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Geosci. Remote Sens., GE-29, 175, 10.1109\u002F36.103308\nCoulter, 1983, Applications of the Bayes decision rule for an automatic water mass classification from satellite infrared data, Vol. II, 589\nGerson, 1982, Detecting the Gulf Stream from digital infrared data pattern recognition, Vol. 12, 19\nHaddon, 1990, Image segmentation by unifying region and boundary information, IEEE Trans. Pattern Anal. Machine Intell., PAMI-12, 929, 10.1109\u002F34.58867\nHaralick, 1982, Pattern recognition of remotely sensed data, 351\nHaralick, 1987, Image analysis using mathematical morphology, IEEE Trans. Pattern Anal. Machine Intell., PAMI-9, 532, 10.1109\u002FTPAMI.1987.4767941\nHolyer, 1989, Edge detection applied to satellite imagery of the oceans, IEEE Trans. Geosci. Remote Sens., GE-27, 46, 10.1109\u002F36.20274\nJanowitz, 1985, Automatic detection of Gulf Stream rings, Office of Naval Research Tech. 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S. F\nBarboux, 2015, Mapping slope movements in Alpine environments using TerraSAR-X interferometric methods, ISPRS J. Photogramm. Remote Sens., 109, 178, 10.1016\u002Fj.isprsjprs.2015.09.010\nBarsch, 1996, Rockglaciers: Indicators for the Present and Former Geoecology in High Mountain Environments\nBerardino, 2002, A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms, IEEE Trans. Geosci. Remote Sens., 40, 2375, 10.1109\u002FTGRS.2002.803792\nBieniek, 2012, Climate divisions for Alaska based on objective methods, J. Appl. Meteorol. Climatol., 51, 1276, 10.1175\u002FJAMC-D-11-0168.1\nCADIS, 2016\nColesanti, 2003, Multi-platform permanent scatterers analysis: first results, 52\nCostantini, 1998, A novel phase unwrapping method based on network programming, IEEE Trans. Geosci. 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A, 2012, 2017\nGong, 2015, Measurement and interpretation of subtle deformation signals at Unimak Island from 2003 to 2010 using weather model-assisted time series InSAR, J. Geophys. Res. Solid Earth, 120, 1174, 10.1002\u002F2014JB011384\nGoogle\nGray, 2000, Influence of ionospheric electron density fluctuations on satellite radar interferometry, Geophys. Res. Lett., 27, 1451, 10.1029\u002F2000GL000016\nHanssen, 2001\nHooper, 2007, Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volcán Alcedo, Galápagos, J. Geophys. Res., 112, B07407, 10.1029\u002F2006JB004763\nHu, 2016, Detecting seasonal landslide movement within the Cascade landslide complex (Washington) using time-series SAR imagery, Remote Sens. Environ., 187, 49, 10.1016\u002Fj.rse.2016.10.006\nIwahana, 2016, InSAR detection and field evidence for thermokarst after a tundra wildfire, using ALOS-PALSAR, Remote Sens., 8, 218, 10.3390\u002Frs8030218\nJorgenson, 2006, Abrupt increase in permafrost degradation in Arctic Alaska, Geophys. Res. Lett., 33, 10.1029\u002F2005GL024960\nLanari, 2004, Satellite radar interferometry time series analysis of surface deformation for Los Angeles, California, Geophys. Res. Lett., 31, 10.1029\u002F2004GL021294\nLindsey, 2015, Line-of-sight displacement from ALOS-2 interferometry: Mw 7.8 Gorkha Earthquake and Mw 7.3 aftershock, Geophys. Res. Lett., 42, 6655, 10.1002\u002F2015GL065385\nLiu, 2010, InSAR measurements of surface deformation over permafrost on the North Slope of Alaska, J. Geophys. Res. Earth Surf., 115, F03023, 10.1029\u002F2009JF001547\nLiu, 2013, Surface motion of active rock glaciers in the Sierra Nevada, California, USA: inventory and a case study using InSAR, Cryosphere, 7, 1109, 10.5194\u002Ftc-7-1109-2013\nLiu, 2014, InSAR detects increase in surface subsidence caused by an Arctic tundra fire, Geophys. Res. Lett., 41, 3906, 10.1002\u002F2014GL060533\nLiu, 2014, Seasonal thaw settlement at drained thermokarst lake basins, Arctic Alaska, Cryosphere, 8, 815, 10.5194\u002Ftc-8-815-2014\nLu, 2014\nLu, 2012\nMassonnet, 1994, Radar interferometric mapping of deformation in the year after the landers earthquake, Nature, 369, 227, 10.1038\u002F369227a0\nMeyer, 2010, A review of ionospheric effects in low-frequency SAR, 2014; signals, correction methods, and performance requirements, 29\nMeyer, 2006, The potential of low-frequency SAR systems for mapping ionospheric TEC distributions, IEEE Geosci. Remote Sens. Lett., 3, 560, 10.1109\u002FLGRS.2006.882148\nRignot, 2001, Penetration depth of interferometric synthetic-aperture radar signals in snow and ice, Geophys. Res. Lett., 28, 3501, 10.1029\u002F2000GL012484\nRosen, 2000, Synthetic aperture radar interferometry - invited paper, Proc. IEEE, 88, 333, 10.1109\u002F5.838084\nSandwell, 2008, Accuracy and resolution of ALOS interferometry: vector deformation maps of the father's day intrusion at Kilauea, IEEE Trans. Geosci. Remote Sens., 46, 3524, 10.1109\u002FTGRS.2008.2000634\nShusun, 1997, Aufeis in the Ivishak River, Alaska, mapped from satellite radar interferometry, Remote Sens. Environ., 60, 131, 10.1016\u002FS0034-4257(96)00167-8\nSimpson, 2016, Investigating movement and characteristics of a frozen debris lobe, south-central Brooks Range, Alaska, Environ. Eng. Geosci., 22\nTong, 2010, Coseismic slip model of the 2008 Wenchuan earthquake derived from joint inversion of interferometric synthetic aperture radar, GPS, and field data, J. 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Atmos. Ocean. Technol., 33, 361, 10.1175\u002FJTECH-D-15-0093.1\nBuchard, 2017, The MERRA-2 aerosol reanalysis, 1980 onward. Part II: evaluation and case studies, J. Clim., 30, 6851, 10.1175\u002FJCLI-D-16-0613.1\nDash, 2010, The SST quality monitor (SQUAM), J. Atmos. Ocean. Technol., 27, 1899, 10.1175\u002F2010JTECHO756.1\nDiaz, 2001, Relationship between errors in AVHRR-derived sea surface temperature and the TOMS aerosol index, Geophys. Res. Lett., 28, 1989, 10.1029\u002F2000GL012446\nGelaro, 2017, The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Clim., 30, 5419, 10.1175\u002FJCLI-D-16-0758.1\nGood, 2012, An infrared desert dust index for the along-track scanning radiometers, Remote Sens. Environ., 116, 159, 10.1016\u002Fj.rse.2010.06.016\nGriggs, 1985, A method to correct satellite measurements of sea surface temperature for the effects of atmospheric aerosols, J. Geophys. Res., 90, 12951, 10.1029\u002FJD090iD07p12951\nHess, 1998, Optical properties of aerosols and clouds: the software package OPAC, Bull. Am. Meteorol. Soc., 79, 831, 10.1175\u002F1520-0477(1998)079\u003C0831:OPOAAC>2.0.CO;2\nIgnatov, 2016, AVHRR GAC SST reanalysis version 1 (RAN1), Remote Sens., 8, 315, 10.3390\u002Frs8040315\nLe Borgne, 2013, Night time detection of Saharan dust using infrared window channels: application to NPP\u002FVIIRS, Remote Sens. Environ., 137, 264, 10.1016\u002Fj.rse.2013.06.001\nLiang, 2011, Monitoring of IR clear-sky radiances over oceans for SST (MICROS), J. Atmos. Ocean. Technol., 28, 10.1175\u002FJTECH-D-10-05023.1\nLiang, 2009, Implementation of the community radiative transfer model in advanced clear-sky processor for oceans and validation against nighttime AVHRR radiances, J. Geophys. Res., 114, 10.1029\u002F2008JD010960\nLiang, 2017, Monitoring of VIIRS ocean clear-sky brightness temperatures against CRTM simulations in ICVS for TEB\u002FM bands, vol. 10402, 104021S\nMay, 1992, A correction for Saharan dust effects on satellite sea surface temperature measurements, J. Geophys. Res., 97, 3611, 10.1029\u002F91JC02987\nMcMillin, 1975, Estimation of sea surface temperatures from two infrared window measurements with different absorption, J. Geophys. Res., 80, 5113, 10.1029\u002FJC080i036p05113\nMcMillin, 1984, Theory and validation of the multiple window sea surface temperature technique, J. Geophys. Res., 89, 3655, 10.1029\u002FJC089iC03p03655\nMerchant, 1999, Toward the elimination of bias in satellite retrievals of sea surface temperature: 1. Theory, modeling and interalgorithm comparison, J. Geophys. Res., 104, 23565, 10.1029\u002F1999JC900105\nMerchant, 2006, Saharan dust in night-time thermal imagery: detection and reduction of related biases in retrieved sea surface temperature, Remote Sens. Environ., 104, 15, 10.1016\u002Fj.rse.2006.03.007\nMerchant, 2009, Retrieval characteristics of non-linear SST from AVHRR, Geophys. Res. Lett., 36\nNalli, 2002, Aerosol correction for remotely sensed sea surface temperatures from the National Oceanic and Atmospheric Administration