Feasibility Study for an Aquatic Ecosystem Earth Observing System Version 1.2.

International audienceMany Earth observing sensors have been designed, built and launched with primary objectives of either terrestrial or ocean remote sensing applications. Often the data from these sensors are also used for freshwater, estuarine and coastal water quality observations, bathymetry and benthic mapping. However, such land and ocean specific sensors are not designed for these complex aquatic environments and consequently are not likely to perform as well as a dedicated sensor would. As a CEOS action, CSIRO and DLR have taken the lead on a feasibility assessment to determine the benefits and technological difficulties of designing an Earth observing satellite mission focused on the biogeochemistry of inland, estuarine, deltaic and near coastal waters as well as mapping macrophytes, macro-algae, sea grasses and coral reefs. These environments need higher spatial resolution than current and planned ocean colour sensors offer and need higher spectral resolution than current and planned land Earth observing sensors offer (with the exception of several R&D type imaging spectrometry satellite missions). The results indicate that a dedicated sensor of (non-oceanic) aquatic ecosystems could be a multispectral sensor with ~26 bands in the 380-780 nm wavelength range for retrieving the aquatic ecosystem variables as well as another 15 spectral bands between 360-380 nm and 780-1400 nm for removing atmospheric and air-water interface effects. These requirements are very close to defining an imaging spectrometer with spectral bands between 360 and 1000 nm (suitable for Si based detectors), possibly augmented by a SWIR imaging spectrometer. In that case the spectral bands would ideally have 5 nm spacing and Full Width Half Maximum (FWHM), although it may be necessary to go to 8 nm wide spectral bands (between 380 to 780nm where the fine spectral features occur -mainly due to photosynthetic or accessory pigments) to obtain enough signal to noise. The spatial resolution of such a global mapping mission would be between ~17 and ~33 m enabling imaging of the vast majority of water bodies (lakes, reservoirs, lagoons, estuaries etc.) larger than 0.2 ha and ~25% of river reaches globally (at ~17 m resolution) whilst maintaining sufficient radiometric resolution

Sathyendranath S., Bracher A., Brockmann C., Platt T., Ramon D., Regner P. (2017) Colour and Light in the Ocean (CLEO) 2016: A Scientific Roadmap from the CLEO Workshop Organised by ESA and PML. Held at ESRIN, Frascati, Italy on 6 - 8 September, 2016.

The Colour and Light in the Ocean (CLEO) Workshop, organized by the European Space Agency (ESA) and the Plymouth Marine Laboratory (PML) was held on the ESRIN, the ESA Centre for Earth Observations, at Frascati, Italy on 6-8 September 2016. The workshop is sponsored through selected SEOM (Scientific Exploitation of Operational Missions) projects, including: Pools of Carbon in the Ocean (POCO), Photosynthetically Active Radiation and Primary Production (PPP), Synergistic Exploitation of Hyper- and Multispectral Sentinel-Measurements to Determine Phytoplankton Functional Types (PFT) (SynSenPFT), and Extreme Case-2 Waters (C2X). Additional partner projects of ESA are: Marine Photosynthesis Parameters from Space (MAPPS), a Pathfinder STSE (Support to Science Element) project; and Ocean Colour Climate Change Initiative (OC-CCI) through the CCI (Climate Change Initiative). The objectives of the workshop were to: Evaluate state-of-art Exchange information with other relevant projects and activities Bring together remote sensing community, in situ data providers, modellers and other users Explore applications in marine ecosystem models Plan for the future: Identify challenge areas and research priorities for future EO data exploitation activities Discuss key science issues and make recommendations to strengthen community engagement Shape ideas for potential new ocean-colour products to be developed in the era of the Sentinel-3 mission The workshop was organized in five themes, developed around the activities of the sponsoring projects. Each t heme had oral, poster and discussion sessions. The workshop attracted some 160 registered participants. The workshop served an important need to connect the community, to provide a forum for lively exchange of ideas, and to recommend priorities for future activities in a collective manner. The workshop brought together scientists working on development of novel products from ocean-colour data and the user community, including, notably, the modeling community. One of the key outputs of the workshop is this report, which provides the Scientific Roadmap for future activities. Another planned outcome is a Special Issue on Colour and Light in the Oceans, to be published in the Journal, which will highlight the major scientific results presented at the workshop. Each section of the report, dealing with one of the themes of the workshop, is self-contained, but cross-references to other sections are provided where appropriate. Some recommendations found common resonance across sections, such as the need for continuous, consistent, ocean-colour data streams from satellites for long-term monitoring of the marine ecosystem; the need for an integrated approach, bringing together the remote-sensing community, the in situ data providers and the modeling community; the need to promote development of novel products and advanced sensors; and the importance of providing high-quality and uninterrupted support to the user community, through easy and free access to data and products. Each section discusses the current state of the art, identifies user requirements and gaps, and priorities for research in the short and medium terms. The workshop served the important function of sounding the community’s aspirations, and presenting them in a concise manner for ESA, through this Scientific Roadmap. One of the recommendations from the participants was that CLEO workshops be organized on a regular basis in the future, to develop the ocean-colour community , to promote exchange of new results and ideas, and to plan future activities. We thank all workshop participants, keynote speakers, authors of the oral presentations and the posters, the Scientific Committee and the Organising Committee, and the Session Chairs for all their contributions to the workshop. For the logistical support and local organization and hospitality, we thank the ESRIN Graphics Bureau, Administration, Catering Service and the Events Office, especially Irene Renis, Anne Lisa Pichler and Giulia Vinicola

