A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques

Remotely sensed data can reinforce the abilities of water resources researchers and decision makers to monitor waterbodies more effectively. Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e., suspended sediments, colored dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as airplanes, are currently used to measure the amount of radiation at different wavelengths reflected from the water’s surface. In this review paper, various properties (spectral, spatial and temporal, etc.) of the more commonly employed spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. Furthermore, this paper investigates the commonly used approaches and sensors employed in evaluating and quantifying the eleven water quality parameters. The parameters include: chlorophyll-a (chl-a), colored dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD)

Sensor capability and atmospheric correction in ocean colour remote sensing

© 2015 by the authors; licensee MDPI, Basel, Switzerland. Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi-and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges

A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans

The need for more effective environmental monitoring of the open and coastal ocean has recently led to notable advances in satellite ocean color technology and algorithm research. Satellite ocean color sensors' data are widely used for the detection, mapping and monitoring of phytoplankton blooms because earth observation provides a synoptic view of the ocean, both spatially and temporally. Algal blooms are indicators of marine ecosystem health; thus, their monitoring is a key component of effective management of coastal and oceanic resources. Since the late 1970s, a wide variety of operational ocean color satellite sensors and algorithms have been developed. The comprehensive review presented in this article captures the details of the progress and discusses the advantages and limitations of the algorithms used with the multi-spectral ocean color sensors CZCS, SeaWiFS, MODIS and MERIS. Present challenges include overcoming the severe limitation of these algorithms in coastal waters and refining detection limits in various oceanic and coastal environments. To understand the spatio-temporal patterns of algal blooms and their triggering factors, it is essential to consider the possible effects of environmental parameters, such as water temperature, turbidity, solar radiation and bathymetry. Hence, this review will also discuss the use of statistical techniques and additional datasets derived from ecosystem models or other satellite sensors to characterize further the factors triggering or limiting the development of algal blooms in coastal and open ocean waters

Satellite estimates of wide-range suspended sediment concentrations in Changjiang (Yangtze) estuary using MERIS data

The Changjiang (Yangtze) estuarine and coastal waters are characterized by suspended sediments over a wide range of concentrations from 20 to 2,500 mg l-1. Suspended sediment plays important roles in the estuarine and coastal system and environment. Previous algorithms for satellite estimates of suspended sediment concentration (SSC) showed a great limitation in that only low to moderate concentrations (up to 50 mg l-1) could be reliably estimated. In this study, we developed a semi-empirical radiative transfer (SERT) model with physically based empirical coefficients to estimate SSC from MERIS data over turbid waters with a much wider range of SSC. The model was based on the Kubelka–Munk two-stream approximation of radiative transfer theory and calibrated using datasets from in situ measurements and outdoor controlled tank experiments. The results show that the sensitivity and saturation level of remote-sensing reflectance to SSC are dependent on wavelengths and SSC levels. Therefore, the SERT model, coupled with a multi-conditional algorithm scheme adapted to satellite retrieval of wide-range SSC, was proposed. Results suggest that this method is more effective and accurate in the estimation of SSC over turbid water

Remote sensing, numerical modelling and ground truthing for analysis of lake water quality and temperature

