Suspended Particulate Matter (SPM) on a Regional Scale (Tuscan Archipelago) from AVHRR and MERIS Data

Maurizio Tommasini(1), Saverio Mori(1), Gabriele Poli(1), Pier Franco Pellegrini(1)

(1) University of Florence - Electronics and Telecommunications Department – Via di S. Marta, 3 – 50139

Firenze – Italy. E-mail: m.tommasini@labtele.det.unifi.it ABSTRACT Recent studies have shown the possibility to obtain concentrations of Suspended Particulate Matter (SPM) from satellite remote-sensed data, using images acquired by passive, multi-spectral sensors such as SeaWIFS, MERIS, and MODIS. Studies performed on the Belgian seafront indicated how it is possible to calibrate a NOAA-AVHRR algorithm for SPM calculations using measurements on chlorophyll-“a” extracted from a local fixed station network. One limitation of this algorithm lies in the difficulty of obtaining chlorophyll-“a” values from larger areas of study. In our work we have explored the possibility of using MERIS chlorophyll-“a” maps to calibrate the SPM NOAA-AVHRR (SPM-AVHRR) algorithm along the Tuscan coast. This procedure has some advantages over the previous one: chlorophyll-“a” coverage of the entire SPM map, not just point-by-point coverage; the data fusion technique adopted permitted the increase of SPM informative contents taken from AVHRR data; the possibility to use near real time chlorophyll-“a” maps; the possibility to use directly acquired NOAA-AVHRR data. Our data are received by the Satellite Receiving Station of University Pole (PIN) of Prato – Italy (43°53',134 N; 11°05',942 E - WGS84). The MERIS algorithm used to calculate chlorophyll-“a” maps was chosen by comparing the results of 4 algorithms and in situ measurements. Sea truth data were taken from the ARPAT (Tuscan Regional Department for Environmental Protection) database. Presuming there is a limited variability of concentration, a single Meris chlorophyll-“a” map can be used to process more SPM-AVHRR maps. This chlorophyll map will be periodically substituted with one that is closer to the last AVHRR image. The validity of this method was also verified through in-situ measurements, as well as SPM maps from MERIS images. The proposed method can be of great interest in continuous remote sensing observations of Turbidity and SPM by using near real time Full Resolution MERIS data. The best results can be acquired using MERIS data together with close-to-point ARPAT sea truth measurements. These data on the Tuscan Sea are at our disposal. 1 SUSPENDED PARTICULATE MATTER AND SEA TURBIDITY ISSUES Suspended Particulate Matter (SPM), Turbidity and Transparency are 3 physical and optical parameters that can be used for sea observation. A logical link between them can be defined. The Sea Turbidity parameter indicates a decrease in transparency due to the presence of suspended and/or dissolved substances. This depends on various elements, such as rain, storms, soil and sea bottom erosion, flooding, aquatic flora and fauna, and salinity, but not by sea colour. For example, if there are many types of tannins present, the colouring is intense but is often very transparent. In turbid waters light is transmitted but also scattered, reflected, and reduced. Turbidity is defined as water’s capability of scattering light, measured in NTU (Nephelometric Turbidity Units), FTU (Formazin Turbidity Units), and JTU (Jackson Turbidity Units). The Transparency parameter evaluates the transparency of the water, which depends on light scattering and colouring as well. It is usually measured in Secchi Depth. Lastly, the suspended solids are the mass of the suspended material and can be measured in [mg/L] ≡ [g m-3]. In literature it is often called Suspended Particulate Matter (SPM), or also Total Suspended Matter (TSM). Unlike the previous parameters, this one is a concentration and not an optic property. The SPM parameter is useful for monitoring the aquatic ecosystem, water drainage projects used as indicators of sedimentation movement, and in studies of land erosion. In compliance with regional, national, and European norms [10], ARPAT has set up a monitoring network of the Tuscan coast. This network consists of 14 measuring sites with 3 stations per site at 500, 1000, and 3000 [m] from the coast, placed perpendicularly to it (transept) [1] (Fig.1). Measurements are taken at various depths from approximately 0.5 to 50 [m] for each station, and also include chlorophyll-“a” [mg/m-3] and Turbidity [FTU]. Suspended solids [mg/L] are seasonally measured within 500[m] of the coast and 0.5 [m] in depth.