advanced very high resolution radiometer, J. Geophys. Res., 107, 3172, 10.1029\u002F2001JC001162\nPetrenko, 2014, Evaluation and selection of SST regression algorithms for JPSS VIIRS, J. Geophys. Res., 119, 4580, 10.1002\u002F2013JD020637\nPetrenko, 2016, Sensor-specific error statistics for SST in the advanced clear-sky processor for oceans, J. Atmos. Ocean. Technol., 33, 345, 10.1175\u002FJTECH-D-15-0166.1\nPierangelo, 2004, Dust altitude and infrared optical depth from AIRS, Atmos. Chem. Phys., 4, 1813, 10.5194\u002Facp-4-1813-2004\nPrabhakara, 1974, Estimation of sea surface temperature from remote sensing in the 11- to 13-μm window region, J. Geophys. Res., 79, 5039, 10.1029\u002FJC079i033p05039\nReynolds, 1993, Impact of Mount Pinatubo aerosols on satellite-derived sea surface temperatures, J. Clim., 6, 768, 10.1175\u002F1520-0442(1993)006\u003C0768:IOMPAO>2.0.CO;2\nSaha, 2012, Selecting a first-guess sea surface temperature field as input to forward radiative transfer models, J. Geophys. Res., 117, 10.1029\u002F2012JC008384\nSaunders, 1999, An improved fast radiative transfer model for assimilation of satellite radiance observations, Q. J. R. Meteorol. Soc., 125, 1407, 10.1002\u002Fqj.1999.49712555615\nXi, 2012, Impact of Asian dust aerosol and surface albedo on photosynthetically active radiation and surface radiative balance in dryland ecosystems, Adv. 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(2001). Tree and terrain measurements using small-footprint multiple-return airborne LiDAR data from mixed Douglas-fir forest in the Pacific Northwest (135 pp.). Master's Thesis. Mississippi State, MS: Mississippi State University.\nHaufler, 1999, Conserving biological diversity using a coarse-filter approach with a species assessment, 107\nHudak, 2002, Integration of LiDAR and Landsat ETM+ data for estimating and mapping forest canopy height, Remote Sensing of Environment, 82, 397, 10.1016\u002FS0034-4257(02)00056-1\nHyyppä, 2001, A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners, IEEE Transactions on Geoscience and Remote Sensing, 39, 969, 10.1109\u002F36.921414\nKraus, 1998, Determination of terrain models in wooded areas with airborne laser scanner data, Journal of Photogrammetry and Remote Sensing, 53, 193, 10.1016\u002FS0924-2716(98)00009-4\nLantham, 1998, A method for quantifying vertical forest structure, Forest Ecology and Management, 104, 157, 10.1016\u002FS0378-1127(97)00254-5\nLefsky, 1999, LiDAR remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests, Remote Sensing of Environment, 70, 339, 10.1016\u002FS0034-4257(99)00052-8\nLevene, 1960, Robust test for equality of variances, 278\nMartin, 1988, Habitat and area effects on forest bird assemblages: Is nest predation an influence?, Ecology, 69, 74, 10.2307\u002F1943162\nMaurer, 1981, Foraging of five bird species in two forests with different vegetation structure, Wilson Bulletin, 93, 478\nMcCombs, 2003, Influence of fusing LiDAR and multispectral imagery on remotely sensed estimates of stand density and mean tree height in a managed pine plantation, Forest Science, 49, 457\nMcCoy, 1991, Habitat structure: The evolution and diversification of a complex topic, 3\nMeans, 1999, Use of large-footprint scanning airborne LiDAR to estimate forest stand characteristics in the Western Cascades of Oregon, Remote Sensing of Environment, 67, 298, 10.1016\u002FS0034-4257(98)00091-1\nMehl, 1998\nNæsset, 2002, Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data, Remote Sensing of Environment, 80, 88, 10.1016\u002FS0034-4257(01)00290-5\nNæsset, 2002, Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve, Remote Sensing of Environment, 79, 105, 10.1016\u002FS0034-4257(01)00243-7\nNelson, 1988, Estimating forest biomass and volume using airborne laser data, Remote Sensing of Environment, 24, 247, 10.1016\u002F0034-4257(88)90028-4\nNilsson, 1996, Estimation of tree heights and stand volume using an airborne LiDAR system, Remote Sensing of Environment, 56, 1, 10.1016\u002F0034-4257(95)00224-3\nParker, R. 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A., Laczniak, R. J., Boyd, R. A., Smith, J. L., & Nylund, W. E. (2003). Estimated ground-water discharge by evapotranspiration from Death Valley, California, 1997–2001. U.S. Geological Survey, Water-Resources Investigations Report 03-4254.\nDevitt, 1997, The effect of applied water on the water use of saltcedar in a desert riparian environment, Journal of Hydrology, 192, 233, 10.1016\u002FS0022-1694(96)03105-8\nEmbry, 1994, Photosynthetic light-harvesting during leaf senescence in Panicum miliaceum, Plant Science, 95, 141, 10.1016\u002F0168-9452(94)90088-4\nField, 1991, Ecological scaling of carbon gain to stress and resource availability, 35\nGillies, 1997, A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature, International Journal of Remote Sensing, 18, 3145, 10.1080\u002F014311697217026\nGoodrich, 2000, Seasonal estimates of riparian evapotranspiration using remote and in situ measurements, Agricultural and Forest Meteorology, 105, 281, 10.1016\u002FS0168-1923(00)00197-0\nHartmann, 2002, Weather, climate and hydrologic forecasting for the US Southwest: A survey, Climate Research, 21, 239, 10.3354\u002Fcr021239\nHuete, 2002, Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment, 83, 195, 10.1016\u002FS0034-4257(02)00096-2\nHunsaker, 2003, Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index, Irrigation Science, 22, 95, 10.1007\u002Fs00271-003-0074-6\nJensen, 1963, Estimating evapotranspiration from solar radiation, Proceedings of the American Society of Civil Engineering, Irrigation and Drainage Divison, 89, 15, 10.1061\u002FJRCEA4.0000287\nJones, 1983\nKustas, 2000, Evaluating the effects of sub-pixel heterogeneity on pixel average fluxes, Remote Sensing of Environment, 74, 327, 10.1016\u002FS0034-4257(99)00081-4\nKustas, 2002, Impact of time averaged inputs for estimating sensible heat flux of riparian vegetation using radiometric surface temperature, Journal of Applied Meteorology, 41, 319, 10.1175\u002F1520-0450(2002)041\u003C0319:IOUDTA>2.0.CO;2\nMeek, 1999, A note on recognizing autocorrelation and using autoregression, Agricultural and Forest Meteorology, 96, 1, 10.1016\u002FS0168-1923(99)00025-8\nMonteith, 1990\nMontgomery, 1982\nMoran, 1994, Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index, Remote Sensing of Environment, 49, 246, 10.1016\u002F0034-4257(94)90020-5\nNagler, 2001, Assessment of vegetation indices for riparian vegetation in the Colorado River delta, Mexico, Journal of Arid Environments, 49, 91, 10.1006\u002Fjare.2001.0844\nNagler, 2003, Comparison of transpiration rates among saltcedar, cottonwood and willow trees by sap flow and canopy temperature methods, Agricultural and Forest Meteorology, 116, 103, 10.1016\u002FS0168-1923(02)00251-4\nNagler, 2004, Leaf area index and Normalized Difference Vegetation Index as predictors of canopy characteristics and light interception by riparian species on the Lower Colorado River, Agricultural and Forest Meteorology, 125, 1, 10.1016\u002Fj.agrformet.2004.03.008\nNishida, 2003, Development of an evapotranspiration index from aqua\u002FMODIS for monitoring surface moisture status, IEEE Transactions on Geoscience and Remote Sensing, 41, 493, 10.1109\u002FTGRS.2003.811744\nNishida, 2003, An operational remote sensing algorithm of land surface evaporation, Journal of Geophysical Research, D: Atmospheres, 108, 10.1029\u002F2002JD002062\nOsmond, 1980\nPearcy, 1991, Measurement of transpiration and leaf conductance, 137\nPrueger, 2004, Characteristic turbulence spectra above and below a tamarisk canopy\nPrueger, 2001, Feasibility of evapotranspiration monitoring of riparian vegetation with remote sensing, vol. 267, 246\nRana, 2000, Measurement and estimation of actual evapotranspiration in the field under Mediterranean climate: A review, European Journal of Agronomy, 13, 125, 10.1016\u002FS1161-0301(00)00070-8\nSala, 1996, Water use by Tamarix ramosissima and associated phreatophytes in a Mojave Desert Floodplain, Ecological Applications, 6, 8, 10.2307\u002F2269492\nSchaeffer, 2000, Transpiration of cottonwood\u002Fwillow forest estimated from sap flux, Agricultural and Forest Meteorology, 105, 257, 10.1016\u002FS0168-1923(00)00186-6\nScott, 2004, Interannual and seasonal variation in fluxes of water and carbon dioxide from a riparian