Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions

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

Estimation of Phytoplankton Chlorophyll-a Concentration in the Western Basin of Lake Erie Using Sentinel-2 and Sentinel-3 Data

Worldwide phenomena called algae bloom has been recently a serious matter for inland water bodies. Temporal and spatial variability of the bloom makes it di cult to use in-situ monitoring of the lakes. This study aimed to evaluate the potential of Sentinel-3 Ocean and Land Colour Instrument (OLCI) and Sentinel-2 Multispectral Instrument (MSI) data for monitoring algal blooms in Lake Erie. Chlorophyll-a (Chl-a) related products were tested using NOAA-Great Lakes Chl-a monitoring data over summer 2016 and 2017. Thematic water processor, fluorescence line height/maximum chlorophyll index (MCI) and S2 MCI, plug-in SNAP were assessed for their ability to estimate Chl-a concentration. We processed both Top of the Atmosphere (TOA) reflectance and radiance data. Results show that while FLH algorithms are limited to lakes with Chl-a < 8 mg m-3, MCI has the potential to be used effectively to monitor Chl-a concentration over eutrophic lakes. Sentinel-3 MCI is suggested for Chl-a > 20 mg m-3 and Sentinel-2 MCI for Chla > 8 mg m-3. The different Chl-a range limitation for the MCI products can be due to the different location of the maximum peak bands, 705 and 709 for MSI and OLCI sensors respectively. TOA radiances showed a signi cantly better correlation with in situ data compared to TOA reflectances which may be related to the poor pixel identi cation during the process of pixel flagging affected by the complexity of Case-2 water. Our fi nding suggests that Sentinel-2 MCI achieves better performance for Chl-a retrieval (R2 = 0.90). However, the FLH algorithms outperformed showing negative reflectance due to the shift of reflectance peak to longer wavelengths along with increasing Chl-a values. Although the algorithms show moderate performance for estimating Chl-a concentration; this study demonstrated that the new satellite sensors, OLCI and MSI, can play a signi ficant role in the monitoring of algae blooms for Lake Erie

Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index

Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R2 lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R2 of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (<30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops

Living up to the hype of hyperspectral aquatic remote sensing: science, resources and outlook

Intensifying pressure on global aquatic resources and services due to population growth and climate change is inspiring new surveying technologies to provide science-based information in support of management and policy strategies. One area of rapid development is hyperspectral remote sensing: imaging across the full spectrum of visible and infrared light. Hyperspectral imagery contains more environmentally meaningful information than panchromatic or multispectral imagery and is poised to provide new applications relevant to society, including assessments of aquatic biodiversity, habitats, water quality, and natural and anthropogenic hazards. To aid in these advances, we provide resources relevant to hyperspectral remote sensing in terms of providing the latest reviews, databases, and software available for practitioners in the field. We highlight recent advances in sensor design, modes of deployment, and image analysis techniques that are becoming more widely available to environmental researchers and resource managers alike. Systems recently deployed on space- and airborne platforms are presented, as well as future missions and advances in unoccupied aerial systems (UAS) and autonomous in-water survey methods. These systems will greatly enhance the ability to collect interdisciplinary observations on-demand and in previously inaccessible environments. Looking forward, advances in sensor miniaturization are discussed alongside the incorporation of citizen science, moving toward open and FAIR (findable, accessible, interoperable, and reusable) data. Advances in machine learning and cloud computing allow for exploitation of the full electromagnetic spectrum, and better bridging across the larger scientific community that also includes biogeochemical modelers and climate scientists. These advances will place sophisticated remote sensing capabilities into the hands of individual users and provide on-demand imagery tailored to research and management requirements, as well as provide critical input to marine and climate forecasting systems. The next decade of hyperspectral aquatic remote sensing is on the cusp of revolutionizing the way we assess and monitor aquatic environments and detect changes relevant to global communities

Remote sensing satellite image processing techniques for image classification: a comprehensive survey

This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors. In remote sensing, the image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification. Image pre-processing is the initial processing which deals with correcting radiometric distortions, atmospheric distortion and geometric distortions present in the raw image data. Enhancement techniques are applied to preprocessed data in order to effectively display the image for visual interpretation. It includes techniques to effectively distinguish surface features for visual interpretation. Transformation aims to identify particular feature of earth’s surface and classification is a process of grouping the pixels, that produces effective thematic map of particular land use and land cover

