Quantification of Suspended Particulate Matter

Transcription

1 Quantification of Suspended Particulate Matter from DAIS/ROISIS Images: case-ii waters S. Salama and J. Monbaliu Hydraulics Laboratory, K.U.Leuven Arenberg, 31 Heverlee, Belgium 1 Abstract On the 5th of June 1 the Digital Airborne Imaging Spectrometer (DAIS) and the Reflective Optics System Imaging Spectrometer (ROSIS) acquired sets of images that covered part of the Belgian coast. The acquisition was concurrent with radiometric and bio-physical in-situ measurements. The images were atmospherically corrected using measurement from the sun-photometer at Oostende. The resulting water leaving spectra were inverted to constituents concentrations using an explicit and an implicit technique. Although the implicit inversion retrieves the concentrations of water constituents simultaneously, it was restricted to the spectra of a few pixels in DAIS/ROSIS images. This paper proposes an explicit approach that uses the near infrared (NIR) band.8 µm and the red band.678 µm to estimate the concentrations of suspended particulate matter and chlorophyll-a respectively. The advantage of this approach is its applicability to the whole data sets of DAIS/ROSIS. The relative difference between the retrieved SPM concentrations from implicit and explicit inversions did not exceed 6% in turbid waters but was up to 1% in clear waters. On the other hand the relative differences between the concentrations of chlorophyll-a, retrieved from 3 Presented at the 3rd EARSEL Workshop on Imaging Spectroscopy, Herrsching, May 3

2 explicit and implicit inversion, did not exceed 5% in clear water but reached up to 36% in turbid waters. The proposed technique is, therefore, suitable for the quantification of SPM in turbid waters and chlorophyll-a in clear waters. Keywords: CHRIS, DAIS, ROSIS, SPM, chlorophyll-a, IOP, inversion, atmospheric correction, Belgian coast, turbid waters. Introduction.1 Processing chain Hyperspectral sensors acquire continuous spectra with more channels at NIR for the aid of atmospheric correction. The Compact High Resolution Imaging Spectrometer (CHRIS) sensor was launched on board of PROBA (PROject for on Board Autonomy) the nd of October 1. CHRIS will acquire sets of images over an area of 18km by 18km within the Belgian coastal zone near Oostende. Each set consists of five images at different looking angles. These multi-viewing angles of CHRIS will facilitate the determination of the aerosol multiple-scattering radiance. The operational mode will be set at 5m-spatialresolution with 63 spectral bands. Within this context a set of DAIS/ROSIS images were used as prototype of CHRIS data over the Oostende site. Radiometric and physical in-situ measurements were carried out (simultaneously with sensors overpass) in the Belgian waters. The processing of DAIS/ROSIS images was subdivided into three steps namely, preprocessing, processing and postprocessing. The objective of the first step was to retrieve high accuracy water leaving reflectance. This was realized through a good design of the flight lines, accurate in-situ measurements and a reliable algorithm for atmospheric correction. The total recorded reflectance at the sensor s level was atmospherically corrected using data of the sun-photometer at Oostende. In the processing step, the inherent optical properties (IOP) of the water (and hence the constituents concentrations) were estimated from DAIS/ROSIS images. This encompassed two subtasks. The first subtask was to simultaneously retrieve the concentrations of the water constituents through an implicit inversion technique. The the implicit inversion approach was applied on few spectra (of a few pixels) which were concurrent with in-situ measurements. The disadvantage of the implicit inversion is, however, the small data set on which it can be applied. For the huge data sets of DAIS and ROSIS another approach is definitely needed to exploit the full coverage of the sensors (i.e. the whole image). This was realized in the

