Global Monthly Vegetation Cover

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2. Information content of AVHRR measurements over land

The AVHRR is flown onboard the NOAA polar-orbiting satellites, the first of which was launched in 1978. These satellites are nearly sun-synchronous flying at the altitude of about 850 km. With each pass, data are collected in a cross-track scanning mode along a swath about 2700 km wide, covering a range of viewing angles up to 56o. Each 24 h, global coverage consisting of 14.1 orbits is achieved, with one look during the daytime and one during the night. Under normal conditions, NOAA operates two polar orbiters, one in the morning and one in the afternoon. The second-generation GVI contains data only from daytime orbits of the afternoon satellites NOAA-9 (April 1985-November 1988) and NOAA-11 (November 1988-September 1994). These satellites cross the equator between 1400 and 1700 local solar time, with the equator crossing time drifting to a later hour as the satellite ages (Price 1991). Satellite orbit drift results in a systematic change of illumination conditions and local time of observation -- one of the main sources of nonuniformity in multiannual satellite time series.

The AVHRR has five channels in the visible, near-infrared and thermal infrared (IR) regions of spectrum: channels 1 (0.58-0.68 12), 2 (0.73-1.0 12), 3 (3.6-3.9 12), 4 (10.3-11.3 12), and 5 (11.5-12.5 12). All these channels have been chosen within the relatively transparent atmospheric windows to allow observations of the surface. The first two channels measure solar-reflected light, whereas the land-atmosphere Planck emittance dominates in the thermal IR channels 4 and 5. Channel 3 is the most complicated case since both emitted and reflected solar components are comparable in this waveband. That was one of the reasons, in addition to frequent noisiness, for excluding channel 3 from the GVI dataset, and, therefore, analysis of its information content is not given here. A brief review below discusses the information content of AVHRR measurements over land in cloud free conditions. In studying the land surface, clouds should be excluded from the data since they obscure the signal from the surface in AVHRR wavebands.

a. Solar channels (channels 1 and 2)

1) Surface Reflectance
The surface reflectance is characterized by the bidirectional reflectance distribution function (BRDF) -- reflectance as a function of the observation-illumination geometry (e.g. Pinty and Verstraete 1992; Roujean et al. 1992; Cihlar et al. 1994). BRDF depends on the type of soil/vegetation and surface topography. If the BRDF is known, one can integrate it hemispherically to estimate the hemispherical spectral surface albedo for a particular band, but the limited range of AVHRR viewing geometry complicates derivation of the hemispherical albedo. Semiempirical relationships (e.g. Laszlo et al. 1988) can be used to convert spectral albedos into an integral over the full spectral domain -- the broadband albedo, needed as an input to some numerical climate models.

2)Vegetation Index
Availability of the 0.73-1.0 12 band makes AVHRR most attractive in studying processes at the biosphere-atmosphere interface. In this channel, the surface reflectance sharply increases for green vegetated surfaces as compared to soil/senescent vegetation. In channel 1, the presence of chlorophyll in green vegetation reduces the observed radiances (see e.g. Curran 1980). Combining the AVHRR solar channels, Ch1(channel 1) and Ch2 (channel 2), allows one to enhance the contrast between green vegetation and soil/senescent vegetation. The most widely used combination is the NDVI, calculated as NDVI=(Ch2-Ch1)/(Ch2+Ch1). This particular combination was first proposed by Rouse et al. (1973) (not specifically for AVHRR) and has proved to be very useful because it partially compensates for changing illumination conditions, surface slope and viewing aspect -- factors that strongly affect observed radiances. The NDVI as a preferred index for monitoring vegetation was reenforced by an analysis of different spectral indices (Tucker 1979). Since the early 1980s, top-of-the-atmosphere NDVI derived from AVHRR has been extensively utilized for mapping vegetation on the continental and global scales (e.g. Tarpley et al. 1984; Tucker et al. 1985).

