The Color of Water from Space: A Case Study for Italian Lakes from Sentinel-2

Lakes are inestimable renewable natural resources that are under significant pressure by human activities. Monitoring lakes regularly is necessary to understand their dynamics and the drivers of these dynamics to support effective management. Remote sensing by satellite sensors offers a significant opportunity to increase the spatiotemporal coverage of environmental monitoring programs for inland waters. Lake color is a water quality attribute that can be remotely sensed and is independent of the sensor specifications and water type. In this study we used the Multispectral Imager (MSI) on two Sentinel-2 satellites to determine the color of water of 170 Italian lakes during two periods in 2017. Overall, most of the lakes appeared blue in spring and green-yellow in late summer, and in particular, we confirm a blue-water status of the largest lakes in the subalpine ecoregion. The color and its seasonality are consistent with characteristics determined by geomorphology and primary drivers of water quality. This suggests that information about the color of the lakes can contribute to synoptic assessments of the trophic status of lakes. Further ongoing research efforts are focused to extend the mapping over multiple years

Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon

The recent PlanetScope constellation (130+ satellites currently in orbit) has shifted the high spatial resolution imaging into a new era by capturing the Earth’s landmass including inland waters on a daily basis. However, studies on the aquatic-oriented applications of PlanetScope imagery are very sparse, and extensive research is still required to unlock the potentials of this new source of data. As a first fully physics-based investigation, we aim to assess the feasibility of retrieving bathymetric and water quality information from the PlanetScope imagery. The analyses are performed based on Water Color Simulator (WASI) processor in the context of a multitemporal analysis. The WASI-based radiative transfer inversion is adapted to process the PlanetScope imagery dealing with the low spectral resolution and atmospheric artifacts. The bathymetry and total suspended matter (TSM) are mapped in the relatively complex environment of Venice lagoon during two benchmark events: The coronavirus disease 2019 (COVID-19) lockdown and an extreme flood occurred in November 2019. The retrievals of TSM imply a remarkable reduction of the turbidity during the lockdown, due to the COVID-19 pandemic and capture the high values of TSM during the flood condition. The results suggest that sizable atmospheric and sun-glint artifacts should be mitigated through the physics-based inversion using the surface reflectance products of PlanetScope imagery. The physics-based inversion demonstrated high potentials in retrieving both bathymetry and TSM using the PlanetScope imagery

Monitoring oil spill in Norilsk, Russia using satellite data.

This paper studies the oil spill, which occurred in the Norilsk and Taimyr region of Russia due to the collapse of the fuel tank at the power station on May 29, 2020. We monitored the snow, ice, water, vegetation and wetland of the region using data from the Multi-Spectral Instruments (MSI) of Sentinel-2 satellite. We analyzed the spectral band absorptions of Sentinel-2 data acquired before, during and after the incident, developed true and false-color composites (FCC), decorrelated spectral bands and used the indices, i.e. Snow Water Index (SWI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). The results of decorrelated spectral bands 3, 8, and 11 of Sentinel-2 well confirmed the results of SWI, NDWI, NDVI, and FCC images showing the intensive snow and ice melt between May 21 and 31, 2020. We used Sentinel-2 results, field photographs, analysis of the 1980-2020 daily air temperature and precipitation data, permafrost observations and modeling to explore the hypothesis that either the long-term dynamics of the frozen ground, changing climate and environmental factors, or abnormal weather conditions may have caused or contributed to the collapse of the oil tank.Open access funding provided by the Qatar National Library

Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice

Multispectral remote sensing may be a powerful tool for areal retrieval of biogeophysical parameters in the Arctic sea ice. The MultiSpectral Instrument on board the Sentinel-2 (S-2) satellites of the European Space Agency offers new possibilities for Arctic research; S-2A and S-2B provide 13 spectral bands between 443 and 2,202 nm and spatial resolutions between 10 and 60 m, which may enable the monitoring of large areas of Arctic sea ice. For an accurate retrieval of parameters such as surface albedo, the elimination of atmospheric influences in the data is essential. We therefore provide an evaluation of five currently available atmospheric correction processors for S-2 (ACOLITE, ATCOR, iCOR, Polymer, and Sen2Cor). We evaluate the results of the different processors using in situ spectral measurements of ice and snow and open water gathered north of Svalbard during RV Polarstern cruise PS106.1 in summer 2017. We used spectral shapes to assess performance for ice and snow surfaces. For open water, we additionally evaluated intensities. ACOLITE, ATCOR, and iCOR performed well over sea ice and Polymer generated the best results over open water. ATCOR, iCOR and Sen2Cor failed in the image-based retrieval of atmospheric parameters (aerosol optical thickness, water vapor). ACOLITE estimated AOT within the uncertainty range of AERONET measurements. Parameterization based on external data, therefore, was necessary to obtain reliable results. To illustrate consequences of processor selection on secondary products we computed average surface reflectance of six bands and normalized difference melt index (NDMI) on an image subset. Medians of average reflectance and NDMI range from 0.80–0.97 to 0.12–0.18 while medians for TOA are 0.75 and 0.06, respectively

