Sub-regional patterns of primary production annual cycle in the Ligurian and North Tyrrhenian seas, from satellite data

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1 Rivista Italiana di Telerilevamento , 42 (2): Italian Journal of Remote Sensing , 42 (2): Sub-regional patterns of primary production annual cycle in the Ligurian and North Tyrrhenian seas, from satellite data Luigi Lazzara 1, Christian Marchese 1, Luca Massi 1, Caterina Nuccio 1, Fabio Maselli 2, Carolina Santini 2, Maurizio Pieri 3 and Valentino Sorani 4 1 Università di Firenze - Dipartimento Biologia Evoluzionistica Leo Pardi, via Romana, 17, Firenze, Italy. 2 CNR Istituto di Biometeorologia (IBIMET), Sesto Fiorentino, Italy 3 Consorzio LaMMa Sesto Fiorentino, Italy 4 Universidad Autónoma del Estado de Morelos, Departamento de Ecología, Mexico Abstract The annual cycle of pelagic primary production (PP) from ocean colour is analysed in a transition area between Ligurian and Tyrrhenian waters, where a general oligo-mesotrophic status is seasonally modified by anthropic impact near the Tuscany coast. Based on the common ecological features six different zones were delimited. Remote sensing data from different satellites (Meteosat, Aqua) were used as input in a primary production model. The daily production of the entire area was computed on pixel by pixel basis (4x4 km) using a modified GIS software. Overwhelming importance of oceanic bloom and high spatial variance of PP (90 ±54 gc m -2 y r -1 ) show that remote sensed data can allow a better estimation of carbon budget even in optically complex waters. Keywords: pelagic primary production, satellite reflectance, MODIS-Aqua, Meteosat, Western Mediterranean. Ciclo annuale della produzione primaria nei Mari Ligure e Nord Tirreno da prodotti satellitari, caratteristiche sub-regionali Riassunto Allo scopo di descrivere il ciclo annuale della produzione primaria (PP) pelagica nelle acque comprese tra il Mar Ligure e il Tirreno settentrionale, viene utilizzato un algoritmo semianalitico del colore del mare di tipo sub-regionale. L area d indagine, oligo-mesotrofica con un certo impatto antropico lungo la costa toscana, è stata suddivisa in sei sottoregioni con caratteristiche ecologiche comuni. Nel modello di produzione primaria sono stati introdotti prodotti satellitari ottenuti da diversi satelliti (Meteosat, Aqua) e la produzione giornaliera per l intera area è stata calcolata per ogni pixel (4x4 km) utilizzando un software GIS modificato. La grande importanza della fioritura oceanica e l alta variabilità spaziale di PP (90 ±54 gc m -2 y r -1 ) indicano che i dati satellitari consentono una migliore valutazione del bilancio del carbonio anche in acque marine otticamente complesse. Parole chiave: produzione primaria pelagica, riflettanza, MODIS-Aqua, Meteosat, Mediterraneo Occidentale. 87

2 Lazzara et al. Primary production annual cycle from satellite data in Ligurian and North Tyrrhenian seas Introduction Since the 80 it has been shown, with the Coastal Zone Colour Scanner (CZCS) sensor, that ocean reflectance observed from satellite can be used to monitor phytoplankton distribution [Gordon and Morel, 1983], but a further achievement was the estimation of oceanic primary production at a global scale [Longhurst et al., 1995; Antoine and Morel, 1996]. The recent increasing availability of global satellite imagery of water leaving radiance (Ocean Colour) allows the detection of primary production changes for the whole biosphere and this could be a main reason for the use of satellite estimates [Field et al., 1998]. For terrestrial ecosystems net primary production (NPP) is calculated from absorbed photosynthetic irradiance (APAR), plant biomass (NDVI) and photosynthetic efficiency [Maselli et al., 2008] but for aquatic ecosystems the knowledge of biomass vertical distribution is also required. Several models to compute oceanic primary production at a global scale, using remote sensed parameters have been proposed, and the degree of explicit resolution in depth, wavelength and time allows to distinguish between four different categories [Behrenfeld and Falkowski, 1997]. Each of them uses an estimate of phytoplankton biomass from chlorophyll concentration, of Photosynthetically Available Radiation (PAR) and an expression of changes in photosynthetic efficiency as a function of incident or absorbed irradiance. A recent comparison of twelve algorithms [Campbell et al., 2002] gives satisfactory results for estimates at a global scale (within a factor of two with respect to in situ values). Since long time, however, the need has been pointed out [Platt and Sathyendranath, 1988] for the development of regional and local models, both of optical seawater constituents and of primary production eco-physiology. Remote sensing of sea colour is based on the study of the reflective properties of the main optically active seawater constituents: phytoplankton (PH), whose concentration is generally estimated by chlorophyll (CHL); non-algal particulate matter (NAP) which, in coastal waters, is mostly represented by suspended sediments (SS); colored dissolved organic matter (CDOM), also named yellow substance (YS). In shallow waters the optical properties of sea bottom and of benthos may also play a major role [Maselli et al., 2005]. Various semi-analytical ocean colour algorithms have been proposed and successfully applied, among which there is the standard OC3M algorithm, that is used as a global product [O Reilly et al., 2000]. Though generally applicable, these algorithms require improvement in many local cases, such as the Mediterranean Sea [Volpe et al., 2007]. For this reason several regional algorithms have been defined for this basin, such as those of Bricaud et al. [2002], D Ortenzio et al. [2002] and more recently, Santoleri et al. [2008]. The performances of estimation methods based on these algorithms are generally good in (Case 1) waters, whose optical properties are determined primarily by phytoplankton, but are poorer in optically complex (Case 2) waters, where the concentration of sediment and/or yellow-substance is usually significant. The need for studying sub-regional or local algorithms is confirmed in the coastal waters of the Tuscany, where standard products of MODIS poorly estimate the concentration of chlorophyll, suspended sediments and yellow substance [Santini et al., 2004]. This has stimulated the recent development of a new inversion algorithm which is tuned on the optical properties of the Ligurian and Northern Tyrrhenian seas [Maselli et al., 2009]. This sub-regional algorithm is capable of providing nearly unbiased estimates of all three optically active constituents, that could be proficiently used to model the net primary 88

