QUALIDADE DA ÁGUA E DINÂMICA TEMPORAL DA
BIOMASSA FITOPLANCTÔNICA EM AÇUDES DO SEMIÁRIDO:
UMA ABORDAGEM ÓTICA
WATER QUALITY AND TEMPORAL DYNAMICS OF THE PHYTOPLANKTON BIOMASS IN MAN-MADE LAKES OF THE BRAZILIAN SEMIARID REGION: AN OPTICAL APPROACH
Universidade Federal do Rio Grande do Norte
Programa de Pós-Graduação em EcologiaDhalton Luiz Tosetto Ventura
Qualidade da água e dinâmica temporal da biomassa fitoplanctônica
em açudes do Semiárido: uma abordagem ótica
Tese apresentada ao Programa de Pós-Graduação em Ecologia da Universidade Federal do Rio Grande do Norte como requisito para a obtenção do título de Doutor em Ecologia.
Orientador: Prof. Dr. José Luiz de Attayde
Natal – RN
2018
Universidade Federal do Rio Grande do Norte - UFRN Sistema de Bibliotecas - SISBI
Catalogação de Publicação na Fonte. UFRN - Biblioteca Setorial Prof. Leopoldo Nelson - -Centro de Biociências - CB
Ventura, Dhalton Luiz Tosetto.
Qualidade da água e dinâmica temporal da biomassa
fitoplanctônica em açudes do Semiárido: uma abordagem ótica / Dhalton Luiz Tosetto Ventura. - Natal, 2019.
120 f.: il.
Tese (Doutorado) - Universidade Federal do Rio Grande do Norte. Centro de Biociências. Programa de Pós-Graduação em Ecologia.
Orientador: Prof. Dr. José Luiz de Attayde.
1. Qualidade da água - Tese. 2. Fitoplâncton - Tese. 3.
Biomassa - Tese. 4. Regime hidráulico - Tese. 5. Monitoramento - Tese. 6. Sensoriamento remoto - Tese. I. Attayde, José Luiz de. II. Universidade Federal do Rio Grande do Norte. III. Título. RN/UF/BSE-CB CDU 556
Universidade Federal do Rio Grande do Norte
Programa de Pós-Graduação em EcologiaDhalton Luiz Tosetto Ventura
Qualidade da água e dinâmica temporal da biomassa fitoplanctônica
em açudes do Semiárido: uma abordagem ótica
BANCA EXAMINADORA
____________________________________ Dr. Venerando Eustaquio Amaro, UFRN
(Examinador Interno)
____________________________________ Dra. Renata de Fátima Panosso, UFRN
(Examinadora Interna)
____________________________________ Dr. Thiago Sanna Freire Silva, UNESP
(Examinador Externo)
____________________________________ Dr. Jean-Michel Martinez, IRD
(Examinador Externo)
____________________________________ Dr. José Luiz de Attayde, UFRN
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AGRADECIMENTOS
Se você já fez doutorado, imagine, a cinco meses do término, descobrir que, na verdade, você não tem cinco, mas somente mais três meses para entregar sua tese. Por uma confusão burocrática, foi o que aconteceu comigo. Eu, que já estava preocupado pelo trabalho estar atrasado, entrei num verdadeiro turbilhão. Lá se foram 86 dias ininterruptos trabalhando freneticamente por 14, 15 horas, comendo mal, dormindo mal, tendo crises de nervoso e dedicando pouca atenção ao meu filho e à minha esposa. Mas eis que estamos chegando ao final. A tese não ficou como eu gostaria, queria ter feito mais e melhor, mas a hora é de extrair o que houve de positivo e de agradecer.
Começo pelo meu orientador, o Coca. Foi ele quem me convidou e me incentivou a entrar nessa “coisa de maluco” que é um doutorado. O Coca, que eu conheci antes de sequer fazer mestrado, numa reunião de trabalho, é dessas pessoas especiais. Alto astral, paciente e motivador. Também agradeço ao Jean-Michel, outro “culpado” por eu ter embarcado nessa. Na primeira vez em que ele foi enviado pelo IRD para passar um período na ANA, onde trabalho, fiz uma espécie de estágio com ele, onde aprendi muita coisa sobre sensoriamento remoto da qualidade da água. E foi aí que tudo começou.
Preciso, claro, agradecer aos meus pais, que estabeleceram um alicerce sólido para todas as minhas empreitadas, incluindo este doutorado. Ao meu filho, nascido durante o doutorado, agradeço por sempre me iluminar com seus sorrisos e me confortar com seus abraços. Nós somos muito apegados e foi uma barra ter de negar a ele a atenção que me pedia. Meu pai, com quem ele adora brincar, ajudou muito, ficando pelo menos algumas horas com ele quase todos os dias. Isso fez muita diferença nessa reta final, durante a qual também precisei muito do apoio e compreensão da minha esposa. Mais que agradecer, preciso dividir com ela esta conquista. Aliás, lá se vão 16 anos juntos... O tempo voa. Estes últimos meses que o digam!
E teve muita gente que participou das coletas e análises de dados. Raúl, Rosemberg, Diogo, Luciane, Nilva e vários servidores da Cogerh, Funceme, UFRN e ANA. É muita gente, fica até difícil listar. Mas uma pessoa não pode deixar de ser citada: o Edson. Ele foi nosso fiel escudeiro nas campanhas de campo, sempre bem-disposto, dirigindo, pilotando o barco e ajudando na coleta e no que mais precisasse.
Quero agradecer também a vários professores que fizeram críticas, sugestões ou deram dicas importantes, como Renata, Vanessa, Dadão e Carlos. E digo obrigado, antecipadamente, aos membros da banca examinadora, de quem certamente receberei críticas valiosas. Por fim, agradeço aos meus familiares e amigos queridos, que alegram a vida e sempre estão prontos para ajudar.
