Monitoring multi-temporal and spatial variations of water transparency in the Jiaozhou Bay using GOCI data

Water transparency, commonly measured as Secchi disk depth (SDD), is essential for describing the optical properties of coastal waters. We proposed a regional linear corrected SDD estimation model based on the North Sea Mathematical Models for GOCI and the mechanical model developed by Lee et al. (2015) in the Jiaozhou Bay. Combined with the multiple variable linear regression analysis, the diurnal SDD variations of the bay inside and the bay mouth are controlled by the solar zenith angle (SZA) and tides. The bay outside mainly varies with SZA. From GOCI observations between 2011 and 2021, wind force influenced the entire area on the inner-annual SDD variations. It exhibits an increasing trend in the inter-annual dynamics, which was more stable inside the bay with an annual increase of 0.035 m, and air temperature was the most significant contribution. However, human activities cannot be ignored in causing water environment changes

Assessment of Normalized Water-Leaving Radiance Derived From Goci Using AERONET-OC Data

The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater properties, a fundamental understanding of the uncertainties of normalized water-leaving radiance (nLw) products introduced by atmospheric correction algorithms is necessarily required. This paper presents the uncertainties by accessing GOCI-derived nLw products generated by two commonly used operational atmospheric algorithms, the Korea Ocean Satellite Center (KOSC) standard atmospheric algorithm adopted in GOCI Data Processing System (GDPS) and the NASA standard atmospheric algorithm implemented in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package), with Aerosol Robotic Network Ocean Color (AERONET-OC) provided nLw data. The nLw data acquired from the GOCI sensor based on two algorithms and four AERONET-OC sites of Ariake, Ieodo, Socheongcho, and Gageocho from October 2011 to March 2019 were obtained, matched, and analyzed. The GDPS-generated nLw data are slightly better than that with SeaDAS at visible bands; however, the mean percentage relative errors for both algorithms at blue bands are over 30%. The nLw data derived by GDPS is of better quality both in clear and turbid water, although underestimation is observed at near-infrared (NIR) band (865 nm) in turbid water. The nLw data derived by SeaDAS are underestimated in both clear and turbid water, and the underestimation worsens toward short visible bands. Moreover, both algorithms perform better at noon (02 and 03 Universal Time Coordinated (UTC)), and worse in the early morning and late afternoon. It is speculated that the uncertainties in nLw measurements arose from aerosol models, NIR water-leaving radiance correction method, and bidirectional reflectance distribution function (BRDF) correction method in corresponding atmospheric correction procedure

Remote sensing of phytoplankton biomass in oligotrophic and mesotrophic lakes: addressing estimation uncertainty through machine learning

