Gayana 68(2) supl. t.I. Proc. : 259-265, 2004 ISSN 0717-652X

A SEARCH FOR CORRELATIONS BETWEEN SEA SURFACE TEMPERATURES AND OPTICAL REFLECTANCES IN CHILE AND BRAZIL DERIVED FROM AVHRR/NOAA IMAGES

Aníbal Gusso¹, Jorge Ricardo Ducati¹, Carlos G. Cotlier² & Diego A. G. Lopez²

1. Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia Universidade Federal do Rio Grande do Sul, C.P. 15044, Porto Alegre, RS, Brazil; anibal.gusso@ufrgs.br,_ ducati@if.ufrgs.br
2. Facultad de Ciencias de Ingenieria y Agrimensura Universidad Nacional de Rosario, C.P. 2000, Rosario, Argentina; ccotlier@fceia.edu.ar,_ dag_lopez@yahoo.com.ar

ABSTRACT

A search is made for indicators of the presence of phytoplankton, using satellite images of the Pacific (Central Chile) and Atlantic (South Brazil). AVHRR/NOAA-16 and -17 visible (1 and 2) and thermal (4 and 5) channels were used to perform a detection test, respectively, the Suspended Particulate Matter (SPM) and the Sea Surface Temperature (SST). In Brazilian waters, a positive correlation is found between SST and SPM's reflectance. This is interpreted as due to phytoplankton being more abundant in colder waters, where nutrients availability are higher because CO₂ dissolution rates, thus being a favorable environment for phytoplankton contents, which when mixed with SPM, tends to reduce the total water reflectance, since organic matter causes absorption at red wavelengths. A comparison is made with results for colder Pacific, where an opposite trend is found. It is noted that the Pacific shelf, off Chile, is narrower than the Atlantic's off Brazil, leading to circulation processes, which have a different influence on particulate matter contents. It's also concluded that NOAA data is suitable for these studies, despite the fact its spectral resolution is poorer comparing to specialized ocean studies satellites, a disadvantage compensed by its wider spectral and radiometric range and higher imaging frequency.

Keywords: phytoplankton biomass, remote sensing, AVHRR/NOAA

INTRODUCTION

Recently, many studies have been carried out on the implications of phytoplanktonic ecology and distribution over primary production and terrestrial climate and atmosphere. Phytoplankton produces organic carbon by photosynthesis and it is the main biological entity in the marine carbon cycle. The photosynthesis is know as the primary production process because it is the first step in organic matter production, in which the low trophic levels (phytoplankton) energy is transfered to the high levels. Phytoplankton pigments consists mainly of chlorophyll a, b, c and carotenoids (Doerffer et al., 1999) but the marine biomass also contains degradation by-products (phaeopigments) composed of polimerized humic and fulvic acids. However, the energy/matter interactions are very complex and depend of a number of interelated factors (Lillesand & Kiefer, 1979). The range of different combinations among organic pigments of photosynthetic or non-photosynthetic marine biota, and suspended sediments, make the study of phytoplanktonic biomass a very complex task, even harder if approached from space observing systems. Over the shelf, waters are usually termed as "Case 2 waters" because of the major influence on the water colour by Suspended Particulated Matter (SPM) (e. g., tidally stirred sediments or riverine fluvial muds) or gelbstoff (organic yellow substances). In this study, following Fischer & Fell (1999) it is considering phytoplankton, DOM and suspended matter the most important optically active constituents in Case 2 waters.

In Case 2 waters, SPM sediments and yellow substances from terrestrial origin, which are not co-varying with chlorophyll a concentration, dramatically increase the errors in chlorophyll concentrations estimates (Gohin et al., 2002). In this situation, even at higher spectral resolution it is difficult to correctly identify, or quantify, isolated elements of suspended matter, least to separate it from marine biomass. However, information on amounts (either relative or absolute) of these parameters are crucial to many studies. The possibility of using information from the NOAA channel 1 (red, 580-680 nm) to detect organic constituents, including phytoplankton biomass concentrations mixed with SPM (Case 2 waters), is tested in this work.