3 second subtask of the processing step. The reflectance at the NIR (.8 µm) was directly inverted to the concentrations of SPM. These concentrations were then feeded to a second explicit inversion of the chlorophyll-a band at.678 µm. The bulk absorption coefficient at this band (.678 µm) was assumed to mainly be due to the absorption of water molecules and the chlorophyll-a.. Characteristics of the Sensors CHRIS is a space-borne sensor with 6 bands which cover the spectral range between. µm and 1.5 µm at a spatial resolution of 5 m. ROSIS is a push-broom airborne sensor with 115 spectral bands distributed between.3 and.86 µm and a spatial resolution of.56 mrad (instantaneous field of view IFOV). DAIS is a 79-channel airborne spectrometer. This sensor covers the spectral range from.5 to 1.3 µm at a spatial resolution of 3.3 mrad. Table (.) summarizes the spectral and geometrical characteristics of the sensors. Table 1: The operational mode of each sensor. Parameter CHRIS DAIS ROSIS Dynamic range [bit] FOV [ ] ± 1.3 ± 6 ±8 spectral range [µm] Number of bands Spatial resolution [m] flight altitude [Km] Method The recorded reflectance at the sensor level ρ (λ) t several components: ρ (λ) t { = T g (λ) T (λ) v ρ (λ) sfc + ρ(λ) path + T v (λ) can be written as the sum of (λ) Where T g (λ) and T v (λ) are respectively the gaseous transmittance (ozone, oxygen and water vapor) and the viewing diffuse-transmittance from ocean to sensor. The subscript of the reflectance represents the contribution from surface reflectance (sfc), the atmospheric path (path) and water (w). The water leaving } (1) 5

4 reflectance (λ) is the desired quantity that is related to the sea water physical and biological properties. Extracting this quantity from the total received reflectance is conventionally called atmospheric correction. The water leaving reflectance can then be related to the bulk inherent optical properties of the water column through the first order (Kirk 199 [1]) semianalytical model of Gordon et al. (1988) []: (λ) b b (λ) =.5πl T (λ) 1 b b (λ) + a(λ) () l 1 =.99 is the subsurface expansion coefficients due to internal refraction, reflection and sun zenith; T (λ) is the solar transmittance from sun-to-target; b b (λ) and a(λ) are the bulk backscattering and absorption coefficients of the surface water, respectively. The constant number.5 describes the fraction of transmitted light. This forward model (), however, omits the fluorescence effects. Thus the model cannot be expected to provide accurate predictions of the chlorophyll-a or DOM concentrations. This uncertainty will lead to errors in the estimated concentrations. The analytical model () can be explicitly or implicity inverted to the governing IOP. Explicit solutions are direct-inversion assuming a one-constituent water model. This method is restricted to case I waters or to the NIR part of the spectrum. The NIR bands are very appropriate for the quantification of suspended straticulate matter (SPM) in turbid waters. This is due to the following: The water column is optically governed by SPM in the NIR. This allows the use of a simple hydro optical model (Gordon et al., 1988 []) and direct inversion. The water surface and bottom reflectance have small values (Tolk et al. [3]). This will reduce the induced errors due to roughened sea surface and bottom albedo. Saturation-of-reflectance occurs at high concentrations of SPM (Althuis and Shimwell 1995 []). The radiance field is not affected by the stratification of the water column (Forget et al., 1 [5]). 6

5 The coupled term is negligible. This allows to introduce some realistic assumptions to facilitate the atmospheric correction (Gordon and Castano 1987 [6]). Implicit solutions are based on minimizing the difference between the modelled and measured radiance (Doeffer et al., 199 [7], Lee et al., 1998 [8] and 1999 [9], Forget et al., 1999 [1] and 1 [5] and Chomko et al.,3 [11]). Implicit inversion solves a sequence of direct problems. The measured reflectance can then be fitted to these pre-generated spectra of water leaving reflectance. Inherent optical properties are retrieved from the modelled-spectrum which has the best-fit to the measurement. The concentrations of water constituents can then be estimated from the retrieved IOP and measured specific inherent optical properties (SIOP). Measurements of the SIOP was carried out (and supplied) by IVM [1] in the North Sea. In this paper both explicit and implicit methods are used. Suspended particulate matter (SPM) and water molecules were assumed to be the only optical active components at the NIR. The reflectance at the NIR is directly (explicitly) inverted to the concentrations of SPM. These concentrations are then used as the input for a second explicit inversion of the red absorption band of chlorophyll-a (.678 µm). This approach tacitly assumed that the bulk absorption coefficient at.678 µm is manly due to the water molecules and the phytoplankton pigment (i.e. chlorophyll-a). On the other hand, the implicit inversion approach was applied on the spectra of the pixels that were concurrent with in-situ measurements. Results and discussions.1 Planning the flight lines The DAIS/ROSIS scanned a sub-region of CHRIS coverage off shore Oostende with three ROSIS and one DAIS. The constraints in preparing a flight line were: Avoid sun glint and specular reflection from the sea surface. Maintain an acceptable signal-to-noise-ratio. Minimize the variations in illumination-intensity across the flight line. Maintain the accessability to the sampling sites which is subjected to the tidal cycle (i.e water level). 7

