Water Quality Observations from Space: A Review of Critical Issues and Challenges

Water is the basis of all life on this planet. Yet, approximately one in seven people in the world do not have access to safe water. Water can become unsafe due to contamination by various organic and inorganic compounds due to various natural and anthropogenic processes. Identifying and monitoring water quality changes in space and time remains a challenge, especially when contamination events occur over large geographic areas. This study investigates recent advances in remote sensing that allow us to detect and monitor the unique spectral characteristics of water quality events over large areas. Based on an extensive literature review, we focus on three critical water quality problems as part of this study: algal blooms, acid mine drainage, and suspended solids. We review the advances made in applications of remote sensing in each of these issues, identify the knowledge gaps and limitations of current studies, analyze the existing approaches in the context of global environmental changes, and discuss potential ways to combine multi-sensor methods and different wavelengths to develop improved approaches. Synthesizing the findings of these studies in the context of the three specific tracks will help stakeholders to utilize, share, and embed satellite-derived earth observations for monitoring and tracking the ever-evolving water quality in the earth’s limited freshwater reserves

Water quality grade identification for lakes in middle reaches of Yangtze River using landsat-8 data with deep neural networks (DNN) model

Water quality grade is an intuitive element for people to understand the condition of water quality. However, in situ water quality grade measurements are often labor intensive, which makes measurement over large areas very costly and laborious. In recent years, numerous studies have demonstrated the effectiveness of remote sensing techniques in monitoring water quality. In order to automatically extract the water quality information, machine learning technologies have been widely applied in remote sensing data interoperation. In this study, Landsat-8 data and deep neural networks (DNN) were employed to identify the water quality grades of lakes in two cities, Wuhan and Huangshi, in the middle reach of the Yangtze River, central China. Additionally, linear support vector machine (L-SVM), random forest (RF), decision tree (DT), and multi-layer perceptron (MLP) were selected as comparative methods. The experimental results showed that DNN achieved the most promising performance compared to the other approaches. For the lakes in Wuhan, DNN gave water quality results with overall accuracy (OA) of 93.37% and Kappa of 0.9028. For the lakes in Huangshi, OA and kappa given by DNN were 96.39% and 0.951, respectively. The results show that the use of remote sensing images for water quality grade monitoring is effective. In the future, our method can be used for water quality monitoring of lakes in large areas at a low cost

Improving Chlorophyll-a Estimation from Sentinel-2 (MSI) in the Barents Sea using Machine Learning

This article addresses methodologies for remote sensing of ocean Chlorophyll-a (Chl-a), with emphasis on the Barents Sea. We aim at improving the monitoring capacity by integrating in situ Chl-a observations and optical remote sensing to locally train machine learning (ML) models. For this purpose, in situ measurements of Chl-a ranging from 0.014–10.81 mg/m 3 , collected for the years 2016–2018, were used to train and validate models. To accurately estimate Chl-a, we propose to use additional information on pigment content within the productive column by matching the depth-integrated Chl-a concentrations with the satellite data. Using the optical images captured by the multispectral imager instrument on Sentinel-2 and the in situ measurements, a new spatial window-based match-up dataset creation method is proposed to increase the number of match-ups and hence improve the training of the ML models. The match-ups are then filtered to eliminate erroneous samples based on the spectral distribution of the remotely sensed reflectance. In addition, we design and implement a neural network model dubbed as the ocean color net (OCN), that has performed better than existing ML-based techniques, including the Gaussian process Regression (GPR), regionally tuned empirical techniques, including the ocean color (OC3) algorithm and the spectral band ratios, as well as the globally trained Case-2 regional/coast colour (C2RCC) processing chain model C2RCC-networks. The proposed OCN model achieved reduced mean absolute error compared to the GPR by 5.2%, C2RCC by 51.7%, OC3 by 22.6%, and spectral band ratios by 29%. Moreover, the proposed spatial window and depth-integrated match-up creation techniques improved the performance of the proposed OCN by 57%, GPR by 41.9%, OC3 by 5.3%, and spectral band ratio method by 24% in terms of RMSE compared to the conventional match-up selection approach

Extracting surface water bodies from sentinel-2 imagery using convolutional neural networks

