Modeling Top of Atmosphere Radiance over Heterogeneous Non-Lambertian Rugged Terrain

Topography affects the fraction of direct and diffuse radiation received on a pixel and changes the sun–target–sensor geometry, resulting in variations in the observed radiance. Retrieval of surface–atmosphere properties from top of atmosphere radiance may need to account for topographic effects. This study investigates how such effects can be taken into account for top of atmosphere radiance modeling. In this paper, a system for top of atmosphere radiance modeling over heterogeneous non-Lambertian rugged terrain through radiative transfer modeling is presented. The paper proposes an extension of “the four-stream radiative transfer theory” (Verhoef and Bach 2003, 2007 and 2012) mainly aimed at representing topography-induced contributions to the top of atmosphere radiance modeling. A detailed account for BRDF effects, adjacency effects and topography effects on the radiance modeling is given, in which sky-view factor and non-Lambertian reflected radiance from adjacent slopes are modeled precisely. The paper also provides a new formulation to derive the atmospheric coefficients from MODTRAN with only two model runs, to make it more computationally efficient and also avoiding the use of zero surface albedo as used in the four-stream radiative transfer theory. The modeling begins with four surface reflectance factors calculated by the Soil–Leaf–Canopy radiative transfer model SLC at the top of canopy and propagates them through the effects of the atmosphere, which is explained by six atmospheric coefficients, derived from MODTRAN radiative transfer code. The top of the atmosphere radiance is then convolved with the sensor characteristics to generate sensor-like radiance. Using a composite dataset, it has been shown that neglecting sky view factor and/or terrain reflected radiance can cause uncertainty in the forward TOA radiance modeling up to 5 (mW/m2·sr·nm). It has also been shown that this level of uncertainty can be translated into an over/underestimation of more than 0.5 in LAI (or 0.07 in fCover) in variable retrieval.

[1]  Klemen Zakšek,et al.  On the proper analytical expression for the sky-view factor and the diffuse irradiation of a slope for an isotropic sky , 2012 .

[2]  S. Adler-Golden,et al.  Atmospheric Correction for Short-wave Spectral Imagery Based on MODTRAN 4 , 2000 .

[3]  W. Verhoef,et al.  Multi-temporal, multi-sensor retrieval of terrestrial vegetation properties from spectral–directional radiometric data , 2015 .

[4]  Frédéric Baret,et al.  Retrieval of vegetation properties from combined hyperspectral/multiangular optical measurements: results from the DAISEX campaigns , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[5]  Benjamin Y. H. Liu,et al.  The interrelationship and characteristic distribution of direct, diffuse and total solar radiation , 1960 .

[6]  Rudolf Richter,et al.  Correction of atmospheric and topographic effects for high-spatial-resolution satellite imagery , 1997, Defense, Security, and Sensing.

[7]  W. Verhoef,et al.  Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models , 2003 .

[8]  W. Verhoef,et al.  A Bayesian object based approach for estimating vegetation biophysical and biochemical variables from APEX at sensor radiance data , 2013 .

[9]  Luis Guanter,et al.  Simulation of Optical Remote-Sensing Scenes With Application to the EnMAP Hyperspectral Mission , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Maria del Carmen,et al.  Optical and radar remote sensing applied to agricultural areas in europe , 2008 .

[11]  D. Xie,et al.  LAI inversion algorithm based on directional reflectance kernels. , 2007, Journal of environmental management.

[12]  T. Faurtyot.  Vegetation water and dry matter contents estimated from top-of-the-atmosphere reflectance data: A simulation study , 1997 .

[13]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[14]  W. Verhoef,et al.  Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data , 2007 .

[15]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[16]  W. Verhoef,et al.  Estimating forest variables from top-of-atmosphere radiance satellite measurements using coupled radiative transfer models , 2011 .

[17]  P. Deschamps,et al.  Evaluation of topographic effects in remotely sensed data , 1989 .

[18]  R. Richter,et al.  Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction , 2002 .