It is important to stress the point that what is being studied here is the alteration in reflected radiance due to the presence of some agent in water, also observed at Gusso et al. (2004). The starting point is the reflectance curve due to inorganic matter, contained in the SPM. The additional presence in water of organic matter, either living or not, will alter the radiance detected by the orbital sensor, since organic matter and phytoplankton have lower reflectances than inorganic matter (or, which is the same, higher absorption), its detection being possible having the suspended sediment as background. Here, the radiance is detected by channel 1, with atmospheric absorption corrected with the channel 2 of AVHRR/NOAA instrument. Additionally, features location of surface thermal zones and upwelling zones frequently are associated to the surface phytoplankton concentration distribution. The colder waters enriched with nutrients (N, P, Mg and Ca) are propicious to all marine biota. Some studies (Traganza et al., 1983; Laurs et al., 1984; Abbot & Chelton, 1991; van Haren et al., 1998) has shown the great association between temperature and higher primary productivity regions. The sea surface temperature can be estimated through a linear combination of channels 4 and 5, tuned to detect to thermal emission. This idea can be tested by comparing reflectance vs. SST for places in continental shelves were both values are available.

The correlation between SST and SPM reflectance is sought regardless of specific place in the ocean. In the case of Brazilian coast, features called "clusters" can be used as sources of SST and reflectance data. Clusters are the result of surface temperature separations due to various causes, including contact between currents and layer mixing, and are often associated with abundance of nutrients, being for this reason useful indicators of fish. Regions with a characteristic temperature (clusters) can be pinpointed and the corresponding temperature, or radiance readings made for pixels within those regions. Nevertheless, the searched correlation involving SST and reflectance can use data from any part of the ocean, provided that a temperature gradient exists. Even in deep waters, where the botton effects are negligeable, the reflectance properties of a water body are not only a function of the water per se, but also the material in the water (Lillesand & Kiefer 1978). Moreover, most of scattering is caused by suspended sediments, whereas the absortion is dominated by chlorophyll-a and colored dissolved or particulate matter. Moore (1977) demonstrated that the peak in reflectance shifts to longer wavelegths as the suspended sediment concentration increases. The absortive in-water components such as chlorophyll-a and CDOM have been shown to lower the reflectance in a substantial way (Curran and Novo, 1988).

MATERIAL AND METHODS

2.1. Study area

This study covers an area at Santa Catarina State, over the South Brazilian outer shelf, with the following limiting latitudes: 28^o 37' S and 28^o 46' S (Figure 1). This area was chosen because an existing upwelling zone near the Santa Marta Grande Cape (28^o 45' S and 48 45' W). In the Chilean coast the observed area is between the latitudes 34^o 27' S and 37^o 37' S. The temperature readings were done on points associated to clusters (as defined in sec. 1), which were distributed all over the shelf. However, the retrieved data were obtained far from Santa Catarina coast (outer shelf), because the oceanic dynamics promotes variations both in particulated material surface distribution and thermal feature configurations. In the Pacific, data points were chosen following the same sample criterium.

2.2. Data acquisition

The orbital data used in this study were received at the satellite stations at the Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia (CEPSRM-UFRGS) at Porto Alegre, Brazil, and at the Estación Terrena Satelital of the Universidade Nacional de Rosario, Argentina. Images were chosen looking for high elevation angles at overpass. Eight images in High Resolution Picture Transmission (HRPT) format were selected for simultaneous radiance and SST horizontal distribution data retrieval by means of the software ERDAS-Imagine (version 8.5), under cloud-free situation over the shelves (Tables 1 and 2).

Figure 1: Graphic scatterplots for coasts of Chile (left column) and Brazil (right column) of SST (C^o) and the water-leaving radiance (Wm^-2sr^-1) at colder and warmer waters for Sept/23/2003, January/07, March/17, March/18/2004 (Chile) and July/14, July/15, July/23 and September/04/2002 (Brazil).

2.3. Data treatment

The method took five steps: a) georreferencing data; b) atmospheric correction; c) water-leaving radiance retrieval; d) SST data retrieval; e) generation of a scatterplot of SST against SPM water-leaving radiance data for the two sets of images.

2.3.1. Georreferencing

Georreferencing was performed with the orbital algorithm for control points calculation, based on the efemerids data of orbital route. This type of georreferencing, with the Spheroid and DATUM WGS84 for Brazilian coast and WGS72 for the Chilean coast, often presents a mean accuracy of 2 pixels (2200m) for this region. Over the ocean, it seems to be accurated enough for this study scale.

2.3.2. Atmospheric correction and water-leaving radiance

A simply procedure for atmospheric correction was implemented, partialy based in that described by Stumpf & Pennock (1989) and Froidefond et al. (1993). Channel 2 (725-1000 nm) radiances are subtracted from channel 1 radiances, being accepted that the observed water-leaving radiance from channel 2 is very close to zero. The purpose is to remove contamination from aerosols and sunglint. The supposedly clear-water pixels were identified in the area of interest and the radiance value of 1.5Wm^-2sr^-1 is subtracted from all images. This procedure intents to reduce contamination due to Rayleigh and aerosol scattering. It results, into a pixel basis, the reflectance value.