Atmospheric Correction for Hyperspectral Ocean Color Retrieval with Application to the Hyperspectral Imager for the Coastal Ocean (HICO)

The classical multi-spectral Atmospheric Correction (AC) algorithm is inadequate for the new generation of spaceborne hyperspectral sensors such as NASA's first hyperspectral Ocean Color Instrument (OCI) onboard the anticipated Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission. The AC process must estimate and remove the atmospheric path radiance contribution due to the Rayleigh scattering by air molecules and scattering by aerosols from the measured top-of-atmosphere (TOA) radiance, compensate for the absorption by atmospheric gases, and correct for reflection and refraction of the air-sea interface. In this work, we present and evaluate an improved AC for hyperspectral sensors developed within NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Data Analysis System software package (SeaDAS). The improvement is based on combining the classical AC approach of multi-spectral capabilities to correct for the atmospheric path radiance, extended to hyperspectral, with a gas correction algorithm to compensate for absorbing gases in the atmosphere, including water vapor. The SeaDAS-hyperspectral version is capable of operationally processing the AC of any hyperspectral airborne or spaceborne sensor. The new algorithm development was evaluated and assessed using the Hyperspectral Imager for Coastal Ocean (HICO) scenes collected at the Marine Optical BuoY (MOBY) site, and other SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and AERosol Robotic NETwork - Ocean Color (AERONET-OC) coastal sites. A hyperspectral vicarious calibration was applied to HICO, showing the validity and consistency of HICO's ocean color products. The hyperspectral AC capability is currently available in SeaDAS to the scientific community at https://oceancolor.gsfc.nasa.gov/

Atmospheric Correction Performance of Hyperspectral Airborne Imagery over a Small Eutrophic Lake under Changing Cloud Cover

Atmospheric correction of remotely sensed imagery of inland water bodies is essential to interpret water-leaving radiance signals and for the accurate retrieval of water quality variables. Atmospheric correction is particularly challenging over inhomogeneous water bodies surrounded by comparatively bright land surface. We present results of AisaFENIX airborne hyperspectral imagery collected over a small inland water body under changing cloud cover, presenting challenging but common conditions for atmospheric correction. This is the first evaluation of the performance of the FENIX sensor over water bodies. ATCOR4, which is not specifically designed for atmospheric correction over water and does not make any assumptions on water type, was used to obtain atmospherically corrected reflectance values, which were compared to in situ water-leaving reflectance collected at six stations. Three different atmospheric correction strategies in ATCOR4 was tested. The strategy using fully image-derived and spatially varying atmospheric parameters produced a reflectance accuracy of ±0.002, i.e., a difference of less than 15% compared to the in situ reference reflectance. Amplitude and shape of the remotely sensed reflectance spectra were in general accordance with the in situ data. The spectral angle was better than 4.1° for the best cases, in the spectral range of 450–750 nm. The retrieval of chlorophyll-a (Chl-a) concentration using a popular semi-analytical band ratio algorithm for turbid inland waters gave an accuracy of ~16% or 4.4 mg/m3compared to retrieval of Chl-a from reflectance measured in situ. Using fixed ATCOR4 processing parameters for whole images improved Chl-a retrieval results from ~6 mg/m3difference to reference to approximately 2 mg/m3. We conclude that the AisaFENIX sensor, in combination with ATCOR4 in image-driven parametrization, can be successfully used for inland water quality observations. This implies that the need for in situ reference measurements is not as strict as has been assumed and a high degree of automation in processing is possible

