Inverting optical reflectance to estimate surface properties of vegetation canopies.
An inversion method using a simple bidirectional reflectance model and data on optical reflectances remotely-sensed from satellites has been improved to derive surface properties such as the leaf area index (LAI). Such properties are important in deriving the resistance of the vegetative canopy to uptake of gaseous trace chemicals from the atmosphere and in the study of radiation transfer processes. We found that a multi-pass retrieval technique can greatly improve a model's ability to retrieve surface properties. Because the sensitivity of the model inversion to initial values is an important issue that depends on (1) the partial derivative of reflectance with respect to each parameters to be retrieved ( R/x ) and (2) the degree of independence among model parameters, we investigated the issue with synthetic data constructed by a bidirectional reflectance model. The results revealed that, although the surface optical properties are mostly independent of each other, their initial values did have some effect on the retrieved value of the LAI, with the worse case caused by leaf angle distribution index, n , at close-to-nadir solar and view zenith angles of the reflectance data. At near nadir angles, n and LAI were strongly correlated, and their retrieval was not unique. When applied to satellite remote sensing data obtained with the advanced very-high-resolution radiometer (AVHRR), the model-retrieved seasonal variation of surface properties agreed reasonably well with independent ground measurements made in the First International Satellite Land-Surface Climatology Project (ISLSCP) Field Experiment (FIFE) campaign. Application to Landsat data to retrieve spatial variation was less successful, largely because of the close-to-nadir solar and view zenith angles in the data.
- Research Organization:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- DOE Contract Number:
- DE-AC02-06CH11357
- OSTI ID:
- 937919
- Report Number(s):
- ANL/ER/JA-23762; IJSEDK; TRN: US200905%%590
- Journal Information:
- Int. J. Remote Sens., Vol. 19, Issue 4 ; 1998; ISSN 0143-1161
- Country of Publication:
- United States
- Language:
- ENGLISH
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