woodland ecosystem, Agricultural and Forest Meteorology, 122, 65, 10.1016\u002Fj.agrformet.2003.09.001\nScott, 2003, The understory and overstory partitioning of energy and water fluxes in an open canopy, semiarid woodland, Agricultural and Forest Meteorology, 114, 127, 10.1016\u002FS0168-1923(02)00197-1\nScott, 2000, The water use of two dominant vegetation communities in a semiarid riparian ecosystem, Agricultural and Forest Meteorology, 105, 241, 10.1016\u002FS0168-1923(00)00181-7\nSmith, 1998, Water relations of riparian 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1993, Imaging spectroscopy: Interpretation based on spectral mixture analysis, 145\nAdams, 1986, Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander I site, Journal of Geophysical Research, 91, 8098, 10.1029\u002FJB091iB08p08098\nAsner, 1998, Estimating vegetation structural effects on carbon uptake using satellite data fusion and inverse modeling, Journal of Geophysical Research—Atmospheres, 103, 28839, 10.1029\u002F98JD02459\nBateson, 1996, A method for manual endmember selection and spectral unmixing, Remote Sensing of Environment, 55, 229, 10.1016\u002FS0034-4257(95)00177-8\nBateson, 2000, Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis, IEEE Transactions on Geoscience and Remote Sensing, 38, 1083, 10.1109\u002F36.841987\nBell, 2002, Low abundance materials at the Mars Pathfinder landing site: An investigation using spectral mixture analysis and related techniques, Icarus, 158, 56, 10.1006\u002Ficar.2002.6865\nBoardman, 1993, Automating spectral unmixing of AVIRIS data using convex geometry concepts, 11\nBoardman, 1995, Mapping target signatures via partial unmixing of AVIRIS data, 23\nBorel, 1994, Nonlinear spectral mixing models for vegetative and soil surfaces, Remote Sensing of Environment, 47, 403, 10.1016\u002F0034-4257(94)90107-4\nChurch, 1974, The maximal covering location problem, Papers of the Regional Science Association, 32, 101, 10.1007\u002FBF01942293\nCohen, 1960, A coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20, 37, 10.1177\u002F001316446002000104\nCongalton, 1991, A review of assessment the accuracy of classifications of remotely sensed data, Remote Sensing of Environment, 37, 35, 10.1016\u002F0034-4257(91)90048-B\nCrist, 1985, A TM tasseled cap equivalent transformation for reflectance factor data, Remote Sensing of Environment, 17, 301, 10.1016\u002F0034-4257(85)90102-6\nCross, 1991, Subpixel measurement of tropical forest cover using AVHRR data, International Journal of Remote Sensing, 12, 1119, 10.1080\u002F01431169108929715\nDennison, 2000, Characterizing chaparral fuels using combined hyperspectral and synthetic aperture radar, 119\nElmore, 2000, Quantifying vegetation change in semiarid environments: Precision and accuracy of spectral mixture analysis and the normalized difference vegetation index, Remote Sensing of Environment, 73, 87, 10.1016\u002FS0034-4257(00)00100-0\nGarcia, 2001, Detection of interannual vegetation responses to climatic variability using AVIRIS data in a coastal savanna in California, IEEE Transactions on Geoscience and Remote Sensing, 39, 1480, 10.1109\u002F36.934079\nGillespie, 1990, Interpretation of residual images: spectral mixture analysis of AVIRIS images, Owens Valley, California, 243\nGolub, 1989\nGreen, 1993, Estimation of aerosol optical depth and additional atmospheric parameters for the calculation of apparent surface reflectance from radiance as measured by the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS), 73\nGreen, 1998, Imaging spectroscopy and the Airborne Visible\u002FInfrared Imaging Spectrometer (AVIRIS), Remote Sensing of Environment, 65, 227, 10.1016\u002FS0034-4257(98)00064-9\nHaboudane, 2002, Land degradation and erosion risk mapping by fusion of spectrally-based information and digital geomorphometric attributes, International Journal of Remote Sensing, 23, 3795, 10.1080\u002F01431160110104638\nHall, 1995, Remote sensing of forest biophysical structure using mixture decomposition and geometric reflectance models, Ecological Applications, 5, 993, 10.2307\u002F2269350\nHalligan, K. Q. (2002). Multiple endmember spectral mixture analysis of vegetation in the northeast corner of Yellowstone national park. Master's Thesis, University of California Santa Barbara.\nHuete, 1986, Separation of soil–plant spectral mixtures by factor analysis, Remote Sensing of Environment, 19, 237, 10.1016\u002F0034-4257(86)90055-6\nKameyama, 2001, Development of WTI and turbidity estimation model using SMA—application to Kushiro Mire, eastern Hokkaido, Japan, Remote Sensing of Environment, 77, 1, 10.1016\u002FS0034-4257(01)00189-4\nKauth, 1976, The tasseled cap—a graphic description of the spectral–temporal development of agricultural crops as seen by Landsat, 4B41\nLi, 2003, Highland contamination in lunar mare soils: Improved mapping with multiple endmember spectral mixture analysis (MESMA), Journal of Geophysical Research—Planets, 108, 7.1, 10.1029\u002F2002JE001917\nMaselli, 1998, Multiclass spectral decomposition of remotely sensed scenes by selective pixel unmixing, IEEE Transactions on Geoscience and Remote Sensing, 36, 1809, 10.1109\u002F36.718648\nMetternicht, 1998, Estimating erosion surface features by linear mixture modeling, Remote Sensing of Environment, 64, 254, 10.1016\u002FS0034-4257(97)00172-7\nMustard, 1996, Buried stratigraphic relationships along the southwestern shores of Oceanus Procellarum—implications for early lunar volcanism, Journal of Geophysical Research—Planets, 101, 18913, 10.1029\u002F96JE01826\nOkin, 2001, Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments, Remote Sensing of Environment, 77, 212, 10.1016\u002FS0034-4257(01)00207-3\nPainter, 2003, Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data, Remote Sensing of Environment, 85, 64, 10.1016\u002FS0034-4257(02)00187-6\nPainter, 1998, The effect of grain size on spectral mixture analysis of snow-covered area from AVIRIS data, Remote Sensing of Environment, 65, 320, 10.1016\u002FS0034-4257(98)00041-8\nPeddle, 2001, A comparison of spectral mixture analysis and ten vegetation indices for estimating boreal forest biophysical information from airborne data, Canadian Journal of Remote Sensing, 27, 627, 10.1080\u002F07038992.2001.10854903\nPeddle, 1999, Spectral mixture analysis and geometric–optical reflectance modeling of boreal forest biophysical structure, Remote Sensing of Environment, 67, 288, 10.1016\u002FS0034-4257(98)00090-X\nPhinn, 2002, Monitoring the composition of urban environments based on the vegetation–impervious surface_soil (VIS) model by subpixel analysis techniques, International Journal of Remote Sensing, 23, 4131, 10.1080\u002F01431160110114998\nPinet, 2000, Local and regional lunar regolith characteristics at Reiner Gamma Formation: Optical and spectroscopic properties from Clementine and Earth-based data, Journal of Geophysical Research—Planets, 105, 9457, 10.1029\u002F1999JE001086\nRay, 1996, Nonlinear spectral mixing in desert vegetation, Remote Sensing of Environment, 55, 59, 10.1016\u002F0034-4257(95)00171-9\nRiano, 2002, Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains, Remote Sensing of Environment, 79, 60, 10.1016\u002FS0034-4257(01)00239-5\nRoberts, 2003, Evaluation of the potential of Hyperion for fire danger assessment by comparison to the airborne visible infrared imaging spectrometer, IEEE Transactions on Geoscience and Remote Sensing, 10.1109\u002FTGRS.2003.812904\nRoberts, 1999, Development of a regionally specific library for the Santa Monica Mountains using high resolution AVIRIS data, 349\nRoberts, 1998, Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models, Remote Sensing of Environment, 65, 267, 10.1016\u002FS0034-4257(98)00037-6\nRoberts, 1997, Optimum strategies for mapping vegetation using multiple endmember spectral mixture models, 108\nRoberts, 1997, Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS, Remote Sensing of Environment, 62, 223, 10.1016\u002FS0034-4257(97)00092-8\nRoberts, 2002, Large area mapping of land-cover change in Rondonia using multitemporal spectral mixture analysis and decision tree classifiers, Journal of Geophysical Research—Atmospheres, 107, 8073, 10.1029\u002F2001JD000374\nRoberts, 1993, Green vegetation nonphotosynthetic vegetation and soils in AVIRIS data, Remote Sensing of Environment, 44, 255, 