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This paper describes a dataset consisting of 47 hyperspectral-reflectance measurements of plastic litter samples. The plastic litter samples include virgin and real samples from the Port of Antwerp. They were measured in dry conditions in the Flemish Institute for Technological Research (VITO) calibration facility, and a selection of the samples were also measured in wet conditions and submerged in a water tank at Flanders Hydraulics. The construction on top of the tank allowed us to submerge the plastics and keep sediments in suspension. The spectral measurements were performed using an Analytical Spectral Devices (ASD) FieldSpec 4 and a Spectral Evolution (SEV) spectrometer. The datasets are available on the 4TU.ResearchData open-access repository (ASD dataset: https://doi.org/10.4121/12896312.v2, Knaeps et al., 2020; SEV dataset: https://doi.org/10.4121/uuid:9ee3be54-9132-415a-aaf2-c7fbf32d2199; Garaba et al., 2020).

While at least 8 million tons of plastic litter are ending up in our oceans every year and research on marine litter detection is increasing, the spectral properties of wet as well as submerged plastics in natural marine environments are still largely unknown. Scientific evidence-based knowledge about these spectral characteristics has relevance especially to the research and development of future remote sensing technologies for plastic litter detection. In an effort to bridge this gap, we present one of the first studies about the hyperspectral reflectances of virgin and naturally weathered plastics submerged in water at varying suspended sediment concentrations and depth. We also conducted further analyses on the different polymer types such as Polyethylene terephthalate (PET), Polypropylene (PP), Polyester (PEST) and Low-density polyethylene (PE-LD) to better understand the effect of water absorption on their spectral reflectance. Results show the importance of using spectral wavebands in both the visible and shortwave infrared (SWIR) spectrum for litter detection, especially when plastics are wet or slightly submerged which is often the case in natural aquatic environments. Finally, we demonstrate in an example how to use the open access data set driven from this research as a reference for the development of marine litter detection algorithms.

With the objective to improve data quality in terms of cloud detection, absolute radiometric calibration and atmospheric correction, the PRoject for On-Board Autonomy-Vegetation (PROBA-V) data archive (October 2013 - June 2020) will be reprocessed to Collection 2 (C2). The product validation is organized in three phases and focuses on the intercomparison with PROBA-V Collection 1 (C1), but also consistency analysis with SPOT-VGT, Sentinel-3 SYN-VGT, Terra-MODIS and METOP-AVHRR is foreseen. First preliminary results show the better performance of cloud and snow/ice masking, and indicate that statistical consistency between PROBA-V C2 and C1 are in line with expectations. PROBA-V C2 data are to be released to the public in September 2021.

International audience; Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA – ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances (̂ρ w ). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. This volume of data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errors in ̂ρ w (560) and ̂ρ w (664) were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15–30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concentration of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20–30% uncertainties in ̂ρ w (490 ≤ λ ≤ 743 nm) yielded 25–70% uncertainties in derived Chla and TSS products for top-performing AC processors. We summarize our results using performance matrices guiding the satellite user community through the OWT-specific relative perfor­mance of AC processors. Our analysis stresses the need for better representation of aerosols, particularly absorbing ones, and improvements in corrections for sky- (or sun-) glint and adjacency effects, in order to achieve higher quality downstream products in freshwater and coastal ecosystems.

High-quality satellite-based ocean colour products can provide valuable support and insights in management and monitoring of coastal ecosystems. Today’s availability of Earth Observation (EO) data is unprecedented including traditional medium resolution ocean colour systems (e.g. SeaWiFS, MODIS-AQUA, MERIS, Sentinel-3/OLCI), high resolution land sensors (e.g. Sentinel-2/MSI, Landsat-8/OLI, Pleiades) and geostationary satellites (e.g. SEVIRI). Each of these sensors offers specific advantages in terms of spatial, temporal or radiometric characteristics.As a new production unit, the high resolution coastal service will be integrated in CMEMS. It offers 12 different products which are covered within the Ocean Colour Thematic Assembly Centre (OCTAC). The products can be categorized in two groups: 1) near real time (NRT) and Multi-Year near real time (MYNRT). The products are generated the coastal waters (20km stripe for the coastline) for all European Seas and are provided in 100m spatial resolution. All products are based on Sentinel-2 MSI data. The primary OCTAC variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral Remote Sensing Reflectance (RRS). This, together with the Particulate Backscatter Coefficient (BBP), constitute the category of the optics products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm. The transparency products include turbidity (TUR) and Suspended Particulate Matter (SPM) concentration. They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions. The geophysical product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging. The NRT products are generally provided withing 24 hours after end of the acquisition day, while monthly averaged products are provided few days after end of the respective month. A third group of products are daily gap-filled products which are provided once in a quarter. Validation of the variables has been performed by match-up analysis with in situ data as well as by comparison of the high resolution products with the well established Low Resolution CMEMS Ocean Colour products. The products will be introduced in the CMEMS service by May 2021. We will present the products themselves as well as the validation results for the different variables. The known limitations will be reported in order to provide a full picture of the new service.

Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA - ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances (??w). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. The volume of our data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errorsin ??w(560) and ??w(664) were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15- 30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concertation of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20- 30% uncertainties in ??w(490

This paper describes a dataset consisting of 47 hyperspectral reflectance measurements of plastic litter samples. The plastic litter samples include virgin and real samples from the Port of Antwerp. They were measured in dry conditions in the VITO calibration facility and a selection of the samples was also measured in wet conditions and submerged in a watertank at Flanders Hydraulics. The construction on top of the tank allowed to submerge the plastics and keep sediments in suspension. The spectral measurements were performed using an Analytical Spectral Devices (ASD) FieldSpec 4 and a Spectral Evolution (SEV) spectrometer. The datasets are available on the 4TU.ResearchData open-access repository (ASD dataset: https://doi.org/10.4121/12896312.v2, Knaeps et al., 2020; SEV dataset: https://doi.org/10.4121/uuid:9ee3be54-9132-415a-aaf2-c7fbf32d2199, Garaba et al., 2020).

In urban environments, due to climate change urban heat waves are predicted to occur more frequently. Urban vegetation and the linked evapotranspiration rate can play a mitigating role. However, a major challenge in urban hydrological modelling remains the mapping of vegetation dynamics and its role in hydrological processes in particular interception storage and evapotranspiration. Conventional mapping of vegetation usually implies intensive labor and time consuming field work. We explore the potential of different remote sensing sensors (Proba-V, Landsat, Sentinel2, Apex) to characterize the urban vegetation dynamics for hydrological modelling. The here proposed remote sensing sensors show differences in the spectral and spatial resolutions as well as in their revisit time. However, in the urban environment we need a high spatial and spectral resolution to distinguish the urban landcover and a frequent revisit time to capture seasonal vegetation dynamics. Therefore, we propose a combination of different remote sensing sensors to derive leaf area index (LAI) timeseries in the urban environment. To improve the consistency in time series generated from different remote sensing sources a harmonization of the multi-sensor time series is proposed and validated with a multi-resolution validation approach using ground-truthing LAI (BELHARMONY project). The LAI timeseries, derived from the different remote sensing sensors, are then introduced into the hydrological modelling framework for a location- and time- specific assessment of the interception storage and evapotranspiration component. The effect of the sensor differences to the LAI timeseries on the hydrological response is analyzed.

PROBA-V (Project for On-Board Autonomy-Vegetation) is a global vegetation monitoring satellite. The spectral quality of the data and the coverage of PROBA-V over coastal waters provide opportunities to expand its use to other applications. This study tests PROBA-V data for the retrieval of turbidity in the North Sea region. In the first step, clouds were masked and an atmospheric correction, using an adapted version of iCOR, was performed. The resulted water leaving radiance reflectance was validated against AERONET-OC stations, yielding a coefficient of determination of 0.884 in the RED band. Next, turbidity values were retrieved using the RED band. The PROBA-V retrieved turbidity data was compared with turbidity data from CEFAS Smartbuoys and ad-hoc measurement campaigns. This resulted in a coefficient of determination of 0.69. Finally, a time series of 1.5 year of PROBA-V derived turbidity data was plotted over MODIS data to check consistencies in both datasets. Seasonal dynamics were noted with high turbidity in autumn and winter and low values in spring and summer. For low values, PROBA-V and MODIS yielded similar results, but while MODIS seems to saturate around 50 FNU, PROBA-V can reach values up till almost 80 FNU.