3 Rivista Italiana di Telerilevamento , 42 (2): Italian Journal of Remote Sensing , 42 (2): production of these waters. This study aims at studying the seasonal dynamics of oceanic, neritic and coastal waters, as far as it concerns pelagic primary production, through partition and analysis of sub-areas differences. Moreover a first reassessment of annual primary production in the Ligurian and North Tyrrhenian seas will be performed, using a first version of the sub-regional algorithm. Study Area The study area (7 30 E 12 E; N 41 N) is mainly represented by a transitional sub-basin where water masses circulation is strongly related to the characteristics of the Ligurian and Tyrrhenian waters. The thermic deficit caused in the Ligurian Sea by the blowing of north-western winds (Mistral) draws the warmer oligotrophic southern Tyrrhenian waters that, mainly during the cold season, spread northwards beyond the Elba Island and the Corsica Channel. During the warm season the basin exchanges are more limited and the colder more eutrophic Ligurian waters spread towards the Tyrrhenian ones [Astraldi et al., 1993]. As a result of this main frame, a climate mitigation and a seasonal waters replacement characterize the Arcipelago Toscano waters. Figure 1 - Study area with differentiation in 6 sub-regional units (a1 = Ligurian oceanic, a2 = Tyrrhenian oceanic, a3 = Ligurian neritic no-coast, a4 = Tyrrhenian neritic no-coast, a5 = Ligurian coastal, a6 = Tyrrhenian coastal) and sites of in situ measurements. 89

4 Lazzara et al. Primary production annual cycle from satellite data in Ligurian and North Tyrrhenian seas The previous surveys carried on from 80s, as oceanographic cruises and short time series, pointed out the main ecological features of these waters [Innamorati et al., 1995], which are generally confirmed from the last investigations in the 00s [Innamorati et al., 2003; Cappella et al., 2008]: a general oligo-mesotrophic status, that becomes eutrophic along the northern Tuscany coast (named Versilia), especially affected by river inputs, and that is mainly exploited by pico-nanoplanktonic producer assemblages. In spring and summer-fall periods microplanktonic (diatom) blooms occur, mainly related to coastal fertilization or from mixing events [Nuccio et al., 1995; Innamorati et al., 2003]. Thus, based on the common physical and biological features, this pelagic region has been partitioned into six sub-regions (Fig.1) corresponding to: (a1) the permanent cyclonic oceanic area in the Ligurian Sea and (a2) a less permanent second cyclonic oceanic area in the Northern Tyrrhenian Sea to the Est of the Strait of Bonifacio, two neritic areas separated by the Elba Island into (a3) a northern and (a4) a southern part and finally two strictly coastal areas, respectively (a5) Ligurian, and (a6) Tyrrhenian. Processing of satellite data SST data Sea surface temperature (SST) was obtained from the MODIS Aqua Global Level 3 Mapped product, which is downloadable from the website oceandata.sci.gsfc.nasa.gov. In particular, monthly averages of daytime satellite overpasses with a spatial resolution of 4 km were used. The SST files of the study year (2006) were cropped for the area of interest and converted into a format suitable for the PP model. PAR data PAR was derived from the Sea Surface Irradiance (SSI) product, which is obtained through the processing of SEVIRI-Meteosat observations and is supplied by the French meteorological service within the O&SI-SAF project (www.osi-saf.org). SSI expresses the solar irradiance that reaches the sea surface in the optical band (0.3-4 m) m) and is produced by the algorithm presented by Brisson et al. [1999]. Pieri et al. [2009] found that O&SI- SAF SSI reproduces the daily evolution of radiation more accurately than the similar PAR product derived from SeaWiFS data. Another advantage of O&SI-SAF SSI is linked to its continuous validation, which is performed by the relevant consortium considering also some weather stations included in the current study area. The daily SSI files of 2006 were downloaded from the website of IFREMER (ftp.ifremer. fr). SSI was then converted into PAR by using a coefficient (0.464) proposed by Iqbal [1983]. Finally, daily SSI data were aggregated on a monthly basis and resampled to 4 km resolution for feeding the PP model. [CHL] data The concentration of chlorophyll ([CHL]) was derived from 1 km MODIS-Aqua imagery. In particular, [CHL] was computed by using two standard ocean colour algorithms and a specific algorithm (SAM-LT) recently proposed by Maselli et al., [2009]. The two standard algorithms are OC3M (global) and MedOC3 (regional), whose theoretical bases are fully described in O Reilly et al., [2000] and Santoleri et al., [2008], respectively. The new algorithm is based on the simulation of remote sensing reflectance, Rrs sim through 90

