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New Sights: Estimating of Foliage Spatial Distribution from Multi-angle Remote Sensing Data
Update time: [November 23, 2016]
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The spatial distribution of vegetation foliage can be described with a canopy clumping index (CI). If leaves are randomly distributed in space, the CI is unity. Most vegetation leaves show clumped spatial distribution, i.e., a CI less than unity. CI is an important factor to characterize the terrestrial ecosystem and model land-surface processes. The distribution and interception of solar radiation and precipitation as well as the distribution of foliar nutrients within canopies would be substantially different even with the same leaf area but different CI. Global gross primary productivity (GPP) and evapotranspiration (ET) would be substantially underestimated if CI is not properly quantified. 

Canopy clumping index can be estimated from multi-angle remote sensing data based on an empirical relationship with the normalized difference between hotspot and darkspot (NDHD) index. However, previous studies have not taken into consideration of the influence of bidirectional reflectance distribution function (BRDF) model and solar zenith angle (ZA), which will substantially influence the accuracy of CI products. Furthermore, CI products are mostly evaluated with a direct point to pixel comparison method in former work. However, this method may be limited because of the scale mismatch. A new validation method is needed for CI product validation. 

Ms. WEI Shanshan and Prof. FANG Hongliang, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), proposed a new method to tackle with the above issues. They found that the CI accuracy is affected by the different configurations of BRDF models and SZA values. For sparsely vegetated areas, they proposed to estimate the CI using oblique SZA. Considering the non-linearity of CI, they propose the method of upscaling the CI from gap fraction, which decrease the uncertainty of CI validation. Their study also shows that CI generally decreases with the increasing leaf area index (LAI).  

Their study provided theoretical basis for the CI estimation using multi-angle and high-resolution satellite data. With the improved method, they have started to generate global CI product, which can further improve the accuracy of GPP and ET estimation. 

This study was supported by National Natural Science Foundation of China. The related results have been published in a recent issue of Remote Sensing of Environment (Wei, S., & Fang, H. (2016). Estimation of canopy clumping index from MISR and MODIS sensors using the normalized difference hotspot and darkspot (NDHD) method: The influence of BRDF models and solar zenith angle. Remote Sensing of Environment, 187, 476-491).

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