Assessment of Multiple Scattering in the Reflectance of Semiarid Shrublands

Multiple scattering within a mixed pixel results in a nonlinear effect on the measured spectra in remotely sensed imagery. This study provides a quantitative assessment of multiple scattering in the reflectance of semiarid shrublands and explores its relationship to the characteristics of shrubs (density and height) and imaging parameters (wavelength and viewing angles). Field measurements were conducted at the southern fringe of the Otindag Sandy Land in China. A Monte Carlo ray tracing model, the Forest LIGHT interaction model (FLIGHT), was applied to simulate the multiple scattering results. FLIGHT simulation results were first evaluated against field measurements and then compared with a Landsat-8 OLI image. Results show that: 1) the contribution of multiple scattering to the spectra of a scene increases linearly with the fractional cover of vegetation and crown height; 2) in general, multiple scattering has a stronger effect on the near-infrared (NIR) domain than on the visible bands; 3) shadows significantly strengthen the multiple scattering effect, specifically within the visible bands; and 4) 80 to 100% of the total multiple scattering is caused by the second-order scattering within the visible bands and 60% to 90% within the NIR band. This study helps to improve our understanding of the multiple scattering effect and to select between linear and nonlinear spectral unmixing models to solve the abundances of shrubs and soil in mixed pixels.

[1]  C. Small.  High spatial resolution spectral mixture analysis of urban reflectance , 2003 .

[2]  G. Gutman,et al.  The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models , 1998 .

[3]  N. A. Quarmby,et al.  Linear mixture modelling applied to AVHRR data for crop area estimation , 1992 .

[4]  S. Gerstl,et al.  Nonlinear spectral mixing models for vegetative and soil surfaces , 1994 .

[5]  Hongjie Xie,et al.  Canopy blockage and scattering effects on apparent soil spectral reflectance and its consequence in spectral mixture analysis of vegetated surfaces , 2008 .

[6]  Jin Chen,et al.  Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction , 2006 .

[7]  M. Schaepman,et al.  Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data , 2008 .

[8]  M. Sunshine,et al.  Spectral Analysis for Earth Science : Investigations Using Remote Sensing Data , 2013 .

[9]  Jerzy Cierniewski,et al.  Influence of soil surface roughness on soil bidirectional reflectance , 1997 .

[10]  F. J. García-Haro,et al.  A Mixture Modeling Approach to Estimate Vegetation Parameters for Heterogeneous Canopies in Remote Sensing , 2000 .

[11]  M. Schaepman,et al.  Effects of woody elements on simulated canopy reflectance: Implications for forest chlorophyll content retrieval , 2010 .

[12]  Laurent Tits,et al.  Quantifying Nonlinear Spectral Mixing in Vegetated Areas: Computer Simulation Model Validation and First Results , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  S. Delalieux,et al.  An automated waveband selection technique for optimized hyperspectral mixture analysis , 2010 .

[14]  Yu-Jun Ma,et al.  Shrub encroachment with increasing anthropogenic disturbance in the semiarid Inner Mongolian grasslands of China , 2013 .

[15]  J. Hogg.  Quantitative remote sensing of land surfaces , 2004 .

[16]  John R. Miller,et al.  Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated‐forest hyperspectral data , 2009 .

[17]  Alfredo Huete,et al.  Separation of soil-plant spectral mixture by factor analysis , 1986 .

[18]  Craig S. T. Daughtry,et al.  A new technique to measure the spectral properties of conifer needles , 1989 .

[19]  D. Lobell,et al.  A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation , 2000 .

[20]  John F. Mustard,et al.  Spectral unmixing , 2002, IEEE Signal Process. Mag..

[21]  J. Poesen,et al.  The European Soil Erosion Model (EUROSEM): A dynamic approach for predicting sediment transport from fields and small catchments. , 1998 .

[22]  Kenji Omasa,et al.  Estimation of vegetation parameter for modeling soil erosion using linear Spectral Mixture Analysis of Landsat ETM data , 2007 .

[23]  Kohei Arai,et al.  Forest parameter estimation by means of Monte-Carlo simulations with experimental considerations , 2009 .

[24]  Ryutaro Tateishi,et al.  Remote Sensing of Fractional Green Vegetation Cover Using Spatially-Interpolated Endmembers , 2012, Remote. Sens..

[25]  Peter R. J. North,et al.  Three-dimensional forest light interaction model using a Monte Carlo method , 1996, IEEE Trans. Geosci. Remote. Sens..

[26]  P. North,et al.  Remote sensing of canopy light use efficiency using the photochemical reflectance index , 2001 .

[27]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .

[28]  John B. Adams,et al.  Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon , 1995 .

[29]  Peter R. J. North,et al.  Radiative transfer modeling of direct and diffuse sunlight in a Siberian pine forest , 2005 .

[30]  Jin Chen,et al.  A Quantitative Analysis of Virtual Endmembers' Increased Impact on the Collinearity Effect in Spectral Unmixing , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Alan T. Murray,et al.  Estimating impervious surface distribution by spectral mixture analysis , 2003 .

[32]  Xuexia Chen,et al.  Spectral mixture analyses of hyperspectral data acquired using a tethered balloon , 2006 .

[33]  W. Verstraeten,et al.  Nonlinear Hyperspectral Mixture Analysis for tree cover estimates in orchards , 2009 .

[34]  T. W. Ray,et al.  Nonlinear Spectral Mixing in Desert Vegetation , 1996 .

[35]  Gregory P. Asner,et al.  Estimating nonlinear mixing effects for arid vegetation scenes with MISR channels and observation directions , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[36]  B. Martínez,et al.  Accuracy assessment of fraction of vegetation cover and leaf area index estimates from pragmatic methods in a cropland area , 2009 .

[37]  M. Raupach,et al.  Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series , 2003 .

[38]  H. Poilvé,et al.  Hyperspectral Imaging and Stress Mapping in Agriculture , 1998 .

[39]  D. Roberts,et al.  Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data , 1993 .

[40]  Mathias Disney,et al.  Monte Carlo ray tracing in optical canopy reflectance modelling , 2000 .