Phytoplankton constitute the bottom of the aquatic food web, produce half of Earth’s oxygen and are part of the global carbon cycle. A measure of aquatic phytoplankton biomass therefore functions as a biological indicator of water status and quality. The abundance of phytoplankton in most lakes on Earth is low because they are weakly nourished (i.e., oligotrophic). It is practically infeasible to measure the millions of oligotrophic lakes on Earth through field sampling. Fortunately, phytoplankton universally contain the optically active pigment chlorophyll-a, which can be detected by optical sensors. Earth-orbiting satellite missions carry optical sensors that provide unparalleled high spatial coverage and temporal revisit frequency of lakes. However, when compared to waters with high nutrient loading (i.e., eutrophic), the remote sensing estimation of phytoplankton biomass in oligotrophic lakes is prone to high estimation uncertainties. Accurate retrieval of phytoplankton biomass is severely constrained by imperfect atmospheric correction, complicated inherent optical property (IOP) compositions, and limited model applicability. In order to address and reduce the current estimation uncertainties in phytoplankton remote sensing of low - moderate biomass lakes, machine learning is used in this thesis. In the first chapter the chlorophyll-a concentration (chla) estimation uncertainty from 13 chla algorithms is characterised. The uncertainty characterisation follows a two-step procedure: 1. estimation of chla from a representative dataset of field measurements and quantification of estimation uncertainty, 2. characterisation of chla estimation uncertainty. The results of this study show that estimation uncertainty across the dataset used in this chapter is high, whereby chla is both systematically under- and overestimated by the tested algorithms. Further, the characterisation reveals algorithm-specific causes of estimation uncertainty. The uncertainty sources for each of the tested algorithms are discussed and recommendations provided to improve the estimation capabilities. In the second chapter a novel machine learning algorithm for chla estimation is developed by combining Bayesian theory with Neural Networks (NNs). The resulting Bayesian Neural Networks (BNNs) are designed for the Ocean and Land Cover Instrument (OLCI) and MultiSpectral Imager (MSI) sensors aboard the Sentinel-3 and Sentinel-2 satellites, respectively. Unlike established chla algorithms, the BNNs provide a per-pixel uncertainty associated with estimated chla. Compared to reference chla algorithms, gains in chla estimation accuracy > 15% are achieved. Moreover, the quality of the provided BNN chla uncertainty is analysed. For most observations (> 75%) the BNN uncertainty estimate covers the reference in situ chla value, but the uncertainty calibration is not constantly accurate across several assessment strategies. The BNNs are applied to OLCI and MSI products to generate chla and uncertainty estimates in lakes from Africa, Canada, Europe and New Zealand. The BNN uncertainty estimate is furthermore used to deal with uncertainty introduced by prior atmospheric correction algorithms, adjacency affects and complex optical property compositions. The third chapter focuses on the estimation of lake biomass in terms of trophic status (TS). TS is conventionally estimated through chla. However, the remote sensing of chla, as shown in the two previous chapters, can be prone to high uncertainty. Therefore, in this chapter an algorithm for the direct classification of TS is designed. Instead of using a single algorithm for TS estimation, multiple individual algorithms are ensembled through stacking, whose estimates are evaluated by a higher-level meta-learner. The results of this ensemble scheme are compared to conventional switching of reference chla algorithms through optical water types (OWTs). The results show that estimation of TS is increased through direct classification rather than indirect estimation through chla. The designed meta-learning algorithm outperforms OWT switching of chla algorithms by 5-12%. Highest TS estimation accuracy is achieved for high biomass waters, whereas for low biomass waters extremely turbid waters produced high TS estimation uncertainty. Combining an ensemble of algorithms through a meta-learner represents a solution for the problem of algorithm selection across the large variation of global lake constituent concentrations and optical properties

Water Quality Modelling Using Multivariate Statistical Analysis and Remote Sensing in South Florida

The overall objective of this dissertation research is to understand the spatiotemporal dynamics of water quality parameters in different water bodies of South Florida. Two major approaches (multivariate statistical techniques and remote sensing) were used in this study. Multivariate statistical techniques include cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), discriminant analysis (DA), absolute principal component score-multiple linear regression (APCS-MLR) and PMF receptor modeling techniques were used to assess the water quality and identify and quantify the potential pollution sources affecting the water quality of three major rivers of South Florida. For this purpose, a 15-year (2000–2014) data set of 12 water quality variables, and about 35,000 observations were used. Agglomerative hierarchical CA grouped 16 monitoring sites into three groups (low pollution, moderate pollution, and high pollution) based on their similarity of water quality characteristics. DA, as an important data reduction method, was used to assess the water pollution status and analysis of its spatiotemporal variation. PCA/FA identified potential pollution sources in wet and dry seasons, respectively, and the effective mechanisms, rules, and causes were explained. The APCS-MLR and PMF models apportioned their contributions to each water quality variable. Also, the bio-physical parameters associated with the water quality of the two important water bodies of Lake Okeechobee and Florida Bay were investigated based on remotely sensed data. The principal objective of this part of the study is to monitor and assess the spatial and temporal changes of water quality using the application of integrated remote sensing, GIS data, and statistical techniques. The optical bands in the region from blue to near infrared and all the possible band ratios were used to explore the relation between the reflectance of a waterbody and observed data. The developed MLR models appeared to be promising for monitoring and predicting the spatiotemporal dynamics of optically active and inactive water quality characteristics in Lake Okeechobee and Florida Bay. It is believed that the results of this study could be very useful to local authorities for the control and management of pollution and better protection of water quality in the most important water bodies of South Florida