After the end of the ‘Satellite Pour l'Observation de la Terre’ (SPOT) VEGETATION (SPOT/VGT) mission in May/2014, the SPOT/VGT data archive, consisting of raw data coming from both the VEGETATION 1 (VGT1) and VEGETATION 2 (VGT2) instruments, was reprocessed, aiming at improved cloud screening and correcting for known artefacts such as the smile pattern in the VGT2 Blue band and the Sun-Earth distance bug in Top-of-Atmosphere reflectance calculation, with the objective of improving temporal consistency. The aim of this paper is to inform the user community of the changes in and the evaluation of the new SPOT/VGT Collection 3 (VGT-C3). The evaluation of the reprocessing is based on (i) the relative comparison between SPOT/VGT Collection 2 (VGT-C2) and VGT-C3 surface reflectances and Normalized Difference Vegetation Index (NDVI), (ii) consistency analysis between VGT1-C3 and VGT2-C3, and (iii) the comparison of the archive with external datasets from METOP/Advanced Very High Resolution Radiometer (AVHRR) and TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS). Surface reflectances are slightly higher after the reprocessing, with larger differences in July compared to January, caused by the corrected Sun-Earth distance modelling. For NDVI, the overall impact of the reprocessing is relatively small and differences show no seasonality. Trends in the differences over the years are related to changes in calibration coefficients. Systematic differences between VGT1-C3 and VGT2-C3 surface reflectance are well below 1%, with largest bias between VGT1 and VGT2 for the NIR band and the NDVI (VGT2 > VGT1, especially for larger NDVI values). Both the comparison with METOP/AVHRR (surface reflectance and NDVI) and TERRA/MODIS (NDVI) reveal trends over time: systematic bias between VGT2 and METOP/AVHRR tends to decrease over time, while comparison with TERRA/MODIS indicates an increasing bias between VGT2 and MODIS. VGT2 NDVI seems to be gradually evolving to slightly larger values, which is consistent with the change in overpass time of VGT2 and the different illumination conditions caused by the orbital drift of the sensor. Results demonstrate however the SPOT/VGT-C3 archive is more stable over time compared to the previous archive, although bidirectional reflectance distribution function (BRDF) normalization is recommended in order to correct for bidirectional effects.

Societal dependence on, and commercial and scientific exploitation of Earth-Oriented remote sensing from satellites is growing at an exponential rate. The comprehensive EU Copernicus programme provides a major contribution to the global effort, but even so, to achieve the necessary global and temporal coverage requires synergistic cooperation and associated interoperability of the Worlds sensors. For a user to exploit Earth Observation (EO) data there must exist confidence in data characteristics, quality and reliable delivery. Although long-term data records for climate may be the most demanding in nature, generation of analysis-ready operational data sets for applications, as diverse as food security to pollution monitoring, all require the user to have some quantitative level of confidence in the data and derived information. A long-term Calibration/Validation (Cal/Val) vision necessitates clear ownership and long-term funding. Delineating the roles of the European Commission (EC), space agencies and member states in long-term Cal/Val would provide clarity. It is clear that the space agencies have the responsibility to meet the mission requirement of their spaceborne instruments but long-term validation is often entrusted to interested parties bringing their own resources to the task. Furthermore, there is a critical need for Fiducial Reference Measurements (FRMs), acquired in operational mode, and comprehensive in coverage both spatially and temporally, to assure that the satellite product accuracies are met. This paper discusses the current status, gaps and challenges regarding long-term Cal/Val of EO satellites and recommends the creation of a European coordinating entity for satellite product calibration and validation. The proposed entity would be an integrative organization coordinating the European Cal/Val activities in partnership with the member states and the space agencies and working together with existing data providers to secure access to satellite and in-situ data of traceable FRM standards.

