Comparison of machine learning algorithms for retrieval of water quality indicators in case-II waters: a case study of Hong Kong

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters

Research trends in the use of remote sensing for inland water quality science: Moving towards multidisciplinary applications

Remote sensing approaches to measuring inland water quality date back nearly 50 years to the beginning of the satellite era. Over this time span, hundreds of peer-reviewed publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30 year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10-15 years has brought about a focal shift within the field, where researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters

Remote sensing of chlorophyll-a in small inland waters

Small inland waters (SIWs) – waterbodies smaller than 100 km2 – are the predominant form of lakes globally, yet they are highly subject to water quality degradation, especially due to harmful algae blooms (HABs). Space-borne remote sensing has proven its capability to detect and map HABs in coastal waters as well as large waterbodies mostly through estimating chlorophyll-a (Chla). However, remote retrieval of near-surface Chla concentration in SIWs is challenging due to adjacency effects in remotely sensed signals and substantial in situ optical interferences of various water constituents. Although various algorithms have been developed or adapted to estimate Chla from moderate-resolution terrestrial missions (~ 10 – 60 m), there remains a need for robust algorithms to retrieve Chla in SIWs. Here, we introduce and evaluate new approaches to retrieve Chla in small lakes in a large lake catchment using Sentinel-2 and Landsat-8 imagery. In situ Chla data used in this study originate from various sources with contrasting measurement methods, ranging from field fluorometry to high-performance liquid chromatography (HPLC). Our analysis revealed that in vivo Chla measurements are not consistent with in vitro measurements, especially in high Chla amounts, and should be calibrated before being fed into retrieval models. Calibrated models based on phycocyanin (PC) fluorescence and environmental factors, such as turbidity, significantly decreased Chla retrieval error and increased the range of reconstructed Chla values. The proposed calibration models were then employed to build a consistent dataset of in situ Chla for Buffalo Pound Lake (BPL) – 30 km length and 1 km width – in the Qu’Appelle River drainage basin, Saskatchewan, Canada. Using this dataset for training and test, support vector regression (SVR) models were developed and reliably retrieved Chla in BPL. SVR models outperformed well-known commonly used retrieval models, namely ocean color (OC3), 2band, 3band, normalized difference chlorophyll index (NDCI), and mixture density networks (MDN) when applied on ~200 matchups extracted from atmospherically-corrected Sentinel-2 data. SVR models also performed well when applied to Landsat-8 data and data processed through various atmospheric correction (AC) processors. The proposed models also suggested good transferability over two optical water types (OWTs) found in BPL. Based on prior evaluations of the models’ transferability over OWTs in BPL, locally trained machine-learning (ML) models were extrapolated for regional retrieval of Chla in the Qu’Appelle River drainage basin. The regional approach was trained on in situ Chla data from BPL and retrieved Chla in other six lakes in the drainage basin. The proposed regional approach outperformed a recently developed global approach (MDN) in terms of accuracy, and showed more applicability than local models given the scarcity of in situ data in most lakes. In addition, ML models, e.g., SVR, performed consistently better than other models when employed in the regional approach. A rare phenomenon of marked blue discoloration of ice and water in winter 2021 in Pasqua Lake, a small lake in Qu’Appelle Watershed, provided an opportunity to assess the regional approaches in estimating chlorophyll-a for waterbodies where enough training data is not available. Therefore, using a developed model based on data from BPL, we produced Chla maps and could successfully relate the discoloration event to a late fall bloom in Pasqua Lake. We included the details of that study in Appendix A. Altogether, the models and approaches introduced in this thesis can serve as first steps toward developing a remote-sensing-based early warning system for monitoring HABs in small inland waters. Results showed that the development of an early warning system for SIWs based on Chla monitoring is currently possible, thanks to advancements in medium-resolution satellite sensors, in situ data collection methods, and machine learning algorithms. However, further steps need to be taken to improve the accuracy and reliability of systems: (a) in situ data need to be consistent for being fed into remote sensing models, (b) retrieval models and AC processors should be improved to provide better estimations of Chla, and (c) regional approaches might be developed as alternatives for local and global approaches in the absence of accurate AC processors and scarcity of in situ Chla data