ii SUMÁRIO
Qualidade da água e dinâmica temporal da biomassa fitoplanctônica em açudes do
Semiárido: uma abordagem ótica ... 1
RESUMO ... 1
ABSTRACT ... 2
INTRODUÇÃO GERAL ... 3
ESTRUTURA DA TESE ... 5
CAPÍTULO 1 – Water quality monitoring of lakes through remote sensing: background and applications ... 7 1 Abstract ... 7 2 Introduction ... 7 3 Background ... 13 4 Suitable sensors ... 17 4.1 Landsat sensors ... 17 4.2 MSI ... 18 4.3 MODIS ... 18 4.4 VIIRS ... 19
4.5 MERIS and OLCI... 19
5 Selected algorithms ... 20
5.1 Secchi Disk Depth (SDD) ... 21
5.1.1 Blue-red combinations ... 21
5.1.2 Red band ... 23
5.2 Diffuse attenuation coefficients Kd(490) and Kd(PAR) ... 23
5.2.1 Blue-red difference ... 24
5.2.2 Dual band ratio ... 24
5.2.3 NIR band ... 25
5.3 Total surface suspended solids (SSS)... 25
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5.3.2 Red band ... 27
5.3.3 NIR band ... 28
5.4 Chlorophyll a concentration (chla) ... 28
5.4.1 Three and two NIR-red bands ... 28
5.4.2 MPH ... 30 5.4.3 FLH ... 32 5.4.4 BNDBI ... 33 5.4.5 Multi-variable regression ... 35 5.5 CDOM ... 35 5.5.1 Green-red ratio ... 36 5.5.2 Green-NIR ratio ... 37 6 Discussion ... 37 7 Conclusions ... 39
CAPÍTULO 2 – Optical properties and water quality of Brazilian semiarid lakes ... 40
1 Abstract ... 40
2 Introduction ... 40
3 Material and Methods ... 44
3.1 Study sites ... 44
3.2 Data collection and analyses ... 45
3.3 Predictive models ... 48 4 Results ... 49 4.1 Water quality ... 49 4.2 Phytoplankton composition ... 52 4.3 Optical properties ... 53 4.3.1 Reflectance ... 53
4.3.2 Diffuse attenuation and euphotic zone ... 55
iv 4.3.4 CDOM absorption ... 57 4.3.5 Pigment absorption ... 58 4.3.6 Scattering ... 60 4.4 Predictive models ... 60 4.4.1 Kd(PAR) and zeu ... 60 4.4.2 SSS ... 61 4.4.3 ISS ... 62 4.4.4 CDOM ... 63 4.4.5 Chla ... 64 4.5 Synthetic data ... 66 5 Discussion ... 67 6 Conclusions ... 74
CAPÍTULO 3 – MODIS remote sensing land surface reflectance data for assessment of long-term series of daily chlorophyll-a concentration in Brazilian semiarid lakes ... 75
1 Abstract ... 75
2 Introduction ... 75
3 Material and methods ... 78
3.1 Study sites ... 78 3.2 Field data ... 80 3.3 MODIS data ... 82 3.4 Modelling ... 83 3.5 Time series ... 84 4 Results ... 85 5 Discussion ... 88 6 Conclusions ... 92 CONCLUSÃO GERAL ... 93 REFERÊNCIAS BIBLIOGRÁFICAS ... 94
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Qualidade da água e dinâmica temporal da biomassa fitoplanctônica
em açudes do Semiárido: uma abordagem ótica
RESUMO
Ao redor do mundo, cientistas, gestores e autoridades têm enfrentado vários problemas ecológicos e econômicos gerados pela eutrofização artificial de lagos, cujo atributo mais visível é uma excessiva biomassa fitoplanctônica, comumente quantificada pela concentração de clorofila a (chla). Enquanto nutrientes têm sabidamente um papel decisivo na dinâmica temporal da biomassa fitoplanctônica, o regime hidráulico de um lago pode exercer um controle externo sobre tal dinâmica. Neste estudo, foi avaliada a influência do regime hidráulico na dinâmica temporal da biomassa fitoplanctônica em açudes do Semiárido. Os açudes são uma fonte crucial de recursos hídricos para consumo humano e irrigação nessa região, mas são frequentemente afetados pela eutrofização e carecem de um monitoramento limnológico adequado. Para superar essa deficiência de dados, avaliaram-se as relações entre a qualidade de água e as propriedades óticas em 13 lagos da região de estudo, validaram-se algoritmos para estimativa da chla a partir de imagens do sensor orbital MODIS e geraram-se séries de 15 anos de dados diários de chla para os três maiores açudes estudados: Orós (OROS), Castanhão (CAST) e Eng.º Armando Ribeiro Gonçalves (EARG). Como os açudes do Semiárido estão sujeitos a um marcante regime hidráulico, com clara separação entre períodos secos e chuvosos, hitpotetizou-se que isso se refletiria na variação temporal da chla. A comparação entre a série de chla produzida e a série histórica de volume dos açudes deu suporte a essa hipótese. Os valores de chla caíram rapidamente com a renovação da água promovida por chuvas intensas e permaneceram altos durante os períodos secos. As intensas chuvas de 2004 reduziram bruscamente a biomassa fitoplanctônica em EARG e OROS e, para este último, os efeitos até mesmo se estenderam aos anos subsequentes. Nossos resultados encorajam a exploração do arquivo de imagens MODIS para estudos sobre a dinâmica temporal do fitoplâncton em lagos naturais e artificiais, tanto em escala sazonal como interanual.
Palavras-chave: qualidade da água; fitoplâncton; biomassa; regime hidráulico; monitoramento; sensoriamento remoto; lago; reservatório; cianobactéria; Semiárido.
2 Ph.D. thesis
Federal University of Rio Grande do Norte, Natal, Brazil
Water quality and temporal dynamics of the phytoplankton biomass in
man-made lakes of the Brazilian semiarid region: an optical approach
Dhalton Luiz Tosetto Ventura
ABSTRACT
All over the world, scientists, managers and policy-makers have been challenged by several ecological and economic issues caused by the artificial eutrophication of lakes, whose most visible effect is the excessive phytoplankton biomass, commonly represented by the chlorophyll a concentration (chla). While nutrients play a primary role in the temporal dynamics of the phytoplankton biomass, the lakes’ hydraulic regime can exert an external control over such dynamics. In this study, we assessed the influence of the hydraulic regime on the temporal dynamics of the phytoplankton biomass in man-made lakes of the Brazilian semiarid region. Lakes are a crucial source of water resources for human consumption and irrigation in that region, but they are frequently affected by eutrophication and lack an adequate limnological monitoring. To overcome this data deficiency, we assessed the relationships among water quality and optical properties in 13 lakes of the study region, validated an algorithm for estimating chla from images of the MODIS orbital sensor, and generated a 15-year time series for the three largest study lakes: Orós (OROS), Castanhão (CAST) and Eng. Armando Ribeiro Gonçalves (EARG). Because lakes in the Brazilian semiarid region are subject to a seasonally marked hydraulic regime, we hypothesized that it would be reflected on the temporal variation of chla. The comparison between the time series of chla and lakes’ volume supported such hypothesis. The concentrations steeply dropped with intense rainfall-driven water renewal and kept high during the dry periods. The intense rainfall of 2004 abruptly reduced the phytoplankton biomass in EARG and OROS lakes and, for the latter, its effects even extended to the subsequent years. Our results encourage the exploration of the MODIS archived imagery for further studying the temporal dynamics of the phytoplankton in natural and man-made lakes, at both seasonal and interannual scales. Keywords: water quality; phytoplankton; biomass; hydraulic regime; monitoring; remote sensing; lake; reservoir; cyanobacteria; semiarid; Brazil.
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INTRODUÇÃO GERAL
Globalmente, cientistas, gestores e autoridades têm sido confrontados pelos inúmeros problemas ecológicos e econômicos causados pela eutrofização artificial de lagos, como a redução na diversidade de espécies, a biomassa fitoplanctônica excessiva, a dominância da comunidade fitoplanctônica por espécies tóxicas ou impalatáveis de algas, a depleção do oxigênio dissolvido na água e uma série de perdas econômicas para setores tão diversos quanto turismo, pesca, abastecimento de água e saúde pública (Pretty et al., 2003; Smith and Schindler, 2009). O crescimento no uso crescente de fertilizantes agrícolas e na geração de esgoto urbano estão levando a uma maior carga de nutrientes e intensificando a eutrofização (J. A. da Costa et al., 2018; Glibert, 2017; Jarvie et al., 2006; Vitousek et al., 1997). Esse agravamento da eutrofização no mundo motivou um aumento do número de estudos sobre o tema, especialmente em lagos (J. A. da Costa et al., 2018), e alguns desses estudos tem mostrado que o processo de eutrofização pode ser reforçado pelo desflorestamento e pelas mudanças climáticas (Brugam, 1988; Costa-Böddeker et al., 2012; Couceiro et al., 2007; Glibert, 2017).
O atributo mais prontamente visível de águas eutróficas é a sua coloração esverdeada, associada à uma elevada biomassa fitoplanctônica. E muitos dos efeitos adversos da eutrofização derivam dessa biomassa elevada. Há muito se sabe que as mudanças na biomassa fitoplanctônica são majoritariamente controladas por mudanças na carga de nutrientes (Brown et al., 2000; Canfield Jr. and Bachmann, 1981; Chow-Fraser et al., 1994; Dillon and Rigler, 1974; Wang and Wang, 2009), mas também tem sido demonstrado que o regime hidráulico pode exercer um controle externo sobre a dinâmica temporal da biomassa fitoplanctônica (Bouvy et al., 2003, 2000; Gomes and Miranda, 2001; Harris and Baxter, 1996; Lins et al., 2017; Loverde-Oliveira et al., 2009; Medeiros et al., 2015; Rangel et al., 2012; Valdes-Weaver et al., 2006). Tal controle é complexo e envolve vias diretas e indiretas. O controle direto ocorre principalmente por meio da “lavagem” do fitoplâncton durante eventos de intenso fluxo hídrico (Angelini et al., 2008; Harris and Baxter, 1996; Loverde-Oliveira et al., 2009; Rangel et al., 2012). Enquanto esse controle direto é independente do nível de nutrientes, disponibilidade de luz e densidade do fitoplâncton, o controle indireto é exercido principalmente por alterações no ambiente luminoso e na carga de nutrientes, com efeitos dependentes de densidade e até antagônicos. Por exemplo, durante períodos chuvosos, a carga de nutrientes geralmente aumenta, favorecendo o crescimento fitoplanctônico (Abell and Hamilton,
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2015; Calijuri and Dos Santos, 2001), mas a concentração de sedimentos em suspensão também aumenta, o que reduz a penetração da luz na coluna d’água e prejudica o crescimento do fitoplâncton (Vanni et al., 2006). O ambiente luminoso ainda pode ser afetado de outras formas. A intensa depleção de reservatórios durante períodos de seca, por exemplo, pode promover uma circulação significativa de água e aumentar a razão entre as profundidades de mistura e da zona eufótica, reduzindo a exposição do fitoplâncton à luz (Naselli-Flores, 2000). Por outro lado, se essa depleção não for tão intensa, a maior estabilidade da coluna d’água durante períodos secos é favorável à acumulação de (Bouvy et al., 2003; Brasil et al., 2016; Costa et al., 2009, 2016; Rocha-Jr et al., 2018).
