The Usage of Band Ratios to Predict Lake Water Quality Parameters using Sentinel-2 L1C Imagery

Band ratios using remote imagery can be useful for monitoring large bodies of water when high quality imagery is available. Sentinel-2 satellite imagery provides frequent, high-resolution coverage of the globe. This study set out to test the usefulness of existing band ratios for estimating chlorophyll a (CHL-a), dissolved organic carbon (DOC), and turbidity with Sentinel-2 imagery. USGS in-situ data was matched to Sentinel-2 imagery of Beaver Lake, Arkansas taken August 2015 to July 2019 and the dark spectrum fitting (DSF) atmospheric correction method in ACOLITE was applied to generate surface reflectance values. CHL-a was estimated using two different methods, the band 5 (B5) peak at 704.1 nm and the ratio of B5 to B4. DOC was estimated using the ratio of B3 to B4. A turbidity estimation equation was created by directly correlating turbidity to B4 reflectance values. The usage of these methods was deemed to be unfit for use under the conditions found at Beaver Lake. Poor correlation was found for CHL-a (R² = 0.0228, R = -0.1510, & R² = 0.0344, R = 0.1855) and for DOC (R² = 0.0548, R = -0.2341). Turbidity was more strongly corelated to the estimate equation (R² = 0.8402, R =0.9166) but considering the poor results for other parameters it is not recommended to apply these methodologies to parameter estimation at Beaver Lake

Chapter Sentinel-2 e campionamenti in situ per il monitoraggio delle acque marine dell’Abruzzo: primi risultati

In this study, the estimate of chlorophyll "a" and the dispersion of sediment in the sea, calculated from Sentinel-2, was compared with real data acquired in situ by a multiparametric probe, along the Abruzzo coast. The ultimate goal is to optimize parameters and algorithms to be able to derive concentration maps of chlorophyll and suspended solids from satellite, taking advantage of the high time frequency and high spatial resolution of the detections. This information is of particular relevance for aquaculture activities, for monitoring water quality and for analyzing sedimentary processes

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

Turbidity and Secchi disc depth with Sentinel-2 in different trophic status reservoirs at the Comunidad Valenciana