Retrieval of Lake Erie Water Quality Parameters from Satellite Remote Sensing and Impact on Simulations with a 1-D Lake Model

Lake Erie is a freshwater lake, and the most southern of the Laurentian Great Lakes in North America. It is the smallest by volume, the fourth largest in surface area (25,700 km2), and the shallowest of the Laurentian Great Lakes. The lake’s high productivity and warm weather in its watershed has attracted one-third of the total human population of the Great Lake’s basin. The industrial and agricultural activities of this huge population has caused serious environmental problems for Lake Erie namely harmful algal blooms, dissolved organic/inorganic matters from river inputs, and sediment loadings. If these sorts of water contaminations exceed a certain level, it can seriously influence the lake ecosystem. Hence, an effective and continuous water quality monitoring program is of outmost importance for Lake Erie. The use of Earth observation satellites to improve monitoring of environmental changes in water bodies has been receiving increased attention in recent years. Satellite observations can provide long term spatial and temporal trends of water quality indicators which cannot be achieved through discontinuous conventional point-wise in situ sampling. Different regression-based empirical models have been developed in the literature to derive the water optical properties from a single (or band ratio of) remote sensing reflectance (radiance). In situ measurements are used to build these regressions. The repeated in situ measurements in space and/or time causes clustered and correlated data that violates the assumption of regression models. Considering this correlation in developing regression models was one of the topics examined in this thesis. More complicated semi-analytical models are applied in Case II waters, aiming to distinguish several constituents confounding water-leaving signals more effectively. The MERIS neural network (NN) algorithms are the most widely used among semi-analytical models. The applicability of these algorithms to derive chl-a concentration and Secchi Disk Depth (SDD) in Lake Erie was assessed for the first time in this thesis. Satellite-observations of water turbidity were then coupled with a 1-D lake model to improve its performance on Lake Erie, where the common practice is to use a constant value for water turbidity in the model due to insufficient in situ measurements of water turbidity for lakes globally. In the first chapter, four well-established MERIS NN algorithms to derive chl-a concentration as well as two band-ratio chl-a related indices were evaluated against in situ measurements. The investigated products are those produced by NN algorithms, including Case 2 Regional (C2R), Eutrophic (EU), Free University of Berlin WeW WATER processor (FUB/WeW), and CoastColour (CC) processors, as well as from band-ratio algorithms of fluorescence line height (FLH) and maximum chlorophyll index (MCI). Two approaches were taken to compare and evaluate the performance of these algorithms to predict chl-a concentration after lake-specific calibration of the algorithms. First, all available chl-a matchups, which were collected from different locations on the lake, were evaluated at once. In the second approach, a classification of three optical water types was applied, and the algorithms’ performance was assessed for each type, individually. The results of this chapter show that the FUB/WeW processor outperforms other algorithms when the full matchup data of the lake was used (root mean square error (RMSE) = 1.99 mg m-3, index-of-agreement (I_a) = 0.67). However, the best performing algorithm was different when each water optical type was investigated individually. The findings of this study provide practical and valuable information on the effectiveness of the already existing MERIS-based algorithms to derive the trophic state of Lake Erie, an optically complex lake. Unlike the first chapter, where physically-based and already trained algorithms were implemented to evaluate satellite derived chl-a concentration, in the next chapter, two lake-specific, robust semi-empirical algorithms were developed to derive chl-a and SDD using Linear Mixed Effect (LME) models. LME considers the correlation that exists in the field measurements which have been repeatedly performed in space and time. Each developed algorithm was then employed to investigate the monthly-averaged spatial and temporal trends of chl-a concentration and water turbidity during the period of 2005-2011. SDD was used as the indicator of water turbidity. LME models were developed between the logarithmic scale of the parameters and the band ratio of B7:665 nm to B9:708.75 nm for log10chl-a, and the band ratio of B6:620 nm to B4:510 nm for log10SDD. The models resulted in RMSE of 0.30 for log10chl-a and 0.19 for log10SDD. Maps produced with the two LME models revealed distinct monthly patterns for different regions of the lake that are in agreement with the biogeochemical properties of Lake Erie. Lastly the water turbidity (extinction coefficient; Kd) of Lake Erie was estimated using the globally available satellite-based CC product. The CC-derived Kd product was in a good agreement with the SDD field observations (RMSE=0.74 m-1, mean bias error (MBE)=0.53 m-1, I_a=0.53). CC-derived Kd was then used as input for simulations with the 1-D Freshwater Lake (FLake) model. An annual average constant Kd value calculated from the CC product improved simulation results of lake surface water temperature (LSWT) compared to a “generic” constant value (0.2 m-1) used in previous studies (CC lake-specific yearly average Kd value: RMSE=1.54 ºC, MBE= -0.08 ºC; generic constant Kd value: RMSE=1.76 ºC, MBE= -1.26 ºC). Results suggest that a time-independent, lake-specific, and constant Kd value from CC can improve FLake LSWT simulations with sufficient accuracy. A sensitivity analysis was also conducted to assess the performance of FLake to simulate LSWT, mean water column temperature (MWCT) and mixed layer depth (MLD) using different values of Kd. Results showed that the model is very sensitive to the variations of Kd, particularly when Kd value is below 0.5 m-1. The sensitivity of FLake to Kd variations was more pronounced in simulations of MWCT and MLD. This study shows that a global mapping of the extinction coefficient can be created using satellite-based observations of lakes optical properties to improve the 1-D FLake model. Overall, results from this thesis clearly demonstrate the benefits of remote sensing measurements of water quality parameters (such as chl-a concentration and water turbidity) for lake monitoring. Also, this research shows that the integration of space-borne water clarity (extinction coefficient) measurements into the 1-D FLake model improves simulations of LSWT