The SeaSWIR dataset consists of 137 ASD (Analytical Spectral Devices, Inc.) marine reflectances, 137 total suspended matter (TSM) measurements and 97 turbidity measurements gathered at three turbid estuarine sites (Gironde, La Plata, Scheldt). The dataset is valuable because of the high-quality measurements of the marine reflectance in the Short Wave InfraRed I region (SWIR-I: 1000-1200 nm) and SWIR-II (1200-1300 nm) and because of the wide range of TSM concentrations from 48 up to 1400 mgĝ€†Lĝ'1. The ASD measurements were gathered using a detailed measurement protocol and were subjected to a strict quality control. The SeaSWIR marine reflectance is characterized by low reflectance at short wavelengths ( < 450 nm), peak reflectance values between 600 and 720 nm and significant contributions in the near-infrared (NIR) and SWIR-I parts of the spectrum. Comparison of the ASD water reflectance with simultaneously acquired reflectance from a three-radiometer system revealed a correlation of 0.98 for short wavelengths (412, 490 and 555 nm) and 0.93 for long wavelengths (686, 780 and 865 nm). The relationship between TSM and turbidity (for all sites) is linear, with a correlation coefficient of 0.96. The SeaSWIR dataset has been made publicly available (https://doi.org/10.1594/PANGAEA.886287).. Fil: Knaeps, Els. Flemish Institute For Technological Research (vito); Bélgica Fil: Doxaran, David. Laboratoire D'océanographie de Villefranche (lov); Francia Fil: Dogliotti, Ana Inés. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina Fil: Nechad, Bouchra. Royal Belgian Institute For Natural Sciences (rbins); Bélgica Fil: Ruddick, K.. Royal Belgian Institute For Natural Sciences (rbins); Bélgica Fil: Raymaekers, D.. Flemish Institute For Technological Research (vito); Bélgica Fil: Sterckx, Sindy. Flemish Institute For Technological Research (vito); Bélgica

Atmospheric correction plays a crucial role among the processing steps applied to remotely sensed hyperspectral data. Atmospheric correction comprises a group of procedures needed to remove atmospheric effects from observed spectra, i.e. the transformation from at-sensor radiances to at-surface radiances or reflectances. In this paper we present the different steps in the atmospheric correction process for APEX hyperspectral data as applied by the Central Data Processing Center (CDPC) at the Flemish Institute for Technological Research (VITO, Mol, Belgium). The MODerate resolution atmospheric TRANsmission program (MODTRAN) is used to determine the source of radiation and for applying the actual atmospheric correction. As part of the overall correction process, supporting algorithms are provided in order to derive MODTRAN configuration parameters and to account for specific effects, e.g. correction for adjacency effects, haze and shadow correction, and topographic BRDF correction. The methods and theory underlying these corrections and an example of an application are presented.

PROBA-V (PRoject for On-Board Autonomy&ndash, Vegetation) was launched in May-2013 as an operational continuation to the vegetation (VGT) instruments on-board the Syst&egrave, me Pour l&rsquo, Observation de la Terre (SPOT)-4 and -5 satellites. The first reprocessing campaign of the PROBA-V archive from Collection 0 (C0) to Collection 1 (C1) aims at harmonizing the time series, thanks to improved radiometric and geometric calibration and cloud detection. The evaluation of PROBA-V C1 focuses on (i) qualitative and quantitative assessment of the new cloud detection scheme, (ii) quantification of the effect of the reprocessing by comparing C1 to C0, and (iii) evaluation of the spatio-temporal stability of the combined SPOT/VGT and PROBA-V archive through comparison to METOP/advanced very high resolution radiometer (AVHRR). The PROBA-V C1 cloud detection algorithm yields an overall accuracy of 89.0%. Clouds are detected with very few omission errors, but there is an overdetection of clouds over bright surfaces. Stepwise updates to the visible and near infrared (VNIR) absolute calibration in C0 and the application of degradation models to the SWIR calibration in C1 result in sudden changes between C0 and C1 Blue, Red, and NIR TOC reflectance in the first year, and more gradual differences for short-wave infrared (SWIR). Other changes result in some bias between C0 and C1, although the root mean squared difference (RMSD) remains well below 1% for top-of-canopy (TOC) reflectance and below 0.02 for the normalized difference vegetation index (NDVI). Comparison to METOP/AVHRR shows that the recent reprocessing campaigns on SPOT/VGT and PROBA-V have resulted in a more stable combined time series.

The atmospheric condition parameters used in the radiative transfer-based atmospheric correction (AC) are often uncertain. This uncertainty propagates to the estimated reflectance. The reflectance, is, however, not equally sensitive to all the parameters. A sensitivity analysis (SA) helps in prioritizing the parameters. The objective of this study was to perform an SA of reflectance to water vapor concentration ( $wv$ ) and aerosol optical thickness ( $AOT$ ). SA was performed using the Fourier amplitude sensitivity test (FAST) method, which computes sensitivity indices ( $\text{SI}$ ) of these parameters. Besides variation in the two parameters, we also studied the effect of surface albedo on the $\text{SI}$ by quantifying $\text{SI}$ for three target surfaces (in the spectral range $\text{0.44{-}0.96}\;\upmu$ m): 1) a dark target (water); 2) a bright target (bare soil); and 3) a target having low albedo in the visible and high albedo in near-infrared range (forest). For $AOT$ , high ( $\approx\!\text{0.9}$ ) $\text{SI}$ values were observed at the nonwater absorption wavelengths. For $wv$ , high $\text{SI}$ values were observed at wavelengths, where strong absorption features are located and when the surface albedo was high. For the dark target, the effect of $AOT$ was prominent throughout the spectral range. We found that the sensitivity of reflectance to $wv$ and $AOT$ is a function of wavelength, strength of the absorption features, and surface albedo. We conclude that $AOT$ is a more important parameter for dark targets than $wv$ even at the principal absorption feature. For bright targets, the importance of $wv$ and $AOT$ depends on the strength of the absorption feature.

Image correction for atmospheric effects (iCOR) is an atmospheric correction tool that can process satellite data collected over coastal, inland or transitional waters and land. The tool is adaptable with minimal effort to hyper- or multi-spectral radiometric sensors. By using a single atmospheric correction implementation for land and water, discontinuities in reflectance within one scene are reduced. iCOR derives aerosol optical thickness from the image and allows for adjacency correction, which is SIMilarity Environmental Correction (SIMEC) over water. This paper illustrates the performance of iCOR for Landsat-8 OLI and Sentinel-2 MSI data acquired over water. An intercomparison of water leaving reflectance between iCOR and Aerosol Robotic Network – Ocean Color provided a quantitative assessment of performance and produced coefficient of determination (R2) higher than 0.88 in all wavebands except the 865 nm band. For inland waters, the SIMEC adjacency correction improved results in the red-edge and near-infrared region in relation to optical in situ measurements collected during field campaigns.

International audience; The SeaSWIR dataset consists of 137 ASD (Analytical Spectral Devices, Inc.) marine reflectances, 137 total suspended matter (TSM) measurements and 97 turbidity measurements gathered at three turbid estuar-ine sites (Gironde, La Plata, Scheldt). The dataset is valuable because of the high-quality measurements of the marine reflectance in the Short Wave InfraRed I region (SWIR-I: 1000-1200 nm) and SWIR-II (1200-1300 nm) and because of the wide range of TSM concentrations from 48 up to 1400 mg L −1. The ASD measurements were gathered using a detailed measurement protocol and were subjected to a strict quality control. The SeaSWIR marine reflectance is characterized by low reflectance at short wavelengths (< 450 nm), peak reflectance values between 600 and 720 nm and significant contributions in the near-infrared (NIR) and SWIR-I parts of the spectrum. Comparison of the ASD water reflectance with simultaneously acquired reflectance from a three-radiometer system revealed a correlation of 0.98 for short wavelengths (412, 490 and 555 nm) and 0.93 for long wavelengths (686, 780 and 865 nm). The relationship between TSM and turbidity (for all sites) is linear, with a correlation coefficient of 0.96. The SeaSWIR dataset has been made publicly available (https://doi.org/10.1594/PANGAEA.886287).

PROBA-V is a new global vegetation monitoring mission, to be launched in the second quarter of 2013. PROBA-V has been developed to show a consistent performance with SPOT-VEGETATION (SPOT-VGT) data, with similar spectral bands but with an improved spatial resolution of 1/3 km. The innovative mission concept has led to several key research topics related to image quality, which are discussed in this article. To support the existing VEGETATION user community, the data products for PROBA-V continue to provide daily top of canopy synthesis (S1-TOC) and 10 day synthesis products (S10-TOC). In addition, the new top of atmosphere daily synthesis (S1-TOA) products and a radiometrically/geometrically corrected (level 1C) product in raw resolution will also be provided for scientific users.