[ES] En los estudios de calidad de aguas por teledetección, uno de los principales indicadores es la transparencia o turbidez del agua. La transparencia puede ser medida in situ mediante la profundidad del disco de Secchi (SD), y la turbidez con un turbidímetro. En las últimas décadas se han utilizado diferentes relaciones entre bandas de diferentes sensores obtenidas por teledetección para la estimación de estos parámetros. En este trabajo, a partir de datos de campo obtenidos a lo largo de 2017 y 2018 en embalses de la cuenca del Júcar con gran variedad de estados tróficos, se han calibrado diferentes índices y bandas para poder estimar la transparencia a partir de imágenes Sentinel-2 (S2). A las imágenes S2 nivel L1C tomadas en el mismo día que los datos de campo, se les han aplicado tres métodos de corrección atmosférica desarrollados para aguas: Polymer, C2RCC y C2X. A partir de los espectros de S2 obtenidos y de los datos de campo de SD se ha observado que el menor error se obtiene con las imágenes corregidas con Polymer y un ajuste potencial del cociente de reflectividades en las bandas azul y verde (R490/R560), que permiten la estimación de SD con un error relativo del 13%. También el método C2X presenta buen ajuste con el mismo cociente de bandas, aunque un error mayor, presentando la corrección C2RCC la peor correlación. Se ha obtenido también la relación entre SD (en m) y turbidez (en NTU), lo que proporciona un método operativo para la estimación de la turbidez con S2. Se muestra, además, la relación para los diferentes embalses entre el SD y la concentración de clorofila-a, sólidos en suspensión y materia orgánica disuelta.[EN] Transparency or turbidity is one of the main indicators in studies of water quality using remote sensing. Transparency can be measured in situ through the Secchi disc depth (SD), and turbidity using a turbidimeter. In recent decades, different relationships between bands from different remote sensing sensors have been used for the estimation of these variables. In this paper, several indices and spectral bands have been calibrated in order to estimate transparency from Sentinel-2 (S2) images from field data, obtained throughout 2017 and 2018 in Júcar basin reservoirs with a great variety of trophic states. Three atmospheric correction methods developed for waters have been applied to the S2 level L1C images taken at the same day as the field data: Polymer, C2RCC and C2X. From the spectra obtained from S2 and the SD field data, it has been found that the smallest error is obtained with the images atmospherically corrected with Polymer and a potential adjustment of the reflectivities’ ratio of the blue and green bands (R490/R560), which allow the estimation of SD with a relative error of 13%. Also the C2X method presents good adjustment with the same bands ratio, although with a greater error, while the correction C2RCC shows the worst correlation. The relationship between SD (in m) and turbidity (in NTU) has also been obtained, which provides an operational method for estimating turbidity with S2. The relationship for the different reservoirs between SD and chlorophyll-a concentration, suspended solids and dissolved organic matter, is also shownEste trabajo ha sido posible gracias al Proyecto ESAQS del Programa Prometeo para grupos de investigación de excelencia de la Conselleria d’Educació, Investigació, Cultura i Esport (GVPROMETEO2016-132) de la Generalitat Valenciana.Delegido, J.; Urrego, P.; Vicente, E.; Sòria-Perpinyà, X.; Soria, J.; Pereira-Sandoval, M.; Ruiz-Verdú, A.... (2019). Turbidez y profundidad de disco de Secchi con Sentinel-2 en embalses con diferente estado trófico en la Comunidad Valenciana. 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Vertical Artifacts in High-Resolution WorldView-2 and Worldview-3 Satellite Imagery of Aquatic Systems

Satellite image artefacts are features that appear in an image but not in the original imaged object and can negatively impact the interpretation of satellite data. Vertical artefacts are linear features oriented in the along-track direction of an image system and can present as either banding or striping; banding are features with a consistent width, and striping are features with inconsistent widths. This study used high-resolution data from DigitalGlobeʻs (now Maxar) WorldView-3 satellite collected at Lake Okeechobee, Florida (FL), on 30 August 2017. This study investigated the impact of vertical artefacts on both at-sensor radiance and a spectral index for an aquatic target as WorldView-3 was primarily designed as a land sensor. At-sensor radiance measured by six of WorldView-3ʻs eight spectral bands exhibited banding, more specifically referred to as non-uniformity, at a width corresponding to the multispectral detector sub-arrays that comprise the WorldView-3 focal plane. At-sensor radiance measured by the remaining two spectral bands, red and near-infrared (NIR) #1, exhibited striping. Striping in these spectral bands can be attributed to their time delay integration (TDI) settings at the time of image acquisition, which were optimized for land. The impact of vertical striping on a spectral index leveraging the red, red edge, and NIR spectral bands—referred to here as the NIR maximum chlorophyll index (MCINIR)—was investigated. Temporally similar imagery from the European Space Agencyʻs Sentinel-3 and Sentinel-2 satellites were used as baseline references of expected chlorophyll values across Lake Okeechobee as neither Sentinel-3 nor Sentinel-2 imagery showed striping. Striping was highly prominent in the MCINIR product generated using WorldView-3 imagery, as noise in the at-sensor radiance exceeded any signal of chlorophyll in the image. Adjusting the image acquisition parameters for future tasking of WorldView-3 or the functionally similar WorldView-2 satellite may alleviate these artefacts. To test this, an additional WorldView-3 image was acquired at Lake Okeechobee, FL, on 26 May 2021 in which the TDI settings and scan line rate were adjusted to improve the signal-to-noise ratio. While some evidence of non-uniformity remained, striping was no longer noticeable in the MCINIR product. Future image tasking over aquatic targets should employ these updated image acquisition parameters. Since the red and NIR #1 spectral bands are critical for inland and coastal water applications, archived images not collected using these updated settings may be limited in their potential for analysis of aquatic variables that require these two spectral bands to derive