Satellite-derived bathymetry with sediment classification using the ICESat-2 and multispectral imagery: cases study in the South China Sea and Australia

Achieving coastal and shallow-water bathymetry is essential for understanding the marine environment and for coastal management. Bathymetry data in shallow sea areas can currently be obtained using SDB (satellite-derived bathymetry) with multispectral satellites based on depth inversion models. In situ bathymetry data are crucial for validating empirical models but are currently limited in remote and unapproachable areas. Instead of the measured water depth data, we used the ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) ATL03 bathymetry data at different acquisition dates and multispectral imagery from Sentinel-2/GeoEye-1 to train and evaluate water depth inversion empirical models in two study regions: Shanhu Island in the South China Sea (SCS), and Heron Island in the Great Barrier Reef (GBR) in Australia. However, different sediment types also influenced the SDB results. Therefore, three types of sediments (sand, reef, and coral/algae) were analyzed for Heron Island, and four types of sediments (sand, reef, rubble and coral/algae) were analyzed for Shanhu Island. The results show that consistency generally improved when sediment classification information was considered in both study areas. For Heron Island, the sand sediments showed the best performance in both models compared to the other sediments, with mean RMSE and R2 values of 1.52 m and 0.90, respectively, representing a 5.6% improvement of the latter metric. For Shanhu Island, the rubble sediments showed the best consistency in both models, and the average RMSE and R2 values were 0.65 m and 0.97, respectively, indicating an RMSE improvement of 15.5%. Finally, bathymetry maps were generated in two regions based on the sediment classification results. These results indicate that the SDB method with sediment classifications can improve the water depth estimation consistency. This technology can also provide an efficient way to generate large-scale bathymetry maps in remote and sensitive shallow water areas for marine applications

Dinâmica espaço-temporal da turbidez no reservatório de Itaipu, na região sul do Brasil, utilizando dados de sensoriamento remoto

