Final Report 27/06/ The. STSE-WaterRadiance. project. Final Report. (ESA Contract: AO /08/NL/CT)

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1 7/06/1 1. Page: 1 of 77 The STSE-WaterRadiance project (ESA Contract: AO /08/NL/CT) Authors: Rüdiger Röttgers, Carsten Brockmann, Roland Doerffer, Jürgen Fischer, Andre Hollstein, Samantha Lavender, Wolfgang Schönfeld, Jonas von Bismarck Revisions: Issue Date Subject Author 0.9 0/01/1 Draft Röttgers 1 1/06/1 Final version Röttgers/Doerffer 1.1 7/06/1 Final version, revised, after comments by MB Doerffer 1

2 7/06/1 1. Page: of 77 Contents 1.Project management...3.objectives of the project and preliminary analysis introduction Project objectives and logic Results of the literature review pure water absorption Temperature and salinity coefficient of pure water absorption scattering by pure water and salt water forward Modelling (water model) inversion techniques Experimental results of pure water IOP measurements pure water IOP measurements pure water absorption measurements of the temperature and salinity coefficients of pure water absorption forward modelling Inversion techniques Sensitivity study Water optical properties processor (WOPP) Description of WOPP (WS) Absorption Scattering Real part of the refractive index Water-leaving radiance model improvements and validation for UV and SWIR HydroLight extension MOMO extensions and validation Polarisation Raman Scattering GIOP SeaDAS Implementation GIOP Theoretical Basis IOP retrieval algorithm improvements Neural Network inversion algorithm and its validation The bio-optical model Training and testing of the NN Inversion procedure Validation Sensitivity Analysis Salinity and Temperature Sensitivity analysis using the neural network based inversion algorithm Effect of temperature on North Sea water Effect of salinity Test with real data: Impact of temperature and salinity on specular reflection The importance of OLCI band 1 (400 nm) Raman Scattering Polarisation Beam processor implementation Generation of Temperature and Salinity Climatology Auxiliary Dataset BEAM Case Regional Processor Water Radiance Release GIOP in BEAM Processor Intercomparison Recommendations Measurements of inherent optical water properties (IOPs) Carry out consistent measurements of the absorption of pure water over the full spectral range of S3 to reduce uncertainty coming from different literature values Carry out measurements of the volume scattering function for different water constituents and natural waters...65

3 7/06/1 1. Page: 3 of Carry out measurements of the polarisation of water constituents Carry out optical closure computations Radiation transfer modelling Set up a data base of bio-optical models Combine polarisation and Raman in one model Study the combined effect of polarisation, Raman scattering, and salinity and temperature on water reflectances or different water types as an extension of this study Generate a large table of atmosphere path radiance, for different cases, which can be combined with water reflectances Explore the impact of a plane parallel model atmosphere on the simulation accuracy and in turn on the atmospheric correction for large solar zenith angles Study the impact of large waves and large solar zenith angles, when the signal comes mainly from the wave body itself Inversion procedures Create a flexible adaptive algorithm system Develop an algorithm to include the SWIR bands of SLSTR into the atmospheric correction procedure for water with high turbidity Further improvements of the current MERIS L processing Include major findings of pure water optics into account for the 4th reprocessing, but with fixed temperature and salinity coefficients For future reprocessing include T and S maps as foreseen for OLCI and base the look-up tables on simulations which take all effects, which have been studied, into account Consequences of the results for OLCI and S Verify the use of spectral bands at 400 and 100 nm concerning atmospheric correction and the retrieval of water IOPs during the commissioning phase Keep the ground processor flexible for required modifications during the commissioning phase and plan major changes in the processing code Develop an atmospheric correction procedure, which utilizes the combined information from OLCI and SLSTR Utilise the recommendations as given under the other items of this roadmap for future upgrades of the OLCI processing system Support tools Extend the MERMAID data base and include case water data and salinity and temperature Provide test data on level 1- level 3 for regional algorithms together with a test system Provide an easy to plug-in interface to existing processing software for different computer languages (C, Python, Java) Summary and Conclusions References Acronyms Project management WaterRadiance was an ESA-STSE (support to science element) project. The project consortium consisted of seven institutional partners and two scientific advisors. Table 1 lists the project participants and advisors, and table the relevant meeting schedule during the run time of the project. The project deliverables are listed in Table 3 as references. Table1 List of WaterRadiance participants. Name Institution Function Marc Bouvet ESA coordinator Carsten Brockmann BC Malik Chami LOV Roland Doerffer HZG Jürgen Fischer FUB Norman Fonferra BC advisor 3

4 7/06/1 1. Edward S. Fry TAMU André Hollstein FUB Samantha Lavender Argans David McKee Uni Strathclyde Rüdiger Röttgers HZG Wolfgang Schönfeld HZG Jonas von Bismarck FUB advisor project manager FUB, Free university of Berlin, BC, Brockmann Consult,TAMU, Texas A&M university, LOV, Laboratoire d'océanographie de Villefranche, HZG, Helmholtz-Zentrum Geesthacht. Table. List of project meetings Meeting Location Date Kick Off ESTEC, Noordwijk 16/04/09 1. Progress meeting HZG, Geesthacht 01/07/09. Progress meeting HZG, Geesthacht 03/11/09 3. Progress meeting by telephone 7/01/10 4. Progress meeting FUB, Berlin 4/03/10 5. Progress meeting by telephone 9/09/10 Mid term meeting ESRIN, Frascati 09/1/10 Technical meeting by telephone 16/03/11 Technical meeting by telephone 04/05/11 6. Progress meeting ESTEC, Noordwijk 7/09/11 Technical meeting by telephone 31/10/11 Technical meeting by telephone 1/1/11 Final meeting ESRIN, Frascati 6/01/1 Table 3. List of project deliverables Document identifier and title D 1: Literature review report D : Xcel sheet with IOP D 3: RB, requirements baseline document D 4: SAP, scientific analysis plan D 5: TN on pure water spectral absorption measurements D 6: Pure water spectral absorption and real part of refractive index model (WOPP) ATBD D 7: Water-leaving radiance model description document D 8: Water-leaving radiance model validation report D9a: Sensitivity analysis report D9b: Sensitivity analysis report - Case 1 temp, salinity and polarisation sensitivity 4 Page: 4 of 77

5 7/06/1 1. Page: 5 of 77 D9c :Sensitivity analysis report - Raman scattering D9d: Validation of the Raman scattering in MOMO D 10: LUT of water-leaving radiances in the NIR (omitted) D 11: Retrieval algorithm of IOPs from MERIS - ATBD D 1: Retrieval algorithm of IOPs from S-3 - ATBD D 13a: MERIS and S-3 retrieval algorithm validation document D 13b: Processor inter-comparisons validation document D 14: Retrieval algorithm of IOPs from MERIS - BEAM documentation (plug-in user manual and online help files) D 15: Retrieval algorithm of IOPs from MERIS - Acceptance test document D 16: Scientific Roadmap D 17: Summary Report D 18: D 19: Technical Data Package: consisting of D 1 to D18 SW 1: Water optical properties processor, SW : Retrieval algorithm of IOPs from MERIS - BEAM plug-in. Objectives of the project and preliminary analysis.1. Introduction The European Space Agency is developing the next generation of European operational satellites in the Sentinel missions. Sentinel-3 will include a mission for operational oceanography following the success of ENVISAT. The relevant sensor (OLCI, ocean and land colour instrument) will have a broader spectral range including bands for observations towards the UV and improving the observations in the IR range. These capacities might allow improvements in the retrieval of ocean colour and water constituents. The WaterRadiance project was motivated by the need to explore such novel capacities, to improve current retrieval methods and to develop solid scientific grounds for the preparation of the new ocean colour products. The MERIS instrument operational since 00, provides optical signature in 15 bands between 41 nm and 900 nm and is focussed on ocean colour. Sentinel-3 (S-3) combines the instruments OLCI and the Sea and Land Surface Temperature Radiometer (SLSTR) with overlapping swaths and spectral bands ranging from the visible to the SWIR spectral range ( nm). As a results of the spectral combination of the two instruments OLCI and SLSTR, channels not formerly exploited by MERIS will be available, e.g.: 400 nm, 100 nm, 1375 nm, 1610 nm, 50 nm and 3740 nm. The interpretation of measurements at these new bands requires an in-depth understanding of the physics in the water body and atmosphere. The interpretation of radiation measurement in the UV to NIR spectral region is based on radiative transfer modelling of water-leaving radiances and their relationship to the inherent optical properties (IOPs) of the water constituents. The IOPs of pure water are fundamental for this modelling and for the product retrieval from ocean colour measurements. An excessive literature of pure water IOPs is available but fundamental knowledge of some properties is missing or often in debate, e.g. the pure water absorption in the UV/VIS or the temperature and salinity dependence of this absorption in the NIR. 5

6 7/06/1 1. Page: 6 of 77 In this context, this project will explore the potential of novel spectral features of Sentinel 3 to improve the current modelling of the water leaving radiance in the UV and the NIR with special focus on the pure water IOPs... Project objectives and logic The activity in the project were, hence, focused on exploration of the potential of novel spectral regions and bands for the Sentinel-3 mission by improving the current modelling of the water-leaving radiance, especially in the UV and NIS/SWIR spectral regions, in view of the preparation for the next generation of ESA s ocean color products. The following issues were the main targets of the project: Improve the current modelling of the water-leaving radiance, especially in the UV and the NIR, by reducing the uncertainties associated with the absorption, the scattering, and the real part of the refractive index of pure water by including temperature and salinity dependencies of these inherent optical properties (IOPs) in the modelling, as well as including Raman scattering and polarization. Quantify the added value of this modelling as well as that of new IOP measurements for S-3 (with respect to MERIS) in the UV and NIR/SWIR spectral range Exploit the above results to improve current MERIS L water product retrieval (and possibly atmospheric correction) Develop an IOP retrieval algorithm for S-3 water-leaving radiance data. Demonstrate the applicability of the above algorithm to MERIS data. For each issue and parameter the associated uncertainties shall be calculated and given with the results. Provide a scientific roadmap for future activities To achieve these goals the following work was carried out: 1. A preliminary analysis was conducted consisting of a literature review study (D1) of IOPs, AOPs and their remote sensing from space, that was focused on pure water but included other water constituents. Based on this study (D1) a scientific analysis plan (D3) was prepared, describing the lack of knowledge of pure water IOPs and the necessary further work. The required experimental and modelling work were than described in a requirement baseline document (RB, D4).. Based on the RB (D4) the pure water spectral IOPs (absorption, scattering, real part of refractive index) were compiled after laboratory experiment for the UV to NIR/SWIR were performed to determine the full spectral information for the temperature and salinity coefficients of pure water absorption that was not available from published work. This compiled IOP data were used to set-up a simple processor for calculation of pure water IOPs at different temperature and salinity (WOPP, water optical properties processor). 3. A water-leaving radiance forward model (MOMO, Hydrolight) and an IOP retrieval model (HZG-NN) were improved by including WOPP in the calculation scheme. Additionally pure water Raman scattering and polarization were included into MOMO. 4. The models were validated and a sensitivity study was performed to examine the influence of temperature, salinity, Raman scattering and polarization on the water-leaving radiance reflection and the IOP retrieval. 5. An ATBDs for the retrieval of IOPs from MERIS and S-3 L water-leaving radiances was developed 6

7 7/06/1 1. Page: 7 of 77 and the retrieval algorithm of IOPs from MERIS L water-leaving radiances implemented in BEAM. 6. At the end recommendations for future improvements for ocean colour are given. Fig. : Compendium of pure water light absorption as a function of wavelength in the spectral region of nm. Absorption axis in log scale! Fig. 1: Pure water light absorption as a function of wavelength in the spectral region of nm. Both axes in log scale! Fig. 3: Temperature coefficient of pure water absorption as a function of wavelength. 7 Fig. 4: Salinity coefficient of pure water absorption as a function of wavelength.