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

Tracing Real-Time Transnational Hydrologic Sensitivity and Crop Irrigation in the Upper Rhine Area over the Exceptional Drought Episode 2018–2020 Using Open Source Sentinel-2 Data

Climate and regional land-use and landcover change (LUCC) impact the ecosystem of the Upper Rhine Area (URA) and transform large parts of the landscape into strongly irrigated agricultural cropland. The increase of long-term drought periods and the trend towards low summer precipitation totals trigger an increase in groundwater scarcity and amplify the negative effects of extensive irrigation purposes and freshwater consumption in a hydrologically sensitive region in Central Europe. This article presents qualitative transnational open source remote sensing temporal series of vegetation indices (NDVI) and groundwater level development to tracing near real-time vegetation change and socio-ecological feedbacks during periods of climate extremes in the Upper Rhine Area (2018–2020). Increased freshwater consumption caused a dramatic drop in groundwater availability, which eventually led to a strong degradation of the vegetation canopy and caused governmental regulations in July 2020. Assessing vegetation growth behavior and linking groundwater reactions in the URA through open source satellite data contributes to a rapidly accessible understanding of the ecosystem’s feedbacks on the local to the transnational scale and further enables risk management and eco-political regulations in current and future decisionmaking processes.Climate and regional land-use and landcover change (LUCC) impact the ecosystem of the Upper Rhine Area (URA) and transform large parts of the landscape into strongly irrigated agricultural cropland. The increase of long-term drought periods and the trend towards low summer precipitation totals trigger an increase in groundwater scarcity and amplify the negative effects of extensive irrigation purposes and freshwater consumption in a hydrologically sensitive region in Central Europe. This article presents qualitative transnational open source remote sensing temporal series of vegetation indices (NDVI) and groundwater level development to tracing near real-time vegetation change and socio-ecological feedbacks during periods of climate extremes in the Upper Rhine Area (2018–2020). Increased freshwater consumption caused a dramatic drop in groundwater availability, which eventually led to a strong degradation of the vegetation canopy and caused governmental regulations in July 2020. Assessing vegetation growth behavior and linking groundwater reactions in the URA through open source satellite data contributes to a rapidly accessible understanding of the ecosystem’s feedbacks on the local to the transnational scale and further enables risk management and eco-political regulations in current and future decisionmaking processes

Assessing ESA Climate Change Initiative data for the monitoring of phytoplankton abundance and phenology in deep lakes: investigation on Lake Geneva

Lake water quality assessment requires quantification of phytoplankton abundance. Optical satellite imagery allows us to map this information within the entire lake area. The ESA Climate Change Initiative (ESA-CCI) estimates Chl-a concentrations, based on medium resolution satellite data, on a global scale. Chl-a concentrations provided by the ESA-CCI consortium were analyzed to assess their representativeness for water quality monitoring and subsequent phenology studies in Lake Geneva. Based on vertically resolved in-situ data, those datasets were evaluated through match-up comparisons. Because the underlying algorithms do not take into account the vertical distribution of phytoplankton, a specific analysis was performed to evaluate any potential biases in remote sensing estimation, and consequences for observed phenological trends. Different approaches to data averaging were performed to reconstruct Chl-a estimates provided by the remote sensing algorithms. Strong correlation (R-value > 0.89) and acceptable discrepancies (rmse ∼ 1.4 mg.m−3) were observed for the ESA-CCI data. This approach permitted recalibration of the ESA CCI data for Lake Geneva. Finally, merging satellite and in-situ data provided a consistent time series for long term analysis of phytoplankton phenology and its interannual variability since 2002. This combination of in-situ and satellite data improved the temporal resolution of the time series, enabling a more accurate identification of the timing of specific spring events characterising phytoplankton phenology