Validation and Calibration of QAA Algorithm for CDOM Absorption Retrieval in the Changjiang (Yangtze) Estuarine and Coastal Waters

Distribution, migration and transformation of chromophoric dissolved organic matter (CDOM) in coastal waters are closely related to marine biogeochemical cycle. Ocean color remote sensing retrieval of CDOM absorption coefficient (ag(λ)) can be used as an indicator to trace the distribution and variation characteristics of the Changjiang diluted water, and further to help understand estuarine and coastal biogeochemical processes in large spatial and temporal scales. The quasi-analytical algorithm (QAA) has been widely applied to remote sensing inversions of optical and biogeochemical parameters in water bodies such as oceanic and coastal waters, however, whether the algorithm can be applicable to highly turbid waters (i.e., Changjiang estuarine and coastal waters) is still unknown. In this study, large amounts of in situ data accumulated in the Changjiang estuarine and coastal waters from 9 cruise campaigns during 2011 and 2015 are used to verify and calibrate the QAA. Furthermore, the QAA is remodified for CDOM retrieval by employing a CDOM algorithm (QAA_CDOM). Consequently, based on the QAA and the QAA_CDOM, we developed a new version of algorithm, named QAA_cj, which is more suitable for highly turbid waters, e.g., Changjiang estuarine and coastal waters, to decompose ag from adg (CDOM and non-pigmented particles absorption coefficient). By comparison of matchups between Geostationary Ocean Color Imager (GOCI) retrievals and in situ data, it reveals that the accuracy of retrievals from calibrated QAA is significantly improved. The root mean square error (RMSE), mean absolute relative error (MARE) and bias of total absorption coefficients (a(λ)) are lower than 1.17, 0.52 and 0.66 m−1, and ag(λ) at 443 nm are lower than 0.07, 0.42 and 0.018 m−1. These results indicate that the calibrated algorithm has a better applicability and prospect for highly turbid coastal waters with extremely complicated optical properties. Thus, reliable CDOM products from the improved QAA_cj can advance our understanding of the land-ocean interaction process by earth observations in monitoring spatial-temporal distribution of the river plume into sea