A turbidez da água é um parâmetro crucial na determinação da qualidade da água. A erosão e o assoreamento estão diretamente ligados à turbidez, influenciando a qualida de das águas e o armazenamento de reservatórios. Mapear padrões de turbidez é importante para a gestão e monitoramento de reservatórios. O monitoramento da turbidez em reservatórios, depende de métodos tradicionais com coletas pontuais, analisadas em labor atório que consomem tempo, dinheiro e mão - de-obra exaustiva. Diante disso, o sensoriamento remoto surge como alternativa para complementar programas de monitoramento, preenchendo lacunas temporais e espaciais. Esta Tese aborda métodos para analisar e quantificar padrões de turbidez da água utilizando dados de sensoriamento remoto e técnicas de processamento digital de imagens. Investigou- se a relação entre turbidez, precipitação e reflectância espectral. Os resultados mostraram alta correlação entre o índic e de turbidez NDTI e a turbidez ( R² = 0,91). A precipitação teve influência determinante, sendo o rio Paraná, nos períodos de maior precipitação, o principal agente no transporte de sedimentos. Os compartimentos laterais do reservatório mostraram menor inf luência no transporte de sedimentos. Também comparou-se o desempenho dos algoritmos Classification and Regression Tree (CART), Naive Bayes (NB) e Random Forest (RF), a partir de classificação supervisionada de imagens, e abordagens em Pixel -Based Image analysis (PBIA) e Geographic Object-Based Image Analysis (GEOBIA), para classificar a turbidez. O classificador RF obteve a maior precisão em ambas as abordagens, seguido por CART e NB. Os índices Kappa e Aná lise Global das classificações GEOBIA foram superiores às classificações PBIA em ambos os algoritmos. Também avali ou-se o potencial de estimativa da Área de Água Superficial e Nível de Água do Reservatório. Testamos séries temporais de imagens ó pticas Landsat 8 e Sentinel-2, radar Sentinel- 1, e validação c om altimetria Jason-3. A metodologia foi desenvolvida na rotina operacional do Google Earth Engine, que agilizou o mapeamento. Os melhores resultados foram entre Sentinel-2 e NDWI com R² = 0,88 e RMSE de 11,59 km². No geral, nossos resultados demonstram o potencial do sensoriamento remoto para identificar e analisar padrões de turbidez no reservatório de Itaipu. O que pode ser extraído deste estudo é que a turbidez da água do reservatório é espectralmente ativa. Em segundo lugar, há uma forte conexão entre materiais supensos, turbidez e precipitação. Os dados multiespectrais de média resolução foram ideais na detecção e análise de turbidez. Nosso estudo mostra que mesmo sem dados in situ, é possível analisar, e quantificar padrões de turbidez do reservatório de Itaipu a partir de sensores acoplados em satélites espaciais.Water turbidity is a crucial parameter in determining water quality. Erosion and siltation are directly linked to turbidity, influencing water quality and reservoir storage. Mapping turbidity patterns is important for reservoir management and monitoring. The monitoring of turbidity in reservoirs depends on traditional methods with punctual collections, analyzed in the laboratory that consume time, money and exhaustive labor. Given this, remote sensing emerges as an alternative to complement monitoring programs, filling temporal and spatial gaps. This Thesis addresses methods to analyze and quantify water turbidity patterns using remote sensing data and digital image processing techniques. The relationship between turbidity, precipitation and spectral reflectance was investigated. The results showed a high correlation between the NDTI turbidity index and turbi dity (R² = 0.91). Precipitation had a decisive influence, with the Paraná River, in periods of greater precipitation, being the main agent in the transport of sediments. The lateral compartments of the reservoir showed less influence on sediment transport. nt transport. The performance of the Classification and Regression Tree (CART), Naive Bayes (NB) and Random Forest (RF) algorithms was also compared, based on supervised image classification, and Pixel-Based Image analysis (PBIA) and Geographic Object approaches. Based Image Analysis (GEOBIA), to classify turbidity. The RF classifier achieved the highest accuracy in both approaches, followed by CART and NB. Kappa indices and Global Analysis of GEOBIA rankings were superior to PBIA rankings in both algorithms. The estimation potential of the Surface Water Area and Water Level of the Reservoir was also evaluated. We tested time series of Landsat 8 and Sentinel-2 optical images, Sentinel-1 radar, and validation with Jason-3 altimetry. The methodology was developed in the operational routine of Google Earth Engine, which streamlined the mapping. ng. The best results were between Sentinel-2 and NDWI with R² = 0.88 and RMSE of 11.59 km². Overall, our results demonstrate the potential of remote sensing to identify and analyze turbidity patterns in the Itaipu reservoir. What can be extracted from this study is that the turbidity of the reservoir water is spectrally active. e. Second, there is a strong connection between suspended materials, turbidity and precipitation. Medium resolution multispectral data were ideal for detecting and analyzing turbidity. Our study shows that even without in situ data, it is possible to analyze and quantify turbidity patterns in the Itaipu reservoir using sensors attached to space satellites

Water Constituents and Water Depth Retrieval from Sentinel-2A—A First Evaluation in an Oligotrophic Lake

Satellite remote sensing may assist in meeting the needs of lake monitoring. In this study, we aim to evaluate the potential of Sentinel-2 to assess and monitor water constituents and bottom characteristics of lakes at spatio-temporal synoptic scales. In a field campaign at Lake Starnberg, Germany, we collected validation data concurrently to a Sentinel-2A (S2-A) overpass. We compared the results of three different atmospheric corrections, i.e., Sen2Cor, ACOLITE and MIP, with in situ reflectance measurements, whereof MIP performed best (r = 0.987, RMSE = 0.002 sr−1). Using the bio-optical modelling tool WASI-2D, we retrieved absorption by coloured dissolved organic matter (aCDOM(440)), backscattering and concentration of suspended particulate matter (SPM) in optically deep water; water depths, bottom substrates and aCDOM(440) were modelled in optically shallow water. In deep water, SPM and aCDOM(440) showed reasonable spatial patterns. Comparisons with in situ data (mean: 0.43 m−1) showed an underestimation of S2-A derived aCDOM(440) (mean: 0.14 m−1); S2-A backscattering of SPM was slightly higher than backscattering from in situ data (mean: 0.027 m−1 vs. 0.019 m−1). Chlorophyll-a concentrations (~1 mg·m−3) of the lake were too low for a retrieval. In shallow water, retrieved water depths exhibited a high correlation with echo sounding data (r = 0.95, residual standard deviation = 0.12 m) up to 2.5 m (Secchi disk depth: 4.2 m), though water depths were slightly underestimated (RMSE = 0.56 m). In deeper water, Sentinel-2A bands were incapable of allowing a WASI-2D based separation of macrophytes and sediment which led to erroneous water depths. Overall, the results encourage further research on lakes with varying optical properties and trophic states with Sentinel-2A

