Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption

The utility of multi-angle optical remote sensing for terrestrial carbon cycle estimation is demonstrated through theoretical development, POLDER data analysis, and a case study of carbon cycle in a boreal forest. Progress in canopy-level photosynthesis modeling suggests that simpler big-leaf photosynthesis models are giving ways to more complex sunlit/shaded leaf separation models. This advancement in ecological modeling has increased the demand for advanced description of canopy architecture. Such demand may be mostly met through the use of multi-angle remote sensing techniques. In addition to leaf area index (LAI), another canopy parameter, the foliage clumping index, can be derived from multi-angle remote sensing. These two parameters are the basis for separating sunlit and shaded leaves. As leaf photosynthesis is nonlinearly related to incident radiation, such separation avoids the problems of big-leaf models that only make use of the total radiation absorption by the canopy without considering the distribution of radiation among leaves. A practical conclusion is that the traditional way of mapping the net primary productivity (NPP) through its correlation with the remotely sensed fraction of photosynthetically active radiation (FPAR) absorbed by plant canopies is only a very crude approximation and could be replaced with mapping LAI and clumping index and modeling NPP with advanced photosynthesis models. This is a step forward in remote sensing applications because single-angle remote sensing can only acquire information on the effective LAI related to the canopy gap fraction in the viewing direction and the amount of shaded leaf area is unknown.