This letter presents the improvements of an absolute calibration reference system based on simulated top-of-atmosphere bidirectional reflectance factor time series over bright desert targets. The current work highlights a case study performed over Committee on Earth Observation Satellites (CEOS) calibration target Libya4, demonstrating that it is possible to achieve a mean accuracy of 3% when simulation is compared with calibrated observations acquired by polar orbiting satellites.

D5.15 INFORM Prototype/Algorithms Validation Report provides validation results for the algorithms described in deliverable D5.14 (INFORM Algorithms Theoretical Basis Document). The report is an update of the preliminary validation results reported in D5.13. D5.15 describes the data used, the applied processing steps with reference to D5.1 and D5.14 for more detailed information, and validation results for T5.1 Atmospheric correction, T5.2 Attenuation and euphotic depth, T5.3 TSM and turbidity, T5.4 Yellow matter, T5.5 Phytoplankton functional types, T5.6 Stratification, T5.7 Macrophytes, T5.8 Phytoplankton primary production and T5.9 Sun-induced chlorophyll fluorescence. For T5.1, T5.2 and T5.3 the developed prototype code is described in D5.2 Sentinel-2 atmospheric correction prototype code, D5.3 Attenuation coefficients and euphotic depth prototype code and D5.4 TSM and turbidity prototype code, respectively. The products developed in T5.2 Attenuation and euphotic depth, T5.3 TSM and turbidity, T5.4 Yellow matter, T5.5 Phytoplankton functional types, T5.6 Stratification, T5.7 Macrophytes, T5.8 Phytoplankton primary production and T5.9 Sun-induced chlorophyll fluorescence are described in D5.5 - D5.12.

One of the inherent challenges in remote sensing is the effect of atmospheric gases and particles on the light received by the sensor. The effects of atmospheric interference need to be adequately corrected for in order to retrieve quantitative biophysical information about a water body from the remotely sensed signal. Atmospheric correction of remotely sensed data is more challenging for inland waters than for open ocean waters due to a number of factors. The proximity of inland waters to various terrestrial sources of atmospheric pollution results in a more optically heterogeneous atmosphere, which complicates atmospheric modeling; the signal received at the sensor is often contaminated by contribution from the adjacent land, which is particularly problematic in cases of raised topography surrounding the water body and complicates atmospheric correction; non-negligible reflectance of water in the near-infrared region due to high sediment concentrations in inland waters (from terrestrial sources such as soil erosion, surface runoff from rainfall, and agricultural and industrial discharge) makes it difficult to accurately estimate and remove the effect of atmospheric aerosol scattering on the received signal. In spite of these and other challenges, a few algorithms have been developed and used for atmospheric correction of remotely sensed data from inland waters with reasonable success. This chapter contains a discussion of the main challenges in atmospheric correction for inland waters and brief descriptions of a few existing atmospheric correction algorithms that are suitable for inland waters.

A spectroradiometer able to measure up to 2500nm has been used to measure the reflectance for the Ocean and Land Color Instrument (OLCI) 1020nm band. In July and October 2010 two measurement campaigns were organized at the Scheldt river to collect reflectance spectra and corresponding Total Suspended Matter (TSM) concentration and turbidity. A wide range of TSM concentration was covered from 15 to 402mgL -1. The measurements show a significant increase in reflectance between 950 and 1150nm, corresponding to a decrease in the pure water absorption coefficient. A high correlation was observed between the reflectance at 1020 and 1071nm and TSM concentration. The results were confirmed by simulations with Hydrolight and by the analysis of airborne Airborne Prism EXperiment (APEX) imagery. © 2012 Elsevier Inc. Fil: Knaeps, E.. Flemish Institute For Technological Research (vito); Bélgica Fil: Dogliotti, Ana Inés. Royal Belgian Institute For Natural Sciences (rbins); Bélgica. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina Fil: Raymaekers, D.. Flemish Institute For Technological Research (vito); Bélgica Fil: Ruddick, K.. Royal Belgian Institute For Natural Sciences (rbins); Bélgica Fil: Sterckx, S.. Flemish Institute For Technological Research (vito); Bélgica