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

Analyse der Wasserfarbe von Seen mithilfe räumlich hoch und mittel auflösender Satelliten

Remote sensing techniques can assist traditional lake monitoring approaches by supplying spatial information on optically active lake ecology indicators, i.e. chlorophyll-a (CHL), total suspended matter (TSM), coloured dissolved organic matter (CDOM), and, especially in optically shallow waters, water depth and substrate composition. The present thesis provides an overview on the current research status concerning lake remote sensing and the benefit of time series analyses for lake ecology. To investigate the suitability of Sentinel-2 and Landsat 8 for lake monitoring and their combination with other sensors this thesis focused on two study areas with highly different optical characteristics, i.e. the oligotrophic Lake Starnberg (southern Germany) and the mesotrophic-eutrophic Lake Kummerow (northern Germany). Using the bio-optical model WASI-2D, Sentinel-2A turned out to be suited for retrieving low TSM and CDOM values. The high spatial resolution enabled the differentiation between bare ground and areas covered by submerged aquatic vegetation. Water depth estimations performed well until half Secchi disk depth. Cross-sensor comparisons demonstrated high correlation of CHL among timely acquired, spatially high and medium resolved sensors. Evaluations with in situ data showed that most of the sensor-in situ match-ups were within an uncertainty range of in situ measurements. Analysing a 9-year MERIS time series with FUB/WeW revealed unprecedented information on temporal trends and seasonal behaviour of CHL, TSM and CDOM at the study area Lake Kummerow. Combining CHL, retrieved with the Modular Inversion and Processing System, from different satellite sensors (MODIS, Landsat 7/ 8, Sentinel-2A) enabled detailed observations of phytoplankton development. Such combinations are a step forward to future lake analyses which may integrate remote sensing data, in situ measurements and environmental modelling.Fernerkundungstechniken können das Seemonitoring mit räumlichen Informationen über optisch aktive Indikatoren der Gewässerökologie liefern, z.B. Chlorophyll-a (CHL), suspendierte Schwebstoffe (TSM), Gelbstoffe (CDOM) und insbesondere in optisch flachen Gewässern, Wassertiefe und Substratbedeckung. Die vorliegende Arbeit gibt einen Überblick über den aktuellen Forschungsstand zur Seefernerkundung und den Nutzen von Zeitreihenanalysen für die Seeökologie. Um die Eignung von Sentinel-2 und Landsat 8 für ein Seenmonitoring und deren Kombination mit anderen Sensoren zu untersuchen, konzentrierte sich diese Arbeit auf zwei Untersuchungsgebiete mit sehr unterschiedlichen optischen Eigenschaften: den oligotrophen Starnberger See (Süddeutschland) und den mesotroph-eutrophen Kummerower See (Norddeutschland). Mit dem bio-optischen Modell WASI-2D erwies sich Sentinel-2A als geeignet, um niedrige TSM- und CDOM-Werte zu bestimmen. Die hohe räumliche Auflösung ermöglichte eine Unterscheidung zwischen unbewachsenem und mit Makrophyten bewachsenem Untergrund. Die Wassertiefenbestimmung verlief bis zur halben Sichttiefe gut. Sensorübergreifende Vergleiche zeigten eine hohe Korrelation von CHL zwischen zeitnah erfassten, räumlich mittel und hoch aufgelösten Sensoren. Auswertungen mit in-situ-Daten zeigten, dass die meisten Sensor-in-situ-Match-ups innerhalb eines Unsicherheitsbereichs von in-situ-Messungen lagen. Die Analyse einer 9-jährigen MERIS-Zeitreihe mit FUB/WeW ergab neue Informationen über zeitliche Trends und saisonales Verhalten von CHL, TSM und CDOM im Untersuchungsgebiet Kummerow See. Die Kombination von CHL aus verschiedenen Satellitensensoren (MODIS, Landsat 7/ 8, Sentinel-2A) mit dem Modular Inversion and Processing System ermöglichte detaillierte Beobachtungen der Phytoplanktonentwicklung. Solche Kombinationen sind ein Schritt für zukünftigen Gewässeranalysen, die Fernerkundungsdaten, in-situ-Messungen und Umweltmodellierung integrieren sollten