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Earth Observations from Space: The First 50 Years of Scientific Achievements
Ecosystems as Seen from Space
Satellite-based studies of the land and ocean ecosystems rely primarily on imaging sensors measuring radiance in the visible and near infrared. These spectral bands were ideally suited to monitor plant biomass and primary production because the chlorophyll a pigment, found in all marine and terrestrial photosynthetic plants, reflects green light while absorbing in the blue and red spectral regions. Because plant leaves contain no molecules with high absorption in the near infrared, they are highly reflective in this region. Therefore, the “greenness” of terrestrial ecosystems can be mapped by employing the ratio of red to infrared bands. However, this ratio does not work for the ocean because water is such a strong absorber in the red and infrared that little or no radiation is reflected out of the ocean at those wavelengths. Instead, the ratio of blue to green bands, after correcting for the atmosphere, has been used to quantify the chlorophyll concentration in the ocean (Box 9.3).
Remote sensing techniques for mapping and studying terrestrial and marine ecosystems have evolved along different paths because of different technological requirements. Compared to the ocean, the land is a bright surface whose features have distinct spectral signatures and generally sharp boundaries. The spatial scale of such features is on the order of tens of meters, thus requiring high spatial resolution, but the features generally change slowly over seasons or longer. In contrast, the ocean is a dark surface with subtle spectral variation that requires high radiometric sensitivity. Reflectance from the atmosphere dominates the signal received by a satellite over the ocean, and this signal must be estimated and removed before the ocean signal can be analyzed. Features in the ocean have spatial scales on the order of tens of kilometers, with fluid boundaries that change on timescales of hours to days. These differences have led to different sensor and mission requirements, but the goals remain similar. Both terrestrial and marine studies have sought to quantify primary productivity and the role of the biosphere in the global carbon cycle.
Converting Radiance to Plant Productivity
Jordan (1969) was the first to use a ratio of near-infrared and red radiation to estimate biomass and leaf area index (leaf area/ground surface area) in a forest understory. This study was quickly followed by application of near-infrared/ red ratios to estimate biomass in rangelands (e.g., Pearson and Miller 1972; Rouse et al. 1973, 1974; Maxwell 1976) and was extended by Carneggie et al. (1974) to the Earth Resources Technology Satellite (ERTS-1) observations of seasonal growth, which showed that the seasonal peak in the near-infrared/red ratio coincided with maximum foliage production, thus effectively tracking the phenological cycle.
Rouse et al. (1974) introduced a spectral index, a normalized ratio that reduced illumination differences and other extrinsic effects by dividing the difference of the two bands by their sum, a ratio adopted as the normalized difference vegetation index (NDVI). A landmark paper by Tucker (1979) established linear relationships between vegetation spectral indices (ratios of visible and near-infrared bands) to leaf area and biomass. Following this paper, vegetation indices rapidly became an established method for analysis of plant biophysical properties using laboratory, field, airborne, and Landsat data. Today, nearly 2,000 papers have been published using the NDVI, and nearly 6,000 have used some type of vegetation index to study vegetation. These early studies established that red and near-infrared satellite bands could track changes in plant growth and development.
the plant canopy used to estimate functional process rates of energy and mass exchange, specifically to calculate rates of photosynthesis, evapotranspiration, and respiration (Figure 9.1). For the first time this measurement provides a consistent observational basis to estimate and monitor global productivity. Time series of LAI allow comparison of phenological patterns among six global terrestrial biome types. LAI is defined as the one-sided leaf area per unit of ground area and is produced by R.B. Myneni, Boston University. An algorithm is used to convert red and near-infrared band reflectances to global maps of LAI with modifications for the six biome types, taking into account the directional Sun and