8 7/06/1 1. Page: 8 of 77 Max & Chapados 009 1,43 Irvine & Pollack 1968 Daimon & Masumura 007 n, refractive index (real part) 1,41 Segelstein 1981 Quan & Fry 1995 (35 PSU, 0 C) 1,39 Quan & Fry 1995 (0 PSU, 0 C) 1,37 1,35 1,33 1,31 1, Fig. 6: Scattering for different temperatures and salinities. The associated % error is much smaller than the size of the symbols wavelength (nm) Fig. 5: Real index of refraction of pure water..3. Results of the literature review In the literature review the information on published values for pure water IOPs is summarised, this includes pure water absorption, scattering, real part of the refractive index, with the necessary temperature and salinity dependencies of these IOPs. Information on IOPs for other water constituents are summarized additionally. Further on overviews are given for AOPs and the global-iop (GIOP) algorithm, radiative transfer modelling with focus on pure water polarization and Raman scattering, as well as on inversion techniques to retrieve IOPs from satellite data. The summarized data for pure water absorption are shown in Fig. 1 and Fig.. The available data of the temperature and salinity coefficient of pure water absorption are shown in Fig. 3 and Fig. 4. Pure water scattering showing temperature and salinity dependencies are depicted in Fig. 6, calculated from the model by Zhang & Hu 009. The data for the real part of refractive index is shown in Fig. 5. The summarized data are given in an Excel sheet (D) The literature review study was used to identify the remaining gap in knowledge of pure water IOPs and to define the requirements for the model improvements (D3). The inherent optical properties of all water components are required for the full spectral range ( nm). This includes pure water absorption, scattering, and the real index of refraction and their changes with temperature and salinity. As well as absorption and scattering properties of other relevant water constituents like inorganic and organic dissolved matter and particulate matter that might be relevant for future improvements. The results and requirements are summarized in the following Pure water absorption It can be seen from literature data that pure water absorption is known with reasonably good accuracy for wavelengths > 500 nm (Fig. 1). Recently the pure water absorption was measured again with better accuracy in the UV by Lu 006 and Wang (008). The values of Pope & Fry 1997, Lu 006 and Wang 008 provided a consistent pure water absorption spectrum from 50 to 730 nm, however values of Quickenden and Irvin 1980 given for the range of nm are lower than those of Fry and co-workers (see Fig. ). The 8

9 7/06/1 1. Page: 9 of 77 values of Pope & Fry 1997, Lu 006 and Wang 008 are considered to be realistic and, together with other measurements for wavelengths >700 nm, provide knowledge of pure water absorption in the range of nm. An independent verification for the range of nm would be useful. The uncertainty in pure water absorption is still high at some wavelengths, partly due to measurement errors, partly due to the fact that the data are given for different temperatures, which leads to significant differences in absorption determination especially in the NIR. In addition the strong variations between data set in the UV can be explained by problems with the purity of the water. The pure water absorption in the UV remains uncertain, but the relevance for remote sensing applications of this uncertainty is low because of the strong absorption of organic substances in this spectral region..3.. Temperature and salinity coefficient of pure water absorption. Langford et al. 001, Sullivan et al. 007, and Röttgers & Doerffer 1997 provided data of the temperature coefficient of pure water absorption, ΨT, for the range 400 to 900 nm (Fig. 3). Accurate temperature coefficients for the UV ( nm) and the SWIR ( nm) are lacking. Another data set was provided by Larouche et al. (008) for >1600 nm. Data of ΨT between 900 and 1600 nm were basically missing. The salinity coefficient of pure water absorption, ΨS, was measured by Sullivan et al. 008, Röttgers & Doerffer 1997 and Röttgers 008 between 300 and 800 nm (Fig. 4). Measurements with an AC-S (Sullivan et al. 006) and a spectrophotometer (Röttgers & Doerffer 007) differ considerably from those using a PSICAM (Röttgers & Doerffer 007, Röttgers 008) for specific wavelength ranges (e.g nm). A NIR data set is provided by Max & Chapados 001 for >1600 nm. Data between 750 and 1600 nm were as well missing Scattering by pure water and salt water. Measurements of scattering by pure water and seawater have only been done for some specific wavelengths, with those of Morel (reviewed in Morel 1974) being commonly accepted. Modelling scattering from physical principles was recently done by Zhang and co-workers (Zhang & Hu 009, Zhang et al. 009) and provides full spectral values that are in very good agreement with the empirical measurements of Morel (1968). Temperature and salinity effects are included in these calculations, and the necessary Matlab code is available. Necessary input variables for this calculations are the real part of refractive index for water and air, and the water depolarization ratio Forward Modelling (water model) The Mueller matrices of sea water can not fully be explained by Mie theory using only homogeneous spheres, but underwater polarization and off principal plane viewing angles can provide relationships between size distribution and refractive index of hydrosol particles. A forward model for the light field in the atmosphere/ocean system should therefore take polarisation and scattering of non spherical particles into account. The inelastic redistribution of energy trough Raman scattering should be incorporated into the model. When the accuracy should exceed 5%, a wavelength increment of less than nm must be used. The uncertainties in top of atmosphere (TOA) radiances due to rough (wind blown) atmosphere ocean interfaces is not covered in the literature and should therefore be studied. No tabulated data of measured Mueller matrices of sea water can be found in the literature therefore only Rayleigh scattering, absorption data of sea water and bio optical models can be used. Measured quantities of Mueller matrices for sea water are needed. Two models were used: (1) an extended MOMO code (Matrix Operator, FUB) and Hydrolight (HL, by Curtis Mobley ). HL is a radiative transfer code, which simulates directional radiances, from which a number of apparent optical properties can be determined, including the directional water leaving radiance reflectance (RLw), down- and upwelling irradiance, attenuation coefficients. It can include a structured vertical profile, bottom effects, inelastic scattering (fluorescence, Raman), rough sea surface and various sky conditions. 9

10 7/06/1 1. Page: 10 of 77 Various bio-optical models can be submitted in form of tables or procedures. It was used for the sensitivity studies concerning temperature and salinity effects Inversion techniques In most inversion procedures the variability of reflectance spectra is described by 3 components: (1) absorption by phytoplankton (aph), absorption by detritus and gelbstoff (adg) and scattering or backscattering by all particles (bp or bbp). The inversion schemes can be summarized in the form of the following categories: (1) purely empirical relationships between a concentration or an IOP and the reflectances at or more spectral bands. The coefficients are derived from regression analysis between the IOP / concentration and reflectance ratios. This includes also special algorithms such as the FLH or MCI. () analytical inversion of a simple model which relates IOPs with reflectances, mainly based on the b b / b b a relationship. Main types are the Linear Matrix Inversion or decomposition techniques. (3) Non-linear optimization techniques, where the IOPs are determined by varying the IOPs within a loop to minimize the difference between the observed reflectances at a number of spectral bands and the corresponding simulated reflectances or reflectance ratios. To keep the computational effort sufficiently small, a rather simple model has to be used or the parametrization of a complex radiative transfer model, e.g. a forward neural network. (4) Look-up table procedures. A look-up table is produced either by a large number of measurements or by simulations using a radiative transfer model. (5) Inversion by linear (PCI method) or non-linear (Neural Network) multiple regression techniques. (6) Inversion by a neural network, which is trained with data, which can be simulated also by a complex radiative transfer model. Only (4), (5), (6) allow the use of complex and bi-directional RTMs, version (3) also, when a forwardnn is used. (3), (4) and (6) can handle also complex non-linear relationships. The success of all models depend on the knowledge of the optical properties and a clever grouping of all substances in a bio-optical model. The simple bb/a models have the disadvantage that the bi-directional reflectance cannot be computed so that variable adaptation parameters have to be used to relate the IOPs a and bb to the apparent variable Rrs. Disadvantage of the NN and PCI method are the production of large training data set, which are computational expensive, when sophisticated models are used. Also the frequency distribution of the simulated data is critical for the successs of the retrieval. However, with present high performance Linuxclusters, this is no longer a big obstacle. As a consequence of this analysis we took into account and analysed optimization procedures using a simple model and a forward NN, the table look-up procedure and the inverse NN technique. All of these categories allow the computation of uncertainties..4. Experimental results of pure water IOP measurements Based on the first task (see D1 and D3) and the results of the scientific analysis plan (SAP) additional IOP measurements and corresponding changes to the models (MOMO, HydroLight) and the inversion procedures were developed. The IOP measurements were focussed on the missing data in the NIR spectral range and on the temperature and salinity coefficients of pure water absorption. This chapter provides an overview of the results and how the effects were used in modelling and the algorithm development. 10