Para se estudar a relação entre o regime hidráulico e a dinâmica temporal da biomassa fitoplanctônica, séries temporais de dados limnológicos e hidrológicos são necessárias. E, considerando-se as flutuações de curto prazo que a biomassa fitoplanctônica pode exibir (Bowes et al., 2016; Dubelaar et al., 2004), requer-se um monitoramento intensivo que gere uma amostragem frequente de dados. No entanto, poucos lagos são monitorados intensivamente e o sensoriamento remoto tem sido cada vez mais utilizado para suplementar o monitoramento tradicional (Espinoza Villar et al., 2013, 2012; Matthews, 2014; Palmer et al., 2015c; Shi et al., 2018). Com a disponibilidade de sensores orbitais de alta resolução temporal e dados de acesso livre, o monitoramento a partir de satélites torna-se muito atrativo.
O sensoriamento remoto da qualidade da água se baseia no princípio simples de que mudanças na cor da água ou, mais especificamente, em sua “assinatura espectral”, são causadas por mudanças na sua composição. Muitos dos constituintes naturalmente presentes na água interagem com a luz, absorvendo e espalhando a radiação em diferentes intensidades ao longo do espectro eletromagnético (Kirk, 2011). Os efeitos combinados desses constituintes oticamente ativos (optically active constituents, OAC) conferem à água uma assinatura espectral característica, a qual pode ser detectada por um sensor remoto e usada em algoritmos que relacionam dados espectrais e de qualidade de água. A detecção remota da biomassa fitoplanctônica é baseada nos atributos espectrais da clorofila a, um pigmento fotossintético ubíquo, presente in todas as algas eucarióticas e nas cianobactérias. Ela absorve a luz fortemente nos comprimentos de onda associados às cores azul e vermelho (Bricaud et al., 1995), o que explica a típica coloração verde de
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águas ricas em fitoplâncton. A tarefa elementar de um algoritmo que estima a concentração de clorofila a a partir de dados espectrais é separar, na assinatura espectral da água, o sinal da clorofila a daquele oriundo dos outros OACs.
No presente estudo, nós testamos e desenvolvemos algoritmos de sensoriamento remoto para estudar a qualidade da água e a dinâmica temporal da biomassa fitoplanctônica em açudes do Semiárido brasileiro. Essa vasta região abrange mais de 1000 cidades em nove estados (Brazilian Ministry of National Integration, 2005) e é caracterizada por um balanço hídrico negativo, com precipitação anual média < 900 mm, concentrada no primeiro semestre, e evapotranspiração potencial anual > 2000 mm (Gheyi et al., 2012; Krol and Bronstert, 2007). Como a maior parte dos cursos d’água são intermitentes e secas severas são frequentes, o armazenamento de água nos açudes tem sido, há muito tempo, uma política governamental para garantir o abastecimento público. Mas, pela necessidade de estocar água suficiente para vencer os períodos de seca, esses lagos têm um longo tempo de detenção hidráulica, o que, somado à intensa insolação e à elevada temperatura da água durante todo o ano, favorece a acumulação de biomassa fitoplanctônica, notadamente de cianobactérias potencialmente tóxicas (Barbosa et al., 2012; Bouvy et al., 2000; Brasil et al., 2016; Costa et al., 2009; Freire et al., 2009; Huszar et al., 2000). Dado o regime hidráulico marcante da região, com longos períodos de baixa afluência intercalados por períodos curtos de afluência intensa, nós hipotetizamos que a concentração de clorofila a permanecerá alta na maior parte do tempo, com declínios acentuados durante períodos chuvosos intensos.
ESTRUTURA DA TESE
Os próximos três capítulos foram escritos como trabalhos individuais, na forma de artigos. Eles serão formatados separadamente e submetidos a periódicos nacionais e internacionais para publicação. As questões principais abordadas em cada capítulo são descritas a seguir:
1) Como a qualidade da água de lagos e reservatórios pode ser monitorada por meio de sensores remotos? Esse capítulo traz uma visão geral sobre o sensoriamento remoto da qualidade da água em ambientes lacustres, dando alicerce aos capítulos subsequentes. Em particular, ele deu suporte à elaboração do capítulo 2, demonstrando quais variáveis óticas e de qualidade água podem ser obtidas de
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forma confiável por sensoriamento remoto; e deu suporte ao capítulo 3 mostrando que algoritmos empíricos baseados em produtos terrestres do sensor MODIS, com correção atmosférica padrão, podem ser usados com sucesso para produzir séries temporais de qualidade da água em lagos.
2) Quais são as relações entre variáveis óticas e de qualidade de água em lagos do Semiárido? Pode a qualidade da água ser predita a partir de dados óticos? Para responder a essas questões, nós coletamos dados óticos e de qualidade da água em 13 açudes, caracterizando suas águas com respeito a propriedades óticas, transparência e constituição, e então testamos e selecionamos modelos para predição de variáveis importantes de qualidade de água a partir de dados de reflectância. Os resultados desse capítulo deram suporte ao capítulo subsequente, deixando claro, por exemplo, que um algoritmo para quantificação da biomassa fitoplanctônica nos açudes estudados deve ser capaz de tolerar a influência da matéria inorgânica em suspensão, ao passo que a influência da matéria orgânica dissolvida não requer atenção especial. Ademais, as propriedades óticas descritas para os açudes estudados podem dar suporte ao desenvolvimento de aplicações de sensoriamento remoto para lagos do Semiárido brasileiro.
3) O regime hidráulico marcante dos açudes do Semiárido se reflete na dinâmica temporal da biomassa fitoplanctônica? Para responder essa questão, nós testamos inúmeros algoritmos e selecionamos um para gerar, a partir de imagens diárias do sensor MODIS, uma série de 15 anos da concentração de clorofila a nos três maiores açudes estudados. A comparação da série produzida com a série histórica do volume armazenado nos açudes revelou uma dinâmica temporal associada, com a biomassa fitoplanctônica permanecendo alta durante os períodos secos e diminuindo nos períodos de intensa renovação de água. Em um ano extraordinariamente chuvoso, a redução da biomassa não só foi intense como persistiu pelos anos subsequentes. Esses resultados deram suporte à hipótese de trabalho e encorajaram a exploração do arquivo de imagens MODIS para o estudo da dinâmica temporal do fitoplâncton em lagos e reservatórios.
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CAPÍTULO 1 – Water quality monitoring of lakes through remote
sensing: background and applications
Dhalton Luiz Tosetto Ventura
1 Abstract
Due to the ecological, social and economic importance of lakes all over the world, water quality monitoring programs are generally implemented by governmental agencies aiming at a better water resources management. But, due to operational and budget constraints, the regular monitoring is generally limited to a minor portion of the existing lakes and even strategic lakes included in a monitoring program may lack an adequate sampling frequency or spatial cover. Remote sensing can improve the water quality monitoring of lakes in a cost-effective manner by allowing for a frequent monitoring of a large number of lakes in a region or country; proportioning a synoptic view of a lake or region of interest; filling gaps and hindcasting time series; extending the monitoring to unmonitored portions of a lake or to totally unmonitored lakes; and helping in the quality control of in situ data through the analysis of discrepancies. Considering such potential, we reviewed the literature and selected remote sensing algorithms applicable to monitoring and hindcasting of water quality descriptors in lakes, constraining the selection to algorithms (1) whose underlying methods allow for an automated data processing; (4) applicable to sensors whose data is of free and easy access, with a regular and adequate acquisition frequency; (3) which have been tested with satellite data and have had encouraging results when validated against an independent data set or through cross validation; (4) which do not require in situ optical data; and (5) which can be easily revalidated or recalibrated with local data. Several promising algorithms are available for the remote estimation of important water quality descriptors, such as the Secchi disk depth (SDD), the diffuse attenuation coefficient (Kd), the total surface suspended solids
concentration (SSS), the chlorophyll a concentration (chla) and the absorption coefficient of the colored dissolved organic matter (aCDOM).
