Production Of The Seawifsvi

To support various application projects dealing with the monitoring of land surfaces of SAI, a fast processing system was developed to generate an ensemble of relevant information on the basis of the SeaWiFS data at about 1.5 km resolution.

The system includes a set of algorithms to 1) classify each SeaWiFS pixel on the basis of multispectral BRF measurements into broad categories of geophysical targets such as clouds and bright objects, vegetated surfaces and water bodies and 2) compute the rectified red and near-infrared bands as well as SeaWiFS-VI for those pixels corresponding to vegetated surfaces. A detailed description of the algorithms and of its technical implementation can be found in Gobron et al. (2000c) for terrestrial surfaces and Bulgarelli and Melin (2000) regarding the retrieval of the optical properties of water pixels. As can be seen from Table 5, the pixel classification is performed on the basis of an ensemble of thresholds using only the values in the bands centered at 443, 670, and 865 nm. These tests were established on the basis of a priori knowledge on the multispectral signatures of each geophysical system. The proposed approach efficiently assigns the vast majority of pixels to these classes without requiring any other ancillary information. A more sophisticated scheme was not deemed necessary or justified given the scientific objectives and computer processing constraints. However, a further screening of undesirable geophysical conditions is imposed such that the values of rectified bands must be within predefined intervals. In practice, for every available individual terrestrial SeaWiFS observation (pixel, date), the algorithm yields either a simple label or, in the case of vegetated surfaces, a string of values including all TOA BRFs, the geometry of illumination and observation, the two rectified bands and the SeaWiFS-VI.

Table 5. Pixel classification criteria

Flag value

Spectra! tests

Categories

0

0 < jo(443) < 0,3 and 0 < ¿<670) < 0.5 and 0 < p(865) < 0.7 and 0 < p(443) < p(865) and ¿>(865) ä 1.25 p(670)

Vegetated surface

1

p(443) < 0.0 or ¿*670) < 0.0 or p(865) < 0.0

"Bad pixel"

2

p(443) > 0.3 or ¿<670) > 0.5 or ¿>(865) > 0.7

Cloud

3

0 <p(443) < 0.3 and 0 < ¿5(670) < 0.5 andO<p(865)< 0,7 and p(443) > p(865)

Water body

4

0 < ¿<443) < 0.3 and 0 < ¿<670) < 0.5 and 0 < p(865) < 0.7 and 0 < ¿>(443) < ¿>(865) and 1.25p(670) > p(865)

Bright surface

5*

¿<R670) < 0 or p(R865) < 0

Undefined

6*

VI<0

No vegetation

7*

VI>1

Vegetation

* Generated by the SeaWiFS-VI algorithm internally

* Generated by the SeaWiFS-VI algorithm internally

For a number of surface applications, it is desirable to ensure a good geographical coverage, which implies the temporal compositing of product time series to fill out the gaps in the daily products created by clouds. Such a procedure is justified to the extent that surface changes occur on a time scale much longer than the one adopted for the compositing. The latter is often performed on the basis of maximum NDVI, over the specified time period, but this procedure has been shown to introduce biases in the resulting data sets due to the preferential selection of measurements collected under specific angular conditions (Holben, 1986 and Meyer et al., 1995). We propose a different scheme that allows the selection of the most representative conditions during the compositing period on the basis of a simple statistical analysis. This analysis, based on the inspection of the daily SeaWiFS-VI values retrieved during each period of ten consecutive days, or monthly period, is implemented as follows. The temporal average and corresponding deviation of the SeaWiFS-VI values over the ten-day (monthly) periods are first estimated:

where Tis the number of available clear sky values during the compositing period (10-day or monthly). VI is the temporal average index value and AtVi is the average deviation of the distribution. The value selected as the most representative for the given ten day (monthly) period is the actual VI value which minimizes the quantity This procedure thus generates maps of geophysical products for every ten-day period, and monthly period, where each individual value represents the actual measurement or product for the day considered the most representative of that period. The geometry of illumination and observation for the particular day selected is saved as part of the final product, which is thus fully documented and traceable.

The various panels of Figures 8 and 9 provide an example of monthly composite products derived from SeaWiFS measurements for the month of May, 1998, over Western Europe.

Panel a (b) of Figure 8 illustrates the geographical distribution of solar (observation) zenith angles that result from this composition process for the indicated period. In this particular example, the solar zenith angle varies approximately between 12° and 50° from the southern to the northern part of the region considered, while the observation zenith angle varies between 20° and 43°, depending on the outcome of the selection procedure for identifying the most representative day in the entire monthly time series. Frames (c) and (d) of the same figure show the results of the rectification process for the red and near-infrared channels, respectively. Finally, Figure 9 exhibits the composited SeaWiFS-VI itself (left panel) and the associated average deviation of the distribution (right panel), respectively. A detailed inspection of the SeaWiFS-VI map does not reveal any particular bias despite abrupt changes in the satellite observation geometry, nor does it show artifacts that could have been induced by the compositing technique. The average deviation throughout this composite remains less than 0.05, indicating that the processing algorithm leads to rather stationary index values during this monthly period.

Figure 8. Illustration of the retrievals from the SeaWiFS-VI land surface algorithm obtained during the monthly period between May 01 and 31,1998. Panels (a) and (b) show the Sun and satellite view zenith angles, respectively. The values of the rectified red and near-infrared bands are mapped in panels (c) and (d), respectively

Figure 8. Illustration of the retrievals from the SeaWiFS-VI land surface algorithm obtained during the monthly period between May 01 and 31,1998. Panels (a) and (b) show the Sun and satellite view zenith angles, respectively. The values of the rectified red and near-infrared bands are mapped in panels (c) and (d), respectively

Figure 9. Same as Figure 8 except for the SeaWiFS-VI/Fapar (panel a) and the associated average deviation calculated over the monthly composite period (panel b)

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