Freshwater accounts for just 2.5% of the earth’s water resources, and its quality and availability are becoming an issue of global concern in the 21st century. Growing human population, over-exploitation of water sources and pressures of global warming mean that both water quantity and quality are affected. In order to effectively manage water quality there is a need for increased monitoring and predictive modelling of freshwater resources. To address these concerns in New Zealand inland waters, an approach which integrates biological and physical sciences is needed. Remote sensing has the potential to allow this integration and vastly increase the temporal and spatial resolution of current monitoring techniques, which typically involve collecting grab-samples. In a complementary way, lake modelling has the potential to enable more effective management of water resources by testing the effectiveness of a range of possible management scenarios prior to implementation. Together, the combination of remote sensing and modelling data allows for improved model initialisation, calibration and validation, which ultimately aid in understanding of complex lake ecosystem processes. This study investigated the use of remote sensing using empirical and semi-analytical algorithms for the retrieval of chlorophyll a (chl a), tripton, suspended minerals (SM), total suspended sediment (SS) and water surface temperature. It demonstrated the use of spatially resolved statistical techniques for comparing satellite estimated and 3-D simulated water quality and temperature. An automated procedure was developed for retrieval of chl a from Landsat Enhanced Thematic Mapper (ETM+) imagery, using 106 satellite images captured from 1999 to 2011. Radiative transfer-based atmospheric correction was applied to images using the Second Simulation of the Satellite in the Solar Spectrum model (6sv). For the estimation of chl a over a time series of images, the use of symbolic regression resulted in a significant improvement in the precision of chl a hindcasts compared with traditional regression equations. Results from this investigation suggest that remote sensing provides a valuable tool to assess temporal and spatial distributions of chl a. Bio-optical models were applied to quantify the physical processes responsible for the relationship between chl a concentrations and subsurface irradiance reflectance used in regression algorithms, allowing the identification of possible sources of error in chl a estimation. While the symbolic regression model was more accurate than traditional empirical models, it was still susceptible to errors in optically complex waters such as Lake Rotorua, due to the effect of variations of SS and CDOM on reflectance. Atmospheric correction of Landsat 7 ETM+ thermal data was carried out for the purpose of retrieval of lake water surface temperature in Rotorua lakes, and Lake Taupo, North Island, New Zealand. Atmospheric correction was repeated using four sources of atmospheric profile data as input to a radiative transfer model, MODerate resolution atmospheric TRANsmission (MODTRAN) v.3.7. The retrieved water temperatures from 14 images between 2007 and 2009 were validated using a high-frequency temperature sensor deployed from a mid-lake monitoring buoy at the water surface of Lake Rotorua. The most accurate temperature estimation for Lake Rotorua was with radiosonde data as an input into MODTRAN, followed by Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2, Atmospheric Infrared Sounder (AIRS) Level 3, and NASA data. Retrieved surface water temperature was used for assessing spatial heterogeneity of surface water temperature simulated with a three-dimensional (3-D) hydrodynamic model (ELCOM) of Lake Rotoehu, located approximately 20 km east of Lake Rotorua. This comparison demonstrated that simulations reproduced the dominant horizontal variations in surface water temperature in the lake. The transport and mixing of a geothermal inflow and basin-scale circulation patterns were inferred from thermal distributions from satellite and model estimations of surface water temperature and a spatially resolved statistical evaluation was used to validate simulations. This study has demonstrated the potential of accurate satellite-based thermal monitoring to validate water surface temperature simulated by 3-D hydrodynamic models. Semi-analytical and empirical algorithms were derived to determine spatial and temporal variations in SS in Lake Ellesmere, South Island, New Zealand, using MODIS band 1. The semi-analytical model and empirical model had a similar level of precision in SS estimation, however, the semi-analytical model has the advantage of being applicable to different satellite sensors, spatial locations, and SS concentration ranges. The estimations of SS concentration (and estimated SM concentration) from the semi-analytical model were used for a spatially resolved validation of simulations of SM derived from ELCOM-CAEDYM. Visual comparisons were compared with spatially-resolved statistical techniques. The spatial statistics derived from the Map Comparison Kit allowed a non-subjective and quantitative method to rank simulation performance on different dates. The visual and statistical comparison between satellite estimated and model simulated SM showed that the model did not perform well in reproducing both basin-scale and fine-scale spatial variation in SM derived from MODIS satellite imagery. Application of the semi-analytical model to estimate SS over the lifetime of the MODIS sensor will greatly extend its spatial and temporal coverage for historical monitoring purposes, and provide a tool to validate SM simulated by 1-D and 3-D models on a daily basis. A bio-optical model was developed to derive chl a, SS concentrations, and coloured dissolved organic matter /detritus absorption at 443 nm, from MODIS Aqua subsurface remote sensing reflectance of Lake Taupo, a large, deep, oligotrophic lake in North Island, New Zealand. The model was optimised using in situ inherent optical properties (IOPs) from the literature. Images were atmospherically corrected using the radiative transfer model 6sv. Application of the bio-optical model using a single chl a-specific absorption spectrum (a*ϕ(λ)) resulted in low correlation between estimated and observed values. Therefore, two different absorption curves were used, based on the seasonal dominance of phytoplankton phyla with differing absorption properties. The application of this model resulted in reasonable agreement between modelled and in situ chl a concentrations. Highest concentrations were observed during winter when Bacillariophytes (diatoms) dominated the phytoplankton assemblage. On 4 and 5 March 2004 an unusually large turbidity current was observed originating from the Tongariro River inflow in the south-east of the lake. In order to resolve fine details of the plume, empirical relationships were developed between MODIS band 1 reflectance (250 m resolution) and SS estimated from MODIS bio-optical features (1 km resolution) were used estimate SS at 250 m resolution. Complex lake circulation patterns were observed including a large clockwise gyre. With the development of this bio-optical model MODIS can potentially be used to remotely sense water quality in near real time, and the relationship developed for B1 SS allows for resolution of fine-scale features such turbidity currents