Estimating the concentration of physico chemical parameters in hydroelectric power plant reservoir

The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines the amazon region and adjacent areas, such as the Pantanal, as world heritage territories, since they possess unique flora and fauna and great biodiversity. Unfortunately, these regions have increasingly been suffering from anthropogenic impacts. One of the main anthropogenic impacts in the last decades has been the construction of hydroelectric power plants. As a result, dramatic altering of these ecosystems has been observed, including changes in water levels, decreased oxygenation and loss of downstream organic matter, with consequent intense land use and population influxes after the filling and operation of these reservoirs. This, in turn, leads to extreme loss of biodiversity in these areas, due to the large-scale deforestation. The fishing industry in place before construction of dams and reservoirs, for example, has become much more intense, attracting large populations in search of work, employment and income. Environmental monitoring is fundamental for reservoir management, and several studies around the world have been performed in order to evaluate the water quality of these ecosystems. The Brazilian Amazon, in particular, goes through well defined annual hydrological cycles, which are very importante since their study aids in monitoring anthropogenic environmental impacts and can lead to policy and decision making with regard to environmental management of this area. The water quality of amazon reservoirs is greatly influenced by this defined hydrological cycle, which, in turn, causes variations of microbiological, physical and chemical characteristics. Eutrophication, one of the main processes leading to water deterioration in lentic environments, is mostly caused by anthropogenic activities, such as the releases of industrial and domestic effluents into water bodies. Physico-chemical water parameters typically related to eutrophication are, among others, chlorophyll-a levels, transparency and total suspended solids, which can, thus, be used to assess the eutrophic state of water bodies. Usually, these parameters must be investigated by going out to the field and manually measuring water transparency with the use of a Secchi disk, and taking water samples to the laboratory in order to obtain chlorophyll-a and total suspended solid concentrations. These processes are time- consuming and require trained personnel. However, we have proposed other techniques to environmental monitoring studies which do not require fieldwork, such as remote sensing and computational intelligence. Simulations in different reservoirs were performed to determine a relationship between these physico-chemical parameters and the spectral response. Based on the in situ measurements, empirical models were established to relate the reflectance of the reservoir measured by the satellites. The images were calibrated and corrected atmospherically. Statistical analysis using error estimation was used to evaluate the most accurate methodology. The Neural Networks were trained by hydrological cycle, and were useful to estimate the physicalchemical parameters of the water from the reflectance of visible bands and NIR of satellite images, with better results for the period with few clouds in the regions analyzed. The present study shows the application of wavelet neural network to estimate water quality parameters using concentration of the water samples collected in the Amazon reservoir and Cefni reservoir, UK. Sattelite imagens from Landsats and Sentinel-2 were used to train the ANN by hydrological cycle. The trained ANNs demonstrated good results between observed and estimated after Atmospheric corrections in satellites images. The ANNs showed in the results are useful to estimate these concentrations using remote sensing and wavelet transform for image processing. Therefore, the techniques proposed and applied in the present study are noteworthy since they can aid in evaluating important physico-chemical parameters, which, in turn, allows for identification of possible anthropogenic impacts, being relevant in environmental management and policy decision-making processes. The tests results showed that the predicted values have good accurate. Improving efficiency to monitor water quality parameters and confirm the reliability and accuracy of the approaches proposed for monitoring water reservoirs. This thesis contributes to the evaluation of the accuracy of different methods in the estimation of physical-chemical parameters, from satellite images and artificial neural networks. For future work, the accuracy of the results can be improved by adding more satellite images and testing new neural networks with applications in new water reservoirs

Design of a generic end-to-end mission performance simulator and application to the performance analysis of the FLEX/Sentinel-3 mission

