Optical water types found in Brazilian waters

Alves TP, Mafra LL (2018) Diel variations in cell abundance and trophic transfer of diarrheic toxins during a massive dinophysis bloom in Southern Brazil. Toxins (Basel). https://doi.org/10.3390/toxins10060232 Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM Symposium on discrete algorithms, SODA. New Orleans, p 11 Augusto-Silva PB, Ogashawara I, Barbosa CCF et al (2014) Analysis of MERIS reflectance algorithms for estimating chlorophyll-a concentration in a Brazilian reservoir. Remote Sens 6:11689–11707. https://doi.org/10.3390/rs61211689 Barbosa C, Ferreira R, Araujo C, Novo E (2014) Bio-optical characterization of two Brazilian hydroelectric reservoirs as support to understand the carbon budget in hydroelectric reservoirs. Int Geosci Remote Sens Symp. https://doi.org/10.1109/IGARSS.2014.6946570 Barbosa C, Lee Z, De Carvalho LS, Novo E (2016) Effect of stratified water column on chlorophyll estimate by remote sensing algorithms in a highly eutrophic hydroelectric reservoir. Ocean Opt 2016:9 Barbosa CCF (2007) Sensoriamento Remoto da Dinâmica da Circulação da Água Do Sistema Planície de Curuai/Rio Amazonas. In: Instituto Nacional de Pesquisas Espaciais (INPE) Cairo C, Barbosa C, Lobo F et al (2020) Hybrid chlorophyll-a algorithm for assessing trophic states of a tropical Brazilian reservoir based on MSI/Sentinel-2 data. Remote Sens 12:40 Cairo CT, Barbosa CCF, de Moraes Novo EML, do Carmo Calijuri M (2016) Spatial and seasonal variation in diffuse attenuation coefficients of downward irradiance at Ibitinga Reservoir, São Paulo, Brazil. Hydrobiologia 784:265–282. https://doi.org/10.1007/s10750-016-2883-7 Curtarelli VP, da Silva EFF, Lobo F de L et al (2019) Water transparency in a Brazilian reservoir. In: Anais do XIX Simpósio Brasileiro de Sensoriamento Remoto da Silva EFF, Luz LFG da, Barbosa CCF, Noernberg MA (2019) Turbidity distribution in a subtropical estuary: the estuarine complex of Paranaguá. In: Anais do XIX Simpósio Brasileiro de Sensoriamento Remoto, pp 2354–2357 de Toledo CE, de Araújo JC, de Almeida CL (2014) The use of remote-sensing techniques to monitor dense reservoir networks in the Brazilian semiarid region. Int J Remote Sens 35:3683–3699. https://doi.org/10.1080/01431161.2014.915593 Eleveld MA, Ruescas AB, Hommersom A et al (2017) An optical classification tool for global lake waters. Remote Sens. https://doi.org/10.3390/rs9050420 Ferreira RMP (2014) Caracterização da Ótica e do Carbono Orgânico Dissolvido no Reservatório de Três Marias/Mg. In: INPE Fraley C (1998) How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput J 41:578–588. https://doi.org/10.1093/comjnl/41.8.578 Galvao LS, Pereira Filho W, Abdon MM et al (2003) Spectral reflectance characterization of shallow lakes from the Brazilian Pantanal wetlands with field and airborne hyperspectral data. Int J Remote Sens 24:4093–4112. https://doi.org/10.1080/0143116031000070382 Holland A, Stauber J, Wood CM et al (2018) Dissolved organic matter signatures vary between naturally acidic, circumneutral and groundwater-fed freshwaters in Australia. Water Res 137:184–192. https://doi.org/10.1016/j.watres.2018.02.043 Hommersom A, Kratzer S, Laanen M et al (2012) Intercomparison in the field between the new WISP-3 and other radiometers (TriOS Ramses, ASD FieldSpec, and TACCS). J Appl Remote Sens 6:063615. https://doi.org/10.1117/1.jrs.6.063615 Johnsen G, Sakshaug E (2007) Biooptical characteristics of PSII and PSI in 33 species (13 pigment groups) of marine phytoplankton, and the relevance for pulseamplitude-modulated and fast-repetition-rate fluorometry. J Phycol 43:1236–1251. https://doi.org/10.1111/j.1529-8817.2007.00422.x Jorge DSF, Barbosa CCF, de Carvalho LAS et al (2017) SNR (signal-to-noise ratio) impact on water constituent retrieval from simulated images of optically complex Amazon lakes. Remote Sens 9:1–18. https://doi.org/10.3390/rs9070644 Kirk JTO (1992) Monte Carlo modeling of the performance of a reflective tube absorption meter. Appl Opt 31:6463. https://doi.org/10.1364/ao.31.006463 Kirk JTO (2011) Light and photosynthesis in aquatic ecosystems, 3rd edn. Cambridge Uiversity Press, Cambridge Le C, Li Y, Zha Y et al (2011) Remote estimation of chlorophyll a in optically complex waters based on optical classification. Remote Sens Environ 115:725–737. https://doi.org/10.1016/j.rse.2010.10.014 Lobo FL, Costa MPF, Novo EMLM (2015) Time-series analysis of Landsat-MSS/TM/OLI images over Amazonian waters impacted by gold mining activities. Remote Sens Environ 157:170–184. https://doi.org/10.1016/j.rse.2014.04.030 Londe LR de (2008) Comportamento Espectral do Fitoplâncton de um Reservatório Brasileiro Eutrofizado—Ibitinga (SP). In: INPE Maciel D, Novo E, de Carvalho LS et al (2019) Retrieving total and inorganic