Remote sensing of inland waters: challenges, progress and future directions

Monitoring and understanding the physical, chemical and biological status of global inland waters are immensely important to scientists and policy makers alike. Whereas conventional monitoring approaches tend to be limited in terms of spatial coverage and temporal frequency, remote sensing has the potential to provide an invaluable complementary source of data at local to global scales. Furthermore, as sensors, methodologies, data availability and the network of researchers and engaged stakeholders in this field develop, increasingly widespread use of remote sensing for operational monitoring of inland waters can be envisaged. This special issue on Remote Sensing of Inland Waters comprises 16 articles on freshwater ecosystems around the world ranging from lakes and reservoirs to river systems using optical data from a range of in situ instruments as well as airborne and satellite platforms. The papers variably focus on the retrieval of in-water optical and biogeochemical parameters as well as information on the biophysical properties of shoreline and benthic vegetation. Methodological advances include refined approaches to adjacency correction, inversion-based retrieval models and in situ inherent optical property measurements in highly turbid waters. Remote sensing data are used to evaluate models and theories of environmental drivers of change in a number of different aquatic ecosystems. The range of contributions to the special issue highlights not only the sophistication of methods and the diversity of applications currently being developed, but also the growing international community active in this field. In this introductory paper we briefly highlight the progress that the community has made over recent decades as well as the challenges that remain. It is argued that the operational use of remote sensing for inland water monitoring is a realistic ambition if we can continue to build on these recent achievements.Output Type: Editoria

Extraction of Spatial and Temporal Patterns of Concentrations of Chlorophyll-a and Total Suspended Matter in Poyang Lake Using GF-1 Satellite Data

Poyang Lake is the largest freshwater lake in China. Its ecosystem services and functions, such as water conservation and the sustaining of biodiversity, have significant impacts on the security and sustainability of the regional ecology. The lake and wetlands of the Poyang Lake are among protected aquatic ecosystems with global significance. The Poyang Lake region has recently experienced increased urbanization and anthropogenic disturbances, which has greatly impacted the lake environment. The concentrations of chlorophyll-a (Chl-a) and total suspended matter (TSM) are important indicators for assessing the water quality of lakes. In this study, we used data from the Gaofen-1 (GF-1) satellite, in situ measurements of the reflectance of the lake water, and the analysis of the Chl-a and TSM concentrations of lake water samples to investigate the spatial and temporal variation and distribution patterns of the concentrations of Chl-a and TSM. We analyzed the measured reflectance spectra and conducted correlation analysis to identify the spectral bands that are sensitive to the concentration of Chl-a and TSM, respectively. The study suggested that the wavelengths corresponding to bands 1, 3, and 4 of the GF-1 images were the most sensitive to changes in the concentration of Chl-a. The results showed that the correlation between the reflectance and TSM concentration was the highest for wavelengths that corresponded to band 3 of the GF-1 satellite images. Based on the analysis, bands 1, 3, and 4 of GF-1 were selected while using the APPEL (APProach by ELimination) model and were used to establish a model for the retrieval of Chl-a concentrations. A single-band model that was based on band 3 of GF-1 was established for the retrieval of TSM concentrations. The modeling results revealed the spatial and temporal variations of water quality in Poyang Lake between 2015 and 2016 and demonstrated the capacities of GF-1 in the monitoring of lake environment