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesWater is an integral part of eco-system with significant role in human life. It is immensely mobilized natural resource and hence it should be monitored continuously. Water features extracted from satellite images can be utilized for urban planning, disaster management, geospatial dataset update and similar other applications. In this research, surface water features from Sentinel-2 (S2) images were extracted using state-of-the-art approaches of deep learning. Performance of three proposed networks from different research were assessed along with baseline model. In addition, two existing but novel architects of Convolutional Neural Network (CNN) namely; Densely Convolutional Network (DenseNet) and Residual Attention Network (AttResNet) were also implemented to make comparative study of all the networks. Then dense blocks, transition blocks, attention block and residual block were integrated to propose a novel network for water bodies extraction. Talking about existing networks, our experiments suggested that DenseNet was the best network among them with highest test accuracy and recall values for water and non water across all the experimented patch sizes. DenseNet achieved the test accuracy of 89.73% with recall values 85 and 92 for water and non water respectively at the patch size of 16. Then our proposed network surpassed the performance of DenseNet by reaching the test accuracy of 90.29% and recall values 86 and 93 for water and non water respectively. Moreover, our experiments verified that neural network were better than index-based approaches since the index-based approaches did not perform well to extract riverbanks, small water bodies and dried rivers. Qualitative analysis seconded the findings of quantitative analysis. It was found that the proposed network was successful in creating attention aware features of water pixels and diminishing urban, barren and non water pixels. All in all, it was concluded that the objectives of the research were met successfully with the successful proposition of a new network

Extracting surface water bodies from Sentinel-2 imaginery using convolutional neural networks

Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2020-2021Water is an integral part of eco-system with significant role in human life. It is immensely mobilized natural resource and hence it should be monitored continuously. Water features extracted from satellite images can be utilized for urban planning, disaster management, geospatial dataset update and similar other applications. In this research, surface water features from Sentinel-2 (S2) images were extracted using state-of-the-art approaches of deep learning. Performance of three proposed networks from different research were assessed along with baseline model. In addition, two existing but novel architects of Convolutional Neural Network (CNN) namely; Densely Convolutional Network (DenseNet) and Residual Attention Network (AttResNet) were also implemented to make comparative study of all the networks. Then dense blocks, transition blocks, attention block and residual block were integrated to propose a novel network for water bodies extraction. Talking about existing networks, our experiments suggested that DenseNet was the best network among them with highest test accuracy and recall values for water and non water across all the experimented patch sizes. DenseNet achieved the test accuracy of 89.73% with recall values 85 and 92 for water and non water respectively at the patch size of 16. Then our proposed network surpassed the performance of DenseNet by reaching the test accuracy of 90.29% and recall values 86 and 93 for water and non water respectively. Moreover, our experiments verified that neural network were better than index-based approaches since the index-based approaches did not perform well to extract riverbanks, small water bodies and dried rivers. Qualitative analysis seconded the findings of quantitative analysis. It was found that the proposed network was successful in creating attention aware features of water pixels and diminishing urban, barren and non water pixels. All in all, it was concluded that the objectives of the research were met successfully with the successful proposition of a new network

Deep learning for the early detection of harmful algal blooms and improving water quality monitoring

Climate change will affect how water sources are managed and monitored. The frequency of algal blooms will increase with climate change as it presents favourable conditions for the reproduction of phytoplankton. During monitoring, possible sensory failures in monitoring systems result in partially filled data which may affect critical systems. Therefore, imputation becomes necessary to decrease error and increase data quality. This work investigates two issues in water quality data analysis: improving data quality and anomaly detection. It consists of three main topics: data imputation, early algal bloom detection using in-situ data and early algal bloom detection using multiple modalities.The data imputation problem is addressed by experimenting with various methods with a water quality dataset that includes four locations around the North Sea and the Irish Sea with different characteristics and high miss rates, testing model generalisability. A novel neural network architecture with self-attention is proposed in which imputation is done in a single pass, reducing execution time. The self-attention components increase the interpretability of the imputation process at each stage of the network, providing knowledge to domain experts.After data curation, algal activity is predicted using transformer networks, between 1 to 7 days ahead, and the importance of the input with regard to the output of the prediction model is explained using SHAP, aiming to explain model behaviour to domain experts which is overlooked in previous approaches. The prediction model improves bloom detection performance by 5% on average and the explanation summarizes the complex structure of the model to input-output relationships. Performance improvements on the initial unimodal bloom detection model are made by incorporating multiple modalities into the detection process which were only used for validation purposes previously. The problem of missing data is also tackled by using coordinated representations, replacing low quality in-situ data with satellite data and vice versa, instead of imputation which may result in biased results

Towards a Generalized Machine Learning Approach for Estimating Chlorophyll Values in Inland Waters with Spectral Data