suspended sediments in Amazon floodplain lakes: a multisensor approach. Remote Sens 11:1744. https://doi.org/10.3390/rs11151744 Maciel DA (2019) Avaliação de modelos empíricos e semi-analíticos para a quantificação do total de sólidos inorgânicos em lagos da planície do baixo Amazonas. In: Instituto Nacional de Pesquisas Espaciais (INPE) MacQUEEN (1967) Some methods for classification and analysis of multivariate observations. In: Fifth Berkeley Symposium, pp 281–297 Matthews MW (2017) Bio-optical Modeling of Phytoplankton Chlorophyll-a. In: Mishra DR, Ogashawara I, Gitelson AA (eds) Bio-optical modeling and remote sensing of inland waters. Candice Janco, Amsterdam, pp 157–188 Miller RL, Belz M, Del Castillo C, Trzaska R (2002) Determining CDOM absorption spectra in diverse coastal environments using a multiple pathlengh, liquid core waveguide system. Cont Shelf Res 22:1301–1310 Mobley CD (1999) Estimation of the remote-sensing reflectance from above-surface measurements. Appl Opt 38:7442–7455. https://doi.org/10.1364/AO.38.007442 Moore TS, Dowell MD, Bradt S, Ruiz Verdu A (2014) An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters. Remote Sens Environ 143:97–111. https://doi.org/10.1016/j.rse.2013.11.021 Morel A, Bricaud A (1981) Theoretical results concerning light absorption in a discrete medium, and application to specific absorption of phytoplankton. Deep Res 28A:1375–1393 Noernberg MA, Luz L, da Silva EFF, Mafra LL (2017) Remote sensing reflectance variability along an intense HAB event in Southern Brazil. In: 2017 international ocean colour science meeting (IOCS-2017), Lisbon Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830. https://doi.org/10.1145/2786984.2786995 Ramsay AJO, Wickham H, Graves S (2018) fda: Functional data analysis. Springer, Berlin Ramsay JO (2006) Functional data analysis. Wiley Online Library, Hoboken Rodrigues T, Ivánová I, Alcântara E et al (2015) Analysis of data quality element’s applicability for radiometric measurements in remote sensing of water: a case study in Nova Avanhandava reservoir, São Paulo, Brazil. Rev Bras Cartogr 2015:1327–1339 Roesler CS, Boss ES (2008) In situ measurement of the inherent optial properties (IOPs) and potential for harmful algal bloom (HAB) detection and coastal ecosystem observations. In: Real-time coastal observing systems for marine ecosystem dynamics and harmful algal blooms: theory, instrumentation and modelling, pp 153–206 Rotta LH, Mishra DR, Alcântara E et al (2019) Kd(PAR) and a depth based model to estimate the height of submerged aquatic vegetation in an oligotrophic reservoir: a case study at Nova Avanhandava. Remote Sens 11:1–21. https://doi.org/10.3390/rs11030317 Rotta LHS, Mishra DR, Alcântara EH, Imai NN (2016) Analyzing the status of submerged aquatic vegetation using novel optical parameters. Int J Remote Sens 37:3786–3810. https://doi.org/10.1080/01431161.2016.1204027 Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65 Shi K, Li Y, Li L et al (2013) Remote chlorophyll-a estimates for inland waters based on a cluster-based classification. Sci Total Environ 444:1–15. https://doi.org/10.1016/j.scitotenv.2012.11.058 Sioli H (1968) Hydrochemistry and geology in the Brazilian Amazon region. Amaz Limnol Oecologia Reg Syst Fluminis Amaz 1:267–277 Sioli H (1951) Alguns resultados e problemas da limnologia amazônica. Bol Técnico 1951:3–44 Sobrinho BF, Luz L, Fernandes LF, Mafra L (2018) Evidence of Noctiluca scintillans grazing during a bloom of toxic Dinophysis acuminata complex, south Brazil. In: The 18th international conference on harmful algae. Nantes Spyrakos E, O’Donnell R, Hunter PD et al (2018) Optical types of inland and coastal waters. Limnol Oceanogr 63:846–870. https://doi.org/10.1002/lno.10674 Sun D, Li Y, Wang Q et al (2010) Partitioning particulate scattering and absorption into contributions of phytoplankton and non-algal particles in winter in Lake Taihu (China). Hydrobiologia 644:337–349. https://doi.org/10.1007/s10750-010-0198-7 Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Ser B Stat Methodol 63:411–423 Uudeberg K, Ansko I, Getter P, Ansper A (2019) Using optical water types to monitor changes in optically complex inland and coastal waters. Remote Sens. https://doi.org/10.3390/rs11192297 Vantrepotte V, Loisel H, Dessailly D, Mériaux X (2012) Optical classification of contrasted coastal waters. Remote Sens Environ 123:306–323. https://doi.org/10.1016/j.rse.2012.03.004 Wet Labs (2009) Spectral absorption and attenuation meter. In: User’s guide Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bull 1:80–83. https://doi.org/10.2307/3001946 Wünsch UJ, Stedmon CA, Tranvik LJ, Guillemette F (2018) Unraveling the size-dependent optical properties of dissolved organic matter. Limnol Oceanogr 63:588–601. https://doi.org/10.1002/lno.10651