Fig.1 - ARPAT Sea Measurement Sites and Stations, and mouths of the principal rivers. The material carried by the

Tuscan Rivers Arno, Serchio and Ombrone, influences the sea areas opposite their mouths (red circle).

2 SPM-AVHRR ALGORITHM FROM NOAA-AVHRR DATA The developed SPM-AVHRR algorithm used NOAA-AVHRR data from the PIN - Satellite Receiving Station database acquired on 15.08.2002 10:35 UTC. The MERIS Full Resolution Level 1B data were obtained on the Tuscan Sea on 15.08.2002 at 09 data were incurred from the ARPAT database. The following are the data processing steps taken.:59 UTC. Sea truth 2.1. Data geolocation algorithm The geolocation of AVHRR viewed areas is based on an original procedure that uses four orbital parameters to reduce the geolocation errors. The calculation of the satellite position uses the ephemerid obtained from a two-line bulletin and the beginning time of acquisition of every scan line (time code). The procedure for the precise geolocation of NOAA- AVHRR images is based on only 2 GCP (Ground Control Point) and it takes into account the satellite orbit, the terrestrial ellipsoid and the possible errors of the inside clock. It makes corrections on four parameters: Ec epoch, Ωc right ascension of the ascending node, ∆γc angular step of scan sensor, and Ac angle between scan line and satellite azimuthal direction [5]. A similar approach was also tested for MERIS Level 1B data, giving very good results. However, slightly better ones were obtained by using tie point coordinates, a bilinear interpolation algorithm for obtaining the coordinates of the other points, and a small translation of all coordinates towards NE, of less than 300 [m]. 2.2. Segmented interpolation algorithm A segmented interpolation algorithm was developed to improve the resolution, in proximity to the coastlines, of the images acquired by satellite. The algorithm of a regular grid pixel (u,v) and of pseudo-regular sensor pixels (x,y) as

either sea, land or coast pixels, and the contamination index calculation of the pixel (x,y). (The contamination index indicates the percentage of Earth or sea that is observed by the sensor). For this goal two masks have been created which supply information on the position of grid pixel (u,v): an Earth-sea mask and a mask containing contamination information. If one pixel (x, y) has the same land-sea classification of the grid pixel (u,v) and if its contamination index is smaller than a prefixed threshold, it is suitable for the interpolation of the grid pixel (u,v). If a point (x,y) is not suitable for the interpolation of the grid pixel (u,v), its coordinate z is recalculated using the value z of neighbouring satellite pixels [14]. 2.3 Atmospheric and radiometric corrections The techniques of implemented calibration allow us to obtain the radiance values of the scene observed from the satellite, and of the brightness temperature for the five NOAA-AVHRR channels of the sensor [13], using separate algorithms for visible, near infrared channels and thermal ones. MERIS Level 1B are already calibrated radiances, so no radiometric correction is necessary. Atmospheric correction was carried out with the SMAC (Simplified Method for Atmospheric Correction) algorithm [6] and the ECMWF (European Centre for Medium-Range Weather Forecasts) data contained in the MERIS Level 1B file, assuming a continental aerosol model and an aerosol optical thickness of 0.2 [m]. 2.4 Cloud detection on MERIS and AVHRR data Algorithms were applied to MERIS and AVHRR data in order to classify cloudy pixels. The algorithm applied to AVHRR data, which uses a series of tests for deciding whether a pixel is cloudy or not, is described in [5]. The MERIS Algorithm uses four consecutive threshold criteria. We modified the algorithm of [15] to use different thresholds on sea and land. The obtained Sea-Land-Cloudy mask was used on all processing algorithms. 2.5 Chlorophyll-“a”maps from MERIS data The SPM-AVHRR algorithm illustrated here uses MERIS chlorophyll-“a” maps (Fig.3). The chlorophyll-“a” algorithm chosen is the MERIS-2005, the last version of the MERIS-ESA [3] (Eq.1), as it offers the best correlation (Tab.1a and Tab.1b) between in-situ measurement and analyzed algorithms: OC4v4 [11], MERIS 2005 [3], SANTINI [9] and MUMM-CHL [8]. The MERIS-2005 algorithm uses the maximum between the logarithmic reflectance ratios of MERIS bands 442, 490, 510 [nm], with respect to band 560 [nm] (ρ442, ρ490, ρ510 and ρ560, [adim.] in Eq.1, while chlorophyll-“a” concentration is chl in [mg m-3]). Assuming that:

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and 321 rrrr ,,max= then: ( ) 432 213629575227254479342450 rrrrchl ⋅+⋅−⋅+⋅−= .....log (1) The closest ARPAT measurements are 2 August and 22 August. From data analyses an average correlation emerged (Tab.1) between remote-sensed and in-situ measurements, with a tendency of overestimating chlorophyll values. (Fig.2). The concentration map obtained is in Fig.3. Tab.1a Correlation between in-situ Chlorophyll-“a” concentrations (all stations) and remote-sensed data, for the various

analysed algorithms Chlorophyll-“a” Algorithms

Correlation Mean Error RMS

OC4v4 0.565 0.965 1.661 MERIS 2005 0.592 1.361 1.99 Santini 0.364 -0.836 1.804 MUMM-CHL -0.129 18.94 20.288

Tab.1b Correlation between in-situ Chlorophyll-“a” concentrations (the stations are classified by distance from the coast) and remote-sensed data, for the various analysed algorithms

Chlorophyll-“a” Algorithms

Correlation 500 [m]

Mean Error 500 [m]

RMS 500 [m]

Correlation 3000 [m]

Mean Error 3000 [m]

RMS 3000 [m]

OC4v4 0.618 0.379 2.281 0.775 1.294 1.479 MERIS 2005 0.663 0.909 2.282 0.752 1.633 2.014 Santini 0.382 -1.688 3.243 0.771 -0.367 0.607 MUMM-CHL 0.093 16.398 18.132 -0.809 20.951 21.769

o-o-o in-situ Chl-“a” (22.08.2002), +-+-+ OC4v4, +-+-+ MERIS 2005, +-+-+ Santini, + + + MUMM-CHL

Fig.2. Chlorophyll-“a” values measured by ARPAT at 0.5 [m] depth on sites in Fig. 1 and values obtained from algorithms listed in Tab.1.

2.6 SPM Maps from NOAA-AVHRR data The developed SPM-AVHRR algorithm uses AVHRR channel 1 (580 – 680 [nm]) data (Eq.2) and was developed based on the method described in [7]. The implemented model was refined integrating the solar radiance and the bio-optic sea model. The chlorophyll-“a” measurement used was incurred from MERIS data (see 2.5). The parameters described in Eq.2 are: LW(λ)N [W cm-2 sr-1 µm-1] water leaving spectral radiance normalized for the incidence angle and atmospherically corrected; SPM [g m-3] SPM concentration; chl [mg m-3] for chlorophyll-“a”; bbw(λ) = 1.103756E-3 [m-1] pure water backscattering spectral coefficient aw(λ) = 0.297151 [m-1] and aw(440) = 0.00635000 [m-1] pure water absorption spectral coefficient; α(λ) = 9.175e-3 [adim.] and β(λ) = 0.099 [adim.] factors for determining the spectral coefficient of phytoplankton specific absorption; k = 0.014 [nm-1] yellow substance spectral gradient; as

* = 0.003 [m2 g-1] e bs* = 0.013 [m2 g-1] SPM empirical parameters used in [7] for Belgian waters and tested

here on Tuscan waters; m = 1.331678 [adim.] mean value of the pure water refraction index module; F0(J) = 0.162195 [W cm-2] average incident extraterrestrial spectral irradiance on the AVHRR channel 1 band corrected for the day of the year. Wavelengths are expressed in [nm].

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Fig.3. - Chlorophyll-“a” concentration [mg m-3] on the sea

obtained from MERIS data (15.08.2002 09:59 UTC).

Fig.4 - SPM concentration on the sea [mg/L] obtained from NOAA AVHRR data (15.08.2002 10:35 UTC).