10.1016\u002F0034-4257(93)90020-X\nRogan, 2002, A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery, Remote Sensing of Environment, 80, 143, 10.1016\u002FS0034-4257(01)00296-6\nSmall, 2001, Multiresolution analysis of urban reflectance, 15\nSmall, 2002, Multitemporal analysis of urban reflectance, Remote Sensing of Environment, 81, 427, 10.1016\u002FS0034-4257(02)00019-6\nSmith, 1985, Quantitative determination of mineral types and abundances from reflectance spectra using principal components analysis, Journal of Geophysical Research, 90, C797, 10.1029\u002FJB090iS02p0C797\nTheseira, 2002, An 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ASABE, 59, 1333\nAngelini, 2012, Patch size-dependent community recovery after massive disturbance, Ecology, 93, 101, 10.1890\u002F11-0557.1\nBlair, 2015\nBrinson, 1980, Litterfall, stemflow, and throughfall nutrient fluxes in an alluvial swamp forest, Ecology, 61, 827, 10.2307\u002F1936753\nBrinson, 1985, Transitions in forested wetlands along gradients of salinity and hydroperiod, J. Elisha Mitchell Sci. Soc., 101, 76\nBrinson, 1995, Multiple states in the sea-level induced transition from terrestrial forest to estuary, Estuaries, 18, 648, 10.2307\u002F1352383\nBuckland, 1994, Use of groundtruth data to correct land-cover area estimates from remotely-sensed data, Int. J. Remote Sens., 15, 1273, 10.1080\u002F01431169408954160\nCahoon, 2019, Evaluating the relationship among wetland vertical development, elevation capital, sea-level rise, and tidal marsh sustainability, Estuar. Coasts, 42, 1, 10.1007\u002Fs12237-018-0448-x\nCharles, 2019, Experimental saltwater intrusion drives rapid soil elevation and carbon loss in freshwater and Brackish everglades marshes, Estuar. Coasts, 42, 1868, 10.1007\u002Fs12237-019-00620-3\nChen, 2016, Influence of sea level rise on saline water intrusion in the Yangtze River Estuary, China, Appl. Ocean Res., 54, 12, 10.1016\u002Fj.apor.2015.11.002\nChurch, 2011, Sea-level rise from the late 19th to the early 21st century, Surv. Geophys., 32, 585, 10.1007\u002Fs10712-011-9119-1\nClaverie, 2018, The harmonized Landsat and Sentinel-2 surface reflectance data set, Remote Sens. Environ., 219, 145, 10.1016\u002Fj.rse.2018.09.002\nConner, 1994, The effect of salinity and waterlogging on growth and survival of Baldcypress and Chinese tallow seedlings, J. Coast. Res., 10, 1045\nConner, 1995, Woody plant regeneration in three South Carolina Taxodium\u002FNyssa stands following Hurricane Hugo, Ecol. Eng., 4, 277, 10.1016\u002F0925-8574(94)00054-9\nConner, 1997, Flooding and salinity effects on growth and survival of four common forested wetland species, Wetl. Ecol. Manag., 5, 99, 10.1023\u002FA:1008251127131\nCormier, 2012, Periodicity in stem growth and litterfall in tidal freshwater forested wetlands: influence of salinity and drought on nitrogen recycling, Estuar. Coasts, 36, 533, 10.1007\u002Fs12237-012-9505-z\nCostanza, 2014, Changes in the global value of ecosystem services, Glob. Environ. Chang. Policy Dimens., 26, 152, 10.1016\u002Fj.gloenvcha.2014.04.002\nCowardin, 1979\nCraft, 2012, Tidal freshwater forest accretion does not keep pace with sea level rise, Glob. Chang. Biol., 18, 3615, 10.1111\u002Fgcb.12009\nCraft, 2009, Forecasting the effects of accelerated sea-level rise on tidal marsh ecosystem services, Front. Ecol. Environ., 7, 73, 10.1890\u002F070219\nDesantis, 2007, Sea-level rise and drought interactions accelerate forest decline on the Gulf Coast of Florida, USA, Glob. Chang. Biol., 13, 2349, 10.1111\u002Fj.1365-2486.2007.01440.x\nDoyle, 2007, 1\nDuchemin, 1999, Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA\u002FAVHRR data, Remote Sens. Environ., 67, 68, 10.1016\u002FS0034-4257(98)00067-4\nEngle, 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands, Wetlands, 31, 179, 10.1007\u002Fs13157-010-0132-9\nFranklin, 1989, Importance and justification of long-term studies in ecology, 3\nGhamisi, 2019, Multisource and multitemporal data fusion in remote sensing: a comprehensive review of the state of the art, IEEE Geosci. Remote Sens. Mag., 7, 6, 10.1109\u002FMGRS.2018.2890023\nGoldberg, 2020, Global declines in human-driven mangrove loss, Glob. Chang. Biol., 10.1111\u002Fgcb.15275\nGorelick, 2017, Google earth engine: planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18, 10.1016\u002Fj.rse.2017.06.031\nHall, 1998, Flooding alters apparent position of floodplain saplings on a light gradient, Ecology, 79, 847, 10.1890\u002F0012-9658(1998)079[0847:FAAPOF]2.0.CO;2\nHong, 2019, Changes in vegetation and flora of abandoned paddy terraces in responses to drawdown, J. Ecol. Environ., 43, 22, 10.1186\u002Fs41610-019-0120-9\nHorton, 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300, Quat. Sci. Rev., 84, 1, 10.1016\u002Fj.quascirev.2013.11.002\nHuenneke, 1986, Microsite abundance and distribution of Woody seedlings in a South Carolina cypress-tupelo swamp, Am. Midl. Nat., 115, 328, 10.2307\u002F2425869\nJi, 2001, Monitoring urban expansion with remote sensing in China, Int. J. Remote Sens., 22, 1441, 10.1080\u002F01431160117207\nJialin, 2011, Study on the seasonal dynamics of zonal vegetation of NDVI\u002FEVI of costal zonal vegetation based on MODIS data: a case study of Spartina alterniflora salt marsh on Jiangsu Coast, China, Afr. J. Agric. Res., 6, 4019\nKaplan, 2010, Linking river, floodplain, and vadose zone hydrology to improve restoration of a coastal river affected by saltwater intrusion, J. Environ. Qual., 39, 1570, 10.2134\u002Fjeq2009.0375\nKirwan, 2019, Sea-level driven land conversion and the formation of ghost forests, Nat. Clim. Chang., 9, 450, 10.1038\u002Fs41558-019-0488-7\nKirwan, 2016, Sea level driven marsh expansion in a coupled model of marsh erosion and migration, Geophys. Res. Lett., 43, 4366, 10.1002\u002F2016GL068507\nKlein, 2005, Wetland drying and succession across the Kenai Peninsula Lowlands, south-central Alaska, Can. J. For. Res. Can. Rech. For., 35, 1931, 10.1139\u002Fx05-129\nKnighton, 1991, Tidal-Creek extension and saltwater intrusion in Northern Australia, Geology, 19, 831, 10.1130\u002F0091-7613(1991)019\u003C0831:TCEASI>2.3.CO;2\nKrauss, 2009, Site condition, structure, and growth of Baldcypress along tidal\u002Fnon-tidal salinity gradients, Wetlands, 29, 505, 10.1672\u002F08-77.1\nKrauss, 2015, Assessing stand water use in four coastal wetland forests using sapflow techniques: annual estimates, errors and associated uncertainties, Hydrol. Process., 29, 112, 10.1002\u002Fhyp.10130\nKrauss, 2016, Component greenhouse gas fluxes and radiative balance from two deltaic marshes in Louisiana: pairing chamber techniques and eddy covariance, J. Geophys. Res. Biogeosci., 121, 1503, 10.1002\u002F2015JG003224\nKrauss, 2020\nLangston, 2017, A casualty of climate change? Loss of freshwater forest islands on Florida’s Gulf Coast, Glob. Chang. Biol., 23, 5383, 10.1111\u002Fgcb.13805\nLi, 2007, Measuring the quality of life in city of Indianapolis by integration of remote sensing and census data, Int. J. Remote Sens., 28, 249, 10.1080\u002F01431160600735624\nLiu, 2017, Forest composition and growth in a freshwater forested wetland community across a salinity gradient in South Carolina USA, For. Ecol. Manag., 389, 211, 10.1016\u002Fj.foreco.2016.12.022\nLovett, 2007, Who needs environmental monitoring?, Front. Ecol. Environ., 5, 253, 10.1890\u002F1540-9295(2007)5[253:WNEM]2.0.CO;2\nLuo, 2018, STAIR: a generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud−\u002Fgap-free surface reflectance product, Remote Sens. Environ., 214, 87, 10.1016\u002Fj.rse.2018.04.042\nMagnuson, 1990, Long-term ecological research and the invisible present - uncovering the processes hidden because they occur slowly or because effects lag years behind causes, Bioscience, 40, 495, 10.2307\u002F1311317\nMagolan, 2020, A multi-decadal investigation of Tidal Creek wetland changes, water level rise, and ghost forests, Remote Sens., 12, 1141, 10.3390\u002Frs12071141\nMcKee, 1989, Response of a freshwater marsh plant community to increased salinty and increased water level, Aquat. Bot., 34, 301, 10.1016\u002F0304-3770(89)90074-0\nMcLeod, 2011\nMiddleton, 2017, Functional integrity of freshwater forested wetlands, hydrologic alteration, and climate change, Ecosyst. Heal. Sustain., 2\nMiddleton, 2015, Hydrologic remediation for the Deepwater horizon