2.3.3. SST data retrieval

The McClain et al. (1985) equation is:

SST = 1.035T4 + 3.046(T4 - T5) - 283.9 (1)

Here, SST is the estimated surface temperature in a pixel basis; T4 e T5 are the retrieved brightness temperature obtained from the channels 4 (10300-11300 nm) and 5 (11500-12500 nm) of AVHRR, respectively.

2.3.4. Scatterplots

Both parameters, radiance and SST, are obtained from the same data base, that is, the same image. It can be seen here the advantage of obtaining from the same digital file the SPM radiance and SST data, with the overpasses frequency provided by any of the operational NOAA series satellites. Since what is being looked for is an eventual dependency of radiance on temperature, sets of points at higher and lower SST are needed. This was done selecting suitable points in the ocean. First, some clusters of colder and warmer areas in the SST distribution over the shelf were identified and selected, based only on images of superficial distribution of SST. Later, the same SST distribution image was used to generate two other images, one of them with higher superficial temperatures, and the other with lower ones. The minimum difference gap (DT) of sea surface temperature between colder and warmer clusters was set to be 2^oC for all images, as shown in Table 3.

Eisolated in distance from each other and the seaboard. Hence, after isolating some regions of higher and lower temperature, separated by at least 2^oC, the procedure was to proceed to the reading of the water-leaving radiance, leading to coupled SST and radiance values in a pixel basis. The geographical position of these points is, obviously, different in each image, since the oceanic dynamics alters continuously the SPM and SST distribution. The average number of pixels collected inside each cluster is around 35.

DISCUSSION

In this paper the authors were concerned with the detection of water-leaving radiance variability in the red portion of spectrum due to bio-optical interactions. In Brazilian waters the colder SST's, in the four overpasses dates, are clearly associated to lower water-leaving radiance values; warmer SST are associated to higher water-leaving radiance values. The mean values and standard deviations of each overpassing/date referring to Figure 1 are shown in Table 4.

The first three image of Brazilian waters were taken at Winter, while the last was taken at Spring, the most propicious period to phytoplanktonic biomass development (van Haren et al., 1998; Siegel et al., 1999); the September/04 data shows a significative radiance increase, along with an increase in SST. This result can indicate more nutrient availability, associated with more SPM, which is reasonable, since seasonal physical circulation processes regulate the availability and consumption of nutrients in shallow waters, with the potential to cause significant changes in physical and biological processes, as reported by Lentini et al. (2000) and Silveira et al. (2000).

The radiance in Chilean waters is lower, but also more stable regarding temperature variations; these temperatures are fairly lower than in the Atlantic. Even if these lower radiances can be indicators of lower SMP contents, their stability tells little about possible organic contents or its variation. The general trend is opposite between the two sets of images. It is known, however, that the Pacific outer shelf, off Chile, is narrower, leading to circulation patterns that can differ significantly from the coast of Brazil, with an influence on contents of particulate matter.

With respect to the contents of phytoplankton or other organics, it is not possible to observe directly the distribution of any of the organic constituents (CDOM, Gelbstoff and detritus) directly or isolated, but only the mixed water-leaving radiance resulting from the bio-optical processes of energy/matter interactions. It is supposed that, in the lower SST clusters, there is a more favorable environment, derived from higher nutrients availability, and in this way the colder waters would cause more absorption than the higher SST clusters, where it's supposed to have a lower phytoplankton biomass concentration. For deeper analysis, an accurate estimate of the suspended sediment concentrations is desirable, along with further studies on the relative influences of co-existing organic and inorganic materials in water on the observed radiance in the red portion of the spectrum.

As emphasized by Gower et al. (1999) the red part (600-720nm) of water-leaving radiance spectra contains information about bio-geo-chemical variables which have, to date, been little used due to the lack of appropriate instruments. In this band of the spectrum the phytoplankton biomass is the only one, inter-alia the main constituents, that presents a significative absorption. In this way, the contribution on the optical properties of suspended organic matter may also be relatively high as a result of the high levels of phytoplankton production (Rabalais et al., 1991 apud Myint & Walker, 2002), an asserption shared by the authors of the present work.

The present results also indicate that it is worthwhile to pursue studies in this field with NOAA data, which presents the important advantages of ready access and high temporal frequency of overpasses.