5 Rivista Italiana di Telerilevamento , 42 (2): Italian Journal of Remote Sensing , 42 (2): the following equation: ) ) bbw + $ bb SS b Rrs sim PH + $ b = $ NAP ) ) ) a + $ a + $ a + $ a W PH NAP YS Where [SS] and [YS] are the concentrations of suspended sediments and the value of the absorption coefficient of yellow substance, respectively, a w is absorption coefficient of pure seawater, a* PH, a* NAP and a* YS are the specific absorption coefficients of phytoplankton, non algal particles and yellow substance, and b bw is backscattering coefficient of pure seawater, b* bph, b* bnap are the specific backscattering coefficients of phytoplankton and non algal particles. The specific coefficients of absorption and backscattering are obtained from a bio-optical survey of the study area. The algorithm simulates a wide range of reflectances by varying the concentrations of the three optically active constituents within equation [1]. Next, a comparative analysis of measured, Rrs meas, and simulated remote sensing reflectances, Rrs sim, is performed in order to look for a minimum of a specific error function. This function is based on the cosine of the angle between the vectors of measured and simulated reflectances, cosθ M,S, which can be found as [Sohn and Rebello, 2002; Chang et al., 2006]: cos Rrsmeas T Rrssim im, S = Rrs Rrs meas where Rrs meas and Rrs sim are the vectors of measured and simulated reflectances in 6 or 7 bands, respectively. From a statistical viewpoint, cosθ M,S is equivalent to the correlation coefficient between the two reflectance series. Similarly to this, cosθ M,S can vary from - 1 (complete negative agreement) to +1 (complete positive agreement) and measures the similarity in shape between the two reflectance vectors without detecting amplitude spectral differences. In this way, the algorithm is intrinsically insensitive to amplitude variations of the measured reflectances, which may be due to the presence of seawater constituents with variable spectral properties and/or to inaccurate atmospheric correction of the satellite data. The accuracy of the three [CHL] estimation algorithms was preliminarily assessed by comparison with a series of sea measurements taken in the last few years. The sea data were collected during 3 oceanographic cruises MedGOOS (May 2004, October 2004, October 2006) and by ARPAT (Regional Agency for Environmental Protection of Tuscany) in 7 cruises from April to October Satellite data used for this test are fully described in Santini et al. [2007a] and Maselli et al. [2009]. The results of the comparisons are summarized in Figure 2. The estimation accuracy is improved when using the algorithm based on the maximization of cosθ M,S in terms of RMSE, up to reaching a high accuracy level. More specifically, the new method almost completely removes the strong [CHL] overestimation brought by the global and the regional algorithms in optically complex waters, and only produces at low [CHL] levels a reduced sensibility and a slight underestimation. Next, both the standard and the new algorithms were applied to a series of daily MODIS images taken during images were used, whose features are fully described in Santini et al., [2007b]. The application of the three algorithms to these images produced daily maps of [CHL], which were averaged on a monthly basis, degraded to 4 km resolution by pixel aggregation and given as input to the PP model. sim 91