incident drove ancillary primary production increase in coastal swamps, Ecohydrology, 8, 838, 10.1002\u002Feco.1625\nMiller, 1991, Short report: reaction time analysis with outlier exclusion: Bias varies with sample size, Q. J. Exp. Psychol. Sect. A, 43, 907, 10.1080\u002F14640749108400962\nMitsch, 2015\nMoran, 1997, Opportunities and limitations for image-based remote sensing in precision crop management, Remote Sens. Environ., 61, 319, 10.1016\u002FS0034-4257(97)00045-X\nMorris, 2002, Responses of coastal wetlands to rising sea level, Ecology, 83, 2869, 10.1890\u002F0012-9658(2002)083[2869:ROCWTR]2.0.CO;2\nMueller-Warrant, 2015, Methods for improving accuracy and extending results beyond periods covered by traditional ground-truth in remote sensing classification of a complex landscape, Int. J. Appl. Earth Obs. Geoinf., 38, 115\nMunns, 2002, Comparative physiology of salt and water stress, Plant Cell Environ., 25, 239, 10.1046\u002Fj.0016-8025.2001.00808.x\nNakaji, 2011, Ground-based monitoring of the leaf phenology of deciduous broad-leaved trees using high resolution NDVI camera images, J. Agric. Meteorol., 67, 65, 10.2480\u002Fagrmet.67.2.3\nNeubauer, 2011, Ecosystem responses of a tidal freshwater marsh experiencing saltwater intrusion and altered hydrology, Estuar. Coasts, 36, 491, 10.1007\u002Fs12237-011-9455-x\nNielsen-Gammon, 2012, 3, 37\nOdland, 2002, Thirteen years of wetland vegetation succession following a permanent drawdown, Myrkdalen Lake, Norway, Plant Ecol., 162, 185, 10.1023\u002FA:1020388910724\nPezeshki, 1988, Effect of salinity on leaf ionic content and photosynthesis of Taxodium-Distichum L, Am. Midl. Nat., 119, 185, 10.2307\u002F2426067\nPierfelice, 2015, Salinity influences on aboveground and belowground net primary productivity in tidal wetlands, J. Hydrol. Eng., 22\nPohlert, 2020\nRaabe, 2016, Expansion of tidal marsh in response to sea-level rise: Gulf Coast of Florida, USA, Estuar. Coasts, 39, 145, 10.1007\u002Fs12237-015-9974-y\nRasmussen, 2013, Assessing impacts of climate change, sea level rise, and drainage canals on saltwater intrusion to coastal aquifer, Hydrol. Earth Syst. Sci., 17, 421, 10.5194\u002Fhess-17-421-2013\nReaver, 2019, Hydrodynamic controls on primary producer communities in spring-fed rivers, Geophys. Res. Lett., 46, 4715, 10.1029\u002F2019GL082571\nRice, 2012, Assessment of salinity intrusion in the James and Chickahominy Rivers as a result of simulated sea-level rise in Chesapeake Bay, East Coast, USA, J. Environ. Manag., 111, 61, 10.1016\u002Fj.jenvman.2012.06.036\nRobertson, 1999, Geomorphic processes and spatial patterns of primary forest succession on the Bogue Chitto River, USA, J. Ecol., 87, 1052, 10.1046\u002Fj.1365-2745.1999.00416.x\nRoss, 1994, Sea-level rise and the reduction in pine forests in the Florida keys, Ecol. Appl., 4, 144, 10.2307\u002F1942124\nRyberg, 2012\nSatyanarayana, 2011, Assessment of mangrove vegetation based on remote sensing and ground-truth measurements at Tumpat, Kelantan Delta, East Coast of Peninsular Malaysia, Int. J. Remote Sens., 32, 1635, 10.1080\u002F01431160903586781\nSchemel, 2001, Simplified conversions between specific conductance and salinity units for use with data from monitoring stations, Interag. Ecol. Progr. Newsl., 14, 17\nSchieder, 2018, Massive upland to wetland conversion compensated for historical marsh loss in Chesapeake Bay, USA, Estuar. Coasts, 41, 940, 10.1007\u002Fs12237-017-0336-9\nSrivastava, 2012, Selection of classification techniques for land use\u002Fland cover change investigation, Adv. Sp. Res., 50, 1250, 10.1016\u002Fj.asr.2012.06.032\nSteyer, 2007, Potential consequences of saltwater intrusion associated with hurricanes Katrina and Rita, 137\nStrayer, 1986, Long-term ecological studies: an illustrated account of their design, operation, and importance to ecology, Occas. Publ. Inst. Ecosyst. Stud., 2\nTaillie, 2020, Widespread mangrove damage resulting from the 2017 Atlantic mega hurricane season\nUlrich, 2019\nUry, 2020, Succession, regression and loss: does evidence of saltwater exposure explain recent changes in the tree communities of North Carolina’s coastal plain?, Ann. Bot., 125, 255\nUSDA, 2018\nVermote, 2015\nWang, 2005, On the relationship of NDVI with leaf area index in a deciduous forest site, Remote Sens. Environ., 94, 244, 10.1016\u002Fj.rse.2004.10.006\nWhite, 2017, Restore or retreat? Saltwater intrusion and water management in coastal wetlands, Ecosyst. Heal. Sustain., 3\nWilliams, 1999, Sea-level rise and coastal forest retreat on the west coast of Florida, USA, Ecology, 80, 2045, 10.1890\u002F0012-9658(1999)080[2045:SLRACF]2.0.CO;2\nWilliams, 2003, Interactions of storm, drought, and sea-level rise on coastal forest: a case study, J. Coast. Res., 19, 1116\nWondie, 2007, Seasonal variation in primary production of a large high altitude tropical Lake (lake Tana, Ethiopia): effects of nutrient availability and water transparency, Aquat. Ecol., 41, 195, 10.1007\u002Fs10452-007-9080-8\nWood, 2017, Forested floristic quality index: an assessment tool for forested wetland habitats using the quality and quantity of woody vegetation at Coastwide Reference Monitoring System (CRMS) vegetation monitoring stations, 10.3133\u002Fofr20171002\nXie, 2020, Change point estimation of deciduous forest land surface phenology, Remote Sens. Environ., 240, 111698, 10.1016\u002Fj.rse.2020.111698\nYeo, 1982, Accumulation and localization of sodium-ions within the shoots of rice (Oryza-Sativa) varieties differing in salinity resistance, Physiol. Plant., 56, 343, 10.1111\u002Fj.1399-3054.1982.tb00350.x\nYin, 1998, Flooding and forest succession in a modified stretch along the Upper Mississippi River, Regul. Rivers-Research Manag., 14, 217, 10.1002\u002F(SICI)1099-1646(199803\u002F04)14:2\u003C217::AID-RRR499>3.0.CO;2-S\nZeiringer, 2018, River hydrology, flow alteration, and environmental flow, 67\nZhang, 2010, Multi-source remote sensing data fusion: status and trends, Int. J. 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Chem. Phys., 11, 7253, 10.5194\u002Facp-11-7253-2011\nBaars, 2012, Aerosol profiling with lidar in the Amazon Basin during the wet and dry season, J. Geophys. Res.-Atmos., 117, D21201, 10.1029\u002F2012JD018338\nBeelen, 2013, Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project, Lancet, 383\nBelgiu, 2016, Random forest in remote sensing: a review of applications and future directions, ISPRS J. Photogramm. Remote Sens., 114, 24, 10.1016\u002Fj.isprsjprs.2016.01.011\nBellouin, 2006, Global estimate of aerosol direct radiative forcing from satellite measurements, Nature, 438, 1138, 10.1038\u002Fnature04348\nBohren, 1983\nBruneau, 2015, 355-nm high spectral resolution airborne lidar LNG: system description and first results, Appl. Opt., 54, 8776, 10.1364\u002FAO.54.008776\nBurton, 2012, Aerosol classification using airborne high spectral resolution Lidar measurements – methodology and examples, Atmos. Meas. Tech., 5, 73, 10.5194\u002Famt-5-73-2012\nBurton, 2016, Information content and sensitivity of the 3β + 2α lidar measurement system for aerosol microphysical retrievals, Atmos. Meas. Tech., 9, 5555, 10.5194\u002Famt-9-5555-2016\nBurton, 2018, Calibration of a high spectral resolution lidar using a Michelson interferometer, with data examples from ORACLES, Appl. Opt., 57, 6061, 10.1364\u002FAO.57.006061\nChemyakin, 2014, Arrange and average algorithm for the retrieval of aerosol parameters from multiwavelength high-spectral-resolution lidar\u002FRaman lidar data, Appl. Opt., 53, 10.1364\u002FAO.53.007252\nChemyakin, 2014, Arrange and average algorithm for the retrieval of aerosol parameters from multiwavelength high-spectral-resolution lidar\u002FRaman lidar data, Appl. Opt., 53, 7252, 10.1364\u002FAO.53.007252\nChemyakin, 2016, Retrieval of aerosol parameters from multiwavelength lidar: investigation of the underlying inverse mathematical problem, Appl. Opt., 55, 2188, 10.1364\u002FAO.55.002188\nChen, 2014, Forested landslide detection using LiDAR data and the random forest algorithm: a case study of the three gorges, China, Remote Sens. Environ., 152, 291, 10.1016\u002Fj.rse.2014.07.004\nCheng, 2015, Field-widened Michelson interferometer for spectral discrimination in high-spectral-resolution lidar: theoretical framework, Opt. Express, 23, 12117, 10.1364\u002FOE.23.012117\nChung, 2016, Global