11 7/06/1 1. Page: 11 of Pure water IOP measurements The experimental part included measurements of the temperature and salinity coefficients of pure water absorption from the UV to the NIR/SWIR spectral range( nm) and measurements of the pure water absorption for nm (see D5 for details) Pure water absorption The missing data as well as the temperature and salinity effects were determined using the point source integrating cuvette absorption meter (PSCIAM) With the PSICAM system used here an absolute precision of ~ m-1, and an accuracy of ~ m-1 could be achieved. For this it was necessary to measure the absorption at a small spectral range with a spectrophotometer with an accuracy of ~ 1%. Results of the measurements and the comparison with published data are: (1) The measurements reproduced published work at wavelengths >500 nm, but gave higher absorption coefficients in the range of nm. These higher coefficients can be explained by methodological errors done with our set-up. Considering this error the data suggest that measurements in the range of nm of Fry & Co-workers are correct, as e.g. the position of spectral shoulders are reproduced. () The absorption coefficients at <400 nm are below those of Fry and co-workers and closer to data of Quickenden & Irvine (1980). Considering that there is still an overestimation by the refractive index error, the absorption of pure water at <400 nm remains questionable. The main error source here might be contamination of the pure water by handling during the experiments Measurements of the temperature and salinity coefficients of pure water absorption The temperature and salinity coefficients of pure water, ΨT and ΨS, were measured in the spectral range of 300 to 700 nm using a spectrophotometer and cuvettes with path lengths between 100 µm and 10 cm. The salinity coefficient was measured by using high concentrated NaCl solution as a substitution for sea water, ignoring the small, influence of other ions in natural sea water on the salinity coefficient. For both coefficients FTIR measurements of the range >4000 nm (Max & Chapados 001, Laroucheet al. 008), were used to verify our measurements of the spectral range between 1600 and 700 nm. The obtained spectra of ΨT and ΨS show basically the same spectral signatures in the VIS range as spectra published by others (Langford et al. 001, Sullivan et al 006). However there are significant difference at specific wavelengths, e.g. at nm for ΨT, and at nm forψs. Some differences might be due to differences in the optical resolution, e.g. the additional trough at ~738 nm in the spectrophotometric measurements compared to the ACS and PSICAM measurements. The discrepancies in the absolute values at some wavelengths can be explained by measurement errors and missing corrections for changes in scattering by water and the refractive index with temperature and salinity. The data between 1600 and 500 nm were confirmed by the (much more noisy) data from the available FTIR measurements (Max & Chapados 001, Larouche et al. 008) For the UV wavelengths it was not possible to determine the temperature and salinity coefficients with the required accuracy. One reason is that the absolute values of the coefficients are considered to be very low as the pure water absorption is very low, and that the general deviation from zero is most likely due to measurement errors. The measured increase in optical density (OD) in the UV with shorter wavelengths is likely due to absorption by traces of organic contaminants in the NaCl solution, which could not be removed. However, it can be assumed that the T and S coefficients are very low and that they can be neglected because the specific absorption coefficient of non-water material (e.g. CDOM) is very high in the UV spectral range. 11

12 7/06/1 1. Page: 1 of Forward modelling The forward model MOMO was extended to take the full Stokes vector into account. The scattering functions were computed using a Mie program (WISCOMBE) or, for Rayleigh scattering, were calculated using an analytical models. The extended version of MOMO is now able to take up to six scattering functions into account, thus also non spherical scatterers could be considered. Due to the expansion of the azimuthal dependencies in a Fourier series it was necessary to truncate the strongly peaked phase functions using quadratic interpolation. The extended version is now able to preserve the polarisation properties of the scattered light field. MOMO was also extended to include Raman scattering. The results of the model validation and a sensitivity study are described in chapter 5 of this report. The overall upgrade for MOMO included the following steps: Phase function I/O program (pha44): Input of up to six tabulated scattering functions for each scattering component. Enhancement of internally predefined functions to take polarisation into account. When necessary truncation of the phase functions while preserving polarisation properties of the scattered radiation. Fourier expansion of azimuthal dependency of the 4x4 scattering matrices. Radiative transfer code (mom44) Polarization Redefinition of the I/O for scattering matrices and stokes vectors. Expansion of internal matrices to account for scattering matrices and stokes vectors. Redefinition of the doubling algorithm to the non scalar case. Convergence checks of the geometric series for the reflection matrices. Inelastic scattering Definitions of source terms. Adjustment of adding and doubling algorithm. Using additional runs of Momo to calculate source terms and Raman effects. Validation of the upgraded RT model Measurements from AMSSP over the ocean (north sea/pacific). For the Rayleigh case values from the paper of Kattawar and Adams have been used. Simulated data using Hydrolight were provided by HZG for the validation of Raman scattering. Hydrolight was extended by a module to include the T and S effects on pure water optics (absorption, scattering, refractive index). Furthermore, it was converted to a version with random variations of IOPs for producing mass data for training of the neural networks at 33 wavelengths Inversion techniques An inversion algorithm was developed, which is based on neural networks and an optimization procedure. The neural networks were trained with simulated data, for which the T and S effects were taken into account. Thus, input to the NNs are now also salinity and temperature, which must be provided e.g. from climatological maps. The inversion procedure including the out of scope detectors and the algorithm to determine uncertainties were tested using simulated and the NOMAD field data. Results of the model validation are describe in chapter 6 of this report. 1

13 7/06/1 1. Page: 13 of Sensitivity study The results of the inversion model sensitivity study are described in chapter 7. The analysis was based on simulations and observational data (NOMAD data set and measurements by HZG). For the simulations the pure water IOPs were used, which were included in the water optical properties processor (WOPP). For the sensitivity analysis different bio-optical models were used. The influence of different pure water models on the water leaving radiance reflectance (RLw) was studied by using a background clear water model (Smith & Baker, 1981) and a pure water model. The effects of temperature and salinity on RLw were quantified as well as the addition of the Raman scattering effect. Different bio-optical models were assumed with various concentrations of phytoplankton pigment, organic and suspended matter, which are typical e.g. for the open Ocean, Baltic Sea or North Sea. 3. Water optical properties processor (WOPP) 3.1. Description of WOPP (WS) The newly performed measurements of pure water IOPs were used together with the information from the literature review to provide the basic knowledge for all necessary pure water optical properties. For each IOP a decision was taken for the best available data set (or sets) and a simple calculation scheme was set up to combine the available physical model for scattering with those for calculation of absorption and refractive index for different temperatures and salinities. This so-called Water Optical Properties Processor (WOPP) is a computer program to calculate the inherent optical properties (IOPs) of pure water at atmospheric pressure and at a specific water temperature and salinity, namely absorption (absorption coefficient) scattering (scattering coefficient for any angle [resolution 1, range ], back-, forward-, and total scattering) real part of the index of refraction The input of the model are: 1. central wavelength, bandwidth or direct spectral window, or full spectrum with nm resolution ( nm). temperature (valid range: - 99 C) 3. salinity (valid range: PSU) 4. depolarization factor (default: 0.039). 5. standard spectra of pure water absorption at 0 C, and the related temperature and salinity coefficients 6. standard spectrum of the real part of refractive index of pure water for 7 C The obtained spectral data can be used for radiative transfer simulations or other applications where knowledge of the specific IOPs dependent on temperature and salinity is necessary, e.g. for interpretation of surface reflection spectra in remote sensing applications or in measurements of other water constituents where changes in the reference water with temperature and salinity need to be corrected for. The general structure of the WOPP is shown in Fig

14 7/06/1 1. Page: 14 of 77 Fig. 7. Structure of the water optical properties processor. For more details see the relevant ATBD (D6). The output of the WOPP includes the uncertainties for each pure water IOP. The relevant uncertainties are due to experimental errors when determining the pure water IOP and the necessary correction coefficients, and due to variations between different published results for each IOP. The WOPP consists of two programs written in the programming language Python. The first program (WOPP.py) is a collection of functions to compute several optical properties of pure water according to varying temperatures and salinities as described in the ATBD. WOPP.py can be executed as a stand-alone program, but requires some knowledge of Python. The second program (WOPP_gui.py; Fig. 8) consist of a simple graphical user interface (GUI) that performs the same computations in interactive mode, using the functions from WOPP.py. The Water Optical Properties Processor can be downloaded from the CEOS Cal/Val Portal: The calculation of scattering is by a physical model, that of absorption and refractive index (real part) are partly based on standard spectra, and the relevant temperature and salinity coefficient. Therefore these standard spectra were taken from published measurements and new measurements as obtained from earlier tasks. The relevant historical data summarised in the literature review (D1) and the measured data of this project (D5) were combined to yield spectra of pure water IOPs (absorption, scattering, and real part of the refractive index) for the full wavelength region of 300 to 4000 nm, as well as the respective spectra of the uncertainty. For the absorption coefficients a spectrum at 0 C and 0 PSU (salinity) is provided together with spectra of the temperature and salinity coefficients of pure water absorption, that are used to calculate the absorption for temperatures different from 0 C and salinities different from 0, for the range of - 99 C and 0-45 PSU. The scattering coefficient and its T and S dependence is based on a published physical 14

15 7/06/1 1. Page: 15 of 77 model. The real part of the refractive index is based on combined data from measurement, and its T and S dependence is calculated from an empirical model based on data for the visible spectral region. Fig. 8: WOPP graphical user interface Absorption The spectrum of pure water absorption, aw0(λ), used in WOPP is a combination of empirical measurements at different temperatures (Pope & Fry 1997, Lu 006, Wang 008, Segelstein 1981 (>700 nm), Wieliczka et al. 1989, and Max & Chapados 009 ( nm, > 500 nm)) that were partly modified to get the absorption at the reference temperature of 0 C using the spectrum of ΨT. This combined pure water absorption spectrum at 0.0 C is shown in Fig. 9. The relevant uncertainty, aw0, at each wavelength is taken from the experimental data. When several measurements were available for the same wavelength it was computed as the standard deviation. The spectrum of ΨT was measured during this project (D5) and combined with measurements in the SWIR by Larouche et al. 001 (see Fig. 10). The relative change in absorption varied from % per C (Fig. Fig. 9: Pure water absorption (± s) in the UV/VIS to SWIR spectral region. 15

16 7/06/1 1. Page: 16 of 77 X). The relevant uncertainty of the temperature coefficient, ΨT, is determined from the experimental error. The spectrum of ΨS is is taken from D5 ( nm) and combined with a spectrum calculated from data for the range nm of Max & Chapados 001 (Fig. 11). The relative change in absorption per PSU varied between % and 0.05 % (see Fig. 6). The relevant uncertainty of the salinity coefficient, ΨS, is determined by the experimental error. Fig. 10: The temperature coefficient of pure water absorption,yt (m-1 C-1), as a function of wavelength. The original data are shown in red. In addition, for specific spectral ranges the spectral features were enlarged by multiplying the values with different factor as indicated in the legend. The errors are shown as s contour lines (dashed lines). Fig. 11: The salinity coefficient of pure water absorption,ys (m-1 PSU-1), as a function of wavelength. The errors are shown as s contour lines (dashed lines) Scattering Scattering by pure water is the result of fluctuations of molecule number density resulting in changes in refractive index and is described by the Einstein-Smoluchowski theory of scattering (Smoluchowski 1908, Einstein 1910). This kind of scattering was theoretical described e.g. by Mobley (1994), Morel (1974), and Zhang & Hu (009). Recently Zhang & Hu (009) reviewed these calculations and presented a new formulation using a physical description of the density fluctuation of the refractive index, n (as ε = n²). The 16

