Abstract
This paper aims to assess the relationship between the surface reflectance derived from ground based and aircraft measurements. The parameters of the Rahman–Pinty–Verstraete (RPV) and Ross Thick-LiSparse (RTLS) kernel based bi-directional reflectance distribution functions (BRDF), have been derived using actual measurements of the hemispherical-directional reflectance factor (HDRF), collected during different campaigns over the Railroad Valley Playa. The effect of the atmosphere, including that of the diffuse radiation on bi-directional reflectance factor (BRF) parameter retrievals, assessed using 6S model simulations, was negligible for the low turbidity conditions of the site under investigation (τ550≤0.05). It was also shown that the effects of the diffuse radiation on RPV spectral parameters retrieval is linear for the isotropic parameter ρ0 and the scattering parameter Θ, and can be described with a second order polynomial for the k-Minnaert parameter. In order to overcome the lack of temporal collocations between aircraft and in-situ measurements, Monte Carlo 3-D radiative transfer simulations mimicking in-situ and remote sensing techniques were performed on a synthetic parametric meshed scene defined by merging Landsat and Multianglhe Imaging Spectroradiometer (MISR) remote sensing reflectance data. We simulated directional reflectance measurements made at different heights for PARABOLA and CAR, and analyzed them according to practices adopted for real measurements, consisting of the inversion of BRF functions and the calculation of the bi-hemispherical reflectance (BHR). The difference of retrievals against the known benchmarks of kernel parameters and BHR is presented. We associated an uncertainty of up to 2% with the retrieval of area averaged BHR, independently of flight altitudes and the BRF model used for the inversion. As expected, the local nature of PARABOLA data is revealed by the difference of the anisotropic kernel parameters with the corresponding parameters retrieved from aircraft loops. The uncertainty of the resultant BHR fell within ±3%.
Highlights
Surface albedo is a crucial parameter in order to understand the behavior of the earth climate system, because it regulates the fraction of solar energy reflected back to space by the surface-atmosphere coupled system [1,2,3]
The bidirectional reflectance factor (BRF) is a convenient way to express the anisotropic properties of the surface reflectance, and is defined by the ratio between the exiting radiance measured in a certain direction identified by the zenith and azimuth angles, and the one that would be reflected by an ideal lambertian surface in the same direction [24,25]
We provide here only a summary of the results obtained for the same geometric conditions and atmospheric properties discussed above, and for altitudes relevant to a specific campaign performed over Railroad Valley (RRV) in 2008 (e.g., 180, 645, 1480 and 3400 m above ground)
Summary
Surface albedo is a crucial parameter in order to understand the behavior of the earth climate system, because it regulates the fraction of solar energy reflected back to space by the surface-atmosphere coupled system [1,2,3]. The albedo was defined as an essential climate variable (ECV) by the Global Climate Observing System (GCOS) of the World Meteorological Organization (WMO) [3,5]. Satellite Earth observation provides the best temporal and spatial coverage to monitor the evolution of the lithosphere, cryosphere and biosphere [6]. To produce fit-for-purpose outputs, accurate instrument calibration and algorithm assessment are necessary. Adequate knowledge of the limits of retrieval algorithms, throughout the process that transforms the measured physical quantities (typically spectral radiances) into derived land or atmospheric products, needs to be guaranteed. A common way to perform calibration and validation activity is based on specific and intensive field measurement, during which in-situ data are collected to define the surface status. Atmospheric optical properties have to be defined to account for the atmospheric contamination of the signal measured by the satellite [7]
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