Pareamento bacia-lagoa usando modelagem hidrológica-hidrodinâmica e sensoriamento remoto

A gestão de recursos hídricos tornou-se cada vez mais complexa devido ao rápido crescimento sócio-econômico e as mudanças ambientais nas bacias hidrográficas nas últimas décadas. Modelos computacionais são importantes ferramentas de suporte na gestão de recursos hídricos e tomada de decisões devido a sua funcionalidade, provendo informações importantes sobre os principais processos físicos, químicos e biológicos, e permitindo melhorar o entendimento desses processos, os quais ocorrem em diferentes escalas espaciais e temporais. Na presente tese, o objetivo foi compreender o funcionamento hidrológico do sistema integrado bacia hidrográfica - lagoa, e os efeitos na hidrodinâmica do lago, utilizando como suporte o acoplamento da modelagem hidrológica - hidrodinâmica, e o uso de técnicas de sensoriamento remoto para o monitoramento de parâmetros de qualidade da água (e.g., clorofilaa, temperatura da superfície d’água e níveis da água). A área de estudo é a bacia hidrográfica da Lagoa Mirim, localizada no sul do Brasil, possuindo uma área total de 58.000 km2 (56% no Uruguai e o restante no Brasil). Foram propostos e testados modelos empíricos para estimativa de clorofila-a emumlago raso subtropical, baseados em imagens do sensor MODIS e técnicas estatísticas. Além disso, foi desenvolvido e avaliado o acoplamento da modelagem hidrológica-hidrodinâmica de grande escala e o sensoriamento remoto. O modelo hidrológico distribuído de grande escala MGBIPH acoplado com o modelo hidrodinâmico IPH-ECO foi utilizado para simular a bacia hidrográfica e os principais componentes hidrodinâmicos da Lagoa Mirim. O modelo mostrou bom desempenho quando comparado com observações de vazões, além de dados provenientes de sensoriamento remoto, através de altimetria espacial. As simulações mostraram importantes aspectos sobre a estrutura de fluxo, campos de velocidade e níveis d’água na lagoa, assim como a influência de grandes rios, forçantes externas como o vento (intensidade e direção) e o impacto do estressor antrópico (retiradas para irrigação) no sistema. As simulações permitiram avaliar aspectos relacionados com as variações espaciais e temporais (diurna, mensal, sazonal e inter-anual) da temperatura da superfície da água, a dinâmica dos fluxos de calor (sensível e latente) e os efeitos de eventos meteorológicos de pequena escala como frentes frias, os quais têm um impacto significativo sobre a temperatura superficial da água e os fluxos de calor na lagoa. Quanto aos modelos empíricos para estimativa de clorofila-a a partir do MODIS, os resultados mostram que um simples e eficiente modelo desenvolvido a partir de análise de regressão múltipla, apresentou ligeiras vantagens sobre os modelos de redes neurais artificiais, modelos multiplicativos não paramétricos e modelos empíricos (e.g., Appel, Kahru, FAI e O14a) usualmente utilizados na estimativa de Chl-a em ambientes aquáticos. Resultados também indicam que é inapropriado generalizar um único modelo desenvolvido a partir do conjunto total de dados, para estimar concentrações de Chl-a na lagoa, o que corrobora a heterogeneidade espacial na distribuição de Chl-a e as diferenças entre regiões (litoral e pelágica). A modelagem hidrológica-hidrodinâmica de grande escala apoiada por informação de sensoriamento remoto, mostrou ser uma abordagem promissora para o entendimento da estrutura e funcionamento de lagoas rasas de grande porte e longo prazo, úteis para a gestão integrada dos recursos hídricos.The last decade, the water resource management is being complex due to the rapid socioeconomic development and environmental changes in river basins. Computations models are important support tools in water resource management and make decision providing important information and allowing a better comprehension of the physical, chemical and biologic processes, which occur in di erent temporal/spatial scales. In this