17 7/06/1 1. Page: 17 of 77 results of Zhang & Hu (009) agreed with the measurements of Morel (1966, 1968) within the measurement errors of %. In sea water an additional scattering is caused by fluctuations of the concentration of salt ions, which on the other side influences the total density fluctuations. Total scattering by sea water is the sum of scattering by density and concentration fluctuations and is, hence, a non-linear function of the total concentration of salt ions, i.e. salinity (Fig. 10 &11). The theoretical approaches for its calculation are recently reviewed in Zhang et al. (009). They presented a new calculation using again a physical description of the density and concentration fluctuations of the refractive index, which agree well with the measurements of Morel (1966, 1968). One last critical parameter is the depolarization factor of scattering, which is only roughly known and which varies in these formulation between to The formulation of Zhang et al. 009 is used to calculate scattering as a function of T and S. The depolarization factor, δ, is by default and necessary nsw data are calculated as described below. The calculation is extended to IR wavelengths by using the complete spectra of nsw(t, S). The results can be expressed in terms of the full phase function of scattering, total scattering, or backscattering. The absolute error for this calculation is considered to be less than the experimental error of the data of Morel 1966, 1968 (see Zhang et al 009, Zhang & Hu 009), therefore the experimental error of % is taken for the scattering data after Morel 1966, Real part of the refractive index Based on data from Austin & Halikas 1976, Quan & Fry (1995) provided an empirical model for accurate data for nm at different T and S values. Max & Chapados (009) provided data at 7 C (0 PSU) for > 1670 nm. The accuracy of both data sets is high as can be seen by comparison with other data. The data of Max & Chapados 009 are similar to those of Bertie & Lan Values for the region of nm are available from Segelstein 1981 but deviate from the other two data sets in the considered wavelength regions. Hence, data for nsw ( nm) were constructed by linear interpolation of the data of Segelstein 1981 between 800 and 1670 nm. For more details see the WOPP-ATBD (D6), the spectrum is shown for 7 C in Fig. 1. Fig. 1: The real part of the index of refraction of pure water and seawater. Combined spectrum at 7 C using formulation of Quan & Fry 1995 and data of Max & Chapados 009, the error is indicated as contour lines (±s, dashed lines). 17

18 7/06/1 1. Page: 18 of Water-leaving radiance model improvements and validation for UV and SWIR 4.1. HydroLight extension The radiative transfer model Hydrolight Version 5 was used to simulate water leaving radiance reflectances for sensitivity studies and, in particular, for the mass production of spectra for training of the neural networks. For these purposes some of the build-in functions of the Fortran code had to be modified, extended or replaced. A new function was written for computing the pure water optical properties according to the results of this project with respect to temperature- and salinity effects and uncertainties. This function returns the spectral refractive indices and the absorption and scattering coefficients with or without the uncertainty range for a given set of wavelengths, temperature and salinity. The function is similar to WOPP, the Water optical properties processor. Other functions were written for providing Hydrolight with the optical properties of water constituents of different bio-optical models. For computing the training data sets the concentrations of all water constituents are randomly varied within pre-defined ranges on either a linear or log scale, furthermore temperature and salinity is randomly varied as well as the solar angle and the wind speed. The spectral range was extended to nm to cover the spectral bands as required by the projects (MERIS and OLCI). The code was then implemented on the High Performance Cluster to run the simulations in parallel, each with a different seed for the random number generator. 4.. MOMO extensions and validation The radiative transfer model MOMO is based on the work of Fischer and Grassl (1984), Fell and Fischer (001) and Bennartz and Fischer (000). It has a long tradition of successfully developed remote sensing applications. Including the sensing of lakes (Heege and Fischer 004), analysis of hyperspectral data to derive surface fluorescence signals (Guanter et al. 010), the analysis of ocean color data from MERIS measurements (Zhang et al. 00), and the retrieval of land surface pressure from MERIS data (Lindstrot et al. 009). It is able to calculate the azimuthal resolved light field in an arbitrary one dimensional atmosphere ocean system. We updated the MOMO code basis to additionally treat effects of polarization and Raman scattering in the ocean. Both effects can have a non negligible impact on both top of atmosphere and water leaving radiances Polarisation MOMO calculates the light field L for any optical thickness τ and viewing geometry (μ,φ) in a combined atmosphere-ocean system. The radiative transfer equation in Fourier space is solved using the matrix operator technique for any given profile of scatterers, defined by a profile of the single scattering albedo ω0 and the phase matrix P: 18

19 7/06/1 1. Page: 19 of 77 In the former MOMO version, only the scalar light field L was calculated, using only the P 11 element the phase function. This is much easier to implement, and saves computation time in the order of a factor of 9 to 16. Since the real light field is a vector field which can be described by the Stokes vector; and the real phase matrix is a 4x4 real matrix which couples on a scattering event all vector elements of the Stokes vector with each other; the scalar approach can lead to significant errors in the computed light field. For a detailed discussion we refer to the model description (D7). In principal we had to expand the former scalar light field representation and the phase matrix representation in the MOMO code, and then replace scalar multiplications by matrix multiplications. In addition to this dimensional expansion, the phase matrix must also undergo rotations into-, and out of the plane of scattering. Additionally we introduced the salinity and temperature dependence of the sea water optical model as described in the WOPP-ATBD (D6). This was straight forward, but up to now there are no complete phase matrix measurements available to us. We set up a bio-optical model that uses available measurements of chlorophyll single scattering albedo and chlorophyll absorption from Bricaud et al. (1983, data from 010 received via personal communication). We use this data as input for Mie calculations. The size distribution parameters are used as free parameters in an optimization scheme that retrieves a parameter set that best reproduces the measurements. We additionally added an offset parameter to the imaginary part of the chlorophyll refractive index and a penalty term if the resulting phase function would show non-physical features. The phase matrix output of the Mie calculations can then be used as input for MOMO. Fig. 13: Measured single scattering albedo spectrum for various chlorophyll concentrations and optimized results of the bio optical model. For validation purposes, we compared results of the updated MOMO code with results of other models (Fig. 13, and Fig. 14). For a detailed discussion please see the validation report (D8). We used tables with results from Rayleigh scattering (Natraj et al. 009), results from the SCIATRAN model (Kokhanovsky et al. 010), and results from the NASA GISS model (Chowdhary et al. 006]. The mean of the relative deviation of the MOMO results and the Rayleigh tables is , so we can conclude that both models agree very well with each other. 19

20 7/06/1 1. Page: 0 of 77 Fig. 14: Relative differences of MOMO results and Rayleigh tables for various viewing directions (μ,φ), surface albedos ω, and optical thicknesses τ. We compared MOMO results with SCIATRAN results for more complicated cases of scattering than only Rayleigh (Fig. 15). The models agree well with each other. We also show the effect of the phase function truncation. Fig. 15: Relative differences of MOMO and SCIATRAN for three selected scattering cases. We show result with truncated and untruncated phase function for the intensity I and the other Stokes parameters Q,U,V. To validate the implementation of the rough sea surface model, we compared MOMO results with simulations made by the NASA GISS model (Fig. 16). The models also showed a good agreement. 0

21 7/06/1 1. Page: 1 of 77 Fig. 16: Comparison of MOMO and NASA GISS model results. We show results for the top of the atmosphere and a Rayleigh optical thickness of 0.1 for various solar angles. The surface wind speed was set to 7m/s. We are confident the current implementation of MOMO is free of major errors and that the model is very well suited for the future development of novel remote sensing applications Raman Scattering It is known that inelastic Raman scattering of the solar light field due to energy absorption by vibrational 1

22 7/06/1 1. Page: of 77 modes of water molecules can contribute significantly to the signals observed by ocean remote sensing satellites. The Raman scattered fraction of the water-leaving radiance for clear water reaches values of over 5% percent in the red and SWIR OLCI channels. Therefore the inclusion of trans-spectral Raman scattering effects into radiative transfer models used for ocean remote sensing applications appears necessary in many cases, even though this adds significant computation time in comparison to monochromatic model runs. In the framework of the WATERRADIANCE project, the combined ocean-atmosphere radiative transfer model MOMO was extended to account for water Raman scattering. This was done not simply to qualitatively approximate the Raman scattering contribution on irradiances, but to accurately model all resulting spectral and angular changes to the light field. A detailed description and discussion of the implementation of water Raman scattering effects in this new MOMO version can be found in the model report (D7). At this point a short overview of the new model and it's capabilities shall be given. Fig. 17: Inverse normalized water Raman spectral redistribution function for five different observation wavelengths. The y-axes shows from which wavelengths radiation is emitted to the corresponding observation wavelength. Fig. 18: The cross section of water Raman scattering, the so called Raman absorption coefficient, following the values published in [Bartlett 1998]. The exponent for the exponential decrease has a value of approximately 5. The spectral shift introduced to radiation that is Raman scattered in the water body required some changes in the so far used monochromatic processing scheme in MOMO, to be able to compute trans-spectral processes. The first step of the processing scheme is to compute the radiation available for inelastic scattering with high spectral resolution, in a spectral range defined by the water Raman spectral redistribution function (Walrafen 1969), shown in Fig. 17, and the absorption cross section (Fig. 18). The angular redistribution of Raman scattering is quite similar to Rayleigh scattering, although the difference between forward/backward and sidewards scattering is less pronounced, as can be seen in Fig. 0 This does, however, not justify to generally treat water Raman scattering as being isotropic, as was proven by our computations carried out for a model inter comparison with Hydrolight. In a final step the inelastically scattered radiation is treated as additional source of light at the observation wavelength, represented by a new source term in the radiative transfer equation. MOMO calculates the Raman source terms by spectral and angular integration of all contributing components, which then allows to compute the light field in the oceanatmosphere system with the included first order Raman contribution. Subsequent program runs can calculate higher orders of Raman scattering, but add significant computation time. However, the second order contribution (meaning that photons have been Raman scattered twice) has turned out to be more than two magnitudes lower than the first order contribution even for clear water. Therefore only first order Raman scattering is discussed in this report.