6 Lazzara et al. Primary production annual cycle from satellite data in Ligurian and North Tyrrhenian seas Figure 2 - Comparisons between [CHL] measured and estimated by the three algorithms: OC3M (a), MedOC3 (b) and SAM-LT (c) (the thinner line indicates the 1:1 relationship; r stands for correlation coefficient, RMSE for root mean square error and MBE for mean bias error). The PP model Oceanic primary production models can range from the simplest, just statistical relationships between biomass and production [Joint and Groom, 2000], to the analytical and semianalytical ones. The most esplicit of them are the time, wavelength and depth resolved models (WRDR) with both spectral and vertical resolution of the underwater light field and of phytoplankton biomass. Here we use the bio-optical, physiologically based and semianalytical model of Morel [1991] for oceanic primary production assessment within the euphotic layer. Combining an atmospheric [Tanré et al., 1979] with a bio-optical model of the water column [Morel, 1988] gives an estimate of photosynthetic radiation at the sea surface and along the water column which, joined to a parameterization of the main physiological processes [Morel et al., 1987; Antoine and Morel, 1996], allows the calculation of daily primary production, starting from the concentration of microalgal biomass. The algal biomass provided by satellite sensors just concerns the top layer of the water column. To estimate and use the entire vertical distribution of biomass, the model uses a data set of about 4000 chlorophyll profiles from different ocean areas, to develop the statistical relationships through which a concentration of remotely sensed phytomass is associated to a vertical distribution of pigments [Morel and Berthon, 1989]. Thus the model of pelagic primary production is based upon the following expression: ) P^z, t, mh = 12 $ Chl^z, t, mh $ PAR $ a ^z, t, m h $ } ^z, t, mh where P is the total organic Carbon fixed per unit of time and volume (gc m -3 s -1 ), 12 is a conversion factor from moles to mass units of Carbon, Chl is the concentration of chlorophyll a at a certain depth in mg Chl m -3, a* is the specific absorption coefficient of phytoplankton (m 2 mg Chl -1 ), Ψμ is the transformation efficiency of absorbed energy into organic carbon (net growth) and PAR is the photosynthetically available radiation at time t and depth z (μmol quanta m -2 s -1 ). After some assumptions and approximations as explicited by Antoine and Morel [1996], expression [3] can be transformed and the computation of daily primary production (P) obtained, through the following equation: n 92

7 Rivista Italiana di Telerilevamento , 42 (2): Italian Journal of Remote Sensing , 42 (2): L D 700 ### P = 12a * Un max Chl(z) $ PUR(z, t, m) $ f7x(z, t) A dxdzdm where L is the day length, D is the euphotic depth, a * is the specific absorption spectrum of phytoplankton, Φ μ max is the maximum quantum yield for phototrophic growth, PUR is the fraction of PAR potentially absorbed by phytoplankton, while f(x) is a function reproducing the P-E curve, when x is the ratio between PUR and Ek (KPUR) the photoadaptation irradiance, including a light inhibition effect and a temperature dependence. In a first approximation the physiological parameters of the model which should correspond to the local phytoplankton characters have been considered to be constant and average, like in Antoine and Morel [1996]. More precisely the phytoplankton specific absorption, the quantum yield for growth and the photoadaptation irradiance have the following values: (m 2 mg Chl -1 ) for a *, for Φ and 80 (μmol quanta max μ max m-2 s -1 ) for KPUR. The main remote sensing inputs used by the model (Fig. 3) are the concentration of chlorophyll-a [CHL SAM-LT], the sea surface temperature (SST) and the daily PAR. Starting from the surface concentration of chlorophyll-a [CHL SAM-LT] the model calculates the chlorophyll profile in the euphotic layer [Morel and Berthon, 1989]. To obtain the vertical profiles of temperature (0-200 m) the sea surface temperature (SST) provided by MODIS- Aqua is associated to a climatological temperature profile from MODB (Mediterranean Oceanic Data Base). The daily PAR derived from the Sea Surface Irradiance (SSI) product is used by the model to reconstruct a depth profile of PAR through the average attenuation coefficient of the euphotic layer [Morel and Berthon, 1989]. Figure 3 - Flux diagram of data processing by the integrated model of pelagic primary production. 93