3) The variation interval of the Sun’s zenith angle is considered 20-60 [deg]; the irradiance ratio R was calculated using a first order-simplified model, with a hypothesis of the diffusion over the entire hemisphere. The computed error is less than 20% [2]. 4) For the acquisition time difference, the MERIS chlorophyll and AVHRR SPM maps have different cloud situations. Given the near coincidental time and scarceness of clouds in the example considered, the same cloud mask was used for both chlorophyll-“a” and SPM. 5) Other limitations are due to the bio-optic models and the spectral tables used. Fig.4 shows the obtained SPM map.

3 Results and comparisons of SPM-MERIS, SPM-AVHRR and in-situ measurements SPM concentration can also be directly acquired from MERIS data. The SPM-MERIS algorithm (Eq.3), which derives from [4], uses MERIS band 708 [nm] for water with low Turbidity (SPM concentration estimated < 4.5 [mg/L]), where there is low sensitivity to non-elevated error with reflectance. For very turbid waters it uses band 753 [nm], whose reflectance values are much lower than the other channel, therefore reducing estimated SPM errors [4].

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Fig.6 shows the acquired SPM map. Results from algorithmic applications for calculating SPM estimates on MERIS data on 15.08.02 give many false peaks on open sea, whereas ARPAT’s measured values show suspended material concentration only on particular areas, such as the mouths of the Arno and Ombrone Rivers. The SPM-MERIS is compared to SPM-AVHRR and in-situ measurements on the ARPAT sites (Fig.1). The graphic in Fig.7 displays the SPM obtained from remote sensed data and the Turbidity and SPM from in- situ measurements.

Fig.5. SST map [°C] from NOAA AVHRR data

(15.08.2002 10:35 UTC). Fig.6. SPM concentration on the sea [mg/L] obtained from

MERIS data (15.08.2002 09:59 UTC).

4 CONCLUSIONS The SPM-AVHRR algorithm described gave results that correspond with the measurements taken by ARPAT. The out-of-scale areas result as being around lakes, where suspended material values can actually be much higher, and in some small areas with probable cloud error detection (Fig.4 and Fig.6). On the contrary, the modified SPM-MERIS algorithm

gave many false peaks on open waters, even if it gave trends similar to the AVHRR ones near the coast (Fig.7). Therefore further studies are necessary on local calibration and validation of the SPM-MERIS algorithm. The illustrated SPM-AVHRR algorithm uses a chlorophyll-“a” concentration map obtained from MERIS data, with the MERIS-2005 algorithm described in 2.5. One advantage of this method is the possibilities to regionally and diffusely calibrate an entire SPM map with a chlorophyll-“a” one, unlike more traditional point-by-point methods. Moreover, using chlorophyll-“a” obtained from MERIS Full Resolution data, the resolution of the consequent SPM AVHRR maps intrinsically improves.

0-0-0 In-situ Turbidity [FTU] (22.08.2002), + + + in-situ SPM [mg/L] (02.09.2002), +-+-+ SPM AVHRR [mg/L], +-+-+ SPM MERIS [mg/L]

Fig. 7. Turbidity and SPM values measured by ARPAT on sites in Fig.1 and values obtained from SPM-MERIS and SPM-AVHRR algorithms

Information about the sea given by the MERIS-AVHRR integrated system is completed by SST maps created from AVHRR data [1] (Fig.5). This method becomes particularly interesting if we think of the availability of MERIS and AVHRR data. MERIS data must be requested at ESA and there are potentially lengthy delivery dates, depending on the type of product requested (however, Near Real Time products are available a few hours after the passage). Moreover, there is a passage every three days on geographic area data. On the contrary, AVHRR data are very available and provide more passages per day. Future research should evaluate the relationship between the variation speed of chlorophyll-“a”, which is rather cyclic and barely influenced by intense events, with the variation of SPM concentration. The stability of the AVHRR SPM algorithm should also be evaluated with respect to the chlorophyll concentration map used. 5 ACKNOWLEDGMENTS The authors would like to thank ARPAT (Tuscan Regional Department for Environmental Protection) for their support in grounding in situ measurement obtained with oceanographic vessel “Poseidon” and to Dr. Michelle Johnson for her very useful collaboration in the organization of the paper and its text.