[19]  Klaus I. Itten,et al.  A physically-based model to correct atmospheric and illumination effects in optical satellite data of rugged terrain , 1997, IEEE Trans. Geosci. Remote. Sens..

[20]  Wout Verhoef,et al.  Simulation of Sentinel-3 images by four-stream surface-atmosphere radiative transfer modeling in the optical and thermal domains , 2012 .

[21]  J. Dozier,et al.  Rapid Calculation Of Terrain Parameters For Radiation Modeling From Digital Elevation Data , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[22]  M. Schlerf,et al.  Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data , 2006 .

[23]  C. Bacour.  Design and analysis of numerical experiments to compare four canopy reflectance models , 2002 .

[24]  Rolando V. Raqueno,et al.  Advanced synthetic image generation models and their application to multi/hyperspectral algorithm development , 1999, Other Conferences.

[25]  Jens Nieke,et al.  OPERATIONAL SIMULATION OF AT SENSOR RADIANCE SENSITIVITY USING THE MODO / MODTRAN4 ENVIRONMENT , 2005 .

[26]  Wout Verhoef,et al.  Inversion of a coupled canopy–atmosphere model using multi-angular top-of-atmosphere radiance data: A forest case study , 2011 .

[27]  N. Goel.  Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data , 1988 .

[28]  Mario Parente,et al.  End-to-End Simulation and Analytical Model of Remote-Sensing Systems: Application to CRISM , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[29]  W. Verhoef,et al.  Remote sensing data assimilation using coupled radiative transfer models , 2003 .

[30]  Gail P. Anderson,et al.  Atmospheric correction for shortwave spectral imagery based on MODTRAN4 , 1999, Optics & Photonics.

[31]  Xiao Zhang,et al.  On Hyperspectral Image Simulation of a Complex Woodland Area , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Michael Berger,et al.  Sentinel-2 optical high resolution mission for GMES operational services , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[33]  S. Goetz.  Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site , 1997 .

[34]  Philip J. Howarth,et al.  Using Landsat-5 thematic mapper and digital elevation data to determine the net radiation field of a Mountain Glacier , 1993 .

[35]  L. Dini,et al.  Estimation of Land Surface Parameters Through Modeling Inversion of Earth Observation Optical Data , 2009 .

[36]  David A. Landgrebe,et al.  Simulation of optical remote sensing systems , 1989 .

[37]  F. Baret,et al.  Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data : Principles and validation , 2006 .

[38]  Yuri Knyazikhin,et al.  Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem , 2003 .

[39]  John R. Schott,et al.  Remote Sensing: The Image Chain Approach , 1996 .

[40]  Hermann Kaufmann,et al.  On the application of the MODTRAN4 atmospheric radiative transfer code to optical remote sensing , 2009 .

[41]  J. Dozier,et al.  A faster solution to the horizon problem , 1981 .

[42]  Jean-Luc Widlowski,et al.  Third Radiation Transfer Model Intercomparison (RAMI) exercise: Documenting progress in canopy reflectance models , 2007 .

[43]  R. Richter,et al.  Sensor: a tool for the simulation of hyperspectral remote sensing systems , 2001 .

[44]  W. Verhoef,et al.  Global sensitivity analysis of the spectral radiance of a soil-vegetation system , 2014 .

[45]  H. Rahman,et al.  Coupled surface‐atmosphere reflectance (CSAR) model: 1. Model description and inversion on synthetic data , 1993 .

[46]  R. Richter,et al.  Correction of satellite imagery over mountainous terrain. , 1998, Applied optics.

[47]  J. Hay.  Calculating solar radiation for inclined surfaces: Practical approaches , 1993 .

[48]  A. M. Vogelmann,et al.  Multispectral sensor data simulation modeling based on the multiple scattering LOWTRAN code , 1988 .

[49]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .

[50]  D. C. Robertson,et al.  MODTRAN cloud and multiple scattering upgrades with application to AVIRIS , 1998 .