fine-mode aerosol radiative effect, as constrained by comprehensive observations, Atmos. Chem. Phys., 16, 8071, 10.5194\u002Facp-16-8071-2016\nConticini, 2020, Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in northern Italy?, Environ. Pollut., 261, 10.1016\u002Fj.envpol.2020.114465\nde Graaf, 2013, Feasibility study of integral property retrieval for tropospheric aerosol from Raman lidar data using principal component analysis, Appl. Opt., 52, 2173, 10.1364\u002FAO.52.002173\nDubovik, 2002, Variability of absorption and optical properties of key aerosol types observed in worldwide locations, J. Atmos. Sci., 59, 590, 10.1175\u002F1520-0469(2002)059\u003C0590:VOAAOP>2.0.CO;2\nEngelmann, 2016, The automated multiwavelength Raman polarization and water-vapor lidar PollyXT: the neXT generation, Atmos. Measur. Tech., 9, 1767, 10.5194\u002Famt-9-1767-2016\nGarrett, 2013, Ground-based remote sensing of thin clouds in the Arctic, Atmos. Meas. Tech., 6, 1227, 10.5194\u002Famt-6-1227-2013\nGroß, 2011, Characterization of Saharan dust, marine aerosols and mixtures of biomass-burning aerosols and dust by means of multi-wavelength depolarization and Raman lidar measurements during SAMUM 2, Tellus Ser. B Chem. Phys. Meteorol., 63, 706, 10.1111\u002Fj.1600-0889.2011.00556.x\nGroß, 2013, Aerosol classification by airborne high spectral resolution lidar observations, Atmos. Chem. Phys., 13, 2487, 10.5194\u002Facp-13-2487-2013\nGurjar, 2010, Human health risks in megacities due to air pollution, Atmos. Environ., 44, 4606, 10.1016\u002Fj.atmosenv.2010.08.011\nGuzmán, 2013, Eruption of the Eyjafjallajökull volcano in spring 2010: multiwavelength Raman Lidar measurements of Sulphate particles in the lower troposphere, J. Geophys. Res.-Atmos., 118\nHair, 2008, Airborne high spectral resolution Lidar for profiling aerosol optical properties, Appl. Opt., 47, 6734, 10.1364\u002FAO.47.006734\nHintze, 1998, Violin plots: a box plot-density trace synergism, Am. Stat., 52, 181\nImaki, 2005, Ultraviolet high-spectral-resolution Doppler lidar for measuring wind field and aerosol optical properties, Appl. Opt., 44, 6023, 10.1364\u002FAO.44.006023\nJerrett, 2015, Atmospheric science: the death toll from air-pollution sources, Nature, 525, 330, 10.1038\u002F525330a\nJin, 2020, Development of a 355-nm high-spectral-resolution lidar using a scanning Michelson interferometer for aerosol profile measurement, Opt. Express, 28, 23209, 10.1364\u002FOE.390987\nKolgotin, 2016, Improved identification of the solution space of aerosol microphysical properties derived from the inversion of profiles of lidar optical data, part 1: theory, Appl. Opt., 55, 9839, 10.1364\u002FAO.55.009839\nLelieveld, 2015, The contribution of outdoor air pollution sources to premature mortality on a global scale, Nature, 525, 367, 10.1038\u002Fnature15371\nLi, 2019, East Asian study of tropospheric aerosols and their impact on regional clouds, precipitation, and climate (EAST-AIRCPC), J. Geophys. Res.-Atmos., 124, 13026, 10.1029\u002F2019JD030758\nLiu, 2012, System analysis of a tilted field-widened Michelson interferometer for high spectral resolution lidar, Opt. Express, 20, 1406, 10.1364\u002FOE.20.001406\nLiu, 2019, Performance estimation of space-borne high-spectral-resolution lidar for cloud and aerosol optical properties at 532 nm, Opt. Express, 27, A481, 10.1364\u002FOE.27.00A481\nLv, 2018, Retrieval of cloud condensation nuclei number concentration profiles from Lidar extinction and backscatter data, J. Geophys. Res.-Atmos., 123, 6082, 10.1029\u002F2017JD028102\nMao, 2018, Vertically resolved physical and radiative response of ice clouds to aerosols during the Indian summer monsoon season, Remote Sens. Environ., 216, 171, 10.1016\u002Fj.rse.2018.06.027\nMcRoberts, 2007, Estimating areal means and variances of forest attributes using the k-nearest neighbors technique and satellite imagery, Remote Sens. Environ., 111, 466, 10.1016\u002Fj.rse.2007.04.002\nMüller, 1999, Microphysical particle parameters from extinction and backscatter lidar data by inversion with regularization: theory, Appl. Opt., 38, 2346, 10.1364\u002FAO.38.002346\nMüller, 2004, Closure study on optical and microphysical properties of a mixed urban and Arctic haze air mass observed with Raman lidar and Sun photometer, J. Geophys. Res.-Atmos., 109, 10.1029\u002F2003JD004200\nMüller, 2019, Automated, unsupervised inversion of multiwavelength lidar data with TiARA: assessment of retrieval performance of microphysical parameters using simulated data, Appl. Opt., 58, 4981, 10.1364\u002FAO.58.004981\nNicolae, 2013, Characterization of fresh and aged biomass burning events using multiwavelength Raman lidar and mass spectrometry, J. Geophys. Res.-Atmos., 118, 1, 10.1002\u002Fjgrd.50324\nNoh, 2009, Optical and microphysical properties of severe haze and smoke aerosol measured by integrated remote sensing techniques in Gwangju, Korea, Atmos. Environ., 43, 879, 10.1016\u002Fj.atmosenv.2008.10.058\nPérez-Ramírez, 2013, Effects of systematic and random errors on the retrieval of particle microphysical properties from multiwavelength lidar measurements using inversion with regularization, Atmos. Meas. Tech., 6, 3039, 10.5194\u002Famt-6-3039-2013\nPérez-Ramírez, 2019, Retrievals of aerosol single scattering albedo by multiwavelength lidar measurements: evaluations with NASA Langley HSRL-2 during discover-AQ field campaigns, Remote Sens. Environ., 222, 144, 10.1016\u002Fj.rse.2018.12.022\nPérez-Ramírez, 2020, Optimized profile retrievals of aerosol microphysical properties from simulated Spaceborne multiwavelength Lidar, J. Quant. Spectrosc. Radiat. Transf., 246, 10.1016\u002Fj.jqsrt.2020.106932\nSawamura, 2017, HSRL-2 aerosol optical measurements and microphysical retrievals vs. airborne in situ measurements during DISCOVER-AQ 2013: an intercomparison study, Atmos. Chem. Phys., 17, 7229, 10.5194\u002Facp-17-7229-2017\nTesche, 2011, Optical and microphysical properties of smoke over Cape Verde inferred from multiwavelength lidar measurements, Tellus B, 63, 677, 10.1111\u002Fj.1600-0889.2011.00549.x\nTesche, 2019, 3+2+X: what is the most useful depolarization input for retrieving microphysical properties of non-spherical particles from lidar measurements using the spheroid model of Dubovik et al. (2006)?, Atmos. Meas. Tech., 12, 4421, 10.5194\u002Famt-12-4421-2019\nTorres, 2017, Advanced characterisation of aerosol size properties from measurements of spectral optical depth using the GRASP algorithm, Atmos. Measur. Tech., 10, 3743, 10.5194\u002Famt-10-3743-2017\nUlrike, 2006, vol. 312, 1375\nVeselovskii, 2002, Inversion with regularization for the retrieval of tropospheric aerosol parameters from multiwavelength lidar sounding, Appl. Opt., 41, 3685, 10.1364\u002FAO.41.003685\nVeselovskii, 2005, Information content of multiwavelength lidar data with respect to microphysical particle properties derived from eigenvalue analysis, Appl. Opt., 44, 5292, 10.1364\u002FAO.44.005292\nVeselovskii, 2012, Linear estimation of particle bulk parameters from multi-wavelength lidar measurements, Atmos. Meas. Tech., 5, 1135, 10.5194\u002Famt-5-1135-2012\nVeselovskii, 2013, Retrieval of spatio-temporal distributions of particle parameters from multiwavelength lidar measurements using the linear estimation technique and comparison with AERONET, Atmos. Meas. Tech., 6, 2671, 10.5194\u002Famt-6-2671-2013\nVeselovskii, 2015, Characterization of forest fire smoke event near Washington, DC in summer 2013 with multi-wavelength lidar, Atmos. Chem. Phys., 15, 1647, 10.5194\u002Facp-15-1647-2015\nVeselovskii, 2018, Characterization of smoke and dust episode over West Africa: comparison of MERRA-2 modeling with multiwavelength Mie–Raman lidar observations, Atmos. Meas. Tech., 11, 949, 10.5194\u002Famt-11-949-2018\nWandinger, 2002, Optical and microphysical characterization of biomass-burning and industrial-pollution aerosols from multiwavelength Lidar and aircraft measurements, J. Geophys. Res.