8 Lazzara et al. Primary production annual cycle from satellite data in Ligurian and North Tyrrhenian seas The application of the model is done through the implementation of the program PPCALC written in Fortran77 [Antoine David, pers. comm.] specially modified for this study. The process of calculating primary production over the entire study area (Fig. 1) is carried out through the script Unix PPSAT specially designed and written for this type of use [Lazzara et al., 2008]. The script PPSAT starting from the remote sensed data of chlorophyll-a, sea surface temperature, daily PAR and using the bathymetric data, repeatedly uses (pixel by pixel) the PPCALC program for the calculation of pelagic primary production in the study area, down to 0.1% of surface irradiance. The bathymetric limit is used by PPSAT to improve the estimates of the PP integrated in the water column, especially necessary to avoid overestimation in neritic and coastal waters. The monthly averages of estimated PP and [CHL] were extracted from the six sub-regions shown in Figure 1 and statistically analyzed through a GIS software for characterizing relevant temporal patterns. Results The typical spatial distributions of primary production in the study area (Fig. 4) are exemplified using the estimates of April and October, which correspond to the spring algal bloom and oligotrophic conditions, respectively. In April, the entire Ligurian cyclonic area presents a widespread bloom (Fig. 4a) with high production levels (up to 0.9 gc m -2 d -1 ) whereas in October (Fig. 4b) the whole area shows a generally oligotrophic condition more evident in the northern area, with slightly higher and more uniform values (0.2 gc m -2 d -1 ) in the southern Tyrrhenian waters. The annual cycles of biomass, daily primary production and productivity for the whole region are presented in Figure 5. A main peak during spring (April) is evident both for production (up to 0.55 gc m -2 d -1 ) and for biomass (0.47 mgchl m -3 ) with a secondary peak in summer for production and only a summer peak for productivity P/B, which is related to the increased daily PAR irradiance. The variance in the spatial distribution of both phytomass and production, can be examined and compared in Table 1, through the relative standard deviations (RSD) of each monthly image. The oceanic areas (a1 and a2) show a lower variance both in [CHL] and PP fields, whereas the coastal ones (a5 and a6) are characterized by a sharp, at least 5-fold, increase of RSD. Also, a lower variance is present in the southern and Tyrrhenian parts of each area and especially for the coastal ones. Primary production, as expected, generally shows a reduced spatial variance with respect to chlorophyll distribution, except for periods of low [CHL] during summer and autumn in the oceanic and neritic areas. In contrast, the temporal variance of the seasonal cycle is always higher in the oceanic waters and lower in the coastal ones, with a more regular pattern which characterizes the CHL cycle. The annual cycles of the 6 sub-areas can be examined in Figure 6. The Ligurian and Tyrrhenian oceanic waters show a quite different seasonal pattern from the coastal waters, for both the primary production and the pelagic phytomass cycles. As far as it concerns production, the difference is regular and progressive from off-shore towards the coast (Fig. 6a): while the relevance of the spring bloom is dominant in the oceanic waters, it decreases with respect to the summer peak in the neritic and even more in the coastal ones. 94

9 Rivista Italiana di Telerilevamento , 42 (2): Italian Journal of Remote Sensing , 42 (2): Figure 4 - Maps of pelagic primary production (gc m - 2 d -1 ) in spring and autumn (April and October, have been choosen as more representative of mesotrophic and oligotrophic conditions, respectively). The coastal cycle is characterized by a continuous season of relatively high production from March to September. Looking at the phytomass standing stock (Fig. 6b), coastal waters are characterized by an anticipated spring bloom (March instead of April) mainly in the northern part of the study area, where also colder waters are present from rivers and land runoff. These features, which prosecute along the eastern Ligurian coast, can be noted clearly in SST and CHL images of February and March. 95

10 Lazzara et al. Primary production annual cycle from satellite data in Ligurian and North Tyrrhenian seas Figure 5 - Total area annual cycle in 2006, of pelagic primary production (PP) of phytomass (CHL) and of productivity (P/B). Figure 6 - (a) Annual cycle of pelagic primary production (PP in gc m -2 d -1 ) in the oceanic (a1-a2), neritic (a3-a4) and coastal (a5-a6) sub-regions of Figure 1; (b) Annual cycle of phytoplankton biomass (Chl in mg/m 3 ) in the same six sub-regional units. 96