ACKNOWLEDGEMENTS

The authors would like to thank the National Oceanic and Atmospheric Administration and NOAA Satellite Information System for the AVHRR/NOAA data and informations continually provided. The authors are also thankfull to the following researchers: Dr. Vitor Paulo Pereira, Dr. Jerry T. Sullivan and Glauber A. Gonçalves for very useful discussions.

REFERENCES

Abbot, M. R., Chelton, D. B. 1991. Advances in remote sensing of the ocean. Rev. Geophys., 23(5): 571-589. [ 1]

Curran, P. J. & Novo, E. M. M. 1988. The relationship between suspended sediment and remotely sensed spectral radiance. Journal of Coastal Research., 4: 351-368. [ 2]

Doerffer, R.; Sfrensen, k. & Aiken, J. 1999. MERIS potential for coastal zones applications. International Journal of Remote Sensing, 20(9): 1809-1818. [ 3]

Fischer, J. & Fell, F. 1999. Simulation of MERIS measurements above selected ocean waters. International Journal of Remote Sensing, 20(9): 1787-1809. [ 4]

Froidefond, J. M.; Castaing, P.; Jouanneau, J. M. & Prud'Homme, R. 1993. Method for the quantification of suspended sediments from AVHRR NOAA-11 satellite data. Int. J. Remote Sensing, 14(5): 885-894. [ 5]

Gohin, F.; Druon, J. N. & Lampert, l. 2002. A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. Int. J. Remote Sensing, 23(8): 1639-1662. [ 6]

Gower, J. F. R.; Doerffer, R. & Borstad, G. A. 1999. Interpretation of the 685nm peak in water-leaving radiance spectra in terms of fluorescence, absorption and scattering, and its observation by MERIS. Int. J. Remote Sensing, 20(9): 1771-1786. [ 7]

Gusso, A. Souza, V., Vieira, C. E. L, & Lima, F. P. 2004. A physical approach to bio-optical variability effect detection in the Brazilian outer shelf by means of AVHRR/NOAA satellite data. Revista Brasileira de Oceanografia. Submitted. [ 8]

Lillesand, T.M., Kiefer, R.W. 1987. Remote Sensing and Image Interpretation, 2nd ed. New York, Wiley & Sons, 721p. [ 9]

McClain, P. E.; Pichel, W. G. & Walton, C. C. 1985. Comparative performance of AVHRR-Based multichannel Sea Surface Temperature. J. Geophys. Res., 90(C6): 11587-11601. [ 10]

Moore, G. F.; Aiken, J. & Lavender, S. J. 1999. The atmospheric correction in water colour and the quantitative retrieval of suspended particulated matter in Case II waters: application to MERIS. Int. J. Remote Sensing, 20(9): 1713-1734. [ 11]

Myint, S. W. , Walker, N. D. 2002. Quantification of surface suspended sediments along a river dominated coast with NOAA AVHRR and SeaWiFS measurements: Louisiana, USA. Int. J. Rem. Sens., 23(16): 3229-3250. [ 12]

Siegel, H.; Gerth, M. & Neumann, T. 1999. Case studies on phytoplankton blooms in coastal and open waters of the Baltic Sea using Coastal Zone Color Scanner data. Int. J. Rem. Sens., 20(7): 1249-1264. [ 13]

Silveira, I. C. A. da; Schimidt, A. C. K.; Campos, E. J. D.; Godoi, S. S. de & Ikeda, Y. 2000. The Brazil current off the eastern brazilian coast ­ Review article . Revista Brasileira de Oceanografia, 48(2): 171-183. [ 14]

Stumpf, R. P. & Pennock, J. R. 1989. Calibration of a general optical equation for remote sensing of suspended sediments in a moderately turbid estuary. J. Geophys. Res., 94: 14363-14371. [ 15]

Traganza, E. D.; Silva, V. M.; Austin, D. M.; Hanson, W. L. & Bronsink, S. H. 1983. Nutrient mapping recurrence of coastal upwelling centres by remote sensing - Its application to primary production and the sediment Record. In: Suess, E. and Thiede, J. eds. Coastal Upwelling - Its Sediment Record Pt. A: Responses of sedimentary regime to present coastal upwelling. New York, Plenum Press, p. 61-83. [ 16]

Van Haren, H.; Mills, D. k. & Wetsteyn, L. P. M. J. 1998. Detailed observations of the phytoplankton spring bloom in the stratifying central North Sea. Int. J. Mar. Res., 56(3): 655-680. [ 17]