-Atmos., 107, 107\nWang, 2021, Development of ZJU high-spectral-resolution Lidar for aerosol and cloud: feature detection and classification, J. Quant. Spectrosc. Radiat. Transf., 261, 10.1016\u002Fj.jqsrt.2021.107513\nWang, 2022, Dual-field-of-view high-spectral-resolution lidar: simultaneous profiling of aerosol and water cloud to study aerosol–cloud interaction, Proc. Natl. Acad. Sci., 119\nWhiteman, 2017, Retrievals of aerosol microphysics from simulations of spaceborne multiwavelength lidar measurements, J. Quant. Spectrosc. Radiat. Transf., 205, 27, 10.1016\u002Fj.jqsrt.2017.09.009\nWu, 2020, Air pollution and COVID-19 mortality in the United States: strengths and limitations of an ecological regression analysis, Sci. Adv., 6, eabd4049, 10.1126\u002Fsciadv.abd4049\nYan, 2021, Understanding global changes in fine-mode aerosols during 2008–2017 using statistical methods and deep learning approach, Environ. Int., 149, 10.1016\u002Fj.envint.2021.106392\nZhang, 2017, Design of iodine absorption cell for high-spectral-resolution lidar, Opt. Express, 25, 15913, 10.1364\u002FOE.25.015913\nZhang, 2020, First observation of tropospheric nitrogen dioxide from the environmental trace gases monitoring instrument onboard the GaoFen-5 satellite, Light Sci. Appl., 9, 66, 10.1038\u002Fs41377-020-0306-z\nZhao, 2018, Growth rates of fine aerosol particles at a site near Beijing in June 2013, Adv. Atmos. Sci., 35, 209, 10.1007\u002Fs00376-017-7069-3\nZiemba, 2013, Airborne observations of aerosol extinction by in situ and remote-sensing techniques: evaluation of particle hygroscopicity, Geophys. Res. 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1995, Oceanic primary production. 2. Estimation at global scale from satellite (Coastal Zone Color Scanner) chlorophyll, Global Biogeochem. Cy., 10, 57, 10.1029\u002F95GB02832\nBarnes, 1998, Prelaunch characteristics of the Moderate Resolution Imaging Spectroradiometer (MODIS) on EOS-AM1, IEEE T. Geosci. Remote, 36, 1088, 10.1109\u002F36.700993\nBidigare, 1990, In-vivo absorption properties of algal pigments, 290\nBinding, 2005, Estimating suspended sediment concentrations from ocean colour measurements in moderately turbid waters; the impact of variable particle scattering properties, Remote Sens. Environ., 94, 373, 10.1016\u002Fj.rse.2004.11.002\nBoczar, 1989, Organization and comparison of chlorophyll-protein complexes from two fucoxanthin-containing algae: Nitzschia closterium (Bacillariophyceae) and Isochrysis galbana (Prymnesiophyceae), Plant Cell Physiol, 30, 1047\nBoyer, 2009, Phytoplankton bloom status: chlorophyll a biomass as an indicator of water quality condition in the southern estuaries of Florida, USA, Ecol. Indic., 9, S56, 10.1016\u002Fj.ecolind.2008.11.013\nCannizzaro, 2006, Estimating chlorophyll a concentrations from remote-sensing reflectance in optically shallow waters, Remote Sens. Environ., 101, 13, 10.1016\u002Fj.rse.2005.12.002\nChen, 2007, Remotely sensed assessment of water quality levels in the Pearl River Estuary, China, Mar. Pollut. Bull., 54, 1267, 10.1016\u002Fj.marpolbul.2007.03.010\nConcha, 2016, Retrieval of color producing agents in Case 2 waters using Landsat 8, Remote Sens. Environ., 185, 95, 10.1016\u002Fj.rse.2016.03.018\nDall'Olmo, 2003, Towards a unified approach for remote estimation of chlorophyll-a in both terrestrial vegetation and turbid productive waters, Geophys. Res. Lett., 30, 1938, 10.1029\u002F2003GL018065\nDarecki, 2003, Optical characteristics of two contrasting Case 2 waters and their influence on remote sensing algorithms, Cont. Shelf Res., 23, 237, 10.1016\u002FS0278-4343(02)00222-4\nDierssen, 2006, Red and black tides: quantitative analysis of water-leaving radiance and perceived color for phytoplankton, colored dissolved organic matter, and suspended sediments, Limnol. Oceanngy., 51, 2646, 10.4319\u002Flo.2006.51.6.2646\nDuan, 2009, Two-decade reconstruction of algal blooms in China’s Lake Taihu, Environ. Sci. Technol., 43, 3522, 10.1021\u002Fes8031852\nFeng, 2005, Modeling spectral reflectance of optically complex waters using bio-optical measurements from Tokyo Bay, Remote Sens. Environ., 99, 232\nFeng, 2019, Monitoring and understanding the water transparency changes of fifty large lakes on the Yangtze Plain based on long-term MODIS observations, Remote Sens. Environ., 221, 675, 10.1016\u002Fj.rse.2018.12.007\nFerrari, 1999, A method using chemical oxidation to remove light absorption by phytoplankton pigments, J. Phycol., 35, 1090, 10.1046\u002Fj.1529-8817.1999.3551090.x\nFukushima, 1996, Dissolved organic carbon in a eutrophic lake; dynamics, biodegradability and origin, Aquat. Sci., 58, 139, 10.1007\u002FBF00877112\nGersberg, 1986, Role of aquatic plants in wastewater treatment by artificial wetlands, Water Res., 20, 363, 10.1016\u002F0043-1354(86)90085-0\nGilerson, 2010, Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands, Opt. Express, 18, 24109, 10.1364\u002FOE.18.024109\nGitelson, 1993, Quantitative remote sensing methods for real-time monitoring of inland waters quality, Int. J. Remote Sens., 14, 1269, 10.1080\u002F01431169308953956\nGitelson, 2008, A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: validation, Remote Sens. Environ., 112, 3582, 10.1016\u002Fj.rse.2008.04.015\nGohin, 2002, A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters, Int. J. Remote Sens., 23, 1639, 10.1080\u002F01431160110071879\nGons, 1999, Optical teledetection of chlorophyll a in turbid inland waters, Environ. Sci. Technol., 33, 1127, 10.1021\u002Fes9809657\nGons, 2002, A chlorophyll-retrieval algorithm for satellite imagery (Medium Resolution Imaging Spectrometer) of inland and coastal waters, J. Plankton Res., 24, 947, 10.1093\u002Fplankt\u002F24.9.947\nGordon, 1983, Remote assessment of ocean color for interpretation of satellite visible imagery: a review, 113, 1983, 10.1029\u002FLN004\nGower, 1999, Interpretation of the 685 nm peak in water-leaving radiance spectra in terms of fluorescence, absorption and scattering, and its observation by MERIS, Int. J. Remote Sens., 20, 1771, 10.1080\u002F014311699212470\nGreen, 1994, Optical absorption and fluorescence properties of chromophoric dissolved organic matter in natural waters, Limnol. Oceanogr., 39, 1903, 10.4319\u002Flo.1994.39.8.1903\nGurlin, 2011, Remote estimation of chl-a concentration in turbid productive waters-return to a simple two-band NIR-red model?, Remote Sens. Environ., 115, 3479, 10.1016\u002Fj.rse.2011.08.011\nHieronymi, 2017, The OLCI Neural Network Swarm (ONNS): a bio-geo-optical algorithm for open ocean and coastal waters, Front. Mar. Sci., 4, 140, 10.3389\u002Ffmars.2017.00140\nHo, 2017, Using Landsat to extend the historical record of lacustrine phytoplankton blooms: a Lake Erie case study, Remote Sens. Environ., 191, 273, 10.1016\u002Fj.rse.2016.12.013\nHu, 2009, A novel ocean color index to detect floating algae in the global oceans, Remote Sens. Environ., 113, 2118, 10.1016\u002Fj.rse.2009.05.012\nHu, 2015, A harmful algal bloom of Karenia brevis in the northeastern Gulf of Mexico as revealed by MODIS and VIIRS: a comparison, Sensors, 15, 2873, 10.3390\u002Fs150202873\nHuang, 2003, The characteristics of nutrients and eutrophication in the Pearl River Estuary, China, Mar. Pollut. Bull., 47, 30, 10.1016\u002FS0025-326X(02)00474-5\nHuang, 2014, Assessment of water constituents in highly turbid productive water by optimization bio-optical retrieval model after optical classification, J. Hydrol., 519, 1572, 10.1016\u002Fj.jhydrol.2014.09.007\nJackson, 2017, An improved optical classification scheme for the Ocean Colour Essential Climate Variable and its applications, Remote Sens. Environ., 203, 152, 10.1016\u002Fj.rse.2017.03.036\nJiang, 2015, Remote sensing of particulate organic carbon dynamics in a eutrophic lake (Taihu Lake, China), Sci. Total Environ., 532, 245, 10.1016\u002Fj.scitotenv.2015.05.120\nJiang, 2019, An absorption-specific approach to examining dynamics of particulate organic carbon from VIIRS observations in inland and coastal waters, Remote Sens. Environ., 224, 29, 10.1016\u002Fj.rse.2019.01.032\nKajiyama, 2018, Algorithms merging for the determination of chlorophyll-a concentration in the Black Sea, IEEE Geosci. Remote S., 16, 677, 10.1109\u002FLGRS.2018.2883539\nKoponen, 2007, A case study of airborne and satellite remote sensing of a spring bloom event in the Gulf of Finland, Cont. Shelf Res., 27, 228, 10.1016\u002Fj.csr.2006.10.006\nLe, 2009, Specific absorption coefficient and the phytoplankton package effect in Lake Taihu, China, Hydrobiologia, 619, 27, 10.1007\u002Fs10750-008-9579-6\nLe, 2009, A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: the case of Taihu Lake, China, Remote Sens. Environ., 113, 1175, 