Estimating PAR absorbed by vegetation from bidirectional reflectance measurements

Journal Article · Wed Mar 01 04:00:00 EST 1995 · Remote Sensing of Environment

DOI:https://doi.org/10.1016/0034-4257(94)00114-3· OSTI ID:85985

Roujean, J L ^[1]; Breon, F M ^[2]

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1. Meteo France/Centre National de Recherches Meteorologiques, Toulouse (France)
2. CEA/Lab. de Modelisation du Climat et de l`Environnement, Gif sur Yvette (France)

In the context of increasing carbon dioxide and global climate change, an improved knowledge of carbon fluxes to and from the terrestrial surface appears fundamental. Satellite remote sensing allows estimation, at a global scale, of the photosynthetically active radiation absorbed (APAR) by the vegetation. Current estimates are based on retrieving the fraction (f{sub APAR}) of PAR absorbed by the canopy from spectral vegetation indices (SVI) derived from combinations of spectral reflectance measurements. The authors show that currently used SVI are strongly affected by the soil reflectance as well as the sun/view geometry, which yields large uncertainties on the SVI-f{sub APAR} statistical relationships. However, the errors can be reduced using optimal geometries and new SVI less affected by the soil reflectance. Such an approach yields an improvement of the f{sub APAR} assessment down to less than 5% of relative standard error. It is relevant for monitoring of the interannual variations of the net primary productivity (NPP) and its analysis to understand the climate sensitivity of the present ecosystems. These will permit more detailed studies of seasonal and regional biospheric carbon sources and sinks that affect the global distribution of atmospheric carbon dioxide.

• OSTI ID: 85985
• Journal Information: Remote Sensing of Environment, Journal Name: Remote Sensing of Environment Journal Issue: 3 Vol. 51; ISSN RSEEA7; ISSN 0034-4257
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
CANOPIES
CARBON CYCLE
CARBON SINKS
CLIMATIC CHANGE
FORECASTING
PLANTS
REMOTE SENSING
TERRESTRIAL ECOSYSTEMS