: 10.1016/j.jag.2018.07.018http://dx.doi.org/10.1016/j.jag.2018.07.018]
Zhang F F, Li J S, Shen Q, Zhang B, Wu C Q, Wu Y F, Wang G L, Wang S L and Lu Z Y. 2015. Algorithms and schemes for chlorophyll a estimation by remote sensing and optical classification for turbid Lake Taihu, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8: 350-364 [DOI: 10.1109/JSTARS.2014.2333540http://dx.doi.org/10.1109/JSTARS.2014.2333540]
Zhang F F, Li J S, Yan B K, Yu J C, Wang C, Wang S L, Shen Q, Wu Y H and Zhang B. 2020. Tracking historical chlorophyll-a change in the Guanting Reservoir, northern China, based on Landsat series inter-sensor normalization. International Journal of Remote Sensing. (In press)
Zhang F F, Li J S, Zhang B, Shen Q, Ye H P, Wang S L and Lu Z Y. 2018. A simple automated dynamic threshold extraction method for the classification of large water bodies from landsat-8 OLI water index images. International Journal of Remote Sensing, 39(11): 3429-3451 [DOI: 10.1080/01431161.2018.1444292http://dx.doi.org/10.1080/01431161.2018.1444292]
Zhang H N, Jiang Q G and Xu J. 2013. Coastline extraction using support vector machine from remote sensing image. Journal of Multimedia, 8, 2 (2013-04-01), 8(2) [DOI: 10.4304/jmm.8.2.175-182http://dx.doi.org/10.4304/jmm.8.2.175-182]
Zhang S and Pavelsky T M. 2019. Remote sensing of lake ice phenology across a range of lakes sizes, ME, USA. Remote Sensing, 11(14) [DOI: 10.3390/rs11141718]
Zhang X, Lai J B, Li J G, Wang L, Zhu L and Cheng Y J. 2019. Remote sensing recognition of black-odor water bodies in Shenzhen City based on GF-1 satellite. Science Technology and Engineering, 19(04): 268-274
张雪,赖积保,李家国,王力,朱利,陈宜金. 2019. 基于高分一号影像的深圳市黑臭水体遥感识别.科学技术与工程, 19(04): 268-274 [DOI: CNKI:SUN:KXJS.0.2019-04-044http://dx.doi.org/CNKI:SUN:KXJS.0.2019-04-044]
相关文章
相关作者
相关机构
京公网安备11010802024621