Multidecadal Remote Sensing of Inland Water Dynamics

Remote sensing approaches to measuring inland water dynamics date back more than 50 years. These approaches rely on the unique spectral properties of different waterbodies to delineate surface extents and estimate optically active water quality parameters. Until recently, inland water remote sensing focused largely on localized study domains due to limitations in modelling methods, computing power, and data access. Recent advances in these areas have created novel opportunities for data-driven-multidecadal remote sensing of inland waters at the landscape scale. Here, I highlight the history of inland water remote sensing along with the dominant methodologies, water quality constituents, and limitations involved. I then use this background to contextualize three macroscale inland water remote sensing studies of increasing complexity. The first combines field measurements with remotely sensed surface water extents to identify the impacts of small-scale gold mining in Peru. Our results suggest that mining is leading to synergistic increases in lake area and mercury loading that are significantly heightening exposure risk for people and wildlife. I move from measuring lake extents in Peru to measuring lake color in over 26,000 lakes across the United States. This analysis shows that lake color seasonality can be generalized into five distinct phenology groups that follow well-known patterns of algae growth and succession. The stability of a given lake (i.e. the likelihood it will move from one phenology group to another) is tied to lake and landscape level characteristics including climate and population density. Finally, I move from simple parameters such as quantity and color to estimating multidecadal changes in water clarity in U.S. lakes. I show that lake water clarity in the U.S. has increased by an average of 0.52 cm yr-1 since 1984, largely as a result of extensive U.S. freshwater pollution abatement measures. In combination, these three studies highlight that data intensive remote sensing approaches are expanding the capabilities of inland water remote sensing from local to global scales, and that macroscale remote sensing of inland waters reveals trends and processes that are unobservable using field data alone.Doctor of Philosoph

Optical remote sensing of water quality parameters retrieval in the Barents Sea

This thesis addresses various aspects of monitoring water quality indicators (WQIs) using optical remote sensing technologies. The dynamic nature of aquatic systems necessitate frequent monitoring at high spatial resolution. Machine learning (ML)-based algorithms are becoming increasingly common for these applications. ML algorithms are required to be trained by a significant amount of training data, and their accuracy depends on the performance of the atmospheric correction (AC) algorithm being used for correcting atmospheric effects. AC over open oceanic waters generally performs reasonably well; however, limitations still exist over inland and coastal waters. AC becomes more challenging in the high north waters, such as the Barents Sea, due to the unique in-water optical properties at high latitudes, long ray pathways, as well as the scattering of light from neighboring sea ice into the sensors’ field of view adjacent to ice-infested waters. To address these challenges, we evaluated the performances of state-of-the-art AC algorithms applied to the high-resolution satellite sensors Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI), both for high-north (Paper II) and for global inland and coastal waters (Paper III). Using atmospherically corrected remote sensing reflectance (Rrs ) products, estimated after applying the top performing AC algorithm, we present a new bandpass adjustment (BA) method for spectral harmonization of Rrs products from OLI and MSI. This harmonization will enable an increased number of ocean color (OC) observations and, hence, a larger amount of training data. The BA model is based on neural networks (NNs), which perform a pixel-by-pixel transformation of MSI-derived Rrs to that of OLI equivalent for their common bands. In addition, to accurately retrieve concentrations of Chlorophyll-a (Chl-a) and Color Dissolved Organic Matter (CDOM) from remotely sensed data, we propose in the thesis (Paper 1) an NN-based WQI retrieval model dubbed Ocean Color Net (OCN). Our results indicate that Rrs retrieved via the Acolite Dark Spectrum Fitting (DSF) method is in best agreement with in-situ Rrs observations in the Barents Sea compared to the other methods. The median absolute percentage difference (MAPD) in the blue-green bands ranges from 9% to 25%. In the case of inland and coastal waters (globally), we found that OC-SMART is the top performer, with MAPD Rrs products for varying optical regimes than previously presented methods. Additionally, to improve the analysis of remote sensing spectral data, we introduce a new spatial window-based match-up data set creation method which increases the training data set and allows for better tuning of regression models. Based on comparisons with in-water measured Chl-a profiles in the Barents Sea, our analysis indicates that the MSI-derived Rrs products are more sensitive to the depth-integrated Chl-a contents than near-surface Chl-a values (Paper I). In the case of inland and coastal waters, our study shows that using combined OLI and BA MSI-derived Rrs match-ups results in considerable improvement in the retrieval of WQIs (Paper III). The obtained results for the datasets used in this thesis illustrates that the proposed OCN algorithm shows better performance in retrieving WQIs than other semi-empirical algorithms such as the band ratio-based algorithm, the ML-based Gaussian Process Regression (GPR), as well as the globally trained Case-2 Regional/Coast Colour (C2RCC) processing chain model C2RCC-networks, and OC-SMART. The work in this thesis contributes to ongoing research in developing new methods for merging data products from multiple OC missions for increased coverage and the number of optical observations. The developed algorithms are validated in various environmental and aquatic conditions and have the potential to contribute to accurate and consistent retrievals of in-water constituents from high-resolution satellite sensors