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6 REFERENCES 1. Bucci M., Serena F., Pellegrini P. F., Contributo All’osservazione Del Mare Dell’arcipelago Toscano Da Satellite, pubblicazione ARPAT, Firenze, October 2004 2. Gordon H. R., Brown O. B., Evans R. H., Brown J. W., Smith R. C., Baker K. S. and Clark D. K., A Semianalytic Radiance Model of Ocean Color, Journal of Geophysical research ,Vol. 93 NO. D9, Pages 10.909-10.924, 20 September 1988 3. Meris Quality Working Group, MERIS 2nd Reprocessing: Changes Description, Version 1, 01/08/2005 4. Nechad B., De Cauwer V., Park Y., Ruddick G., Suspended Particulate Matter (SPM) Mapping From MERIS Imagery. Calibration Of A Regional Algorithm For The Belgian Coastal Waters, Proc. MERIS User Workshop, Frascati, Italy, 10 – 13 November 2003 (ESA SP-549, May 2004) URL: http://envisat.esa.int/workshops/meris03/participants/139/paper_EPD_BN.pdf 5. Pellegrini P.F., Bucci M., Tommasini M., e Innocenti M., Monthly Averages of Sea Surface Temperature, International Journal of Remote Sensing, accepted on 15.04.2005, to be published 6. Rahman H., Dedieu G., SMAC : A Simplified Method For The Atmospheric Correction Of Satellite Measurements In The Solar Spectrum, International Journal of Remote Sensing, vol. 15, no. 1, 123-143, 10 January 1994 7. Ruddick K., Ovidio F., Van den Eynde D., Vasilkov A., The Distribution And Dynamics Of Suspended Particulate Matter In Belgian Coastal Waters Derived From AVHRR Imagery, Proceedings of 9th Conference on Satellite Metereology and Oceanography, Paris, 25-29 May 1998 8. Ruddick K., Park Y., Nechad B., MERIS Imagery Of Belgian Coastal Waters: Mapping Of Suspended Particulate Matter and chlorophyll-A, Proc. MERIS User Workshop, Frascati, Italy, 10 – 13 November 2003 (ESA SP-549, May 2004) URL:http://envisat.esa.int/workshops/meris03/participants/206/paper_59_ruddick.pdf 9. Santini C., Santoro E., Pieri M., Massi L., Maselli F., Le Immagini Del Sensore MERIS Per Lo Studio Delle Acque Marino-Costiere Della Regione Toscana, Rivista Italiana di Telerilevamento, 32, pg. 35-46, 2005. 10. Regione Toscana e ARPAT, La Qualità Delle Acque Marine Costiere In Toscana, EDIFIR edizioni, Firenze, Ottobre 2004 11. Berastegui D. A., Jorgensen P. V., Hansen L. B., Fell F., 2.3 ATBD Control Sheet –EOP-CHL-SEA, Informus, Version 0.4, Date 2004-11-23 URL: www.siscal.net/documents/EOP_CHL_SEA_V04.pdf 12. Vermote E., Tanrè D., 6S User Guide Version 2, Laboratoire d'Optique Atmosphérique, URA CNRS 713 Université des Sciences et Technologies de Lille, 1997 13. Goodrum G., Kidwell K.B. and Winston W., NOAA KLM user's guide with NOAA-N, 2005 URL: http://www2.ncdc.noaa.gov/docs/klm/ 14. Tommasini M., Poli G., Pellegrini P.F., Segmented Interpolation along the Coastline for AVHRR NOAA Images, EARSel eProceedings, 2002 URL: http://has.physik.uni–oldenburg.de/eProceedings/ 15. Gobron N., Aussedat O., Pinty B. , Taberner M., Verstraete M., Medium Resolution Imaging Spectrometer (MERIS) - Level 2 Land Surface Products Algorithm Theoretical Basis Document, Institute for Environment and Sustainability Joint Research Centre, TP 440 I-21020, Ispra (VA), Italy, Revision 3.0, November 25, 2004, JRC Publication No. EUR 21387 EN URL: envisat.esa.int/instruments/meris/pdf/atbd_mgvi.pdf