23 7/06/1 1. Page: 3 of 77 An effect that is neglected in many radiative transfer codes that include water Raman scattering effects so far, is the azimuthal dependence of the Raman source. This will still allow to compute accurate irradiances, but lead to errors in the angular dependency of radiances (see Fig. 1). The new MOMO version allows to either include the azimuthal dependency, if accurate radiances are of interest, or to save computation time by azimuthal averaging if irradiances are to be computed. Fig. 1 shows the maximum errors in terms of waterleaving radiances, if the azimuthal dependence of the Raman scattering is neglected for clear water. In the principle plane (azimuth angles of 0 and 180 ), the errors reach values of several percent. Even at an azimuth angle of 90, a maximum error of two percent is reached outside the atmospheric absorption bands. With the exception of an estimation formula for the maximum Raman contribution to water-leaving irradiances (Gordon 1999), no complete analytic solutions of the radiative transfer equation were available for a realistic water body that included Raman scattering effects. Therefore the commercial water-body radiative transfer code Hydrolight (Version 5.3.1) served as a validation reference for the new MOMO version. This code is also a basis for the remote sensing algorithm development within the project framework. As a test case, a flat surface, a clear water body, and a solar constant of one was chosen. Atmospheric absorption and scattering was set to zero in MOMO for the comparison, since Hydrolight does not perform atmospheric radiative transfer calculations. Fig. 19: Raman scattered fraction of the upwelling irradiance just below the water surface. HydroLight results (solid line) and MOMO results (dashed line) show an overall good agreement. the dash/dotted lines stand for values computed with MOMO assuming an isotropic and a Rayleigh style phase function for Raman scattering. The inter-comparison of MOMO and Hydrolight showed a very good agreement for the elastic scattering case, as can be seen in Fig. 19. Small scale spectral features present at the excitation band have a slightly stronger impact on the HydroLight results. This difference is likely caused by the different way, the spectral redistribution of Raman scattered light is implemented in the models. In MOMO, the light from a single excitation wavelength is not scattered to a single emission wavelength, but spread out using the spectral 3

24 7/06/1 1. Page: 4 of 77 Fig. 0: Phase functions for inelastic water Raman scattering and elastic water Rayleigh scattering. Fig. 1: Maximum error (in the range of 0-80 solar zenith angle and 0-60 observation zenith angle) in terms of water leaving radiances, if the azimuthal dependence of water Raman scattering is neglected, for a U.S. Standard atmosphere and a clear waterbody. 4

25 7/06/1 1. Page: 5 of 77 redistribution function discussed further up, leading to a spectral smoothing of features in the excitation spectra. In addition to the results generated with the default Raman phase function, MOMO results under the assumption of an isotropic and a Rayleigh style angular dependence of the Raman scattering were generated for comparison. Fig. : Angular dependency of the radiances just below the water surface.zero stands for the forward direction, 90 sideways scattered light, and 180 backward scattered light. The azimuth angle is set to 0. The stars denote the MOMO results with Raman scattering, the pluses without. Diamonds denote MOMO results with an isotropic Raman phase function, boxes stand for a Rayleigh style Raman phase function. Triangles denote the HydroLight results with Raman scattering, and circles without. For the computed radiances including Raman scattering,as shown in Fig., a very good agreement of the MOMO and the Hydrolight results in terms of angular dependence was not expected. This is due to a computation time efficient approach implemented in Hydrolight, in favour of an exact modelling of the angular redistribution by Raman scattering. The angular distribution of the Hydrolight Raman radiances is generally more pronounced than the one of the MOMO radiances, with the latter agreeing better with our expectations of Raman scattered radiation. However, the trends are similar, with the maximum Raman contribution lowest at the 90 angle, and local contribution maxima at 0 and 180 with the global maximum being at 0. A more detailed discussion of the model comparison can be found in the Raman Validation Report (D9d). 5. GIOP NASA's Ocean Biology Processing Group (OBPG) initiated the GIOP (Generalized Inherent Optical Property) algorithm process by offering to provide the data sets, processing framework and an international forum within which a new generation of global IOP (Inherent Optical Property) products could be developed and evaluated. This was an extension of the previous IOCCG activity reported in the IOCCG Report 5 (IOCCG, 006) with scientific organisations provided the details of their published approaches such that they 5

26 7/06/1 1. Page: 6 of 77 could be both understood and implemented by NASA. By deconstructing the Semi-Analytical Algorithms (SAAs) NASA were able to identify similarities and uniqueness. The main aim was to achieve communitywide consensus on a unified SAA with which to generate global satellite IOP products, but also to determine an approach to implement uncertainties. Face to face discussions were first held at an international IOP algorithm workshop at the Ocean Optics XIX conference (008). A second workshop was held alongside the at the Ocean Optics XX conference (010), for which a white paper was prepared (Franz and Werdell, 010). Since that point, discussions have been online via the forum ( SeaDAS Implementation NASA implemented the GIOP approach within the SeaWiFS Data Analysis System (SeaDAS) (Baith et al., 001) as part of the lgen executable also called the Multi-Sensor Level-1 to Level- processing code (MSl1). The manual for GIOP is currently the 010 white paper (Franz and Werdell, 010). Within the WaterRadiance proposal it was initially proposed that ARGANS would write and Algorithm Theoretical Basic Document (ATBD) based on the GIOP discussions/nasa implementation and then Brockmann Consult would implement this within BEAM. However at the nd Progress Meeting it was agreed that BEAM would call the SeaDAS code and hence only the interface would be implemented. This would avoid the issue of the SeaDAS and BEAM code becoming out of sync. 5.. GIOP Theoretical Basis The default settings for the GIOP model produce the operational IOP product generation by OBPG (currently these are classed as being under Test, see Figure 1), which will produce the Garver-SiegelMaritorena (GSM) model (Maritorena et al., 00). However, the model components and optimization methods can be modified through the use of command-line or Graphical Unser Interface (GUI) parameters associated with 5 options: 1. Model optimization scheme: default is Levenberg-Marquardt. WaterRadiance Neural Network uses the same scheme.. Absorption spectra of phytoplankton: default is Bricaud et al. (1995). WaterRadiance Neural Network uses the mean normalized absorption spectrum of Prieur and Sathyndranath (1981); comparisons shown as in Fig. 3 from Franz and Werdell (010). 3. Absorption spectra of non-algal particles (detritus): default is a tabulated version of the GSM model; exponential with a fixed exponent of WaterRadiance Neural Network has a fixed exponent of Spectral backscattering of particles: default is a tabulated version of the GSM model; powerlaw with a fixed exponent of WaterRadiance Neural Network uses Hydrolight. 5. Relationship between the total absorption and backscattering versus sub-surface reflectance: default is Gordon et al. (1988) quadratic parameters. 6

27 7/06/1 1. Page: 7 of IOP retrieval algorithm improvements 6.1. Neural Network inversion algorithm and its validation For determining the inherent optical properties of water constituents from reflectance spectra two types of neural network based inversion schemes had been considered for this project: (1) the direct inversion with reflectances as input to the NN and the IOPs as output and () the use of a forward NN with the IOPs as input and the reflectances as output to fit the measured reflectances within an optimizing loop. The latter option was selected, because it allows more flexibility with respect to the used spectral bands, the number of input parameters and input ranges, and the use of validation data with different numbers and positions of spectral bands. Within the optimization loop the neural network functions as the forward model. Its properties are set by the training data, which in turn are based on simulations using Hydrolight as the radiative transfer model. The search in the loop is optimized by a Levenberg-Marquard (LM) or non-linear Nelder-Mead simplex optimization procedure. The final deviation between the modelled and measured reflectance spectrum can be used as an indicator of the success of the retrieval or the out of scope conditions. Furthermore, the LM-procedure produces the co-variance matrix of the results from the quasi Hessian matrix. The diagonal of which provides the confidence / uncertainty estimates. In case of the simplex method, the Hessian matrix is computed within a separate module. For setting up the procedure, the following steps were performed: Definition of a bio-optical model with optical properties, ranges, co-variances Implementation of the bio-otpical model in a radiative transfer code Simulation of water leaving radiance reflectances Training and testing of a neural network Implementation of the NN in the iterative procedure (breadboard) Testing of the algorithm, validation Implementation in a breadboard processor Test using satellite data Implementation in the final processor with verification. The procedure is summarized in the diagram (Fig. 4) The bio-optical model Two different bio-optical models were used. One is based on the absorption and scattering properties of substances, the other on independent scattering and absorption components. The first bio-optical model is based on our own measurements and analysis as well as on optical properties, which are included in Hydrolight. The water optical properties are described by 5 components: pure water with temperature and salinity effects phytoplankton, absorption and scattering 7

28 7/06/1 1. Page: 8 of 77 Fig. 3: Snapshot of the NASA OBPG Level 3 Composite Products Webpage ( showing an example of the GIOP Test Products total absorption at 443 nm (a_443_giop). detritus, absorption and scattering dissolved organic matter (gelbstoff), only absorption mineralic suspended matter, absorption and scattering The second version is based on 6 independent components: pure water with temperature and salinity effects absorption coefficient of phytoplankton pigments absorption coefficient of detritus absorption coeffcients of dissolved organic matter (gelbstoff) scattering coefficient of mineralic suspended matter scattering coefficient of white scatterers Further controlling parameters are: sun zenith angle view zenith angle difference between viewing and sun azimuth angle temperature (range 0-36 deg salinity (range 0-43 PSU) 8

29 7/06/1 1. Page: 9 of 77 Using Hydrolight about cases have been simulated. For each case the concentrations of the components of water constituents were randomly selected with a uniform random number on the log scale. Furthermore, solar zenith angle, wind speed, temperature and salinity were randomly selected. In addition, the concentration of each component were multiplied with a random number between 0 and 1 to get sufficient cases were all components have a low concentration to meet case 1 water conditions. Otherwise, a bias would be generated around the mean coefficient of all components. For each case randomly selected 1-5 different viewing angles were extracted, so that altogether about samples were available for training and testing of the NNs. Output of the simulations are the bi-directional water leaving radiance reflectances in 33 spectral bands in the range nm, Rlw = Lw/Ed, where Lw(λ,θ,φ) is the directional water leaving radiance, and Ed( λ), the downwelling irradiance just above the surface. Furthermore, the downwelling irradiance attenuation coefficient at 490 nm was computed (kd490) and the minimum kd_min of all 33 simulated spectral bands. define bio-optical model components IOPs IOP conc relationships implement bio-optical model in radiative transfer model simulate large range of water leaving radiance reflectances define archictecture of a forward neural network train network measured RLw RLw_nn NN RLw compare ok? yes no start values IOPs angles T,S IOPs LM procedure modifies IOPs compute co-variance uncertainties compare with test case fixed values angles T,S Fig. 4: Outline of the set up and use of the forward neural network as the algorithm to retrieve water IOPs and concentrations 9

30 7/06/1 1. Page: 30 of Training and testing of the NN The forward neural network was designed with 3 hidden layers with 7, 17, 41 neurons and with the following input and output variables for the bio-optical model I: 9 Input variables sun_zenith angle view_zenith angle azi_difference between sun and viewing direction temperature salinity log_conc_chlor log_conc_det log_conc_agelb log_conc_min output variables: log_rlw for 33 bands in the nm log_kd490, the downwelling irradiance attenuation coefficient at 490 nm log_kdmin, the downwelling irradiance attenuation coefficient for the wavelength with minimum kd For training cases were used, for the continuous testing cases. The overall mean error of the network is , and the error ratio train/test The results of the test of the NN can be seen in Fig. 5, Fig. 6, Fig. 7for the bands at 30 and 100 nm, and for kdmin. Fig. 5: Test of NN, rlw 30 nm, log scale. 30

31 7/06/1 1. Page: 31 of 77 Fig. 6: Test of NN, rlw 100 nm, log scale. Fig. 7: Test of kdmin, log scale. The NN for the second pure IOP bio-optical model were trained with a random error of 5% on the water leaving radiance reflectances. This was done to make the NN more robust against errors of the atmospheric correction. Consequence is a higher noise in the test plot. 31