6 1 solar zenith solar azimuth : UT : UT angles (degrees) 5 Latitude : UT 15:3 UT Shorline Sea Land : UT UT time of the day (a) Solar angles as function of time Longitude (b) Variations of the solar azimuth at Oostande. Figure 1: The variation of the flight line as function of solar angles, position and date. Three factors were considered, the position, date and time of day and the section to be scanned (i.e. being perpendicular or along the shore line). Thus, any flight line will be time dependent taking its beginning as a fixed location on the shore. It was desirable to have a perpendicular scan (approximately or South North) to the coast to provide the necessary information about the offshore variation of water quality parameters, and differentiation between case I and case II waters. To achieve a reasonable flexibility, several time dependent-scenarios were realized in figures (1(a) and 1(b)). Figure (1) is a scenario for a point near Oostende (Lat: 51.33, Long:.933) and for the 5th of June 1 (the DAIS/ROSIS over-flight). Let us note (figure 1(a)) that at the midday UT time the zenith and azimuth angles are at their minimum values. The above conditions can be fulfilled at sun zenith between 3-5 degrees and the sun is at the back or front of the aircraft. To achieve the last requirement the beginning point of the flight line (the square in the middle of figure 1(b)) was fixed. Then the flight line rotated following the sun azimuth. The best flight line was depending on the alignment of the shore line and water level. The shoreline (in figure 1(b)) was modelled as a line with an angle of 57 w.r.t. the north line in the clockwise direction. 8

7 . In-situ measurements During the DAIS/ROSIS over flight the water leaving reflectance was measured at 1 sampling sites (near Oostende) using the SIMBADA radiometer (figure (a)). The sun measurement, from this device, were unreliable as reported by the provider. Only 6 of these sites were in the DAIS scene (figure ). The water water leaving reflectance P1 P1+ P P3 P3+ P P5 P5+ P5++ P6 SPM specific absorption m mg P1 P P3 P P5 P wavelength µm wavelength µm (a) SIMBADA water leaving reflectance. (b) The specific absorption spectra of SPM. Figure : In-situ measurements of some AOP and IOP during the DAIS campaign the 5th of June 1. transparency was assessed using Secchi depth measurements. Water samples were collected and filtered through GF/F.7 filters to quantify their content of SPM s concentrations (table ) and absorptions (figure (b)). The absorption coefficient of the SPM retained on GF/F filter was determined from the optical density (OD) of the filter (Fargion and Muller [13]). The specific absorption coefficient was calculated by dividing the total absorption coefficient of SPM over its measured concentration. Let us note that the site P6 (figure (b)) has the highest value of the absorption coefficient (at.3 µm). This might be due to the high concentration of SPM at this site (table 3). This is because the spectrophotometer was not equipped with an integrating sphere to eliminate the effect of light backscattering by the particles (Tassan and Ferrari 1995 [19]). 9

8 Table : The sites of in-situ measurements during the DAIS/ROSIS flight campaigns. The stars indicate no data. site local coordinates turbidity ID time lat long Secchi depth [m] SPM [g.m 3 ] P1 1: P1+ 11: *** *** P 11: P3 11: P3+ 11: *** *** P 1.: P5 1: P5+ 1: *** *** P5++ 1: *** *** P6 13: Atmospheric correction The total recorded reflectances of DAIS/ROSIS were corrected for gaseous absorption, Rayleigh and surface reflectance. Data from the sun photometer, situated at Oostende, were then used to generate the aerosol scattering at the sensor level (Vermote et al [1]). A good atmospheric correction should produce close estimates between the reflectance recorded by SIMBADA and DAIS. Figure (3) shows that the water leaving reflectances estimated from DAIS agreed with the readings of SIMBADA for all sites at the band.555 µm. This agreement at.555 µm gives more confidence on the atmospheric correction. This also means that the only reliable values of the water leaving reflectances are at the band.555 µm. Moreover the model (equation with measured IOP as inputs) could not predict the measurements of SIMBADA neither the readings of DAIS. This was except for the site P at which the readings from SIMBADA were reproduced by the model. This discrepancy can be explained by the variations in sampling volumes of the different devices (i.e. DAIS, ROSIS and SIMBADA) (Zanveld 199 [15]). Let us observe that the reflectances at the NIR (figure 3) are relatively large. Peter Gege (3)(Personal communication) suggested that the high reflectance at the DAIS NIR bands are more likely to be due to sensor artifacts. 3