11 Rivista Italiana di Telerilevamento , 42 (2): Italian Journal of Remote Sensing , 42 (2): Table 1 - Relative standard deviations (RSD) of CHL and PP values in the six subregional units (a1-a6), for each month of 2006, with the average value of the areas and temporal variance, throughout the year. [CHL] - Months a1 a2 a3 a4 a5 a6 1 9% 13% 27% 20% 173% 47% 2 30% 11% 37% 20% 140% 89% 3 24% 25% 132% 32% 120% 85% 4 22% 27% 36% 25% 153% 48% 5 35% 36% 17% 10% 159% 9% 6 4% 0% 1% 2% 100% 13% 7 2% 0% 17% 21% 74% 5% 8 10% 3% 8% 9% 51% 68% 9 9% 4% 2% 2% 161% 52% 10 9% 1% 13% 4% 104% 203% 11 44% 1% 29% 1% 139% 164% 12 93% 35% 87% 61% 146% 124% average spatial RSD 24.2% 13.0% 33.9% 17.2% 126.8% 75.7% N pixel temporal VAR temporal RSD 105% 106% 113% 100% 30% 82% PP - Months a1 a2 a3 a4 a5 a6 1 6% 12.7% 28% 31% 127% 76% 2 14% 14.1% 30% 32% 124% 61% 3 12% 13.7% 37% 33% 99% 71% 4 12% 19.3% 32% 33% 93% 51% 5 19% 21.6% 25% 30% 109% 47% 6 3% 11.8% 25% 31% 69% 48% 7 1% 12.5% 26% 32% 89% 50% 8 6% 11.8% 26% 33% 81% 72% 9 5% 14.1% 26% 31% 134% 49% 10 6% 11.3% 27% 31% 98% 124% 11 17% 12.0% 26% 32% 85% 97% 12 38% 23.9% 50% 44% 128% 101% averagespatial RSD 11.5% 14.9% 29.9% 32.7% 103.0% 70.7% N pixel temporal VAR temporal RSD 87% 28% 25% 12% 20% 36% 97

12 Lazzara et al. Primary production annual cycle from satellite data in Ligurian and North Tyrrhenian seas Discussion and conclusions The annual integration of daily primary production for the whole region gives a value of 90 ± 54 gc/m 2 (mean ± st. dev.), with a weighted contribution of the six sub-areas respectively of 51 and 24 % for the Ligurian and Tyrrhenian oceanic waters, of 10 and 8 % for the neritic and of 3.4 and 2.4% for the coastal waters, in the northern and in the southern parts, respectively. A comparison with published values for this area, indicates that these results are similar to the annual PP estimates obtained from in situ measurements and modelling in different but recent years: 104 gc m -2 in the Northern Tyrrhenian Sea, by Innamorati et al., [1995]; 106 gc m -2 in the Ligurian Sea by Levy et al. [1998] and gc m -2 from 14 C measurements in the Ligurian Sea by Marty and Chiaverini [2002]. The other annual PP estimates from satellite data, for the years 90, are rather higher: gc m -2 in the Western Mediterranean [Bricaud et al., 2002] and gc m -2 in the Ligurian Sea [Bosc et al., 2004]. Hence, the absolute value of this annual PP estimate, is consistently lower (about 50%) than the previous ones from satellite data. The main reason of this finding is due to the lower chlorophyll concentrations obtained by the sub-regional SAM-LT algorithm with respect to the other ocean colour algorithms. As previously noted (Fig. 2), these lower estimates are in closer agreement with the in-situ [CHL] measurements taken in the study area, and should therefore provide more accurate inputs to the applied PP model. It should be noted, however, that the SAM-LT sub-regional inversion algorithm has been specifically designed for optical complex waters in which the specific absorption coefficients of optical constituents show a prevalent variability in amplitude [Maselli et al., 2009]. The algorithm is therefore particularly efficient for the study of coastal waters (mainly Case 2). Since the current study area includes both oceanic (Case 1) and coastal (Case 2) waters, the accuracy of the algorithm in the former case should be assessed by more specific and exhaustive tests. In particular, these tests should verify if the SAM-LT algorithm requires functional refinement in order to improve its sensibility in oceanic and oligotrophic conditions. Also, a bottom effect must be considered as a lowering factor of annual PP with respect to the published values. Taking in account the real bottom of the neritic water columns, in fact, reduces the PP estimates of 4-6% for the total area, of % for the neritic waters and even of 240% if only the coastal waters are considered. About the shape of the PP annual cycle: a main spring peak and a secondary summer maximum are commonly observed for production in the Western Mediterranean [Innamorati et al., 1995; Bosc et al., 2004]. Moreover, through a comparison of satellite and in situ estimates of pelagic primary production, Morel and André [1991] could verify the great importance of the spring season for PP in the Western Mediterranean, together with significantly high productions during summer. In general, the feature of a high summer production is more evident in the Eastern Mediterranean or in the Adriatic Sea (Fig. 10 in Bosc et al., [2004]; Antoine et al. [1995]) where the spring biomass peak is nearly absent or less important. About the timing of the standing stock phytomass bloom, a pluriennal comparison of annual cycles from ocean colour in this region (http://reason.gsfc.nasa.gov/giovanni/) since 1998, shows that the typical spring bloom occurs in March or in February rather than in April; but it is noteworthy that it also occurred in April during 2005 and A winter-spring 98