2 Introduction
Natural and man-made lakes all over the world are habitat for uncountable species and are crucial for the human population, providing water and food and sustaining agriculture, livestock, aquaculture, recreation, tourism and other economic activities. They also have
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a determinant role in the global carbon cycle, processing, emitting and storing large amounts of carbon (Tranvik et al., 2009). For the ecological, sanitary and economic importance of lakes, governmental agencies directly or indirectly promote water quality monitoring programs, counting on the gathered information to better manage the water resources. But, due to operational and budget constraints, the regular monitoring is generally limited to a minor portion of the existing lakes and even strategic lakes included in a monitoring program may lack an adequate sampling frequency or spatial cover. Satellite remote sensing has been proved a valuable monitoring approach, as evidenced by the operational use of data collected by orbital sensors in fields as diverse as oceanography, meteorology, geology and forest management. Limnology and water resources management can benefit from an operational use of remote sensing data as well. Such data can improve the water quality monitoring of lakes in a cost-effective manner by (1) allowing for a frequent monitoring of a large number of lakes in a region or country with a and near-simultaneous data acquisition for all of them; (2) proportioning a synoptic view of a lake or region of interest; (3) filling gaps in time series or hindcasting data, i.e., extending time series back to unmonitored periods; (4) extending the monitoring to unmonitored portions of a lake or to totally unmonitored lakes; and (5) helping in the quality control of in situ data through the analysis of discrepancies. Through remote sensing it is possible to estimate several traditional descriptors of water clarity, constitution and temperature. Whereas the remote estimation of temperature is based on the thermal region of the electromagnetic radiation spectrum, water clarity and constitution descriptors, which are the focus of our review, have their estimation based on the water spectral signature in the visible and near-infrared (NIR) regions.
Water clarity is a simple yet effective indicator of water quality in lakes (Heiskary and Walker Jr., 1988), being inversely related to the amounts of phytoplankton and suspended matter (Swift et al., 2006). It has long been measured as the Secchi Disk Depth (SDD), the depth at which a black and white disk disappears from view (Preisendorfer, 1986). For its sometimes strong relationship with the chlorophyll a concentration, SDD is a traditional indicator of the trophic state of lakes (Carlson, 1977). In a study covering a 20-year period and the whole state of Minnesota, Olmanson et al. (2013) estimated, from Landsat data, the SDD and the related trophic state index in more than 10,000 lakes for the summer seasons of 1985, 1990, 1995, 2000 and 2005. Most of these lakes were not
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regularly monitored. The statewide and a long-term view of the water transparency revealed differences among state ecoregions and an overall stability during the two decades covered by the study. In a similar study, the SDD was estimated for over 2,000 lakes in Wisconsin, showing a statewide trend of increasing between 1980 and 2000 (Peckham and Lillesand, 2006). Hicks et al. (2013) produced a 10-year time series of Landsat-derived SDD and suspended solids concentration for 34 lakes in New Zealand. They developed an automated procedure for the data estimation from Landsat images. Recently, Shi et al. (2018) combined SDD data derived from MODIS images and from the conventional monitoring to analyze its temporal variation in the Lake Taihu from 1993 to 2015. They detected a decreasing trend of the water clarity, likely associated to a decrease in wind speed that led to an augmented cyanobacteria bloom surface area. A more accurate measurement of water clarity is given by the diffuse attenuation coefficient of the downwelling irradiance (Kd), which quantifies the exponential
extinction of light in the water column (Kirk, 2011). This coefficient may be determined for any specific wavelength, but in regarding the light availability for photosynthetic organisms, its integration from 400 through 700 nm is more meaningful, being usually named diffuse attenuation coefficient of downwelling photosynthetically active radiation (Kd(PAR)). The euphotic zone depth (zeu) is directly calculated from Kd(PAR) and at least one
of these variables is frequently involved in the modeling of the phytoplankton primary production (Brighenti et al., 2018; Khanna et al., 2009; Zhang et al., 2007). The Kd at 490
nm (Kd(490)) is commonly used interchangeably with Kd(PAR) due to the typically close
relationship between them (Pierson et al., 2008; Son and Wang, 2015; Zhang et al., 2012). Hinging on such relationship and on the direct calculation of zeu from Kd(PAR), Majozi et
al. (2014) mapped zeu in Lake Naivasha (Kenya) from MERIS images. MERIS images
were also used in the estimation of Kd(PAR) for the study of its seasonal and spatial
distributions in Lake Taihu (China) from 2003 to 2010 (Shi et al., 2014). Again in Lake Taihu, Kd(490) was successfully mapped from images of the OLCI sensor, successor of
MERIS (Shen et al., 2017), and of the MODIS sensor (Huang et al., 2017). Not many studies like these have aimed at the remote estimation of Kd(PAR) or Kd(490) for inland
waters. In open oceanic waters, Kd is mainly determined by the phytoplankton optical
properties and its estimation is commonly based on the chlorophyll a concentration (Morel et al., 2007). But in inland waters, the attenuation caused by inorganic particles
10
may be very strong (Shen et al., 2017; Shi et al., 2014). Hence, algorithms for retrieving the Kd directly from the spectral data are preferable.
Despite the effectiveness of water clarity measurements, water quality monitoring programs must also include measurements of water constituents. In fact, water clarity and color are determined by the amounts of optically active constituents (OACs) in the water. The OACs are substances that interact with light by absorbing and/or scattering it. Thus, they can be detected by remote sensing techniques. They can be generically grouped as phytoplankton pigments (chlorophylls, carotenoids and phycobiliproteins), tripton (non-living suspended particles) and colored dissolved organic matter (CDOM) (sometimes the term chromophoric is adopted in lieu of colored). Unlikely the clear oceanic waters, known as Case I waters (Morel and Prieur, 1977), where the phytoplankton pigments are the main source of alterations in the water reflectance spectra, the inland, Case II, waters commonly have substantial and noncovariant amounts of the other OACs (Gurlin et al., 2011). The optical complexity of inland waters poses a great challenge for the remote quantification of their constituents, which is further complicated by physical factors such as the optical influence of marginal lands and of the atmosphere.
Tripton is the non-living part of the seston (also referred to as suspended solids, suspended matter, suspended sediments and particulate matter), which also comprises the phytoplankton. The remote quantification of the seston is assumably easy due to its strong signal, but separately quantifying the tripton is more difficult. In waters where the tripton, which has an organic and an inorganic fraction, is optically dominant, it is commonly quantified as the surface seston, i.e., the total surface suspended solids (SSS) (Doxaran et al., 2009a; Long and Pavelsky, 2013; Martinez et al., 2015; Yepez et al., 2018). The surface suspended solids have important ecological and sanitary roles by limiting the light penetration in the water column, thus reducing its availability for the phytoplankton, and by retaining and transporting nutrients and heavy metals (Cloern et al., 2014; Jones et al., 2014; Pourabadehei and Mulligan, 2016). Lobo et al. (2015) took advantage of the extensive series of data obtained by the Landsat missions and generated a 40-year times series of SSS in the Tapajós River basin (Brazil). The results revealed a clear relationship of the mining activity in the basin and the SSS levels, with peaks in SSS associated with peaks in the gold production. In Lake Taihu, a MODIS-derived SSS time series covering the 2003-2013 period allowed for the identification of temporal and spatial patterns of
11
variation in SSS (Shi et al., 2015). For the same lake, the MODIS-derived SSS was used to study the extension of the plume of suspended solids entering the lake and its association with heavy rainfall events occurred from 2004 to 2013 (Yunlin Zhang et al., 2016). Gaps in the in situ SSS time series of the Madeira river (Brazil) were filled by the SSS estimated from MODIS products (Espinoza Villar et al., 2013) and a whole new SSS time series was generated from MODIS data for one of the formers of the Amazon River, the Ucayali River (Peru), which lacked in situ monitoring data (Espinoza Villar et al., 2012).