Assessing Interactions between Estuary Water Quality and Terrestrial Land Cover in Hurricane Events with Multi-sensor Remote Sensing

Estuaries are environmentally, ecologically and environmentally important places as they act as a meeting place for land, freshwater and marine ecosystems. They are also called nurseries of the sea as they often provide nesting and feeding habitats for many aquatic plants and animals. These estuaries also withstand the worst of some natural disasters, especially hurricanes. The estuaries as well as the harbored ecosystems undergo significant changes in terms of water quality, vegetation cover etc. and these components are interrelated. When hurricane makes landfall it is necessary to assess the damages as quickly as possible as restoration and recovery processes are time-sensitive. However, assessment of physical damages through inspection and survey and assessment of chemical and nutrient component changes by laboratory testing are time-consuming processes. This is where remote sensing comes into play. With the help of remote sensing images and regression analysis, it is possible to reconstruct water quality maps of the estuary affected. The damage sustained by the vegetation cover of the adjacent coastal watershed can be assessed using Normalized Difference Vegetation Index (NDVI) The water quality maps together with NDVI maps help observe a dynamic sea-land interaction due to hurricane landfall. The observation of hurricane impacts on a coastal watershed can be further enhanced by use of tasseled cap transformation (TCT). TCT plots provide information on a host of land cover conditions with respect to soil moisture, canopy and vegetation cover. The before and after TCT plots help assess the damage sustained in a hurricane event and also see the progress of recovery. Finally, the use of synthetic images obtained by use of data fusion will help close the gap of low temporal resolution of Landsat satellite and this will create a more robust monitoring system

Etude du panache du fleuve Rouge dans le golfe du Tonkin à partir d'une analyse en clusters et de simulations d'ensemble