13 Rivista Italiana di Telerilevamento , 42 (2): Italian Journal of Remote Sensing , 42 (2): phytomass bloom is typical of coastal waters in the Mediterranean Sea [Estrada et al., 1985], and is generally determined by diatoms (e.g. Skeletonema costatum) in February. Examples of such bloom are observed along the Southern Tuscany coast [Lenzi-Grillini and Lazzara, 1980] and the French or Spanish Mediterranean coast [Jacques, 1970]. The main findings of this study can be summarized as follows: i) the overwhelming importance of the oceanic bloom in the annual C budget is evident for the whole sub-region, since more than 75% of the annually fixed C is originated in the two oceanic areas; ii) there is a strong differentiation among coastal, neritic and oceanic seasonal cycles, both in timing (earlier) and spatial variance (higher) for the coastal waters; iii) the annual PP estimates are generally lower if compared to the previous ones obtained through ocean colour; iv) significant differences can be identified in the annual cycles of microalgal biomass and production between the northern and the southern coastal areas, which are mostly eutrophic and definitely oligotrophic, respectively. Overall, the analysis and elaboration of ocean colour remote sensing data appear to be a powerful tool in biological oceanography. A reason of strong interest for use of remote sensing data in oceanography is its potential in allowing the definition of ecological indicators of pelagic ecosystems [Platt and Sathyendranath, 2008] such as: total annual production, seasonal cycle of phytomass, spatial variance in biomass and production or distribution of phytoplankton functional types. In fact, a further improvement of pelagic primary production estimates will come from studying and introducing in the PP model ecophysiological parameters of the local phytoplankton functional types, which characterize the study area. Moreover, facilitating the partition in sub-regions can help both ecological understanding and management of the biological resources, by reaching the identification of provinces or sub-regions with ecological meaning [Longhurst, 2007]. Finally, the partition in sub-areas here proposed on the basis of a priori knowledge, has proven to be useful and up to now confirmed by this first analysis of remotely sensed oceanographic data in the Ligurian and North Tyrrhenian Sea. References Antoine D., Morel A., André J.M. (1995) - Algal pigment distribuition and primary production in the eastern Mediterranean as derived from costal zone color scanner observation. Journal of Geophsical Research, Vol. 100 (C8): Antoine D., Morel A. (1996) - Oceanic primary production. 1. Adaptation of a spectral light photosynthesis model in view of application to satellite chlorophyll observations. Global Biogeochemical Cycles, 10: Astraldi M., Bacciola D., Borghini M., Dell Amico F., Galli C., Gasparini G.P., Lazzoni E., Neri P.L., Raso G. (1993) - Caratteristiche stagionali delle masse d acqua nell Arcipelago Toscano. In: Ferretti O., Immordino F., Damiani V. (eds.), Arcipelago Toscano. Studio oceanografico, sedimentologico, geochimico e biologico. ENEA, serie studi Ambientali, Roma: Behrenfeld M.J., Falkowski P.G. (1997) - A consumer s guide to phytoplankton primary productivity models. Limnology and Oceanography, 42: Bosc E., Bricaud A., Antoine D. (2004) - Seasonal and interannual variability in algal biomass and primary production in the Mediterranean Sea, as derived from 4 years of SeaWiFS observations. Global Biogeochemical Cycles, Vol. 18, GB1005, doi: /2003GB