La Observación de la Tierra mediante técnicas de teledetección con instrumentos ópticos en satélite tiene como objetivo monitorizar los procesos bio-geofísicos en la superficie y atmósfera terrestre, adquiriendo datos a diferentes longitudes de onda del espectro electromagnético. Con el fin de asegurar el mantenimiento de las observaciones y las capacidades para entender el sistema Tierra, nuevas misiones satelitales están siendo desarrolladas por agencias espaciales nacionales e internacionales así como organizaciones de investigación. En este contexto, los simuladores de misiones espaciales (E2ES por sus siglas en inglés, End-to-End Mission Performance Simulator) ofrecen a los científicos e ingenieros un marco único para entender el impacto de la configuración del instrumento en los productos finales de la misión y, por tanto, acelerar el desarrollo de una misión desde la fase conceptual hasta el lanzamiento. Al mismo tiempo, estas herramientas permiten definir una metodología para la consolidación de los requisitos y la evaluación de la actuación de estas misiones satelitales, estableciendo criterios para la selección de una misión por las diferentes agencias espaciales. Mientras que el concepto de un E2ES es simple, el diseño de nuevos E2ES y la evolución de los ya existentes tienen una falta de guias y metodología estandarizadas, lo cual se traduce en un caro y complejo proceso de re-ingeniería. Esta tesis cubre dos objetivos principales. Por un lado, se pretende armonizar el trabajo hecho en el campo de los E2ES durante las últimas décadas y proponer una serie de guias y metodologías para desarrollas E2ES para misiones satelitales futuras con instrumentos ópticos pasivos. El primer objetivo es por tanto "Diseñar un simulador de misión genérico que pueda ser fácilmente adaptado para reproducir la mayoría de misiones satelitales, presentes y futuras, con sensores ópticos pasivos". Por otro lado, la misión FLEX/Sentinel-3 de la ESA se usa para validar, a través de la implementación de su propio E2ES, el diseño de la arquitectura genérica tratada en el punto anterior. De este modo, el E2ES para la misión FLEX permite evaluar la actuación de la misión para la obtención de la fluorescencia inducida por radiación solar emitida por la vegetación terrestre. La misión FLEX/Sentinel-3 es una candidata óptima para esta tarea de validación dada la complejidad de la misión (p.ej. vuelo en tandem, multi-plataforma/-instrumento, múltiples rangos y resoluciones espectrales, observaciones multi-angulares, sinergia de productos). El segundo objetivo de esta tesis es por tanto "Evaluar la misión FLEX para la la observación de la emisión de fluorescencia emitida por la vegetación usando un E2ES desarrollado de acuerdo con una arquitectura genérica". La razón fundamental tras esta Tesis es promocionar el uso de una arquitectura genérica común para los E2ES que permita comparar misiones satelitales en procesos de selección competitiva como los Earth Explorer de la ESA así como acelerar el análisis de los requisitos técnicos y el rendimiento de la misión a nivel científico. Particularmente, esto se muestra mediante la implementación de esta arquitectura genérica para el caso específico de la misión FLEX/Sentinel-3 demostrando que: (1) la misión es capaz de obtener con la precisión requerida la emisión de fluorescencia por la vegetación ; y (2) el concepto de esta arquitectura genérica es apto para reproducir la complejidad de la misión FLEX/Sentinel-3 y por tanto se espera que esta metodología pueda ser también aplicable para un gran abanico de misiones ópticas pasivas. Esta base lógica se consigue a partir de una categorización de varias misiones satelitales y la identificación y análisis de los elementos principales que afectan en el rendimiento de la misión e impactan en la arquitectura de un simulador de misión. La arquitectura genérica para E2ES propuesta se valida mediante la implementación del E2ES de la misión FLEX/Sentinel-3 de la ESA teniendo en cuenta ambos satélites, sus instrumentos, y evaluando con este E2ES el rendimiento de la misión FLEX. En esta Tesis, los capítulos 1 y 2 introducen los principales temas de esta Tesis y definen los conceptos básicos. Los capítulos 3 al 5 describe el diseño de la arquitectura genérica para los E2ES en misiones ópticas pasivas. Finalmente, el capítulo 6 resume los principales resultados y las conclusiones derivadas de esta Tesis.Earth observation by satellite optical remote sensing aims to monitor bio-geophysical processes happening in the Earth surface and the atmosphere by acquiring data at different wavelengths of the electromagnetic spectrum. In order to ensure sustained observations and capabilities to fill scientific gaps in our current understanding of the Earth system, new satellite missions are being developed by national and international space agencies and research organisations. In this context, End-to-End Mission Performance Simulator (E2ES) tools offer scientists and engineers a unique framework to understand the impact of instrument configuration in the final mission products and to accelerate the mission development from concept to deployment. At the same time, these cost-effective and flexible tools are capable of defining a methodology for the consolidation of requirements and performance assessment of these new satellite missions, setting the criteria for mission selection by the various space agencies’ programme boards. While the concept of an E2ES is simple, the design of new E2ES and the evolution of existing ones lack from a standard methodology and guidelines, which translates into a complex and costly re-engineering process. This Thesis covers two main objectives. On the one hand, it aims to harmonize the work done in the field of E2ES during the last decades and to propose a set of guidelines or methodology to develop E2ES for future remote sensing satellite passive optical missions. The first main objective, therefore, is: ’To design a generic end-to-end mission performance simulator that can be easily adapted to reproduce most present or future passive optical spaceborne instruments’. On the other hand, the ESA’s FLEX/Sentinel-3 tandem mission is used to validate, through the implementation of its E2ES, the designed generic E2ES architecture and to evaluate the performance of the FLEX mission for the retrieval of Sun-induced fluorescence. The FLEX/Sentinel- 3 mission is optimally suitable for this validation task due to the complexity of the mission (e.g. tandem flight, multi-platform/-instrument mission, multiple spectral ranges and resolutions, multi-angular observations, synergy of products). The second main objective, therefore, is: ’To evaluate the FLEX mission for Sun-induced fluorescence retrievals using a newly developed E2ES in agreement with the designed generic E2ES architecture.’. The rationale behind this Thesis is promoting the use of a common generic E2ES architecture that allows comparing missions in competitive selection process (e.g., ESA’s Earth Explorers) and speeding-up the analysis of the mission technical requirements and scientific performances. Particularly, this is shown by implementing this generic E2ES architecture for the specific case of FLEX/Sentinel-3 mission demonstrating that: (1) the mission is capable of retrieving Sun-induced fluorescence within the required accuracy; and (2) the conceptual generic E2ES architecture is suitable toreproduce the complexity of the FLEX/Sentinel-3 tandem mission and thus it is expected to be also applicable for a wide range of passive optical missions. This rationale is achieved by categorising several satellite missions to identify and analyse the main elements that affect the mission performance and impact the simulator architecture. The proposed generic E2ES architecture is validated by implementing the ESA’s FLEX/Sentinel-3 E2ES, both satellites and their instruments, and testing it through the performance assessment of the FLEX mission products. In this Thesis, Chapters 1 and 2 introduce the main research questions and sets the background concepts. Then Chapters 3–5 describe the design of a generic E2ES architecture for passive optical missions. Finally, Chapter 6 summarizes the main results and conclusions derived in this Thesis