32 7/06/1 1. Page: 3 of Inversion procedure The inversion, i.e. the retrieval of the concentrations and / or the IOPs is based on an iterative adaptation of the modelled Rlw_nn to the measured Rlw_m by varying the concentrations. The neural network is used as the forward model. To optimize this adaptation the box-constrained Levenberg-Marquard optimization procedure by M. Lourakis or the Nelder-Mead non-linear simplex method (own code) is used. The lower and upper constraints are taken from the range of the input variables of the NN. The solar and viewing angles and the temperature and salinity are passed through the LM procedure to the NN. The inversion can be performed for the reflectances of all simulated bands, which are available of the NN or only for a subset, which is relevant for an instrument. By this the same NN can be used as the forward model for different instruments, such as MERIS, MODIS, SeaWIFS and OLCI. The training data set and in turn the NN can be designed for various different optical properties. In the iterations, some of the variables can be fixed and excluded as a variable for the iteration, depending on the type of water. Furthermore, also the constraints can be reduced and adapted to the expected solution range. Details of the procedure and the validation are described in the ATBD (D11/1) 6.. Validation The validation of the algorithm was performed using the NOMAD data set, which has been provided by Ocean Colour group of NASA. It is a quality checked compilation of observations from very different places of the oceans from different cruises under very different conditions (cloudy and sunny weather etc), which were carried out by different teams. To get a larger common data set only 6 spectral bands can be used. Since the nn algorithms provides 33 different bands, only the corresponding 6 bands could be used for fitting. Furthermore, for all stations a salinity of 35 PSU was assumed, while the SST is provided with the data. The results of the retrieval for total scattering, total absorption, absorption by pigments and absorption by gelbstoff and detritus, all at 443 nm, are provided in Fig. 8 - Fig. 31 The comparison of the scattering coefficient b could only be done with the backscattering coefficient of the Nomad data. Fig. 8: Comparison with the NOMAD data set for the total scattering coefficient at 443 nm. 3

33 7/06/1 1. Page: 33 of 77 Fig. 9: Comparison with the NOMAD data set for the total aborption coefficient at 443 nm. Fig. 30: Comparison with the NOMAD data set for the phytoplankton pigment aborption coefficient at 443 nm. 33

34 7/06/1 1. Page: 34 of 77 Fig. 31: Comparison with the NOMAD data set for the absorption coefficient of gelbstoff and detritus (agd) at 443 nm. 7. Sensitivity Analysis 7.1. Salinity and Temperature Sensitivity studies were performed concerning the effect of temperature and salinity on water leaving reflectances and on the fresnel reflection just above the water and at top of atmosphere using Hydrolight, the HZG Monte Carlo code as well as the neural networks. To see the effect on reflectance spectra these spectra have been computed for different water types and then for different combinations of temperature and salinity (see Fig Fig. 36). The effects are only noticeable in the water leaving radiance reflectance spectrum of oligotrophic water. The temperature effect is weak in all cases of natural waters. The salinity effect is significant in oligotrophic water with low concentration of scattering constituents. Here it can enhance the reflectance in the blue spectral range by 0% when compared to fresh water. A further impact on reflectance spectra comes from the uncertainty of the temperature and salinity effect. It comes mainly from temperature and is in the order of 46% in the red and near infrared spectral range for all cases. The maximum is spectrally located at the maximum of the temperature anomaly around 70 nm. In the blue green spectral range the impact depends mainly on the temperature. At low temperatures the impact is small, at high temperatures (36 deg C) it can be as large as in the NIR spectral range. 34

35 7/06/1 1. Page: 35 of 77 Fig. 3: Relative deviations of Rlw due to lower and upper bounds of uncertainties of pure water absorption and scattering for S=35, T=15, apig=0.1, adet=0.15, ays=0.15, bpart=10.0, bwit= Sensitivity analysis using the neural network based inversion algorithm For analyzing the sensitivity the inverse modelling procedure was used, which is based on a forward neural network and a Levenberg-Marquard optimization procedure. The forward neural network produces as output water leaving radiance reflectance spectra for up to 33 spectral bands in the spectral range nm, covering the spectral bands of MERIS, MODIS, SeaWIFS and OLCI. Input to the NN are solar zenith angle, viewing zenith angle, difference between sun and viewing azimuth angle, temperature, salinity, pigment absorption coefficient, absorption coefficient of detritus, absorption coefficient of gelbstoff and the scattering coefficient of mineralic suspended matter, all at 443 nm. This NN has been trained with radiative transfer simulations using the extended version of Hydrolight. For the sensitivity test different cases were simulated using the neural network and then in a second step it was tried to retrieve the independent components by using the same NN in combination with an LevenbergMarquard optimization code. This code allows to set box-constrained boundaries and to compute the covariance matrix from the quasi Hessian matrix, from which the retrieval uncertainties can be retrieved for each independent component. A number of parameters can be adjusted in this program, such as the starting values, the lower and upper boundaries for the independent variables, the step for each iteration. In particular the start values and the adjustment of the step are rather critical for the success of the procedure. 35

36 7/06/1 1. Page: 36 of 77 Fig. 33: Water leaving radiance reflectance of pure water Fig. 34: Water leaving radiance reflectance of Baltic Sea for temperatures 5, 15, 5 deg C, salinity 35. water for temperatures 5 and 5 deg C, salinity 0, chl 1.0 mg m-3, ys(440 nm) 0.3, SPM 1.0 g m-3. Fig. 35: Water leaving radiance reflectance of oligotrophic water for temperature 15 deg C, salinity 0 and 35, chl 0.1 mg m-3, ys(440 nm) 0.01, SPM 0.01 g m-3. Fig. 36: Water leaving radiance reflectance of oligotrophic water for temperature 15 deg C, salinity 0, 0 and 35, chl 1.0 mg m-3, ys(440 nm) 0.3, SPM 1.0 g m3. The lower and upper boundaries (constraints) are normally set with the minima and maxima of the neural network, which is used in the procedure. However, a further restriction is possible if e. g. the range of an IOP is known for a local area. A number of test cases were then defined with optical properties, which are typical for some marine provinces (s. detailed report). In this summary only some of these cases will be demonstrated as examples. For the tests three spectra were defined: (1) the true, () the measured, which includes uncertainties, and (3) the retrieved. First example is a case 1 water test without including any uncertainties, just to see the error in this case (Fig. 37). 36

37 7/06/1 1. Page: 37 of 77 Test case 1 water no error RLw [sr-1] rlw_true rlw_ret wavelength [nm] Fig. 37: True (modelled and retrieved water leaving radiance reflectance spectrum case 1 water test case, without induced uncertainties and changes in temperature and salinity. Variable conc_true 0.1 chlorophyll [mg m-3] 0.01 detritus [g m-3] gelbstoff a443 [m-1] 0.01 min. SPM [g m-3] 0.01 kdmin_true conc_ret stdev kdmin_ret kdmin_err % reldev% kd490_true dif_true reldev_true% kd490_ret kd490_err %

38 7/06/1 1. Page: 38 of 77 Water retrieval case North Sea RLw [sr-1] rlw_true rlw_ret wavelength [nm] Fig. 38: True (modelled and retrieved water leaving radiance reflectance spectrum case water test case, without induced uncertainties and changes in temperature and salinity. Variable chlorophyll [mg m-3] detritus [g m-3] gelbstoff a443 [m-1] min. SPM [g m-3] conc_true 1.000e e e e+00 conc_ret 9.998e e e e+00 stdev 1.918e e e e-05 reldev% 1.918e e e e-03 dif_true e e e e-04 reldev_true% e e e e-0 kd_min true:.893e-01 kd_min_ret:.893e-01 deviation: 0.000e+00 kd_490_true: 4.534e-01 kd_490_ret: 4.536e-01 deviation: 3.39e Effect of temperature on North Sea water The retrieval is performed for a temperature of 15 degc and 35 PSU. In the first test the water has a temperature of 0 degc and 35 PSU (Fig. 39). 38

39 7/06/1 1. Page: 39 of 77 Water retrieval case T effect 1 deg C 6 rel. RLw [%] 4 diftrue% wavelength [nm] Fig. 39: Relative differences in water leaving radiance reflectances due to uncertainties in temperature. Variable chlorophyll [mg m-3] detritus [g m-3] gelbstoff a443 [m-1] min. SPM [g m-3] conc_true kdmin_true kdmin_ret conc_ret stdev kdmin_err kd490_true reldev% dif_true reldev_true% kd490_ret kd490_err An assumed temperature of 35 degc gives similar results, but with inverted signs: Variable chlorophyll [mg m-3] detritus [g m-3] gelbstoff a443 [m-1] min. SPM [g m-3] conc_true conc_ret stdev kdmin_true kdmin_ret kdmin_err kd490_true reldev% kd490_ret In both cases the detritus concentration is the most sensitive component. 39 dif_true reldev_true% kd490_err 1.745

40 7/06/1 1. Page: 40 of Effect of salinity As expected from the analysis of forward modelling, uncertainties in salinity gives much higher errors in the retrieval for case 1 water (Fig. 40). Again the retrieval parameters were set to 15 degc and 35 PSU. In case the water has a salinity of only 1 PSU the following error in the retrieved spectrum occurs: Water retrieval case 1 rel. RLw [%] effect of salinity, 1 PSU diftrue% wavelength [nm] Fig. 40: Relative differences in water leaving radiance reflectances due to uncertainties in salinity. For the retrieved variables the uncertainty is much larger: Variable chlorophyll [mg m-3] detritus [g m-3] gelbstoff a443 [m-1] min. SPM [g m-3] conc_true conc_ret stdev kdmin_true kdmin_ret kdmin_errkd490_true reldev% kd490_ret dif_true reldev_true% kd490_err For the case that the water has a salinity of 4 PSU, as possible in the Red Sea, the uncertainties in the retrieved parameters are partly significant smaller. such as for the retrieval of chlorophyll. 40