14 Lazzara et al. Primary production annual cycle from satellite data in Ligurian and North Tyrrhenian seas Bricaud A., Bosc E., Antoine D. (2002) - Algal biomass and sea surface temperature in the Mediterranean Basin Intercomparison of data from various satellite sensors, and implications for primary production estimates. Remote Sensing of Environment, 81: Brisson A., LeBorgne P., Marsouin A. (1999) - Development of algorithms for Surface Solar Irradiance retrieval at O&SI SAF low and Mid Latitude, February Météo- France/DP/Centre de Météorologie Spatiale, BP 147, Lannion, France. Campbell J., Antoine D., Armstrong R., Arrigo K., Balch W., Barber R., Behrenfeld M., Bidigare R., Bishop J., Carr M.-E., Esaias W., Falkowski P., Hoepffner N., Iverson R., Kiefer D., Lohrenz S., Marra J., Morel A., Ryan J., Vedernikov V., Waters K., Yentsch C., Yoder J. (2002) - Comparison of algorithms for estimating ocean primary production from surface chlorophyll, temperature and irradiance. Global Biogeochemical Cycles, 16 (3), 1035, /2001GB Cappella S., Nuccio C. e Lazzara L. (2008) Diversità delle comunità fitoplanctoniche nelle acque costiere toscane. In: Domenici V., Lenzi A. & Montesarchio E. (Eds.) Codice Armonico 2008 Secondo Congresso di scienze naturali Ambiente toscano, Edizioni ETS, Pisa, pp Chang G.C., Barnard A.H., McLean S., Egli P.J., Moore C., Zaneveld J.R.V., Dickey T.D., Hanson A. (2006) - In situ optical variability and relationships in the Santa Barbara Channel: implications for remote sensing. Applied Optics, 45 (15): D Ortenzio F., Marullo S., Ragni M., Ribera d Alcà M., Santoleri R. (2002) - Validation of empirical SeaWIFS algorithms for chlorophyll-a retrieval in the Mediterranean Sea. A case study for oligotrophic seas. Remote Sensing of Environment, 82: Estrada M., Vives F., Alcaraz M. (1985) - Life and Productivity of the Open Sea. In: Western Mediterranean R. Margalef (Ed.) Pergamon Press, Oxford, pp Field C.B., Beherenfeld M.J., Randersn J.T., Falkowski P. (1998) - Primary production of the biosphere: integrating terrestrial and oceanic components. Science, 281: Gordon H. R., Morel A. (1983) - Remote assessment of ocean color for interpretation of satellite visible imagery. A review. Springer-Verlag, New York. pp Innamorati M., Lazzara L., Nuccio C., Mori G., Massi L. (1995) - Nutrienti, biomassa e produzione fitoplanctonica nell Alto Tirreno Toscano. Atti della Società Toscana di Scienze Naturali, mem. Serie A, Suppl. CII: Innamorati M., Mori G., Nuccio C., Massi L., Melillo C., Mannucci M., Terreri B., De Pasquale A., Polonelli F. (2003) - Indagine sulle mucillaggini nel Mar Tirreno. In: Programma di monitoraggio e studio sui Processi di formazione delle Mucillagini nell Adriatico e nel Tirreno - MAT. Rapporto finale giugno 1999-maggio ICRAM, Roma. Iqbal M. (1983) - An Introduction to Solar Radiation, Academic Press, New York, 7, pp Jacques G. (1970) - Aspects quantitatifs du phytoplancton de Banyuls sur mer (Golfe de Lion). Biomasse et production. Vie et Milieu, 21: Joint I., Groom S.B. (2000) Estimation of phytoplankton production from space: current status and future potential of satellite remote sensing. Journal of Experimental Marine Biology and Ecology, 250: Lazzara L., Marchese C., Maselli F., Massi L., Nuccio C., Pieri M., Santini C., Sorani V. (2008) - Studio della produzione primaria delle acque marine toscane. Atti 12 a Conf. Naz. ASITA, L Aquila, Ottobre 2008, Vol. II: Lenzi Grillini C., Lazzara L. (1980) - Ciclo annuale del fitoplancton nelle acque costiere del 100

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16 Lazzara et al. Primary production annual cycle from satellite data in Ligurian and North Tyrrhenian seas sensing at local and regional scales. Science, 241: Platt T., Sathyendranath S. (2008) - Ecological indicators for the pelagic zone of the ocean from remote sensing. Remote Sensing of Environment, 112: Santini C., Pieri M., Santoro E., Massi L., Maselli F. (2004) - Analisi di dati MERIS e MODIS nello studio delle acque marino costiere della Regione Toscana. Atti della 8 a Conferenza Nazionale ASITA, Roma, Vol. II: Santini C., Maselli F., Massi L., Pieri M. (2007a) - Use of MODIS images to monitor water constituent concentrations in the Tuscany Sea. In SPIE Conference: Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2007, September 2007, Palazzo degli Affari Conference Centre, Florence, Italy, Proceedings of SPIE Vol Santini C., Maselli F., Massi L., Pieri M. (2007b) Applicazione di un modello ottico regionale per lo studio delle acque marine della Toscana. Atti 11 a Conferenza Nazionale ASITA, Torino, Vol. II: Santoleri R., Volpe G., Marullo S., Buongiorno Nardelli B. (2008) - Open Waters Optical Remote Sensing of the Mediterranean Sea, In Remote Sensing of the European Seas, Springer Netherlands, ( / ), Part 2: Sohn Y., Rebello N.S. (2002) - Supervised and Unsupervised Spectral Angle Classifiers. Photogrammetric Engineering and Remote Sensing, 68: Tanré D., Herman M., Deschaps P.Y., De Leffe A. (1979) - Atmospheric modelling for space measurements of ground reflectances, including bidirectional properties. Applied Optics, 18: Volpe G., Santoleri R., Vellucci V., Ribera d Alcalà M., Marullo S., D Ortenzio F. (2007) - The colour of the Mediterranean Sea: Global versus regional bio-optical algorithms evaluation and implication for satellite chlorophyll estimates. Remote Sensing of Environment, 107(4): Received 15/03/2010, accepted 14/05/

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