Canopy blockage and scattering effects on apparent soil spectral reflectance and its consequence in spectral mixture analysis of vegetated surfaces

Abstract

Remote sensing technique has become the most efficient and common approach to estimate surface vegetation cover. Among various remote sensing algorithms, spectral mixture analysis (SMA) is the most common approach to obtain sub‐pixel surface coverage. In the SMA, spectral endmembers (the number of endmembers may vary), with invariant spectral reflectance across the whole image, are needed to conduct the mixture procedure. Although the nonlinear effect in quantifying vegetation spectral reflectance was noticed and sometimes addressed in the SMA analysis, the nonlinear effect in soil spectral reflectance is seldom discussed in the literature. In this paper, we investigate the effects of vegetation canopy on the inter‐canopy soil spectral reflectance via mathematical modelling and field measurements. We identify two mechanisms that lead to the difference between remotely sensed apparent soil spectral reflectance and actual soil spectral reflectance. One is a canopy blockage effect, leading to a reduced apparent soil spectral reflectance. The other is a canopy scattering effect, leading to an increased apparent soil spectral reflectance. Without correction, the first (second) mechanism causes an overestimated (underestimated) areal coverage of the low‐spectral‐reflectance endmember. The overall effect of canopy to soil, however, tends to overestimate fractional vegetation cover due to the relative significance of the canopy blockage effect, even though the two mechanisms vary with spectral wavelengths and spectral difference between different vegetation and soil. For the SMA of vegetated surface using multiple‐spectral remote sensing imagery (e.g., LandSat), it is recommended that infrared bands of low vegetation spectral reflectance (e.g. band 7) be first considered; if both visible and infrared bands are used, combination of bands 3, 4, and 5 is appropriate, while use of all six bands could overestimate fraction vegetation cover.

Acknowledgements

Valuable discussion with Xiaobing Zhou from Montana Tech is appreciated. Blake Weissling helped with some field works. This project was partially supported by a TexasView Remote Sensing Consortium (a member of USGS/AmericanView Consortium) grant and a US Department of Education grant (P120A050061). The authors would like to thank two anonymous reviewers for their helpful comments that improved the paper.