9 P P simulated SIMBADA DAIS P P wavelength µm wavelength µm Figure 3: The water leaving reflectance measured by DAIS and SIMDABA and predicted from equation () and in-situ measurements.. Retrieving the water IOP from DAIS/ROSIS spectra..1 Implicit inversion Six spectra of DAIS where selected for the implicit inversion. These spectra were concurrent with in-situ measurements. Figure () shows that the implicit inversion of the model () was able to reproduce DAIS spectra with maximum value of the root-mean-square (of the residuals) being less than.75%. The confidence interval around the spectra were estimated following the approach of Bates and Watts (1988 [16]). Figure () shows that the image spectra are within the 95% confidence of the modelled values except for the bands.678 and.8 µm. The large values of reflectance at the NIR were interpreted (by the model) as high concentrations of SPM. Then to compensate the resulting high signals 31

10 8 6 modelled DAIS C up C low P P P P P wavelength µm 8 6 P wavelength µm Figure : Modelled versus DAIS water leaving reflectances with 95% of confidence with upper bound Con u p and lower bound Con l ow. The numbers at each panel represent the root-mean-square of the residuals. at shorter wavelengths the model predicted high concentrations of chlorophyll-a and DOM. The retrieved values of the IOP are illustrated in figure (5)... Explicit inversion The reflectance at the NIR.8 µm was explicitly inverted to the concentrations of SPM. These concentrations are then used as the input for a second explicit inversion of the red absorption band of chlorophyll-a at.678 µm. This approach tacitly assumed that the bulk absorption coefficient at this band is manly due to the water molecules and the phytoplankton pigment (i.e. chlorophyll-a). Let us note that the used NIR band.8µm has high value of the water leaving 3

11 chlorophyll a absorption [m 1 ] DOM absorption [m 1 ] P1 P1+ P P P5++ P total absorption [m 1 ] 3 1 SPM backscattering [m 1 ] wavelength [µm] wavelength [µm] Figure 5: The retrieved inherent optical properties of the water surface layer from the implicit inversion of DAIS spectra. reflectance due to the, relatively, weak absorption of water molecules. The estimated concentrations from implicit and explicit inversions are compared in table (3). The concentrations were calculated from the retrieved IOP (implicit and explicit inversion) and the measured SIOP (IVM measurement [1]). Table (3) illustrates that the retrieved SPM concentrations from implicit and explicit inversions are within a good agreement especially at turbid water sites, namely P1, P1+, P5++ and P6 (see figure and table ). On the other hand the relative differences between the concentrations of chlorophyll-a, retrieved from explicit and implicit inversion, decreased with decreasing water turbidity (namely the sites P and P). In other words, the proposed technique is suitable for the quantification of SPM in turbid waters and chlorophyll-a in clear waters. Both constituents (SPM and chlorophyll-a) were, however, underestimated in turbid 33

12 Table 3: The retrieved concentrations of SPM and chlorophyll-a from implicit and explicit inversions with their relative differences. site implicit inversion explicit inversion relative difference Chl-a SPM Chl-a SPM Chl-a SPM mg.m 3 g.m 3 mg.m 3 g.m 3 P P P P P P waters and overestimated in clear waters when using the explicit technique. The estimated concentrations form both methods (implicit and explicit inversion) were, however, very large in comparison to the measured values. This might be due to measurement errors, sensor calibration errors, bottom reflectance and model, scale and measurements closure. Moreover, the values of these concentrations were found to vary with wavelengths. This suggests that the SIOP might be spatially variable (Mikkelsen [17]). This variability can be linked to the SPM particle-size distribution and index of refraction..5 Instrument readings and calibration errors Calibration is the most important step in any measurement. Each instrument (DAIS, ROSIS and SIMBADA) has suffered from reading errors. Major difficulties encountered during the processing of the DAIS and ROSIS images were: Spectral shift in ROSIS with unknown magnitude at each wavelength. The oxygen band-a was employed to correct for this shift. ROSIS spectra have negative reflectances at the blue range (<.55 µm). Therefore it was difficult to assess the absorption properties of phytoplankton and yellow substance. The DAIS water spectra have large values of reflectances at the NIR. The experimental procedure of SIMBADA is to make, consecutively, one dark measurement, three sun measurements, three sea measurements, three sun 3