41 Variable chlorophyll [mg m-3] detritus [g m-3] gelbstoff a443 [m-1] min. SPM [g m-3] 7/06/1 1. conc_true kdmin_true conc_ret kdmin_ret stdev kdmin_err kd490_true reldev% Page: 41 of 77 dif_true reldev_true% kd490_ret kd490_err.066 For case waters the effect of uncertainties in salinity are much smaller, also as expected from the simulations: Here again temperature and salinity was set to 15 deg C and 35 PSU. For the case that the water has a salinity of 1 PSU the uncertainties in the retrieved parameters are: Variable chlorophyll [mg m-3] detritus [g m-3] gelbstoff a443 [m-1] min. SPM [g m-3] kdmin_true conc_true conc_ret stdev kdmin_ret kdmin_err kd490_true reldev% kd490_ret dif_true reldev_true% kd490_err For 15 degc, 4 PSU Variable chlorophyll [mg m-3] detritus [g m-3] gelbstoff a443 [m-1] min. SPM [g m-3] conc_true kdmin_true kdmin_ret conc_ret kdmin_err stdev kd490_true reldev% kd490_ret dif_true reldev_true% kd490_err Test with real data: For a further sensitivity test MERIS RR data were used. For this purpose it was necessary to include the atmospheric correction into the test. The atmospheric correction is also performed with a forward neural network, which simulates the atmospheric path radiance for different aerosol optical thicknesses up to 1.0, different angstrom coefficients in the range 0 -., different wind speeds for the fresnel reflection at the surface and the full range of solar and viewing angles. Further inputs to the nn are sea surface temperature and salinity. Output are the path radiances and the up and downward transmittances. This NN is combined with the water NN to compute rho_tosa, the reflectance at top of atmosphere for a standard surface pressure 41

42 7/06/1 1. Page: 4 of 77 of hpa and without absorption by ozone. Thus, rho_tosa has first to be determined from the measured rho_toa spectrum to make it comparable with the output of the neural network. rhow= nn_water(sun_zenith, view_zenith, azimuth_diff, T, S, apig, adet, agelb, bpart, bwit) rho_path=nn_atmosphere1( sun_zenith, view_zenith, azimuth_diff, T, S, aot, ang, wind) t_down = nn_atmosphere( sun_zenith, view_zenith, azimuth_diff, T, S, aot, ang, wind) t_up = nn_atmosphere3( sun_zenith, view_zenith, azimuth_diff, T, S, aot, ang, wind) rho_tosa = rho_path + t_down * rhow * t_up Eight variables have to be determined by the optimization routine are: apig, adet, agelb, bpart, bwit, aot, ang, wind. To test the sensitivity for temperature and salinity effects a cloud-free transect crossing the Pacific ocean off Baja California was selected from a MERIS scene (Fig.: Due to the low sun this full swath transect is without sun glint. 41 Fig. 41: MER_RR PRBCM _175519_ _00356_04418_0193_reflec_ with transect (MEGS 8) Three different temperature and salinity combinations were used, all other parameters were kept constant. The total absorption for the 3 combinations are plotted along the transect in Fig. 4., note the log10 scale. Obviously the combination T 0deg, S 43 PSU shows a higher absorption than the combination T36, S0. The ratio of both along the transect can be seen in Fig 43. A higher value of atot of around 0% can be seen on the left side, where the absorption is lower then on the right side. This relationship can seen also when the ratio is plotted against the total absorption (s. Fig. 44). This relationship can be explained by the fact that at low T and high S the scattering of water is by 0 % higher in clear oligotrophic water, while in water with higher concentrations of scattering and absorbing constituents, this effect decreases. Since the neural network is trained with the T, S effects, the higher water reflectances at low T and high S has to be compensated by a higher absorption in order to get the right match with the measured toa reflectances. On the right side of the transect, the concentrations of phytoplankton is > 5 times higher, so that no increase in scattering has to be expected by the NN and accordingly be compensated. 4

43 7/06/1 1. Page: 43 of 77 Fig. 4: Total absorption along the California transect, which are derived with the NNs and simplex optimization from Rtoa data with 3 different combinations of T and S as input to the NNs. Fig. 43: Ratio between the total absorption derived with the NNs with different T and S combination of T0S43 / T36S0 43

44 7/06/1 1. Page: 44 of 77 Fig. 44: Scatter plot with regression line (red) of the absorption ratio for the T and S combinations and the total absorption As one can see in Fig the data are rather noisy. This is mainly a problem of the green to red bands, which go into the algorithms. It can be seen in the reflectance spectra, which were retrieved from MERIs L1 top of atmosphere radiances by using the standard atmospheric correction and the NN based of this study (Fig. 45). Fig. 45: Water reflectances for MERIS bands 1 and 4, standard L data, MEGS 8.0, blue, and the match using the inversion scheme with the forward neural network (green) 44

45 7/06/1 1. Page: 45 of 77 Since all 1 bands of MERIS go into the atmospheric correction, the noise makes it difficult to isolate the effects of temperature in cases, where the difference is not as large as demonstrated in this test. 7.. Impact of temperature and salinity on specular reflection One part of the upward directed radiance at top of atmosphere comes from the specular reflection at the air/sea interface. It can b direct sun light without scattering in the atmosphere, which is defined as sun glint, or specularly reflected photons which are scattered in the atmosphere before or after the reflection at the surface, which are defined as sky glint. The degree of reflection depends on the refractive index and the incidence angle. The refractive index in turn changes with wavelength, temperature and salinity. In the present MERIS ground processor a constant value of n = 1.34 is used for all MERIS bands. Case Lam/zeni T=0, S=43 difference T=36, S=0 ratio difference ratio 400 / 10.91E E E E / 10.58E E E E / E E E E / E E E E-001 Table 1: Change of the specular reflectance of pure sea water relative to a water with a temperature of 15 deg C and a saliniy of 35 PSU, for a temperature/salinity combination of 0/4 and for 36/0., computed for wavelengths (400 and 100 nm) and incidence angles (10 and 70 deg). Table 1 shows that the temperature and salinity effect on the specular reflectance may produce an error of about up to 5%, when only mean temperature and salinity conditions are assumed. The contribution of the sky glint to the top-of-atmosphere reflectivity was computed using a Monte Carlo photon tracing code. The photons were labelled and counted separately in 3 categories: (1) photons, which were only scattered in the atmosphere, () photons, which were scattered in the atmosphere and specularly reflected at the water surface (sky glint), and (3) photons, which were specularly reflected but not scattered in the atmosphere (sun glint) (Fig. 46. Fig. 47 shows that the contribution by sky glint is in the order of 5 % for most sun zenith angles and nadir view. Only for sun zenith angles > 50 deg the contribution increases up to 0% for a sun zenith angle of 70 deg. Then also the the wavelength dependence becomes significant. However, one has to note that differences below 3% cannot be seen, because of the noise of the Monte Carlo simulation. This is in particular important for the test of different refractive indices, which cannot be resolved with this test (Fig. 48). However, as demonstrated in the sensitivity report (D9) the effect of temperature and salinity on the sun glint can be high. The change in specular reflected radiance relative to the radiance reflected with a constant refractive index of 1.34 can be in the same order of the water leaving radiance. Thus, for sun glint correction the inclusion of temperature and salinity in the algorithm is of importance when applied to waters with different temperatures and salinities. 45

46 7/06/1 1. Page: 46 of 77 Fig. 46: Nadir view of TOA radiance reflectances [sr-1] for 41 nm for photons with different history: scattered in the atmosphere, sky glint, sun glint and the sum of all 3 Fig. 47: Fraction of sky glint of top of atmosphere radiance reflectance, when sun glint is not considered. Note: due to Monte Carlo noise all 3 curves have uncertainties of a few percent 46

47 7/06/1 1. Page: 47 of 77 Skyglint at toa for different refractive indices Skyglint at toa for different refractive indices 865 nm, nadir view 865 nm, nadir view RL [sr-1] RL [sr-1] T15/S35 T15/S35 T36/S0 T36/S0 T0/S43 T0/S sun zenith angle [deg] sun zenith angle [deg] Fig. 48: Sky glint at top of atmosphere for different T and S combinations and a fixed refractive index of 1.34 for 865 nm and nadir view 7.3. The importance of OLCI band 1 (400 nm) A new spectral band of OLCI compared to MERIS is located at 400 nm. Part of the sensitivity study of the project was to analyse the importance of this band for the retrieval of the absorption coefficient of organic matter in water. Since the absorption by organic matter increases with decreasing wavelength one can expect a higher impact on the reflectance spectra at shorter wavelength. Here we consider only water leaving radaince just above the water, not the impact of the atmosphere. For this test the water reflectance was simulated for an absorbing case water and for a clear ocean. Then the absorption coefficient was retrieved using the forward neural network inversion procedure. For the match with the simulated spectrum the 400 nm band was included or omitted. In summary the results are: In coastal water yellow substance and detritus can be retrieved with sufficient accuracy over the full range used for the simulation. The retrieval of the pigment absorption coefficient is sufficient accurate down to 0.01 m-1 Below this value the retrieval is affected by higher concentrations of detritus and yellow substance, which masks the effect of pigment absorption on reflectance spectra. OLCI band 1 (400 nm) does not improve the retrieval of the IOPs of the 3 substances (Fig. 49). The retrieval accuracy is reduced significantly when the red bands (OLCI bands 10-1) in the range are omitted in the optimization function. These are obviously important for the higher concentrations. In oligotrophic water the retrieval uncertainty is significant lower. The retrieval of a_pig and a_ys is improved when OLCI band 1 (400 nm) is included in computing the chi cost function (Fig. 49.and 49) 47

48 7/06/ Page: 48 of 77

49 7/06/1 1. Page: 49 of 77 Fig. 49: Retrieval test for the absorption coefficient of yellow substance (left) and phytoplankton pigment (right), with OLCI band at 400 nm (upper panels) and without (middle panels), and for only band 1-9, lower panel Fig. 50: relative uncertainty (ratio a_pig_nn / a_pig ) for the test with OLCI band 1 (left) and without (right) Fig. 51: Relative uncertainty (ratio a_ys_nn / a_ys ) for the test with OLCI band 1 (left) and without (right) 7.4. Raman Scattering The new version of the MOMO radiative transfer code, described in the chapter MOMO extensions and validation, was used to perform a sensitivity study regarding the effect of water Raman scattering on waterleaving and top-of-atmosphere radiances under varying chlorophyll and salt concentrations for the OLCI channels. An in-depth description of the IOP models used for the study, as well as a detailed discussion of the results, can be found in sensitivity analysis (D9c). At this place only a short summary regarding the results in 49

50 7/06/1 1. Page: 50 of 77 terms of irradiances shall be given as an overview. For a discussion of the angular dependence of water leaving radiances please refer to (D9c). Fig. 5: The Raman scattered fraction of the water-leaving irradiance for clear water with a temperature of 15 C and a salinity of 35PSU (circles) at a solar zenith angle of 41. The error bars stand for the estimated maximum deviation for a temperature variation in the range of 5 and 30 C and a salinity variation between 0 and 35PSU.The hyper-spectral results are integrated with respect to the spectral response functions of the MERIS/OLCI bands and plotted at the central wavelength position of each channel. For atmospheric absorption and scattering computations the U.S. Standard Atmosphere model without an aerosol layer was chosen. The clear water IOP where taken from WOPP-ATBD (D6), and the bio-optical model for chlorophyll absorption and phytoplankton scattering was adapted from the model used in the HydroLight software package. The results for clear water can be seen in 5 and Fig. 53 The Raman scattered fraction of the water-leaving irradiance in clear water reaches values of 5% in the blue, between 0 and 5% in the red, and up to over 35% in the SWIR. Qualitatively this is in agreement to values published by in [Waters 1995] and [Gordon 1999] for the nm range. A shift from saltwater (35 PSU) to fresh water causes a rise of the Raman fraction of up to 8 percent (absolute), whereas a variation of water temperature between 5 and 30 C has only little impact on the Raman contribution. For the top-of-atmosphere upwelling irradiances, the Raman fraction reaches a maximum of 1.4% in the 490nm channel, followed by a strong decrease in the red and NIR due to the strong absorption in the water, resulting in weak signal leaving the ocean compared to back scattered light from within the atmosphere. 50