The Use of Landsat 8 for Monitoring of Fresh and Coastal Waters

The most interaction between humankind and water occurs in coastal and inland waters (Case 2 waters) at a scale of tens or hundred of meters, but there is not yet an ocean color product (e.g. chlorophyll-a product) at this spatial scale. Landsat 8 could potentially address the remote sensing of these kinds of waters due to its improved features. This work presents an approach to obtain the color producing agents (CPAs) chlorophyll-a, colored dissolved organic material (CDOM) and suspended material (SM) from water bodies using Landsat 8. Adequate atmospheric correction becomes an important first step to accurately retrieving water parameters since the sensor-reaching signal due to water is very small when compared to the signal due to the atmospheric effects. We developed the model-based empirical line method (MoB-ELM) atmospheric correction method. The Mob-ELM employs pseudo invariant feature (PIF) pixels extracted from a reflectance product along with the in-water radiative transfer model HydroLight. We used a look-up-table-based (LUT-based) inversion methodology to simultaneously retrieve CPAs. The LUT of remote-sensing reflectance spectra was created in Hydrolight using inherent optical properties (IOPs) measured in the field. The retrieval algorithm was applied over three Landsat 8 scenes. The CPA concentration maps exhibit expected trends of low concentrations in clear waters and higher concentrations in turbid waters. We estimated a normalized root mean squared error (NRMSE) of about 14% for Chlorophyll-a, 11% for the total suspended solid (TSS), and 7% for colored dissolved organic matter (CDOM) when compared with in situ data. These results demonstrate that the developed algorithm allows the simultaneous mapping of concentration of all CPAs in Case 2 waters and over areas where the standard algorithms are not available due to spatial resolution. Therefore, this study shows that the Landsat 8 satellite can be utilized over Case 2 waters as long as a careful atmospheric correction is applied and IOPs are known