13 measurements and one dark measurement (consult the user manual [18]). SIM- BADA measurements were performed from an inflatable boat. This platform was too unstable when aiming to the sun with the radiometer. Normally the radiometer should be pointed to the target for 1 seconds. During this time the device reads the signals with a frequency of 8 Hz. The mean of these readings is accepted as being the sought signal if the readings have a small standard deviation (defined by the provider). Due to the instability of the platform, sunmeasurements were not accepted according to the standard specifications of the provider. The SIMBADA is equipped with a bulb that facilitates the positioning of the device towards the sun. However this bulb allowed some light to enter the device during the dark and sea measurements. Therefore the device could not be calibrated correctly. The conclusion is that water measurements might be subjected to a substantial amount of errors due to the dark correction and the noise entering the bulb. Finally, the spectrophotometer was not equipped with an integrating sphere to eliminate the effect of light backscattering by the particles (Tassan and Ferrari 1995 [19]). Thus we can not rely on the measured values of the absorption coefficient of turbid samples. 5 Conclusion The DAIS/ROSIS flight campaign was organized with simultaneous in-situ measurements. The data of the sun-photometer at Oostende were used to atmospherically correct the DAIS/ROSIS images. The DAIS water leaving reflectances at bands other than the.555 µm were far from the readings of SIMBADA. This might be due to the variations in the sampling volumes of the different sensors (DAIS, ROSIS and SIMBADA). These variations in the sampling volumes will result in errors due to scale closure that are difficult to be quantified. The IOP of the surface waters were retrieved through an implicit and an explicit inversion technique. Implicit inversion was applied on the spectra of the pixels that were concurrent with in-situ measurements. The atmospherically corrected reflectances were fitted to pre-generated spectra of water leaving reflectance. Inherent optical properties were retrieved from the modelled-spectrum which had the best-fit to the measurement. On the other hand, the reflectances at the NIR and red bands were explicitly inverted to the concentrations of SPM and chlorophyll-a, respectively. First the NIR band.8 µm was used to estimate the concentrations of SPM. These concentration were then used as the 35

14 input for a second explicit inversion of the red absorption band of chlorophyll-a at.678 µm. In this approach we tacitly assumed that the bulk absorption coefficient at the red band is manly due to the water molecules and the phytoplankton pigment (i.e. chlorophyll-a). The relative difference between the retrieved SPM concentrations from implicit and explicit inversions did not exceed 6% in turbid waters but was up to 1% in clear waters. On the other hand the relative differences between the concentrations of chlorophyll-a, retrieved from explicit and implicit inversion, did not exceed 5% in clear water but reached up to 36% in turbid waters. The proposed technique is, therefore, suitable for the quantification of SPM in turbid waters and chlorophyll-a in clear waters. Both constituents (SPM and chlorophyll-a) were, however, underestimated in turbid waters and overestimated in clear waters when using the explicit technique. The estimated concentrations were very large in comparison to the measured values. This might be due to measurement errors, sensor calibration errors, bottom reflectance and model, scale and measurements closure. Considerable amounts of errors were found in the measured values of water leaving reflectances using SIMBADA and DAIS. The IOP (absorption coefficients) are expected to be erroneously measured. This is because the spectrophotometer was not equipped with an integrating sphere. Moreover, the values of the estimated concentrations were found to vary with wavelengths. This might be due to the assumption of constant SIOP. The SIOP of a constituent vary on a spatial and temporal scale. These variations are due to the compositions and shapes of the constituents suspended in the water column. Acknowledgment The authors would like to thank the German Aerospace Center DLR for providing DAIS/ROSIS data under the HySens 1 project; The Institute for Environmental Studies (IVM), Free University of Amsterdam for providing the specific inherent optical properties of Belgian waters; The Laboratoire d Optique Atmosphérique de la Université des Sciences et Technologies de Lille for providing SIMBADA and its derived data; The Flanders Marine Institute (VLIZ) for their support in providing instrumentation; The Management Unit of Mathematical Models of the North Sea (MUMM), for providing the inflatable boat. Dr. Christine Peeters and Rik Deliever form the Teaching Support Unit, faculty of agricultural and applied biological science at K.U.Leuven are acknowledged for providing the spectrophotometer and assisting in the absorption measure- 36

