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United States Department of Agriculture

Agricultural Research Service

Title: A constrained inverse and forward canopy reflectance modeling system for mapping key biophysical properties using remotely sensed reflectance observations

item Houborg, Rasmus
item Anderson, Martha
item Daughtry, Craig

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 10/2/2007
Publication Date: 12/10/2007
Citation: Houborg, R., Anderson, M.C., Daughtry, C. 2007. A contrained inverse and forward canopy reflectance modeling system for mapping key biophysical properties using remotely sensed reflectance observations [abstract]. American Geophysical Union. 88(52):B21A-0030.

Interpretive Summary:

Technical Abstract: Accurate quantitative estimates of leaf chlorophyll content (Cab), which can assist in determining vegetation stress and photosynthetic productivity, and leaf area index (LAI) are important for optimizing estimates of regional scale energy and carbon exchange. Here refinements to the biophysical parameter retrieval system presented in Houborg & Boegh (2007, doi:10.1016/j.rse.2007.04.012) are discussed and the performance of the modified model is demonstrated using high-resolution aircraft and SPOT satellite data. Since a unique relationship between a single biophysical canopy variable and a spectral signature does not exist, a canopy reflectance model (CRM) was employed in inverse and forward mode to build multiple crop and site dependent formulations relating LAI and Cab to various spectral reflectance signatures. Leaf inclination angle, Markov clumping characteristics and leaf mesophyll structure were assumed spatially and temporally invariant within the field boundaries of each agricultural land cover class and estimated by iteratively inverting the CRM using multiple intra-field green ('green), red and near-infrared ('nir) reflectance observations. Only pixels originating from medium to high density vegetation areas were included to maximize the sensitivity of the reflectance signal to the crop specific canopy parameters. New techniques were implemented to constrain the parameter space to relatively few plausible values reducing the number of the computationally demanding inversions, and to simultaneously consider the fraction of dead leaves in the canopy. The inversion was further constrained by separating the retrieval of the soil background reflectance signal from the retrieval of the canopy parameters. Finally, a family of model generated spectral reflectance relationships, each a function of soil and canopy characteristics, was employed for a fast pixel-wise mapping of LAI and Cab. The application of LAI–NDVI, LAI– 'nir, and Cab– 'green relationships provided reliable quantitative estimates of LAI and Cab for green as well as partially senescent agricultural fields in Maryland, U.S.A. characterized by contrasting architectures and leaf biochemical constituents. The model was able to detect decreases in leaf chlorophyll content caused by stressed environmental conditions. The biophysical parameter retrieval system is completely image-based, does not require a priori ground based information, is fast enough for regional-scale applications, and can quite easily be implemented for other regions.

Last Modified: 06/23/2017
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