**2.4 Correlation coefficient**

126 Remote Sensing of Biomass – Principles and Applications

measurement. Because of these errors, the first altimetry satellites such as Seasat or Geosat did not provide very usable and useful data. In 1993, the French Centre National d'Etudes Spatiales (CNES) and the US National Aeronautics and Space Administration (NASA) launched TOPEX/Poseidon satellite, which included a very precise positioning technique. Since then, new accurate altimetry missions were launched: ERS1/2 (in 1993), Geosat Follow-On (in 2000), Jason-1 (in 2002), TOPEX/Poseidon interleaved (in 2002), ENVISAT (in 2003) and Jason-2 (in 2008). The combination of these satellites enables high-precision

It is now generally accepted that at least three altimeter missions are required to resolve the ocean mesoscale variability (Le Traon & Dibarboure, 1999; Pascual et al., 2007). However, merging multi-satellite data requires consistent SLA data sets. Homogeneous and intercalibrated SLA fields in the Mediterranean Sea created by merging TOPEX/Poseidon, ERS1/2, Geosat Follow-On, Jason-1/2, TOPEX/Poseidon interleaved, and ENVISAT altimeter measurements, are obtained from AVISO (http://www.aviso.oceanobs.com/) for the period October 1997 to December 2009. The data set includes 7-day maps of SLA on a 0.125º x 0.125º regular grid interpolated in time and space using a global objective analysis (Le Traon et al., 1998). The length scale of the interpolation and the e-folding time scale were set to 100 km and 10 days (Pujol & Larnicol, 2005). The SLA data is re-binned in space onto a 0.25º x 0.25º to reduce small-scale variability and in time to the satellite CHL 8-day window

The first instrument that demonstrated the viability of satellite ocean color measurements was the US National Oceanic and Atmospheric Administration (NOAA) and the NASA CZCS Experiment aboard the Nimbus-7 satellite (Gordon et al., 1983). Although other instruments had sensed ocean color from space, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use, whereas every parameter in CZCS was optimized for use over water to the exclusion of any other type of sensing. The CZCS ocean color data, available from 1978 to 1986, allowed a considerable progress in the knowledge of spatial and temporal variations in surface CHL in various regions of the world ocean (Antoine et al., 1996; Behrenfeld & Falkowski, 1997; Platt & Sathyendranath,

The CZCS provided justification for future ocean color missions such as the Japanese National Space Development Agency (NASDA) Ocean Color and Temperature Scanner (OCTS) aboard the Advanced Earth Observing Satellite (ADEOS) from 1996 to 1997 (Kishino et al., 1997) or the NASA Sea Viewing Wide Field of View Sensor (SeaWiFS) aboard the Orbital Science Corporation (OSC) Orbview-II satellite from 1997 to 2010 (Hooker & McClain, 2000). Presently, the NASA Moderate Resolution Imaging Spectrometer (MODIS-A) aboard the NASA Aqua satellite (Esaias et al., 1998), and the European Space Agency (ESA) Medium Resolution Imaging Spectrometer (MERIS) aboard the ENVISAT satellite (Rast et al., 1999), both launched in 2002, provide a global monitoring of the ocean biomass. Other missions exist, with more limited coverage however, such as the Indian OCM

To maintain the level of uncertainty of the derived products within predefined requirements, SeaWiFS and MODIS-A ocean color observations are calibrated using longterm in-situ field data (Bailey and Werdell, 2006). The calibration includes an adjustment of

(see below) in order to be consistent with the temporal resolution of CHL data.

altimetry and improves their spatial and temporal resolution.

(Chauhan et al., 2002) or the Korean OSMI (Yong et al., 1999).

**2.2 Ocean color satellite data** 

1988).

The analysis of the relationships between any two satellite data sets involving large number of grid points and time series can be performed in different ways. Correlation is a simple method available when the spatial and time domains of data sets are equal. The Pearson's correlation coefficient between two time series *p*(*t*) and *q*(*t*) with means *p* and *q* and standard deviations *σ*p and *σq* is defined as

$$r\_{pq} = \frac{1}{(T-1)\sigma\_p \sigma\_q} \sum\_{k=1}^{T} (p\_k - \overline{p})(q\_k - \overline{q}) \tag{1}$$

where *T* is the total number of observations. We compute the Pearson's correlation coefficient for each grid point.