15 ments. Also thanks to Dr. Roberto Padilla-Hernandez for his assistance during the in-situ campaign. The financial support of ESA PRODEX Experiment Arrangement No. 918, is gratefully acknowledged. References [1] Kirk J., 199: The relationship between the inherent and apparent optical properties of surface waters and its dependence on the shape of the volume scattering function. Oxford University Press. [] Gordon H., Brown O., Evans R., Brown J., Smith R., Baker K., and Clark D.,1988: A semianalytical radiance model of ocean color. Journal of Geophysical Research, (93): [3] Tolk B., Han L., and Rundquist D., : The impact of bottom brightness on spectral reflectance of suspended sediments. International Journal of Remote Sensing, 1(11): [] Althuis I. and Shimwell S.,1995: Modelling of remote sensing reflectance spectra for suspended matter concentration detection in coastal waters. In EARSeL Advances in Remote Sensing, volume, pages [5] Forget P., Broche P., and Naudin J., 1: Reflectance sensitivity to solid suspended sediment stratification in coastal water and inversion: a case study. Remote Sensing of Environment, 77:9 13. [6] Gordon H. and Castano D.,1987: Coastal color scanner atmospheric correction algorithm: multiple scattering effects. Applied Optics, 6(11): [7] Doerffer R. and Fischer J., 199: Concentration of chlorophyll, suspended matter, and gelbstoff in case ii waters derived from satellite coastal zone color scanner with inverse methods. Journal of Geophysical Research, 99(C): [8] Lee Z., Carder K., Mobley C., Steward R., and Patch J., 1998: Hyperspectral remote sensing for shallow waters. 1. a semianalytical model. Applied Optics, 37(7):

16 [9] Lee Z., Carder K., Mobley C., Steward R., and Patch J., 1999: Hyperspectral remote sensing for shallow waters:. deriving bottom depths and water properties by optimization. Applied Optics, 38(18): [1] Forget P., Ouillon S., Lahet F., and Broche P., 1999: Inversion of reflectance spectra of nonchlorophyllous turbid coastal waters. Remote Sensing of Environment, 68(3):6 7. [11] Chomko R., Gordon H., Maritorena S., and Siegel D., 3: Simultaneous retrieval of oceanic and atmospheric parameters for ocean color imagery by optimization: a validation. Remote Sensing of Environment, 8:8. [1] IVM. Measurements of the SIOP in the North Sea. Personal communication, [13] Fargion S. and Muller J., : Ocean optics protocol for satellite ocean color sensor validation, revision. Protocol Tm , NASA. [1] Vermote E., Tanre D., Deuze J., Herman M., and Morcrette J., 1997: Second simulation of the satellite signal in the solar spectrum, 6s: An overview. IEEE Transactions on Geoscience and Remote Sensing, 35(3): [15] Zanveld J., 199: Optical closure: from theory to measurement. Oxford University Press. [16] Bates D. and Watts D., 1988: Nonlinear Regression Analysis and Its Applications. John Wiley and Sons, NY. [17] Mikkelsen O., : Variation in the projected surface area of suspended particles: Implication for remote sensing assessment of TSM. Remote Sensing of Environment, 79:3 9. [18] Laboratoire d Optique Atmosphérique. SimbadA users s guide. Université des Sciences et Technologies de Lille, F Villeneuve d Ascq Cedex, FRANCE. [19] Tassan S. and Ferrari G., 1995: An alternative approach to absorption measurements of aquatic particles retained on filters. Limnology and Oceanography, (8):