Use of Hyperspectral Remote Sensing to Estimate Water Quality

Approximating and forecasting water variables like phosphorus, nitrogen, chlorophyll, dissolved organic matter, and turbidity are of supreme importance due to their strong influence on water resource quality. This chapter is aimed at showing the practicability of merging water quality observations from remote sensing with water quality modeling for efficient and effective monitoring of water quality. We examine the spatial dynamics of water quality with hyperspectral remote sensing and present approaches that can be used to estimate water quality using hyperspectral images. The methods presented here have been embraced because the blue-green and green algae peak wavelengths reflectance are close together and make their distinction more challenging. It has also been established that hyperspectral imagers permit an improved recognition of chlorophyll and hereafter algae, due to acquired narrow spectral bands between 450 nm and 600 nm. We start by describing the practical application of hyperspectral remote sensing data in water quality modeling. The surface inherent optical properties of absorption and backscattering of chlorophyll a, colored dissolved organic matter (CDOM), and turbidity are estimated, and a detailed approach on analyzing ARCHER data for water quality estimation is presented

Estimating the water quality condition of river and lake water in the Midwestern United States from its spectral characteristics

This study focuses on developing/calibrating remote sensing algorithms for water quality retrieval in Midwestern rivers and lakes. In the first part of this study, the spectral measurements collected using a hand-held spectrometer as well as water quality observations for the Wabash River and its tributary the Tippecanoe River in Indiana were used to develop empirical models for the retrieval of chlorophyll (chl) and total suspended solids (TSS). A method for removing sky and sun glint from field spectra for turbid inland waters was developed and tested. Empirical models were then developed using a subset of the field measurements with the rest for model validation. Spectral characteristics indicative of waters dominated by different inherent optical properties (IOPs) were identified and used as the basis of selecting bands for empirical model development. The second part of this study focuses on the calibration of an existing bio-geo-optical model for studying the spatial variability of chl, non-algal particles (NAP), and colored dissolved organic matter (CDOM) in episodic St. Joseph River plumes in southern Lake Michigan. One set of EO-1 Hyperion imagery and one set of boat-based spectrometer measurements were successfully acquired to capture episodic plume events. Coincident water quality measurements were also collected during these plume events. A database of inherent optical properties (IOPs) measurements and spectral signatures was generated and used to calibrate a bio-geo-optical model. Finally, a comprehensive spectral-biogeochemical database was developed for the Wabash River and its tributaries in Indiana by conducting field sampling of the rivers using a boat platform over different hydrologic conditions during summer 2014. In addition to the various spectral measurements taken by a handheld field spectrometer, this database includes corresponding in situ measurements of water quality parameters (chl, NAP, and CDOM), nutrients (TN, TP, dissolved organic carbon (DOC)), water-column IOPs, water depths, substrate types and bottom reflectance spectra. The temporal variability of water quality parameters and nutrients in the rivers was analyzed and studied. A look-up table (LUT) based spectrum matching methodology was applied to the collected observations in the database to simplify the retrieval of water quality parameters and make the data accessible to a wider range of end users. It was found that the ratio of the reflectance peak at the red edge (704 nm) with the local minimum caused by chlorophyll absorption at 677 nm was a strong predictor of chl concentrations (coefficient of determination ( R2) = 0.95). The reflectance peak at 704 nm was also a good predictor for TSS estimation (R2 = 0.75). In addition, we also found that reflectance within the NIR wavelengths (700–890 nm) all showed strong correlation (0.85–0.91) with TSS concentrations and generated robust models. Field measured concentrations of NAP and CDOM at 67% of the sampled sites in the St Joseph River plume fall within one standard deviation of the retrieved means using the spectrometer measurements and the calibrated bio-geo-optical model. The percentage of sites within one standard deviation (88%) is higher for the estimation of chl concentrations. Despite the dynamic nature of the observed plume and the time lag during field sampling, 77% of sampled sites were found to have field measured chl and NAP concentrations falling within one standard deviation of the Hyperion derived values. The spatial maps of water quality parameters generated from the Hyperion image provided a synoptic view of water quality conditions. Analysis highlights that concentrations of NAP, chl, and CDOM were more than three times higher in conjunction with river outflow and inside the river plumes than in ambient water. It is concluded that the storm-initiated plume is a significant source of sediments, carbon and chl to Lake Michigan. The temporal variability of water quality parameters and nutrients in the Wabash River was closely associated with hydrologic conditions, while no significant correlations existed between these parameters and streamflow for the Tippecanoe River, probably due to the two upstream reservoirs. The poor relationship between CDOM and DOC indicates that most DOC in the rivers was from human sources such as wastewater. It was also found that the source of water (surface runoff or combined sewer overflows (CSO)) to a river, water temperature, and nutrients are important factors controlling instream concentrations of phytoplankton. The LUT retrieved chl and NAP concentrations were in good agreement with field measurements with slopes close to 1.0. The average estimation errors for NAP and chl were within 4.1% and 37.7%, respectively, of independently obtained lab measurements. The CDOM levels were not well estimated and the LUT retrievals for CDOM showed large variability, probably due to the small data range collected in this study and the insensitivity of remote sensing reflectance, Rrs, to CDOM change. (Abstract shortened by ProQuest.

Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission

AbstractFreshwater ecosystems underpin global water and food security, yet are some of the most endangered ecosystems in the world because they are particularly vulnerable to land management change and climate variability. The US National Research Council's guidance to NASA regarding missions for the coming decade includes a polar orbiting, global mapping hyperspectral satellite remote sensing mission, the Hyperspectral Infrared Imager (HyspIRI), to make quantitative measurements of ecosystem change. Traditionally, freshwater ecosystems have been challenging to measure with satellite remote sensing because they are small and spatially complex, require high fidelity spectroradiometry, and are best described with biophysical variables derived from high spectral resolution data. In this study, we evaluate the contribution of a hyperspectral global mapping satellite mission to measuring freshwater ecosystems. We demonstrate the need for such a mission, and evaluate the suitability and gaps, through an examination of the measurement resolution issues impacting freshwater ecosystem measurements (spatial, temporal, spectral and radiometric). These are exemplified through three case studies that use remote sensing to characterize a component of freshwater ecosystems that drive primary productivity. The high radiometric quality proposed for the HyspIRI mission makes it uniquely well designed for measuring freshwater ecosystems accurately at moderate to high spatial resolutions. The spatial and spectral resolutions of the HyspIRI mission are well suited for the retrieval of multiple biophysical variables, such as phycocyanin and chlorophyll-a. The effective temporal resolution is suitable for characterizing growing season wetland phenology in temperate regions, but may not be appropriate for tracking algal bloom dynamics, or ecosystem responses to extreme events in monsoonal regions. Global mapping missions provide the systematic, repeated measurements necessary to measure the drivers of freshwater biodiversity change. Archival global mapping missions with open access and free data policies increase end user uptake globally. Overall, an archival, hyperspectral global mapping mission uniquely meets the measurement requirements of multiple end users for freshwater ecosystem science and management

Remote sensing of inland waters: challenges, progress and future directions

Monitoring and understanding the physical, chemical and biological status of global inland waters are immensely important to scientists and policy makers alike. Whereas conventional monitoring approaches tend to be limited in terms of spatial coverage and temporal frequency, remote sensing has the potential to provide an invaluable complementary source of data at local to global scales. Furthermore, as sensors, methodologies, data availability and the network of researchers and engaged stakeholders in this field develop, increasingly widespread use of remote sensing for operational monitoring of inland waters can be envisaged. This special issue on Remote Sensing of Inland Waters comprises 16 articles on freshwater ecosystems around the world ranging from lakes and reservoirs to river systems using optical data from a range of in situ instruments as well as airborne and satellite platforms. The papers variably focus on the retrieval of in-water optical and biogeochemical parameters as well as information on the biophysical properties of shoreline and benthic vegetation. Methodological advances include refined approaches to adjacency correction, inversion-based retrieval models and in situ inherent optical property measurements in highly turbid waters. Remote sensing data are used to evaluate models and theories of environmental drivers of change in a number of different aquatic ecosystems. The range of contributions to the special issue highlights not only the sophistication of methods and the diversity of applications currently being developed, but also the growing international community active in this field. In this introductory paper we briefly highlight the progress that the community has made over recent decades as well as the challenges that remain. It is argued that the operational use of remote sensing for inland water monitoring is a realistic ambition if we can continue to build on these recent achievements.Output Type: Editoria

Towards high fidelity mapping of global inland water quality using earth observation data

