AI Article Synopsis
- Canopy leaf area index (LAI) is crucial for understanding how vegetation absorbs pollutants in ecosystems, and this study introduces a new, efficient algorithm for retrieving LAI using satellite data.
- The algorithm utilizes Simple Ratios of near-infrared to red reflectance and is based on a physics-based model to accurately simulate how light interacts with vegetation.
- Applied to 1-km resolution satellite images, the method provides reliable monthly LAI values for the contiguous USA and shows promise for assessing regional pollutant deposition and gas exchange based on comparisons to ground measurements.
Article Abstract
Canopy leaf area index (LAI) is an important structural parameter of the vegetation controlling pollutant uptake by terrestrial ecosystems. This paper presents a computationally efficient algorithm for retrieval of vegetation LAI and canopy clumping factor from satellite data using observed Simple Ratios (SR) of near-infrared to red reflectance. The method employs numerical inversion of a physics-based analytical canopy radiative transfer model that simulates the bi-directional reflectance distribution function (BRDF). The algorithm is independent of ecosystem type. The method is applied to 1-km resolution AVHRR satellite images to retrieve a geo-referenced data set of monthly LAI values for the conterminous USA. Satellite-based LAI estimates are compared against independent ground LAI measurements over a range of ecosystem types. Verification results suggest that the new algorithm represents a viable approach to LAI retrieval at continental scale, and can facilitate spatially explicit studies of regional pollutant deposition and trace gas exchange.
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| http://dx.doi.org/10.1016/j.envpol.2005.08.059 | DOI Listing |
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