Many pigments are found among the phytoplanktonic organisms, with photoprotective or photosynthetic functions, but the most important and most studied pigment is the chlorophyll a, which is present in all the photosynthetic eukaryotes and in the cyanobacteria. Chlorophyll a absorbs light strongly in the blue and red regions of the spectrum (Bricaud et al., 1995) and its concentration (chla) has been traditionally used as a proxy for the phytoplankton biomass and as an indicator of trophic state and productivity (Carvalho et al., 2013; Huot et al., 2007). The remote sensing of chla in Case II waters is challenging because they commonly have substantial amounts of tripton and CDOM, which strongly absorbs blue light, thus overlapping the chlorophyll a absorption in this region. As such, the most accurate algorithms for the Case II waters are those designed to explore the chlorophyll-induced features in the red and NIR regions of the spectrum (Dörnhöfer and Oppelt, 2016; Gitelson et al., 2008; Le et al., 2009; Matthews et al., 2012). Ideally, chla should be monitored under a high sampling frequency so to account for the quick biomass variations that the phytoplankton may exhibit (Bowes et al., 2016; Dubelaar et al., 2004). Sensors with a high temporal resolution, such as MODIS, VIIRS, MERIS and OLCI, are suitable for the study of both short- and long-term temporal dynamics of the phytoplankton biomass, as demonstrated by Matthews (2014), Palmer et al. (2015c) and Feng et al. (2015), who produced 10-year series of chla from MERIS imagery for 50 lakes in South Africa, for Lake Balaton (Hungary) and for Poyang Lake (China), respectively.
CDOM (also known as yellow substance or gelbstoff) refers to the portion of the dissolved organic matter that absorbs light and, therefore, may be detected by remote sensing. It is linked to many important ecological and sanitary aspects, such as microbial decomposition, carbon and nutrient cycling, transport of metals, formation of
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trihalomethanes, treatment of drinking water, photochemical transformation of compounds, light availability for primary production and thermal balance of the water column (Golea et al., 2017; Houser, 2006; Lindell et al., 1995; Markager and Vincent, 2000; Tranvik et al., 2009; Wetzel, 1992; Williamson et al., 1999). CDOM amount is generally measured as the absorption coefficient of the dissolved matter at 440 nm or at shorter wavelengths (Hestir et al., 2015; Kallio et al., 2001; Kobayashi et al., 2011; Zhu et al., 2014) and it is expected that its quantification allows for inferences on the amounts of dissolved organic carbon (DOC). But, among the major optically active constituents of inland waters, CDOM is possibly the most challenging for remote sensing (Brezonik et al., 2015), for, (1) unlike phytoplankton and tripton, that affect the water-leaving radiance by absorbing and scattering light, CDOM only absorbs light, which means that the higher the CDOM levels, the weaker is the water-leaving signal; (2) in the visible spectrum, its absorption coefficient is strongest in the blue region, where absorption by tripton and phytoplanktonic pigments is also strong and where atmospheric scattering is high; (3) there are no clear distinctive features in the water spectral signature, such as peaks or troughs, associated to CDOM; (4) similar CDOM levels may be associated to very different reflectance spectra, depending on the phytoplankton and tripton concentrations; and (5) the high uncertainty in the CDOM-DOC relationship makes the usefulness of CDOM estimation dependent upon local validation of the related DOC amounts. Despite these issues, there has been some successful efforts in developing algorithms for estimating CDOM levels in inland waters from satellite data (Brezonik et al., 2005; Chen et al., 2017b; Griffin et al., 2011; Kutser et al., 2005a; Olmanson et al., 2016).
In this work, we reviewed the literature and selected remote sensing algorithms applicable to monitoring and hindcasting of the above-discussed water quality descriptors in lakes, constraining the selection to algorithms (1) whose underlying methods allow for an automated data processing; (4) applicable to sensors whose data is of free and easy access, with a regular and adequate acquisition frequency; (3) which have been tested with satellite data and have had encouraging results when validated against an independent data set or through cross validation; (4) which do not require in situ optical data; and (5) which can be easily revalidated or recalibrated with preexisting limnological data. The automation potential and the data accessibility are important not only when processing a long series of archived images, but also to ensure that a large number of water bodies can
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be continuously monitored in a time- and cost-effective manner. Not requiring in situ optical data is important to ensure the applicability of the algorithms, given that many lakes in the world are not properly monitored for basic water quality variables, let alone for optical parameters. Before describing the algorithms, we briefly present the background of the water quality remote sensing and then list the most suitable sensors for monitoring water quality in lakes. The list includes current and past sensors whose imagery archives are freely available online.
3 Background
When the solar electromagnetic radiation reaches a water body, a part is reflected by the water surface and the other part enters the water body, being attenuated exponentially according to the Lambert-Beer law (Baker and Smith, 1982):
𝐼 = 𝐼0× exp(−𝐾𝑑× 𝑧) (1)
where I is the irradiance at depth z (m), I0 is the irradiance at the surface and Kd is the
diffuse attenuation coefficient of downwelling irradiance. In water, the radiation interacts with water itself and with the water OACs. This interaction may be quantified for each radiation wavelength by the water total absorption (a, in m−1) and total scattering (b, in m−1) coefficients, which are treated as inherent optical properties of the water for not depending on the ambient light field, but only on the attributes of the water itself and of the OACs (Kirk, 2011). The total absorption coefficient, a, can be subdivided into additive components linked to the OACs: a = aw + aph + atripton + aCDOM; where the
subscripts stand for water, phytoplankton, tripton and CDOM, respectively (Figure 1). These components can be further subdivided. For example, aph can be represented as the
sum of the absorption coefficients of as many as phytoplankton pigments are active in the water (e.g. chlorophyll a, chlorophyll b, phycocyanin, etc.): aph = achla + achlb + apc (…).
When the phytoplankton is considered as a whole, it is common to consider aph ~ achla
due to the ubiquity of chlorophyll a and to its strong signal. The scattering coefficient b may be subdivided into bw + bph + btripton. Because a remote sensor can only detect the
in-water radiation that eventually leaves the in-water body, it is also important to consider the backscattering coefficient, bb, which represents the proportion of the scattering directed
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The OACs absorb and scatter radiation in different intensities at each wavelength (Figure 1). Their combined effects result in a water “spectral signature” that may be used for modeling water clarity and constitution. Such signature is usually represented by the variation of the remote sensing reflectance (Rrs, in steradian−1) along the visible and NIR
regions of the electromagnetic spectrum (Figure 2). Rrs, classified as an apparent optical
property for being somewhat affected by the ambient light field, is the ratio of the water-leaving radiance (Lu) to the solar downwelling irradiance (Ed), measured above the water
surface at a particular wavelength (Kirk, 2011). It can be expressed in terms of a and bb
(Gitelson et al., 2008):
𝑅𝑟𝑠 ∝ 𝛾 × 𝑏𝑏⁄(𝑎 + 𝑏𝑏) (2)
where γ represents the geometry of the light field emerging from water.
Figure 1. Examples of absorption coefficient spectra from tripton (18 mg L−1), CDOM (aCDOM(440) = 0.6 m−1)
and chlorophyll a (30 µg L−1). The latter spectrum was calculated from the specific absorption coefficient data from Bidigare et al. (1990). The water absorption spectrum (from Pope and Fry, 1997) is also shown. The inset illustrates the addictive effect of the absorption coefficients.
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Figure 2. Examples of the spectral signatures of waters with nearly the same chlorophyll a concentration (~ 50 µg L−1) and increasing concentrations of inorganic tripton (0.3, 6.5 and 17 mg L−1).