This body of work aims to contribute advancements towards developing globally applicable water quality retrieval models using Earth Observation data for freshwater systems. Eutrophication and increasing prevalence of potentially toxic algal blooms among global inland water bodies have become a major ecological concersn and require direct attention. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products. This study provides one of the first assessments of the state-ofthe-art for trophic status (chlorophyll-a) retrievals for small water bodies using Sentinel-3 Ocean and Land Color Imager (OLCI). Multiple fieldwork campaigns were undertaken for the collection of common aquatic biogeophysical and bio-optical parameters that were used to validate current atmospheric correction and chlorophyll-a retrieval algorithms. The study highlighted the difficulties of obtaining robust retrieval estimates from a coarse spatial resolution sensor from highly variable eutrophic water bodies. Atmospheric correction remains a difficult challenge to operational freshwater monitoring, however, the study further validated previous work confirming applicability of simple, empirically derived retrieval algorithms using top-of-atmosphere data. The apparent scarcity of paired in-situ optical and biogeophysical data for productive inland waters also hinders our capability to develop and validate robust retrieval algorithms. Radiative transfer modeling was used to fill this gap through the development of a novel synthetic dataset of top-of-atmosphere and bottom-of-atmosphere reflectances, which attempts to encompass the immense natural optical variability present in inland waters. Novel aspects of the synthetic dataset include: 1) physics-based, two-layered, size and type specific phytoplankton IOPs for mixed eukaryotic/cyanobacteria 6 assemblages, 2) calculations of mixed assemblage chl-a fluorescence, 3) modeled phycocyanin concentration derived from assemblage based phycocyanin absorption, 4) and paired sensor-specific TOA reflectances which include optically extreme cases and contribution of green vegetation adjacency. The synthetic bottom-of-atmosphere reflectance spectra were compiled into 13 distinct optical water types similar to those discovered using in-situ data. Inspection showed similar relationships and ranges of concentrations and inherent optical properties of natural waters. This dataset was used to calculate typical surviving water-leaving signal at top-of-atmosphere, as well as first order calculations of the signal-to-noise-ratio (SNR) for the various optical water types, a first for productive inland waters, as well as conduct a sensitivity analysis of cyanobacteria detection from top-of-atmosphere. Finally, the synthetic dataset was used to train and test four state-of-the-art machine learning architectures for multi-parameter retrieval and cross-sensor capability. Initial results provide reliable estimates of water quality parameters and inherent optical properties over a highly dynamic range of water types, at various spectral and spatial sensor resolutions. It is hoped the results of this work incrementally improves inland water Earth observation on multiple aspects of the forward and inverse modelling process, and provides an improvement in our capabilities for routine, global monitoring of inland water quality

Remote sensing and bio-geo-optical properties of turbid, productive inland waters: a case study of Lake Balaton

Algal blooms plague freshwaters across the globe, as increased nutrient loads lead to eutrophication of inland waters and the presence of potentially harmful cyanobacteria. In this context, remote sensing is a valuable approach to monitor water quality over broad temporal and spatial scales. However, there remain several challenges to the accurate retrieval of water quality parameters, and the research in this thesis investigates these in an optically complex lake (Lake Balaton, Hungary). This study found that bulk and specific inherent optical properties [(S)IOPs] showed significant spatial variability over the trophic gradient in Lake Balaton. The relationships between (S)IOPs and biogeochemical parameters differed from those reported in ocean and coastal waters due to the high proportion of particulate inorganic matter (PIM). Furthermore, wind-driven resuspension of mineral sediments attributed a high proportion of total attenuation to particulate scattering and increased the mean refractive index (n̅p) of the particle assemblage. Phytoplankton pigment concentrations [chlorophyll-a (Chl-a) and phycocyanin (PC)] were also accurately retrieved from a times series of satellite data over Lake Balaton using semi-analytical algorithms. Conincident (S)IOP data allowed for investigation of the errors within these algorithms, indicating overestimation of phytoplankton absorption [aph(665)] and underestimation of the Chl-a specific absorption coefficient [a*ph(665)]. Finally, Chl-a concentrations were accurately retrieved in a multiscale remote sensing study using the Normalized Difference Chlorophyll Index (NDCI), indicating hyperspectral data is not necessary to retrieve accurate pigment concentrations but does capture the subtle heterogeneity of phytoplankton spatial distribution. The results of this thesis provide a positive outlook for the future of inland water remote sensing, particularly in light of contemporary satellite instruments with continued or improved radiometric, spectral, spatial and temporal coverage. Furthermore, the value of coincident (S)IOP data is highlighted and contributes towards the improvement of remote sensing pigment retrieval in optically complex waters