There is, however, an ambiguity in what NDVI measures, which stems from the unresolved combination of the amount and state of the vegetation in the radiometer field of view (Curran 1980). Usually, these two quantities are correlated since development of vegetation is associated with the increase of both chlorophyll amount and area coverage by the vegetation. The NDVI signal, however, saturates before other measures of vegetation amount, such as leaf area index (LAI) (e.g. Carlson et al. 1990). In any event, because of strong seasonal and spatial signals, this "simple" index of vegetation has been used extensively by the research community for more than a decade. In addition to analyzing vegetation distribution, monitoring its seasonal and interannual variability, and relating it to ecological variables (e.g. Malingreau 1986; Cihlar et al. 1991), it has also been suggested that NDVI be used in numerical models (see review by Gutman (1990)) for estimating the ratio of actual to potential evapotranspiration (Mintz and Walker 1990), the ratio of soil heat flux to net radiation (Kustas et al. 1994), canopy resistance and photosynthesis (Sellers 1985), and green vegetation fraction and LAI (Carlson et al. 1990; Price 1990), [i.e. geophysical parameters identified by GEWEX (Global Energy and Water Cycle Experiment) as important for studying the land surface energy and water budget].

3) Atmospheric effect.
The reflectances in AVHRR channels 1 and 2, and , are affected by scattering/absorption processes in the atmosphere. The scattering is by molecules and aerosols (that may also absorb). Gaseous absorption is due to ozone in channel 1 and to water vapor in channel 2. Atmospheric effects are coupled in a complicated manner with surface BRDF. Thus, derivation of the surface albedo and surface vegetation index from satellite measurements require atmospheric corrections, discussed in detail by Tanré et al. (1992) and Arino et al. (1992).

b. Thermal IR (channels 4 and 5)

The brightness temperatures in AVHRR channels 4 and 5, T4 and T5 (K), depend mainly upon the surface temperature, total-column atmospheric water vapor and surface-atmosphere temperature gradient, so that the former two parameters can be estimated using split-window techniques (see e.g. Dalu 1986; Kerr et al. 1992; Prata 1993).

1) Land Surface Temperature
A useful piece of information for surface characterization is land surface temperature (LST). However, there is some ambiguity in its definition because of the complex character of the land surface -- its heterogeneity, roughness, and multilevel vegetation (e.g. Li and Becker 1993). The split-window technique for LST retrieval usually is based on a linear combination of T4 and T5. The accuracy of the estimated LST is restricted mainly by the effect of unknown and spectrally variable emissivity, which is a function of soil/vegetation type, topography, and observation geometry (Prata 1993).

2) Precipitable Water Index
Dalu's (1986) theoretical considerations show that under certain assumptions total-column atmospheric water vapor amount (total precipitable water) can be derived over the ocean in clear conditions using the difference between the two AVHRR brightness temperatures. A precipitable water index (PWI) can thus be introduced as PWI=T4-T5. Recent investigations analyze the potential and limitations of using PWI to estimate water vapor amount over land (Justice et al. 1991; Eck and Holben 1994). Variable aerosols, surface emissivity and surface-atmosphere temperature gradients, are the major factors affecting the relationship between PWI and the actual water vapor amount.

c. Combining solar and thermal IR channels
A combination of NDVI and LST was proposed as a method for assessing the surface moisture status and fractional vegetation cover over nonuniform land surface (Carlson et al. 1990; Price 1990; Nemani et al. 1993). The basic physical assumption is that the more heavily vegetated surfaces are associated with greater evapotranspiration and hence should be cooler than the less vegetated ones. Another reason that soil temperatures are higher than canopy foliage temperatures is the greater efficiency of the leaves at shedding absorbed energy (Choudhury 1989). Friedl and Davis (1994) indicate that in "well-watered" conditions the proportion of the soil background in the radiometer field of view, rather than evapotranspiration, explains the observed NDVI-LST negative correlation.

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