The remote sensing of water OACs basically relies on algorithms that seek to directly or indirectly determine the contribution of the constituent of interest to the water spectral signature and infer its concentration. For example, in waters optically dominated by low to moderate phytoplankton biomass, an algorithm for remote estimation of the chlorophyll a concentration (chla) may be based on the effect of the chlorophyll a absorption on the reflectance in the blue spectral region (around 440 nm) (e.g. Lesht et al., 2013) or on the effect of the chlorophyll a fluorescence on the reflectance at 685 nm (e.g. Gower et al., 1999). In productive and optically complex waters, the algorithm would better be based on the effect of the chlorophyll a absorption on the reflectance in the red region (around 670 nm) (e.g. Dall’Olmo and Gitelson, 2005) or on the effect of the phytoplankton backscattering on the reflectance in the green (e.g. Zhang et al., 2011) or NIR (e.g. Yuchao Zhang et al., 2016) regions.
There are several types of algorithms. The simplest and most used are the empirical algorithms, in which statistical relationships are established between observed concentrations of an OAC (or a water clarity descriptor) and some spectral index, such as the reflectance at a single wavelength or an operation (ratio, difference or mixed operations) involving the reflectances at two or more wavelengths. Empirical algorithms (e.g. Bonansea et al., 2017) have the advantage of not requiring the inherent optical properties to be known, but the disadvantage of generally being valid for one or few
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locations only. The semi-analytical (e.g. Zhu and Yu, 2013) and the matrix inversion algorithms use models based on the radiative transfer theory to derive the OAC concentration. They have the advantage of being physically-based, but may still depend on the empirical estimation or previous knowledge of some optical parameters. The artificial neural networks (e.g. Doerffer and Schiller, 2007) are a kind of algorithm that may deal with complex non-linear relationships, but they require large training data sets. The spectral matching algorithms (e.g. Liu and Miller, 2008) aims at matching the observed spectral signature to a spectral signature whose underlying optical properties are known. It depends on the use of a large spectral database and require, as input, the reflectance at several different wavelengths. Anyhow, regardless of its type, an algorithm can only be considered reliable upon validation for specific water bodies or for waters of known optical properties. In this review, we focused on empirical algorithms for their easiness of application and of adaptation to a specific water body.
Retrieving the water spectral signature from space is challenging for several reasons. First, the water-leaving signal is very weak – even in waters with high concentrations of suspended solids, the intensity of the water-leaving radiation usually corresponds to less than 6% of the incident solar radiation (Doxaran et al., 2002; Martinez et al., 2015). This weak signal generally accounts for no more than 10% of the signal detected by the orbital optical sensor (Gitelson and Kondratyev, 1991) – most of the signal comes from the atmosphere. Thus, depending on the radiometric resolution of the sensor, i.e. its capacity of detecting variations in the signal intensity, it may not be feasible to estimate the concentration of an OAC, especially CDOM (Kutser, 2012). Second, in its way to the sensor, the water-leaving radiation interacts with the atmosphere. Thus, besides being weak, the detected water signal is not pure, but contaminated with atmosphere information. This unwanted spectral information must be somehow removed or minimized so that the water signature can be reconstituted (Mouw et al., 2015). Third, the detected signal is also influenced by the radiation reflected on the water surface, which does not carry information on the water constitution and, ideally, must be corrected (Zhu et al., 2014). Such correction is especially necessary when sun glint occurs, that is, when the radiation reflection is specular (Overstreet and Legleiter, 2017). Fourth, if the water area viewed by the sensor is next to land (this is virtually always the case in inland waters), the signal may also be contaminated with spectral information from soil, vegetation or urban areas (Halabisky et al., 2016; Tarrant et al., 2010). Due to the so-called adjacency
17
effects, even if no land area is included in a given pixel, the mere proximity of land may affect the water spectral signature, notably along the infrared region (Mouw et al., 2015). Fifth, the sensors do not detect a complete signal, but a signal segmented in different bands. Thus, if a sensor does not have a high spectral resolution, with several narrow bands distributed along the spectrum, it will not be able to retrieve the detailed features of the water spectral signature, such as punctual peaks and troughs. This is illustrated in Figure 3 by the comparison of the bands of a low- and a high-spectral resolution sensor. Despite all these complicating factors, the technology and methodology associated with water remote sensing have greatly advanced in the last decades and, as we shall demonstrate, many studies have been successful in the remote estimation of water clarity and constituents in inland waters (Dörnhöfer and Oppelt, 2016; Palmer et al., 2015b).
Figure 3. Spectral resolution of the ETM+ and OLCI orbital sensors. The 21 OLCI narrow bands (darker rectangles) allow for a much finer representation of the water spectral signature, capturing sharp reflectance variations (e.g. between 660 and 710 nm) that the ETM+ bands cannot distinguish.
4 Suitable sensors
4.1 Landsat sensors
Landsat can be considered the most successful remote sensing program, providing a continuous data acquisition since July 1972, when Landsat 1 was launched. Ever since, Landsat 2, 3, 4, 5, 7 and 8 were successfully launched (Landsat 6 launch failed). The data continuity is warranted by the partial or full compatibility among a series of sensors: Multi
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Spectral Scanner (MSS, Landsat 1-5), Thematic Mapper (TM, Landsat 4-5), Enhanced Thematic Mapper Plus (ETM+, Landsat 7) and Operational Land Imager (OLI, Landsat 8). Landsat 7, launched in April 1999, and 8, launched in February 2013, are still active. Since 2003, it lacks an operational Scan Line Corrector (SLC), which causes ca. 22% of the scene to be lost. Landsat 5 operated for impressive 29 years, from March 1984 through June 2013. The current operational sensors, ETM+ and OLI, have similar configuration. ETM+ has 8 bands: one panchromatic band (15-m spatial resolution); one thermal band (60 m); and six wide bands in the visible and infrared (30 m). In its turn, OLI does not have the thermal band (allocated in a co-operating sensor), but has two new bands, one centered at 443 nm and the other at 1375 nm. The other bands are fully or partially compatible with ETM+ in terms of position, width and spatial resolution, and OLI bands have a better radiometric resolution. In 2020, Landsat 9 will be launched, with basically the same configuration of Landsat 8. Although Landsat sensors were designed primarily for land applications and its temporal resolution of 16 days is not appropriate for monitoring some highly variable water quality parameters, the good spatial resolution and the comprehensive imagery archive have motivated many applications of Landsat data to the study of inland waters.
4.2 MSI
MSI (multi-spectral instrument) is on board Sentinel-2A and Sentinel-2B (ESA), launched in June 2015 and March 2017, respectively, with a combined temporal resolution of 5 days or less, depending on the latitude. It has 13 bands: four 10-m spatial resolution bands in the visible and NIR regions and the remaining are 20- or 60-m spatial resolution bands, most in the infrared region. Besides its high spatial resolution, MSI has two bands positioned in the red-NIR transition that, along with the red band, can be useful for chla estimation algorithms.
4.3 MODIS
MODIS (Moderate-Resolution Imaging Spectroradiometer) is aboard of two NASA satellites: Terra, launched in December 1999, and Aqua, launched in May 2002. Both are still operating, but, considering that their expected life span has been long exceeded, they may stop working at any moment. Each sensor captures images of virtually the whole globe every 1-2 days (1 day in most of the globe) in 36 bands. Bands 1-7 were designed
19
primarily for land applications, 8-16 for ocean color and 17-36 for atmospheric and temperature applications. The spatial resolution is 250 m for bands 1-2 (red and 2 NIR), 500 m for bands 3-7 (blue, green and three infrared bands) and 1 km for the rest. The ocean color bands are narrower and have greater sensitivity than the land bands, but their application to inland waters is hindered by the 1-km spatial resolution.
4.4 VIIRS
VIIRS (Visible Infrared Imaging Radiometer Suite) was launched in October 2011 on board the Suomi NPP satellite (NOAA/NASA) and produce daily images across the globe. It is the MODIS’s successor, with a partially compatible configuration, and, importantly, an improved instantaneous field of view, with a smaller resolution difference between angled and nadir data acquisition. It has 23 bands: M1-16 (750-m spatial resolution); I1-5 (37I1-5 m); and one panchromatic (7I1-50 m). The VIIRS bands designed for land applications have a worse spatial resolution than the MODIS land bands (375 vs 250 m and 750 vs 500 m) and, moreover, the VIIRS red band is considerably wider. On the other hand, the narrow VIIRS ocean-color bands have a better spatial resolution than the MODIS ocean-color bands (750 vs 1000 m), what increases the applicability of such bands to inland waters.