Wasser ist ein wesentliches Element des Lebens. Seine Qualität ist jedoch bedroht, zum Beispiel durch schädliche Algenblüten oder anthropogene Verschmutzungen. Regelmäßige Kontrollen ermöglichen das Erkennen von Veränderungen der Wasserqualität von Binnengewässern. Konventionelle Wasserqualitätskontrollen werden hauptsächlich mittels In-situ-Probenahmen durchgeführt, eine teure und arbeitsintensive Vorgehensweise. Spektrale Fernerkundung kann eine Alternative zu In-situ-Beprobungen sein. Die sichtbare und nahinfrarote Strahlung, die von einem Sensor aufgenommen wird, hat mit dem Wasserkörper und dessen Inhaltsstoffen interagiert. Dadurch enthält die Strahlung Informationen über Absorptions- und Streuprozesse in der Wassersäule. Ein Parameter, der stark mit der Strahlung wechselwirkt, ist das pflanzliche Pigment Chlorophyll a. Chlorophyll a ist ein Proxy für die Phytoplanktonabundanz und kann daher mit der Wasserqualität in Verbindung gebracht werden. Die spektrale Überlappung mit anderen Wasserinhaltsstoffen erschwert die Bestimmung des Chlorophyll a-Gehalts mit spektralen Daten in der Wassersäule. Daher ist ein zuverlässiger Modellierungsansatz erforderlich, um diese nicht-lineare Regressionsaufgabe zu lösen und damit kontinuierliche Chlorophyll a-Werte aus Spektraldaten zu gewinnen. Eine zusätzliche Anforderung an einen solchen Ansatz ist die Anwendbarkeit auf die meisten der weltweiten Binnengewässer, da der Mangel an Referenzdaten nicht erlaubt, spezialisierte Modelle für jeden einzelnen See zu generieren. Diese Generalisierungsanforderung passt perfekt zu Ansätzen des überwachten maschinellen Lernens. Ein Hauptziel dieser Arbeit ist daher das Trainieren und Evaluieren von überwachten maschinellen Lernverfahren zum Schätzen kontinuierlicher Chlorophyll a-Werte von mehreren Binnengewässern. Die untersuchten Studien stützen sich dabei vollständig auf spektrale In-situ-Messungen. Dieser Aufbau erlaubt eine detailliertere Analyse der Beziehungen zwischen spektralen Daten und Wasserparametern. Außerdem wird der Einfluss der Atmosphäre verringert. Drei verschiedene Datensätze wurden im Rahmen dieser Arbeit aufgenommen, um den Generalisierungsprozess der generierten Modelle zu untersuchen. Die Variabilität der Datensätze nimmt dabei sukzessive zu. Daher wurden für diese Datensätze drei Studienkonfigurationen entworfen, die sukzessive die Anforderung zur Generalisierung der Modelle erhöhen. In der ersten Konfiguration werden lediglich Modelle untersucht, die sich auf ein einzelnes Gewässer beziehen. Im Gegensatz dazu stützt sich die letzte Konfiguration auf einen vollständig simulierten Datensatz für den Trainingsprozess der Modelle, während deren Evaluierung auf einem völlig unabhängigen Datensatz mit elf verschiedenen Binnengewässern erfolgt. Die Idee hinter diesem Konzept ist, wenn die Modelle die Chlorophyll a-Werte der elf völlig unbekannten Binnengewässer schätzen können, werden sie vermutlich auch weltweit, die Werte ähnlicher Binnengewässer schätzen können. Ein eindimensionales CNN als Vertreter der Deep-Learning-Verfahren hat sich dabei als das Modell mit den besten Generaliserungseigenschaften bei zufriedenstellender Schätzgenauigkeit erwiesen. Ein weiteres Augenmerk wird auf die spektrale Auflösung gelegt. Eine Verringerung der spektralen Auflösung von hyperspektral auf multispektral ist mit einem Informationsverlust verbunden. Die Schätzungsergebnisse aus dem eindimensionalen CNN zeigen, dass eine hyperspektrale Auflösung für ein vollständig generalisierendes Modell notwendig ist. Eine multispektrale Auflösung ist jedoch ausreichend für weniger generalisierende Modelle. Diese Erkenntnisse sind wichtig um im Hinblick auf ein zukünftiges Forschungsvorhaben den Upscaling-Ansatz auf reale Satellitendaten zu realisieren und damit eine flächendeckende Überwachung der Wasserqualität zu verwirklichen

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

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing