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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.

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.

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

There is a clear trend toward the use of higher spatial resolution satellite sensors. Due to the low revisit time of these sensors and frequent cloud coverage, many applications require data from different sensors to be combined in order to have more frequent observations. This raises concerns regarding data interoperability and consistency. The initial pre-requisite is that there are no radiometric differences in top-of-atmosphere (TOA) observations. This paper aims to quantitatively assess differences in the TOA signal provided by PROBA-V, Sentinel-2A and Sentinel-2B, Landsat-8, and Deimos-1 by using observations over both the Libya-4 desert calibration site and the RadCalNet sites. The results obtained over the Libya-4 site indicate that for all sensors investigated, the inter-sensor deviations are negligible, i.e., within ±2% for comparable spectral bands, with the exception of the Deimos-1 Green band. Clear BRDF (bi-directional reflectance distribution function) effects were observed over the RadCalNet sites, thereby preventing consistent conclusions on inter-sensor deviations from being made. In order to fully explore the potential of the RadCalNet sites, it is recommended that BRDF characterizations be additionally incorporated into the RadCalNet simulations and made publicly available through the distribution portal.

For water quality monitoring using satellite data, it is often required to optimize the low radiance signal through the application of radiometric gains. This work describes a procedure for the retrieval of radiometric gains to be applied to OLI/L8 and MSI/S2A data over coastal waters. The gains are defined by the ratio of the top of atmosphere (TOA) reflectance simulated using the Second Simulation of a Satellite Signal in the Solar Spectrum—vector (6SV) radiative transfer model, REF, and the TOA reflectance acquired by the sensor, MEAS, over AERONET-OC stations. The REF is simulated considering quasi-synchronous atmospheric and aquatic AERONET-OC products and the image acquisition geometry. Both for OLI/L8 and MSI/S2A the measured TOA reflectance was higher than the modeled signal in almost all bands resulting in radiometric gains less than 1. The use of retrieved gains showed an improvement of reflectance remote sensing, Rrs, when with ACOLITE atmospheric correction software. When the gains are applied an accuracy improvement of the Rrs in the 400–700 nm domain was observed except for the first blue band of both sensors. Furthermore, the developed procedure is quick, user-friendly, and easily transferable to other optical sensors.

To validate the iCOR atmospheric correction algorithm applied to the Sentinel-3 Ocean and Land Color Instrument (OLCI), Top-of-Atmosphere (TOA) observations over land, globally retrieved Aerosol Optical Thickness (AOT), Top-of-Canopy (TOC) reflectance, and Vegetation Indices (VIs) were intercompared with (i) AERONET AOT and AERONET-based TOC reflectance simulations, (ii) RadCalNet surface reflectance observations, and (iii) SYN Level 2 (L2) AOT, TOC reflectance, and VIs. The results reveal that, overall, iCOR’s statistical and temporal consistency is high. iCOR AOT retrievals overestimate relative to AERONET, but less than SYN L2. iCOR and SYN L2 TOC reflectances exhibit a negative bias of ~−0.01 and −0.02, respectively, in the Blue bands compared to the simulations. This diminishes for RED and NIR, except for a +0.02 bias for SYN L2 in the NIR. The intercomparison with RadCalNet shows relative differences R2 = 0.80–0.93 and R2 = 0.92–0.96 for TOC reflectance and VIs, respectively. iCOR’s higher temporal smoothness compared to SYN L2 does not propagate into a significantly higher smoothness for TOC reflectance and VIs. Altogether, we conclude that iCOR is well suitable to retrieve statistically and temporally consistent AOT, TOC reflectance, and VIs over land surfaces from Sentinel-3/OLCI observations.

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.

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.

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.

PROBA-V is a remote sensing satellite mission for global monitoring of vegetation. It is designed to offer almost daily coverage of all land masses and to provide data continuity with the VGT2 instrument aboard SPOT-5. Accurate radiometric calibration is key to the success of the mission; therefore, a comprehensive system for in-flight radiometric calibration has been developed. Without no onboard calibration devices, this in-flight calibration will rely fully on vicarious methods. In total nine techniques for vicarious calibration have been implemented and tested in order to meet the radiometric mission requirements. Three key methods that contribute largely to the calibration performance are presented in this paper: Rayleigh, deep convective clouds, and cross-sensor calibration over stable desert sites. As the PROBA-V sensor has still to be launched, calibration algorithm verification is performed using data from the spectrally very similar SPOT-VGT1 and SPOT-VGT2 sensors.

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

A method for the detection and correction of water pixels affected by adjacency effects is presented. The approach is based on the comparison of spectra with the near infrared (NIR) similarity spectrum. Pixels affected by adjacency effects have a water-leaving reflectance spectrum with a different shape to the reference spectrum. This deviation from the similarity spectrum is used as a measure for the adjacency effect. Secondly, the correspondence with the NIR similarity spectrum is used to quantify and to correct for the contribution of the background radiance during atmospheric correction. The advantage of the approach is that it requires no a priori assumptions on the sediment load or related reflectance values in the NIR and can therefore be applied to turbid waters. The approach is tested on hyperspectral airborne data (Compact Airborne Spectrographic Imager (CASI), Airborne Hyperspectral Scanner (AHS)) acquired above coastal and inland waters at different flight altitudes and under varying atmospher...

A seasonally robust algorithm for the retrieval of Suspended Particulate Matter (SPM) in the Scheldt River from hyperspectral images is presented. This algorithm can be applied without the need to simultaneously acquire samples (from vessels and pontoons). Especially in dynamic environments such as estuaries, this leads to a large reduction of costs, both in equipment and personnel. The algorithm was established empirically using in situ data of the water-leaving reflectance obtained over the tidal cycle during different seasons and different years. Different bands and band combinations were tested. Strong correlations were obtained for exponential relationships between band ratios and SPM concentration. The best performing relationships are validated using airborne hyperspectral data acquired in June 2005 and October 2007 at different moments in the tidal cycle. A band ratio algorithm (710 nm/596 nm) was successfully applied to a hyperspectral AHS image of the Scheldt River to obtain an SPM concentration map.

Hyperspectral airborne remote sensing images and in-situ data are combined to assess the spatial and temporal sediment dynamics in the tidal Scheldt river. A log-linear empirical relationship has been developed between a near-infrared reflectance difference and total suspended matter. The relationship was shown to be relatively insensitive to the varying cirrus cloud cover occurring during data acquisition. The produced sediment maps show good agreement with known variations of turbidity over the tidal cycle: maximum turbidity around high water, gradual settling of the sediment in the succeeding slack water and resuspension at the onset of the ebb flow stage.

The launch of several new satellites such as Sentinel-2, Sentinel-3, HyspIRI, EnMAP and PRISMA in the very near future, opens new perspectives for the inland and coastal water community. The monitoring of the water quality closer to the coast, within estuaries or small lakes with satellite data will become feasible. However for these inland and nearshore coastal waters, adjacency effects may hamper the correct retrieval of water quality parameters from remotely sensed imagery. Here, we present a sensor-generic adjacency pre-processing method, SIMilarity Environment Correction (SIMEC). The correction algorithm estimates the contribution of the background radiance based on the correspondence with the Near-INfrared (NIR) similarity spectrum. The performance of SIMEC was tested on MERIS FR images both above highly reflecting waters with high SPM loads, as well as dark lake waters with high CDOM absorption. The results show that SIMEC has a positive or neutral effect on the normalized remote sensing reflectance above optically-complex waters, retrieved with the MERIS MEGS or C2R processor.

In ocean colour remote sensing, the use of Near Infra Red (NIR) spectral bands for the retrieval of Total Suspended Matter (TSM) concentration in turbid and highly turbid waters has proven to be successful. In extremely turbid waters (TSMN 100 mgL−1) however, these bands are less sensitive to increases in TSM. Here it is proposed to use Short Wave Infra Red (SWIR) spectral bands between 1000 and 1300 nm for these extreme cases. This SWIR spectral region is subdivided into two regions, SWIR-I (1000 nm to 1200 nm) and SWIR-II (1200 nm to 1300 nm) which correspond to local minima in the pure water absorption spectrum. For both spectral regions the water reflectance signal was measured in situ with an ASD spectrometer in three different extremely turbid estuarine sites: Scheldt (Belgium), Gironde (France), and Río de la Plata (Argentina), along with the TSMconcentration.A measurable water reflectance was observed for all sites in SWIR-I, while in the SWIR-II region the signal was not significant compared to the Signal-to-Noise Ratio (SNR) of current Ocean Colour (OC) sensors. For the spectral band at 1020 nm (present in Ocean and Land Colour Instrument ? OLCI, onboard Sentinel-3) and at 1071 nm, an empirical single band TSM algorithm is defined which is valid for both the Gironde and Scheldt estuarine sites. This means that a single algorithm can be applied for both sites without expensive recalibration.The relationship between TSM and SWIR reflectance at 1020 and 1071 nm is linear and did not show any saturation for the concentrations measured here (up to 1400 mg L−1), while saturation was observed for the NIR wavelengths, as expected. Hence, for extremely turbid waters it is advised to switch from NIR to SWIR-I wavelengths to estimate TSM concentration. This was demonstrated for an airborne hyperspectral dataset (Airborne Prism Experiment, APEX) from the Gironde estuary having several spectral bands in the SWIR-I. The empirical single band SWIR TSM algorithm was applied to the atmospherically corrected scene providing a TSM concentration map of the Gironde from mouth to more upstream with concentrations expected in this region ranging from a few to several hundreds mg L−1. These results, i.e. the existence of a single relationship for the Scheldt and Gironde, not showing any decrease of sensitivity, highlights the importance of having SWIR bands in future ocean colour sensors for studying extremely turbid rivers, coastal areas and estuaries in the world. A further implication of these results is that there is a TSMlimit for application of atmospheric correction algorithms which assume zero SWIR marine reflectance. That limit is defined here as function of wavelength and sensor noise level. Fil: Knaeps, E.. Flemish Institute for Technological Research (VITO); Bélgica Fil: Ruddick, K. G.. Flemish Institute for Technological Research ; Bélgica Fil: Doxaran, D.. Laboratoire d; 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, B.. Royal Belgian Institute for Natural Sciences (RBINS); Bélgica Fil: Raymaekers, D.. Flemish Institute for Technological Research; Bélgica Fil: Sterckx, S.. Flemish Institute for Technological Research; Bélgica

This study aims to quantify sensitivity of two atmospheric condition parameters: water vapour concentration (wv) and visibility in atmospheric correction processes in an operational processing chain. wv and visibility are important atmospheric condition parameters when retrieving surface reflectance from at-sensor radiance. To save cost, these parameters are often estimated using an image-based method. Their values are therefore uncertain, which in turn propagates to the surface reflectance in the atmospheric correction process. This study proposes an e-FAST based methodology that quantifies sensitivity of the two parameters in order to calculate their relative importance. The methodology is demonstrated with HyMap data. The results show that the two parameters have high sensitivity indices (> 0.8) and are dependent on the wavelength. This indicates that both parameters are important. The uncertainty in the parameters can possibly be further reduced by improving the estimation method.

This paper aims to solve the critical problem of uncertainty optimization of two atmospheric condition parameters: water vapor concentration (WV) and visibility. Both are used in atmospheric correction (AC) processes when retrieving surface reflectance from at-sensor radiance. Parameter uncertainty is often over or under estimated. A framework is proposed that determines the relation (indirect) of the parameters with estimated surface reflectance and uses this to reduce the uncertainty. Results show that optimizing the uncertainty of the parameters reduces the range of visibility from 18-42 to 31-39 km and the range of wv from 1.6-2.9 to 2.05-2.30 g cm(-2) for the class agriculture. Also, the total number of simulations that is needed for further studies such as an uncertainty or a sensitivity analysis in AC is reduced. The study concludes that there is an inverse effect of wv and visibility on the surface reflectance.

APEX system calibration has reached on operational level with calibration procedures at the CHB being well established and the APEX CAL IS enabling an efficient and transparent data storage and processing for the timely generation of calibration cubes required for the radiometric calibration of APEX imagery. Overall, these achievements result in shorter times from laboratory calibration to processed end user products.

Pre-flight radiometric calibration is critical to ensure that sensors can demonstrate that they meet their design performance specification. However, following launch it is critical that this performance is verified and in particular that the radiometric accuracy is validated or if necessary corrected. As Earth observation data becomes widely used it becomes increasingly important that data from each satellite sensor can be relied upon in the short and longer term and any potential biases between sensors can be removed. This post-launch calibration/validation is thus recognized as a key activity by all satellite operators. There are various methods available for carrying out this post-launch vicarious calibration but one of the most common and generic approaches is to use a dedicated and characterised “test site”. In such a method, ground based measurements of surface reflectance/radiance using similar solar illumination angles and sensor view angles (or at least corrected for these) is propagated to the top of the atmosphere (TOA) using a radiative transfer code. For the highest accuracy, surface measurements should be made within a few minutes of the satellite overpass and the characteristics of the atmosphere at that time also measured, particularly its optical depth.