4.5 MERIS and OLCI
MERIS (Medium Resolution Imaging Spectrometer) was aboard the Envisat platform (European Space Agency – ESA), launched in March 2002. Its operation officially ended in May 2012. MERIS had a temporal resolution of less than 3 days and an excellent spectral resolution, with 15 narrow bands of 300-m spatial resolution distributed along the visible and NIR regions, including bands that capture key phytoplankton spectral features, such as the phycocyanin absorption peak near 620 nm, the chlorophyll a absorption and fluorescence peaks around 665 and 685 nm, respectively, and the reflectance peak near 700 nm associated with the phytoplankton backscattering.
OLCI (Ocean and Land Colour Instrument), the MERIS successor, is on board the Sentinel-3A and Sentinel-3B (ESA), launched in February 2016 and April 2018, respectively. OLCI has an even better spectral resolution, with six new bands in the visible and NIR regions in addition to the 15 MERIS bands. The spatial resolution is the same, but the OLCI bands have a better signal to noise ratio and a higher radiometric
20
resolution. The combined operation of the two Sentinel-3 satellites warrants a temporal resolution of less than 2 days.
5 Selected algorithms
The selected algorithms are presented below (Table 1; sections 5.1 to 5.5). Since the MERIS’s bands are all present in the OLCI sensor, all MERIS algorithms discussed here should be suitable for OLCI, although adjustments may be necessary to the algorithms’ coefficients and to procedures of atmospheric correction. Likewise, algorithms developed for the TM and ETM+ Landsat sensors are compatible with the more recent OLI sensor. Among the selected studies, different “reflectance levels” were adopted: top-of-atmosphere reflectance (RTOA); bottom-of-Rayleigh reflectance (RBR); total water
reflectance (Rt); and remote sensing reflectance (Rrs). RTOA is the reflectance calculated
from the radiance measured by the sensor without any correction for atmospheric effects. RBR is corrected only for Rayleigh scattering and gaseous absorption; Rt is further
corrected for the influence of aerosols; and Rrs is further corrected for the radiation
reflection on the water surface, corresponding to the signal that effectively comes from within the water. Because a reliable removal of the water surface contribution requires data not always available (e.g. wind speed), many studies adopt Rt, but it is not always
made clear and it is possible that studies claiming to be using Rrs are, in fact, using Rt. As
for RTOA or RBR, they have been adopted for some studies because atmospheric correction
is complex and frequently causes unwanted distortions on the water’s spectral signature, leading to a poor performance of algorithms with the corrected data.
Table 1. Selected algorithms for retrieval of water clarity and constituents. The accuracy and range fields refer to the validation performance. Accuracy indicators varied among the studies: Pearson correlation coefficient (r); determination coefficient (r2); root mean squared error (RMSE); bias; relative error (RE);
mean relative error (MRE), which, when in percentage, is the same as the mean absolute percent error (MAPE) and the mean normalized bias (MNB); and normalized root mean squared error (NRMS). RMSE, bias and range are in the units of the corresponding variables.
References Location Sensor Accuracy Range Section
SDD (m) (Kloiber et al., 2002b; Olmanson et al., 2008) 10,000+ lakes in Minnesota. TM, ETM+ r2: 0.78 ~0.13-16 5.1.1
(Shi et al., 2018) Lake Taihu, China MODIS r2: 0.68; RMSE: 0.18; MAPE (%): 25.4
0.15-1.1 5.1.2
Kd(PAR) and Kd(490) (m−1)
(Song et al., 2017) 20 lakes in China ETM+, OLI RMSE: 0.971; MRE: 0.33 ~0-16 5.2.1
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References Location Sensor Accuracy Range Section
(Shen et al., 2017) Lake Taihu, China OLCI r2: 0.83; RMSE: 1.06; RMSE (%): 30.81; MNB (%): −3.89; NRMS (%): 31.63
~1-8 5.2.2
(Shi et al., 2014) idem MERIS r2: 0.73; RMSE: 1.7; RE:
0.004-1.32; MAPE: 0.294
1.3-11.2 5.2.3 SSS (mg L−1)
(Long and Pavelsky, 2013) Lake Athabasca, Canada
MERIS Spearman’s ρ: 0.97 3.9-3602 5.3.1
(Espinoza Villar et al., 2013) Madeira River, Brazil MODIS - ~0-2000 Idem
(Shi et al., 2015) Lake Taihu, China MODIS r2: 0.80; RMSE: 14; MAPE: 24.6%
13.9-301.3
5.3.2 (Yepez et al., 2018) Orinoco River,
Venezuela
OLI r2: 0.91 ~15-200 5.3.3
chla (µg L−1)
(Gitelson et al., 2008; Moses et al., 2009a)
Taganrog Bay and Azov Sea, Russia
MERIS RMSE: 5.02 and 3.65 ~15-50 5.4.1
(Matthews, 2014; Matthews et al., 2012; Matthews and Odermatt, 2015)
50 lakes in South Africa
MERIS r: 0.64; RMSE: 55.7; bias:
16.6
1-350 5.4.2
(Palmer et al., 2015c) Lake Balaton, Hungary
MERIS r2: 0.87 ~0-50 5.4.3
(Yuchao Zhang et al., 2016) Lakes Chaohu, Taihu, Hongzehu and Poynaghu, China
MODIS r2: 0.78; RMSE (%): 85.2
~10-1000 5.4.4
(Bonansea et al., 2017) Río Tercero Reservoir, Argentina TM, ETM+, OLI r2: 0.89; RMSE (%): 18.47 ~0-300 5.4.5 aCDOM(440) (m−1)
(Chen et al., 2017b) Lake Huron, USA OLI r2: 0.95; RMSE: 0.504; bias: 0.123; MRE (%): 27.4
~0-8 5.5.1
(Chen et al., 2017a) idem MSI r2: 0.88; RMSE: 0.731;
RMSE (%): 28.02; bias: -0.1
idem 5.5.2
5.1 Secchi Disk Depth (SDD)
The study of Kloiber et al. (2002b) and several subsequent studies (Chipman et al., 2004; Kloiber et al., 2002a; Olmanson et al., 2016, 2008) pointed to combinations of the blue and red bands as the most suitable for the remote estimation of SDD. We present the corresponding algorithms below. However, since the blue band is known to be more subject to atmospheric effects, we also present a single-red-band algorithm (Shi et al., 2018).
5.1.1 Blue-red combinations
Kloiber et al. (2002b) defined a procedure for remote estimation of the SDD from Landsat images. They recommended the utilization of the log-transformed SDD in a linear regression, instead of fitting a power function, to ensure a normal distribution of the regression residuals. After testing the predictive ability of several algorithms with
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different band combinations, they concluded that the most effective algorithm for the TM and ETM+ sensors has the form:
ln(𝑆𝐷𝐷) = 𝑎 + 𝑏 × (𝑅𝑇𝑂𝐴(𝑇𝑀1)/𝑅𝑇𝑂𝐴(𝑇𝑀3)) + 𝑐 × 𝑅𝑇𝑂𝐴(𝑇𝑀1) (3)
where ln stands for natural logarithm; RTOA stands for top-of-atmosphere (TOA)
reflectance; TM1 and TM3 are the blue and red bands of the TM and ETM+ sensors; and a, b and c are the regression coefficients.
