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

Abstract

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" , 2007) 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 OPEN ACCESSRemote Sens. 2015, 7 8020 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/m 2 ·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.

A New Method for the Estimation of Broadband Apparent Albedo Using Hyperspectral Airborne Hemispherical Directional Reflectance Factor Values

“…This result was also discussed in Nicodemus et al [32] and has been used to simplify the equations of radiative transfer models when applied to a uniform Lambertian surface [33]. To calculate the spectral albedo, the BRDF must be known.…”

Potential and Limitations of Photometric Reconstruction Through a Flock of Dove Cubesats

“…The imagery was collected between the 11 th and 16 th of December 2016, with varying sun angles. The imagery was transformed to near surface reflectance values without estimating reflectance contributed from other terrain (Mousivand et al, 2015). However, the indirect reflection or adjacency term (A) was assumed to be stable within a small region, thus when estimating, the topography of a template is taken and the atmospheric adjacency effect seen as a local constant bias (see equation 4).…”

“…Because of the isotropy assumption we adopt here, the reflectance (R) can be seen as a constant in time and space. Furthermore for simplicity of formulation, we exclude just for now in-path absorption, however for a full account see (Mousivand et al, 2015). In this simple formulation the sensor has a certain transmittance (T ), where after the energy over a certain bandwidth over different wavelengths (λ) is collected and transformed (C) to a digital number.…”

“…These given spectral response curves (C(λ)) serve in the present study as input for a MODTRAN-based atmospheric correction estimation (Mousivand et al, 2015). For this atmospheric modeling only a small amount of parameters is needed (water vapor, visibility and CO2 mixing).…”

Evaluation of Burned Areas With Sentinel-2 Using Snap: The Case of Kineta and Mati, Greece, July 2018

“…Solar radiation reflected by the Earth's surface to satellite sensors is affected by its interaction with the atmosphere. The objective of applying an atmospheric correction is to determine true surface (Bottom-Of-Atmosphere, BOA) reflectance values from the Top-Of-Atmosphere (TOA) reflectance values, by removing atmospheric effects (Mousivand et al, 2015). Atmospheric correction is especially important in cases where multi-temporal images are compared and analysed as it is in this case (pre-fire and post-fire images).…”