This paper presents the improvements of an absolute calibration reference system based on simulated TOA BRF time series over bright desert targets. The current work highlights a case study performed over Libya4 demonstrating that it is possible to achieve a mean accuracy of 3% when simulation are compared with calibrated observations. This activity is part of VITO contribution to CEOS/WGCV/IVOS calibration mission.

In this study a new curve fitting approach is presented to derive TSM, CHL and CDOM concentrations in inland and coastal waters from water leaving-reflectance spectra. The approach is based on the wavelet transform and is tested on simulated water-leaving reflectance spectra. For simulations SIOPS and water concentrations, representative for the Scheldt river, were used. The results shown that the approach is less sensitive to errors in the atmospheric correction or specific sensor noise. The idea is based on the development of a new minimization criteria for curve fitting. Instead of minimizing the difference between modeled and measured spectra using a simple RMSE, the RMSE is now combined with specific wavelet features. Several types of errors and noise are added to the simulated spectra to find robust features. Two minimization criteria were found which are almost insensitive to a white error and less sensitive to adjacency effects.

This paper provides an overview of the coastal and marine applications which make use of Imaging Spectroscopy (IS), recently under development in Vito. It should be considered as a concise overview rather than an in depth presentation of one application or development. The first two applications focus on sediment mapping; firstly, a classification of sediment habitat types of the Molenplaat, a tidal sand bank in the Westerschelde, is presented. By means of feature selection and a supervised binary classification approach the sediment is classified according to its grain size, moisture content, organic matter content and chlorophyll-a concentration. The second application uses airborne IS to classify the different sand types present along the Belgian coast, in combination with airborne laserscanning to derive accurate erosion maps. The combination of both data products results in a method which proves to be very suited to monitoring the sand transport processes along the Belgian coast. Afterwards two aquatic applications are presented; in the first, coral reef communities in Indonesia are classified. Extensive field work served to collect a spectral library which is used to classify the coral reef communities in as much detail and as accurately as possible. The second aquatic application addresses the difficult challenge of quantifying the amount of suspended sediment and chlorophyll-a in water and rivers; this is performed by inversion of a bio-optical model using a set of Specific Inherent Optical Properties (SIOP’s) measured in-situ. Many marine applications ask for some specific processing steps which are inherent to the aquatic environment. Therefore the last study in this overview focuses on the atmospheric correction above water bodies. Due to the high absorption and transmission of water bodies the reflected radiation level is low compared to land. To extract this small signal from a much greater base of other radiance a very accurate atmospheric correction algorithm is required. Therefore a specific atmospheric correction algorithm, WATCOR, has been developed to account for the marine atmospheric conditions as well as for the air-water interface. www.witpress.com, ISSN 1743-3541 (on-line) © 2006 WIT Press WIT Transactions on Ecology and the Environment, Vol 88, doi:10.2495/CENV060311 Environmental Problems in Coastal Regions VI 327

The color of the sea is determined by the contents of the water, especially the concentrations of suspended particulate matter (SPM), phytoplankton pigments such as chlorophyll (CHL) and colored dissolved organic matter (CDOM). Reversely, optical sensors that measure the water-leaving reflectance spectra allow us to calculate the desired concentration products. In this paper, a method is introduced that is valid for both case 1 and 2 waters. To this end, model is fitted to reflectance spectra, using simulated annealing for optimizing the mean square of the reflectance over all spectra.

This paper studies the detection of vegetation stress in orchards via remote sensing. During previous research, it was shown that stress can be detected reliably on hyperspectral reflectances of the fresh leaves, using a generic wavelet based hyperspectral classification. In this work, we demonstrate the capability to detect stress from airborne/spaceborne hyperspectral sensors by upscaling the leaf reflectances to top of atmosphere (TOA) radiances. Several data sets are generated, measuring the foliar reflectance with a portable field spectroradiometer, covering different time periods, fruit variants and stress types. We concentrated on the Jonagold and Golden Delicious apple trees, induced with mildew and nitrogen deficiency. First, a directional homogeneous canopy reflectance model (ACRM) is applied on these data sets for simulating top of canopy (TOC) spectra. Then, the TOC level is further upscaled to TOA, using the atmospheric radiative transfer model MODTRAN4. To simulate hyperspectral imagery acquired with real airborne/spaceborne sensors, the spectrum is further filtered and subsampled to the available resolution. Using these simulated upscaled TOC and TOA spectra in classification, we will demonstrate that there is still a differentiation possible between stresses and non-stressed trees. Furthermore, results show it is possible to train a classifier with simulated TOA data, to make a classification of real hyperspectral imagery over the orchard.