Due to the difficulty in defining an atmospheric correction that worked well for all the images, instead of calibrating a single standard model applicable to the atmospherically-corrected data of any image, they calibrated individual models for each image and did not apply atmospheric correction, only a brightness normalization of the images. Each calibrated model was, then, applied to unmonitored lakes of the same image. This procedure is certainly not as practical as the application of a single standard predictive model, but when a conventional monitoring program only covers a small subset of a large set of lakes of interest, the procedure allows for extending the monitoring to the unmonitored lakes. Their procedure showed a great potential, not only in the Kloiber et al. (2002b) study, but also in the subsequent works of Chipman et al. (2004) and Olmanson et al. (2013), where SDD was estimated, respectively, for over 8,000 lakes in Wisconsin in the 1999-2001 time period, and over 10,000 lakes in Minnesota for the years 1985, 1990, 1995, 2000 and 2005. They followed the procedure of calibrating several individual models. Importantly, Olmanson et al. (2013) validated the models’ predictions against a data set with more than 6,000 in situ observations (r2 = 0.78) and they also provided an additional validation, using the overlapping areas of adjacent images to cross-validate the models of each image. Due to the limited performance gain when adopting atmospherically corrected data, they decided not to apply atmospheric corrections nor brightness normalization. Of course, an efficient and automated atmospheric correction would be preferred, avoiding the necessity of in situ data for every image and allowing for the adoption of a standard predictive model. A practical solution to have a single model applicable to any image could be the adoption of an algorithm with smaller accuracy, but great tolerance to atmospheric effects. Interestingly, Kloiber et al. (2002b) and Chipman et al. (2004) found that the red to blue and blue to red ratios alone had a good correlation with measured SDD and were relatively consistent in predicting SSD
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for images of different dates. The algorithms, in such cases, have the form of a simple linear regression:
ln(𝑆𝐷𝐷) = 𝑎 + 𝑏 × (𝑅𝑇𝑂𝐴(𝑇𝑀3)/𝑅𝑇𝑂𝐴(𝑇𝑀1)) (4)
ln(𝑆𝐷𝐷) = 𝑎 + 𝑏 × (𝑅𝑇𝑂𝐴(𝑇𝑀1)/𝑅𝑇𝑂𝐴(𝑇𝑀3)) (5)
5.1.2 Red band
By means of an empirical algorithm, Shi et al. (2018) retrieved from MODIS images the SDD in Lake Taihu from 2003 through 2015 and detected a decreasing trend in transparency. The single-band algorithm, based on the atmospherically-corrected reflectance at MODIS band 1 (red), was calibrated through an exponential regression (r2
= 0.67; n = 150) and validated with in situ data [r2 = 0.68; mean absolute percent error
(MAPE, %) = 25.4; RMSE = 0.18 m; n = 150]:
𝑆𝐷𝐷 (m) = 1.259 × exp(−46.02 × 𝑅𝑟𝑠(𝑀𝑂𝐷1)) (6)
where MOD1 stands for MODIS band 1.
It would be interesting if the blue-red combinations reported as successful for Landsat data (previous topic) had been tested in this study, but there was no mention about it. Anyhow, as the MODIS blue band has a worse spatial resolution (500 m), using only the 250-m red band had the advantage of yielding more detailed maps of SDD and making the algorithm more suitable for small lakes, besides the already-mentioned vulnerability of the blue band to atmospheric effects.
5.2 Diffuse attenuation coefficients Kd(490) and Kd(PAR)
As described above, algorithms based on combinations of the blue and red bands have been proved suitable for remote estimation of SDD. Interestingly, Song et al. (2017) pointed to the blue-red difference as the most suitable algorithm for Kd(PAR). But many
other band combinations have been proposed for either Kd(PAR) or Kd(490), which have a
close statistical relationship (Lee et al., 2005; Zhang et al., 2012). Shen et al. (2017) concluded that the best algorithm for Kd(490) was a multiple regression with two ratios
involving the green, red and NIR bands, while a single-NIR-band algorithm was selected by Shi et al. (2014) for estimation of Kd(PAR).
24 5.2.1 Blue-red difference
Song et al. (2017) tested empirical algorithms for retrieval of Kd(PAR) from OLI and
MODIS data. They found that the best algorithm for both cases was the difference between the atmospherically corrected reflectance at the blue (OLI band 2; MODIS band 3) and red bands. The regression models for OLI and MODIS data were, respectively:
𝐾𝑑(PAR) (m−1) = −199.75 × (𝑅𝑡(𝑂𝐿𝐼2)− 𝑅𝑡(𝑂𝐿𝐼3)) + 11.702 (7)
𝐾𝑑(PAR) (m−1) = −215.23 × (𝑅𝑡(𝑀𝑂𝐷3)− 𝑅𝑡(𝑀𝑂𝐷1)) + 0.112 (8)
The algorithm was calibrated and validated through a 10-fold cross validation method (162 samples for Landsat and 97 for MODIS). The calibration gave r2 = 0.83 and RMSE
= 0.95 m−1 for Landsat and r2 = 0.86 and RMSE = 0.91 m−1 for MODIS data. The validation gave RMSE = 0.971 m−1 and mean relative error (MRE) = 0.33 for Landsat and RMSE = 0.91 m−1 and MRE = 0.19 for MODIS data. Although both algorithms showed good performance, the MODIS algorithm systematically predicted a higher Kd(PAR) and, for the same areas of the images, the blue-red difference from MODIS
was also higher. Among several possible explanations, the differences in atmospheric correction seemed to be the most important one, as evidenced by the fact that for MODIS the blue-red difference was generally negative, as expected for Case II waters, whereas for Landsat the opposite was observed. The atmospheric correction of MODIS images was the standard correction of the MYD09GA product, while for Landsat they applied the dark-object subtraction (DOS) method.
5.2.2 Dual band ratio
An algorithm based on two band ratios was developed for retrieving the Kd(490) in the
25
𝐾𝑑(490) (m−1) = 11.89 × (𝑅𝑟𝑠(𝑂𝐿𝐶𝐼10)/𝑅𝑟𝑠(𝑂𝐿𝐶𝐼6)) + 6.81 × (𝑅𝑟𝑠(𝑂𝐿𝐶𝐼12)/
𝑅𝑟𝑠(𝑂𝐿𝐶𝐼6)) − 6.17
(9)
where OLCI6, 10 and 12 refer to the OLCI bands centered at 560, 681, and 754 nm. The algorithm showed good accuracy in both the calibration [r2 = 0.81; RMSE = 0.99 m−1; RMSE (%) = 25.36; mean normalized bias (MNB, %) = 2.81; normalized root mean square error (NRMS, %) = 25.73] and the validation [r2 = 0.83; RMSE = 1.06 m−1; RMSE (%) = 30.81; MNB (%) = -3.89; NRMS (%) = 31.63]. The combination of two band ratios granted some tolerance to the variation in the optically dominant constituent (inorganic tripton versus phytoplankton). Even though the first ratio (Rrs(OLCI10)/Rrs(OLCI6)) alone had
acceptable accuracy [r2 = 0.66; RMSE = 1.32 m−1; RMSE (%) = 31.58; MNB (%) = 6.41; NRMS (%) = 31.56], working well for areas of high inorganic turbidity, it failed for phytoplankton bloom areas. The dual band-ratio algorithm also had better performance than empirical and semi-analytical algorithms selected from the literature that were tested with the same data. An additional comparison was made with a semi-analytical algorithm developed for retrieval of Kd(490) from MODIS images (Huang et al., 2017), with
encouraging results. The spatial distribution patterns of the MODIS-derived and OLCI-derived Kd(490) showed great agreement. As for the applicability to other lakes, the
algorithm was tested for the Lake Chaohu without recalibration (r2 = 0.60; RMSE = 1.38 m−1) and with recalibration for local data (r2 = 0.80; RMSE = 0.54 m−1).
5.2.3 NIR band
A different algorithm was proposed, however, for the same Lake Taihu. After comparing the results for MERIS data with and without atmospheric correction, Shi et al. (2014) concluded that the best algorithm for estimating the Kd(PAR) was a simple regression of
the TOA reflectance at band 10 (MER10, 754 nm):
𝐾𝑑(PAR) (m−1) = 11.535 × 𝑙𝑛(𝑅𝑇𝑂𝐴(𝑀𝐸𝑅10)) − 29.99 (10)
After calibration [n = 48; r2 = 0.74; RMSE = 1.6; MAPE (%): 29.8], the algorithm was successfully validated [n = 48; r2 = 0.73; RMSE = 1.7; mean relative error (MRE) = 0.004-1.32; MAPE = 0.294].