thesis, the objective was to understand the hydrological functioning of the integrated basin- lake system and its e ects on hydrodynamics, using hydrodynamic - hydrodynamic modeling and water quality monitoring (e.g., chlorophyll-a, water surface temperature and water levels) from remote sensing techniques. The study area is the Lake Mirim basin, located between Brazil and Uruguay (basin total area 58.000 km2). Empirical models were proposed and tested to chlorophyll-a estimation in a shallow subtropical lake, based on MODIS imagery and statistics techniques. In addition, we developed and assessed the coupling of large scale hydrological/hydrodynamic modeling and remote sensing techniques. The large-scale distributed hydrological model MGB-IPH coupled with the hydrodynamic model IPHECO were used to simulate the river basin and the hydrodynamic components of the Lake Mirim. The coupled model showed good performance when compared to in-situ measurements and satellite altimetry data. The simulations showed important aspects relate to flow structure, velocity fields and lake water levels, as well as the influence of large rivers, external forcing as such the wind (intensity and direction), and the impact of anthropogenic stressors (irrigation withdrawals) in the system. The simulations allowed assessing the spatial and temporal variations (diurnal, monthly, seasonal and inter-annual) in the water surface temperature, heat fluxes dynamics (sensible and latent) and the e ects of short time-scale events, as such cold fronts passages over the lake, which cause strong impacts on the water surface temperature and heat fluxes in the lake. Regarding the empirical models developed to chlorophyll-a estimation from MODIS imagery, the results showed that a simple and e cient model developed from multiple regression analysis, performed best in comparison with artificial neural network models, non-parametric multiplicative models, and empirical models (e.g., Appel, Kahru, FAI and O14a) common used in the Chl-a estimation in aquatics environments. Results also indicated that is inappropriate to generalize a single model developed from the total datasets to estimates Chl-a in the lake, which corroborates the spatial heterogeneity (Chl-a distribution) and the di erences among regions (littoral and pelagic). The synergy between large-scale hydrological-hydrodynamic modeling, in situ measurements and remote sensing techniques provided a promising approach to improve the comprehension of the structure and ecosystem functioning of large shallow lakes in long-term time scale, useful to water resources management

Bio-Optical Modeling in a Tropical Hypersaline Lagoon Environment

In this chapter, we attempted to present an overview of the use of remote sensing to monitor water quality parameters, mainly chlorophyll-a (chl-a) and turbidity. We summarized the main concepts of bio-optical modeling and presented a case study of the application of the Hyperspectral Imager for the Coastal Ocean (HICO) for the monitoring of water quality in a tropical hypersaline aquatic environment. Using HICO, we evaluated a set of different semi-empirical bio-optical algorithms for chl-a and turbidity estimation developed for inland and oceanic waters in the Araruama Lagoon, RJ, Brazil, which is an extreme environment due to its high salinity values. We also developed an empirical algorithm for both water quality parameters and compared the performances. Results showed that for chl-a estimation all models have a low performance with a normalized root mean square error (NRMSE) varying from 24.13 to 30.46. For turbidity, the bio-optical algorithms showed a better performance with the NRMSE between 15.49 and 28.04. Overall, these results highlight the importance of including extreme environments, such as the Araruama Lagoon, on the validation of bio-optical algorithms as well as the need for new orbital hyperspectral sensors which will improve the development of the field