10.1016\u002Fj.rse.2009.02.005\nLe, 2011, Remote estimation of chlorophyll a in optically complex waters based on optical classification, Remote Sens. Environ., 115, 725, 10.1016\u002Fj.rse.2010.10.014\nLe, 2013, Evaluation of chlorophyll-a remote sensing algorithms for an optically complex estuary, Remote Sens. Environ., 129, 75, 10.1016\u002Fj.rse.2012.11.001\nLee, 2002, Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters, Appl. Opt., 41, 5755, 10.1364\u002FAO.41.005755\nLee, 2016, On the modeling of hyperspectral remote-sensing reflectance of high-sediment-load waters in the visible to shortwave-infrared domain, Appl. Opt., 55, 1738, 10.1364\u002FAO.55.001738\nLi, 2010, Assessment of soil erosion and sediment yield in Liao watershed, Jiangxi province, China, using USLE, GIS, and RS, J. Earth Sci-China., 21, 941, 10.1007\u002Fs12583-010-0147-4\nLi, 2013, An inversion model for deriving inherent optical properties of inland waters: establishment, validation and application, Remote Sens. Environ., 135, 150, 10.1016\u002Fj.rse.2013.03.031\nLohrenz, 2003, Phytoplankton spectral absorption as influenced by community size structure and pigment composition, J. Plankton Res., 25, 35, 10.1093\u002Fplankt\u002F25.1.35\nLoiselle, 2009, Optical characterization of chromophoric dissolved organic matter using wavelength distribution of absorption spectral slopes, Limnol. Oceanogr., 54, 590, 10.4319\u002Flo.2009.54.2.0590\nLubac, 2007, Variability and classification of remote sensing reflectance spectra in the eastern English Channel and southern North Sea, Remote Sens. Environ., 110, 45, 10.1016\u002Fj.rse.2007.02.012\nMa, 2011, Approximate bottom contribution to remote sensing reflectance in Taihu Lake, China, J. Great Lakes Res., 37, 18, 10.1016\u002Fj.jglr.2010.12.002\nMatthews, 2012, An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters, Remote Sens. Environ., 124, 637, 10.1016\u002Fj.rse.2012.05.032\nMcClain, 2009, A decade of satellite ocean color observations, Annu. Rev. Mar. Sci., 1, 19, 10.1146\u002Fannurev.marine.010908.163650\nMishra, 2012, Normalized difference chlorophyll index: a novel model for remote estimation of chlorophyll-a concentration in turbid productive waters, Remote Sens. Environ., 117, 394, 10.1016\u002Fj.rse.2011.10.016\nMobley, 1999, Estimation of the remote sensing reflectance from above-surface measurements, Appl. Opt., 38, 7442, 10.1364\u002FAO.38.007442\nMobley, 2013\nMonolisha, 2018, Optical classification of the coastal waters of the Northern Indian Ocean, Front. Mar. Sci., 5, 87, 10.3389\u002Ffmars.2018.00087\nMoore, 2001, A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms, IEEE T. Geosc. Remote, 39, 1764, 10.1109\u002F36.942555\nMoore, 2014, An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters, Remote Sens. Environ., 143, 97, 10.1016\u002Fj.rse.2013.11.021\nMorel, 1977, Analysis of variations in ocean color, Limnol. Oceanogr., 22, 709, 10.4319\u002Flo.1977.22.4.0709\nMoses, 2017, Chapter 3 atmospheric correction for inland waters, 69\nMueller, 2003\nMurphy, 2011, Using VIIRS to provide data continuity with MODIS, 3, 1212\nO’Reilly, 1998, Ocean color chlorophyll algorithms for SeaWiFS, J. Geophys. Res., 103, 24937, 10.1029\u002F98JC02160\nO’Reilly, 2000, Ocean color chlorophyll a algorithms for seawifs, oc2, and oc4: version 4, volume 11, 9\nQi, 2015, VIIRS observations of a Karenia brevis bloom in the northeastern Gulf of Mexico in the absence of a fluorescence band, IEEE Geosci. Remote S., 12, 2213, 10.1109\u002FLGRS.2015.2457773\nRaqueño, 2003\nRaqueño, 2000, Hyperspectral analysis tools for themultiparameter inversion of water quality factors in coastal regions, Imaging Spectrometry VI. SPIE., 4132, 10.1117\u002F12.406601\nRöttgers, 2014, Mass-specific light absorption coefficients of natural aquatic particles in the near-infrared spectral region, Limnol. Oceanogr., 59, 1449, 10.4319\u002Flo.2014.59.5.1449\nRuddick, 2000, Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters, Appl. Opt., 39, 897, 10.1364\u002FAO.39.000897\nSalgado-Hernanz, 2019, Trends in phytoplankton phenology in the Mediterranean Sea based on ocean-colour remote sensing, Remote Sens. Environ., 221, 50, 10.1016\u002Fj.rse.2018.10.036\nSathyendranath, 2004, Discrimination of diatoms from other phytoplankton using ocean colour data, Mar. Ecol-Prog. Ser., 272, 59, 10.3354\u002Fmeps272059\nSaulquin, 2019, Interpolated fields of satellite-derived multi-algorithm chlorophyll-a estimates at global and European scales in the frame of the European Copernicus-Marine Environment Monitoring Service, J. Oper. Res. Oceanogr., 12, 47\nSchalles, 1998, Estimation of chlorophyll a from time series measurements of high spectral resolution reflectance in an eutrophic lake, J. Appl. Phycol., 34, 383, 10.1046\u002Fj.1529-8817.1998.340383.x\nShen, 2010, Medium resolution imaging spectrometer (MERIS) estimation of chlorophyll-a concentration in the turbid sediment-laden waters of the Changjiang (Yangtze) Estuary, Int. J. Remote Sens., 31, 4635, 10.1080\u002F01431161.2010.485216\nShi, 2018, Deriving total suspended matter concentration from the near-infrared-based inherent optical properties over turbid waters: a case study in Lake Taihu, Remote Sens., 10, 333, 10.3390\u002Frs10020333\nSmith, 2016, 6\nSmith, 2018, An optimized chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters, Remote Sens. Environ., 215, 217, 10.1016\u002Fj.rse.2018.06.002\nSpyrakos, 2018, Optical types of inland and coastal waters, Limnol. Oceanogr., 63, 846, 10.1002\u002Flno.10674\nSteinmetz, 2011, Atmospheric correction in presence of sun glint: application to MERIS, Opt. Express, 19, 9783, 10.1364\u002FOE.19.009783\nStrickland, 1972\nSun, 2012, Specific inherent optical quantities of complex turbid inland waters, from the perspective of water classification, Photoch. Photobio. Sci., 11, 1299, 10.1039\u002Fc2pp25061f\nTilstone, 2011, An assessment of chlorophyll-a algorithms available for SeaWiFS in coastal and open areas of the Bay of Bengal and Arabian Sea, Remote Sens. Environ., 115, 2277, 10.1016\u002Fj.rse.2011.04.028\nVan Der Woerd, 2008, HYDROPT: a fast and flexible method to retrieve chlorophyll-a from multispectral satellite observations of optically complex coastal waters, Remote Sens. Environ., 112, 1795, 10.1016\u002Fj.rse.2007.09.001\nWang, 2007, The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing, Opt. Express, 15, 15722, 10.1364\u002FOE.15.015722\nWang, 2013, Impacts of VIIRS SDR performance on ocean color products, J. Geophys. Res.-Atmos., 118, 10347, 10.1002\u002Fjgrd.50793\nWang, 2013, Remote sensing of water optical property for China’s inland lake Taihu using the SWIR atmospheric correction with 1640 and 2130 nm bands, IEEE J-STARS, 6, 2505\nWang, 2016, NIR- and SWIR-based on-orbit vicarious calibrations for satellite ocean color sensors, Opt. Express, 24, 20437, 10.1364\u002FOE.24.020437\nWu, 2013, An approach for developing Landsat-5 TM-based retrieval models of suspended particulate matter concentration with the assistance of MODIS, ISPRS J. Photogramm., 85, 84, 10.1016\u002Fj.isprsjprs.2013.08.009\nWu, 2015, Statistical model development and estimation of suspended particulate matter concentrations with Landsat 8 OLI images of Dongting Lake, China, Int. J. Remote Sens., 36, 343, 10.1080\u002F01431161.2014.995273\nXie, 2005, Organ distribution and bioaccumulation of microcystins in freshwater fish at different trophic levels from the eutrophic Lake Chaohu, China, Environ. Toxicol., 20, 293, 10.1002\u002Ftox.20120\nXue, 2017, An approach to correct the effects of phytoplankton vertical nonuniform distribution on remote sensing reflectance of cyanobacterial bloom waters, Limnol. Oceanogr-Meth., 15, 302, 10.1002\u002Flom3.10158\nXue, 2019, Optical classification of the remote sensing reflectance and its application in deriving the specific phytoplankton absorption in optically complex lakes, Remote Sens., 11, 184, 10.3390\u002Frs11020184\nYang, 2010, An enhanced three-band index for estimating chlorophyll-a in turbid Case-II waters: case studies of Lake Kasumigaura, Japan and Lake Dianchi, China, IEEE Geosci. Remote S., 7, 655, 10.1109\u002FLGRS.2010.2044364\nYu, 2010, Long-term water temperature variations in Daya Bay, China using satellite and in situ observations, Terr. Atmos. Ocean. 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