Cette étude vise à mieux comprendre la variabilité du panache du Fleuve Rouge dans le Golfe du Tonkin (GOT) dans la zone proche de l'embouchure et plus au large, en utilisant la modélisation numérique. Comprendre la variabilité du panache et le devenir des eaux du delta est d'une importance capitale pour une connaissance approfondie et une meilleure capacité de prédiction de la circulation océanique et de l'hydrologie dans le GOT, ainsi que pour une meilleure gestion des eaux côtières et surveillance des écosystèmes côtiers. Dans la première partie de la thèse, une configuration du modèle SYMPHONIE est mise en place avec des forçages réalistes et une grille à haute-résolution variable, sur la base de la configuration de V. Piton (2019). Une simulation sur une période de 6 ans (2011-2016) est réalisée pour étudier la variabilité journalière à interannuelle du panache du Fleuve Rouge et de trois rivières dont l'embouchure est voisine. La simulation est ensuite comparée à plusieurs sources d'observations. Ensuite, le panache est identifié à l'aide de traceurs passifs injectés dans la simulation. En utilisant un algorithme d'apprentissage automatique non supervisé (K-means), les principaux régimes du panache et leur évolution dans le temps sont classifiés et analysés selon quatre clusters, puis liés à différentes conditions environnementales. En hiver, le panache est étroit et reste la plupart du temps le long de la côte en raison du courant côtier et du vent de nord-est. Au début de l'été, le vent de la mousson du sud-ouest fait s'écouler le panache vers le large. Le panache atteint sa plus grande couverture en septembre, après le pic du débit. Sur la verticale, l'épaisseur du panache montre également des variations saisonnières. En hiver, le panache est mélangé sur toute la colonne d'eau, alors qu'en été, le panache peut être détaché à la fois du fond et de la côte. Le panache peut s'approfondir au large en été, en raison de vents forts (en mai, juin) ou spécifiquement en raison d'un tourbillon récurrent se produisant près de 19°N (en août). Cette première partie a fait l'objet d'une publication en 2021. L'analyse en clusters ci-dessus montre que, quel que soit le cluster, le panache est fortement affecté par le vent. Par conséquent, dans la deuxième partie de cette thèse, j'utilise un ensemble de simulations pour évaluer la réponse du modèle aux perturbations ajoutées au vent forçant. La sensibilité de la simulation présentée dans la première partie est évaluée statistiquement en calculant la dispersion et la distribution des variables d'intérêt à partir d'un ensemble de 50 membres. En raison des contraintes de calcul et de mémoire, cette étude est réalisée sur une courte période, de juin à août 2015, correspondant à la saison de fort débit. Tout d'abord, l'erreur sur le vent forçant est estimée par comparaison avec un produit satellitaire. Ensuite, son impact sur le modèle est évalué pour les variables de surface et de subsurface. Pour la température et la salinité de surface, l'incertitude est plus élevée près de la côte vietnamienne et du delta du fleuve Rouge. Sur la verticale, l'incertitude est la plus forte à la surface pour la salinité et en sub-surface pour la température. J'analyse ensuite la sensibilité du panache de la rivière. La dispersion de la surface du panache est maximale en août, qui est aussi la période où la surface du panache est la plus grande. L'analyse en clusters montre quelques changements de clusters entre les différents membres de l'ensemble, mais le cluster le plus susceptible de se produire est toujours celui de la simulation de référence (avec le vent non perturbé). Ces changements limités suggèrent que les résultats de la partie I sont effectivement robustes aux erreurs de forçage du vent. Enfin, l'ensemble est vérifié en utilisant les jeux d'observations disponibles.This study aims at better understanding the variability of the Red River plume in the Gulf of Tonkin (GOT) in the mid and far field area using a numerical modeling approach. Understanding the plume variability and the fate of the delta waters is of primary importance for an in-depth knowledge and a better prediction capacity of the ocean circulation and hydrology in the GOT, for an improved management of coastal waters and monitoring of the coastal ecosystems. In the first part of the thesis, the SYMPHONIE model is configured with realistic forcings and a high-resolution variable grid relying on the configuration of V. Piton (2019). It is then run over a 6-year (2011-2016) period to study the daily to interannual variability of the Red River plume and of three rivers whose mouths are nearby. It is then validated with several observational data sources. Then, the plume is identified using simulated passive tracers. Using a K-means unsupervised machine learning algorithm, the main patterns of the plume and their evolution in time are classified in four clusters, analyzed and linked to different environmental conditions. In winter, the plume is narrow and sticks along the coast most of the time due to the downcoast current and northeasterly wind. In early summer, the southwest monsoon wind makes the plume flow offshore. The plume reaches its highest coverage in September after the peak of runoff. Vertically, the plume thickness also shows seasonal variations. In winter, the plume is mixed over the whole water depth, while in summer, the plume can be detached both from the bottom and the coast. The plume can deepen offshore in summer, due to strong wind (in May, June) or specifically due to a recurrent eddy occurring near 19°N (in August). This first part was published in 2021. The clustering analysis above shows that whatever the cluster, the plume is strongly affected by the wind. Therefore, in the second part of this thesis, I use an ensemble of simulations to assess the model response to perturbations added to the wind forcing. The sensitivity of the simulation presented in the first part is statistically evaluated by calculating the spread and the distribution of the variables of interest from an ensemble of 50 members. Due to computing and memory constraints, this study is performed over a short period, from June to August 2015, corresponding to the high runoff season. Firstly, the error of the forcing wind is estimated by comparing it with a satellite product. Then, its impact onto the model is assessed for surface and subsurface variables. For the sea surface temperature and salinity, the uncertainty is higher near the Vietnamese coast and the Red River delta. Vertically, the uncertainty is highest at the surface for salinity and at the sub surface for temperature. The sensitivity of the river plume is then analyzed. The spread of the plume area is highest in August, which is the same time when the plume area reaches its peak. The clustering analysis shows some cluster shifts between different members of the ensemble, but the cluster that is most likely to occur is still the one from the reference simulation (with unperturbed wind). These limited changes suggest that the results of part I are indeed robust to the wind forcing errors. Finally, the ensemble is verified using the available observational datasets

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future