51 7/06/1 1. Page: 51 of 77 Fig. 53: Raman fraction results for the upwelling irradiance at the top of the atmosphere. Fig. 54: The dependence of the water-leaving radiance on the chlorophyll concentration, for sea water with a temperature of 15 C and a salinity of 35 PSU. Results for six roughly equidistant OLCI channels were plotted representatively. The solar zenith angle is

52 7/06/1 1. Page: 5 of 77 The results in terms of Raman scattered fractions of the water leaving irradiance in simulated Case-1 waters are shown in Fig. 54. The decrease of the Raman fraction with growing chlorophyll concentration is strong in every channel. Between 0.1 and 10 mg/m³, the Raman fraction decreases 1.5 magnitudes in the red and green,and about one magnitude in the blue channels. At very turbid cases with concentrations higher than 5 mg/m³, the Raman fraction is lower than one percent in all shown channels. Concluding, water Raman scattering in very clear water makes up for a fraction of 5-37% of the waterleaving irradiance and radiance in the examined OLCI bands (channels from 400 to 885nm, excluding the O A-band). For moderately turbid cases with a chlorophyll concentration of 0.1 mg/m³, which represents the global mean, the Raman scattered contribution still ranges between and 10%. These results show, that a neglection of water Raman scattering effects in a retrieval scheme would lead to unacceptable errors in many cases Polarisation Scalar radiative transfer neglects the vector nature of the light field and the matrix nature of scattering, and therefore can introduce significant errors if compared to results from vector radiative transfer. If an ocean colour retrieval scheme is based on the inversion of a radiative transfer model, these errors can have a significant influence on the retrieval results. In Fig. 55 we show a radiance field in the 41.5nm channel (MERIS/OLCI) for the top of atmosphere and water leaving radiances. The bottom part of each plot shows the relative deviation of vector and scalar radiative transfer results. The relative error is in the order of ±8% and shows a directional pattern with a strong dependence on viewing geometry and also solar position. This shows that there is no simple way to account for the errors of scalar radiative transfer, other then running a full vector model. 5

53 7/06/1 1. Page: 53 of 77 Fig. 55: Hemispheric radiance field (top of each plot) in the 41.5nm channel (MERIS/OLCI) and relative deviation of scalar and vector radiative transfer. Left column shows top of atmosphere values and the right column shows water leaving radiances. Top row is for the sun at 5.9 and the bottom row is for the sun at 50.3 zenith angle. The chlorophyll concentration is 0.1 μg/l and the wind speed is 7 m/s. 53

54 7/06/1 1. Page: 54 of 77 Table 4. Relative deviation of scalar and vector radiative transfer results for zenith top of atmosphere radiance. The sun is located at zenith. The deviations in the radiative transfer results depend in general on viewing direction, channel, and in water constituents. In table 4 and 5 we show the dependency on the (MERIS) channels with respect to the chlorophyll content of the ocean. For channels with the lowest sea water absorption, the polarization effect varies up to a percent for varying chlorophyll content. With increasing wavelength the polarization error for the shown geometry becomes almost independent from the chlorophyll content and is mainly driven by sea water scattering and surface reflections. Table 5. Relative deviation of scalar and vector radiative transfer results for zenith water leaving radiance. The sun is located at the zenith. For the water leaving radiance the effect is in the same order of magnitude (see Table 5); but with increasing wavelength the effect doesn't become independent from the chlorophyll content. This is caused by the fact that the water leaving radiance, compared with top of atmosphere radiance, becomes smaller and smaller with increasing wavelength due to the increasing sea water absorption. 54

55 7/06/1 1. Page: 55 of 77 Fig. 56: Variation of the top of atmosphere degree of polarization with respect to viewing angle in the 41.5nm channel. Wind speed if 7m/s. The top of atmosphere degree of polarization itself is sensitive to a change in chlorophyll concentration (see Fig. 56). The highest sensitivity can be seen for very low chlorophyll concentrations. For a more detailed discussion see the sensitivity analysis (D9b). There we also show and discuss very similar results for the 400nm channel. 55

56 7/06/1 1. Page: 56 of Beam processor implementation The objective of this activity is to provide to the scientific user community an improved ocean colour processor for MERIS that takes into account the achievements of the improved pure water model. This task was realised by five activities: Preparation of a climatology of temperature and salinity as an auxiliary dataset for the processor Development of a first new version of the BEAM Case Regional Processor with inverse neural nets, which have been trained with the new pure water absorption model, and which use the temperature and salinity maps Development of a second new version of the BEAM Case Regional Processor with iterative solution of forward neural nets, also trained with the new pure water model and using temperature and salinity maps Development of a new processor that connects BEAM to lgen in order make GIOP accessible from within BEAM A systematic inter comparison of different processors in order to assess the changes introduced by the new water radiance pure water model We will describe these activities and their main results in the following chapters; 8.1. Generation of Temperature and Salinity Climatology Auxiliary Dataset Temperature and salinity data of the World Ocean Atlas 009 (WOA09, have been used as source for the Water Radiance processor. WOA09 is a set of objectively analysed (1 grid) climatological fields of in situ temperature, salinity, and a large number of other oceanographic parameters at standard depth levels for annual, seasonal, and monthly compositing periods for the World Ocean. It also includes associated statistical fields of observed oceanographic profile data interpolated to standard depth levels on both 1 and 5 grids. We used the statistical mean of the monthly climatology data. Fig. 57 shows as an example the WOA09 salinity and temperature data that were used. The WOA09 data are not gap free and hence could not be used directly. The new water model requires T and S as input, i.e. for every pixel to be processed these values have to be provided, or the pixel would be flagged as algorithm failure. This applies not only to ocean but also to inland waters. 56

57 7/06/1 1. Page: 57 of 77 Fig. 57: Example of the WOA09 dataset (March statistical mean, top: salinity, bottom: temperature) 57

58 7/06/1 1. Fig. 58: S & T interpolated climatologies visualised in VISAT. The colour scales is in Fig Page: 58 of 77

59 7/06/1 1. Page: 59 of 77 We implemented a growing algorithm to fill the gaps. This algorithm takes an existing value and spreads it into gaps. This growing is performed in the spatial and also in the temporal dimension of the dataset. The gap filling includes the coastal zone and extends and little bit into land, but basically stops at land surfaces, i.e. inland waters are not included. A map of inland water bodies (e.g. SRTM rivers and lakes) would be an option to identify those and to set the salinity to 0. The temperature is much more difficult to get for lakes. However, addressing inland waters in an appropriate way is not foreseen in the water radiance project. The result of the gap filling procedure are two datafiles, for temperature and salinity, in netcdf format, each with 1 datasets for the months. The data files can be opened by any programme capable to read netcdf, including BEAM. Fig. 58 shows the interpolated maps for March, which can be compared directly with Fig. 57. Please note that gradients for low salinity are not resolved in this presentation; Fig. 59 shows salinity of the Baltic Sea with an adapted colour scale. Fig. 59: Enlargement of the salinity oft he Baltic Sea area with adapted colour scale 8.. BEAM Case Regional Processor Water Radiance Release HZG had trained neural networks for atmospheric correction as well as for the water inversion which takes as additional input the temperature and salinity; the net has also other minor modification compared with previous versions. The BEAM CaseR processor, version 1.5., which is the latest pubic release (BEAM ), has been upgraded with this new neural network. This required Importing the T&S climatologies Verification of T&S imported data Retrieval of T&S for given pixel Adding two new bands to the output product for T and S used Modification of the software interface to the neural network Modification of the user interface: o option to choose climatology T&S values or to provide constant values to be used for every pixel o option to write the T and S values as bands into the output product 59

60 7/06/1 1. Page: 60 of 77 The user interface of the new version of the processor, called CaseR-1.7-WR (for Water Radiance Release) is shown in Fig. 60. Fig. 60: User Interface of the CaseR-1.7.-WR (Water Radiance release) Case Regional Processor Iterative Solution Version The latest development of the neural network technique is the iterative solution of the forward model, where this is approximated by a neural network. We have been working together with HZG since the first version of this new technique became available in December 011. The new technique requires a major modification of the CaseR processor: Implementation of the iteration procedure (currently favoured: Levenberg-Markquart) Implementation of the new interface to the forward neural network Prototype implementations have been completed and tests are performed on MERIS images. However, in its current state, the processing is far too slow to be applicable, and unstable. The work will be continued under the CoastColour project GIOP in BEAM GIOP is an evolving, experimental platform for IOP retrieval in NASAs L processor lgen. In particular because of this evolutionary character of GIOP it was considered inappropriate to take the GIOP development status at project start, and transfer this into a Java BEAM processor; instead a much better solution was to link the C coded GIOP to BEAM; as long as the interface remains stable any new GIOP 60

61 7/06/1 1. Page: 61 of 77 development will automatically be available in BEAM. NASA was very supportive to this idea; they are currently working on a BEAM user interface and analysis layer to lgen. As part of WaterRadiance BC supported NASA in their activities (without NASA funding) and in return the GIOP integration in BEAM can make use of software components developed by NASA, and will finally include the complete lgen processor. GIOP (as part of lgen) is running under LINUX only. Hence, also the use of the GIOP (lgen) processor in BEAM requires running BEAM under LINUX. Windows user can realise this by using a virtual machine, in the same way, as SeaDAS on Windows runs in Linux VM. A user interface like any other BEAM processor is used to configure GIOP(Fig. 61). On opening the standard configuration which is currently also used by OBPG in SeaDAS is presented as default. If a product is opened in VISAT this is used as default input product. The user can modify the processing parameters and then run the processing. In the background the configuration is written to a file which is read by the executable GIOP (lgen) program. After successful termination of GIOP the resulting product is opened in VISAT. In this way the look-and-feel is identical to any other BEAM processor; however, the technical realisation is completely different. The main challenge of the implementation this way was to establish a communication interface between BEAM and lgen (process invocation, termination, status report, product handing over). Fig. 61: User Interface of GIOP (lgen) from BEAM Processor Intercomparison Note: due to an angular issue in the simulation of the atmosphere the following comparison concerning CaseR 1.7 is of limited accuracy, any conclusions should be treated with caution. Meanwhile the code has been corrected. The results of all other chapters are not affected or have been repeated with the 61