In this study, the effects of the Lambertian assumption on the top-of-atmosphere reflectance are evaluated through comparison with calculations derived using a more detailed bidirectional reflectance distribution function under different atmospheric, lighting, and viewing conditions. The numerical experiments are performed for background, dusty, and cloudy models of the atmosphere in spectral channels of 0.44 and 0.87 μm. In the case of a background or dusty medium over the terrestrial surface, the overestimation of the top-of-atmosphere reflectance in the forward-scatter viewing direction and underestimation in the backscatter one are observed. The angular range as well as magnitude of the discrepancy is noticeably narrower and lower, respectively, when the atmosphere is more turbid and the wavelength is shorter. The use of the Lambertian assumption instead of "true" ocean reflectance leads to a significant underestimation of the top-of-atmosphere reflectance in the forward-scatter direction and a moderate overestimation of reflectance in the backscatter one. The ocean reflectance generally exhibits a high dependence on wind, which affects the reflected solar radiation distribution around the forward-scatter direction. Analysis of simulation results for an overcast sky showed that, in general, the multiple scattered radiation smoothes the anisotropy effects. However, there are conditions at which the choice of a bidirectional reflectance distribution function model is significant: in the case of thin cirrus cloudiness over the ocean at large solar zenith angles and stratus cloudiness with an optical thickness of at least 5 over a vegetation cover or ocean in the near-infrared region of the spectrum.

Download full-text PDF

Source
http://dx.doi.org/10.1364/AO.57.006345DOI Listing

Publication Analysis

Top Keywords

top-of-atmosphere reflectance
16
lambertian assumption
8
reflectance
8
bidirectional reflectance
8
reflectance distribution
8
distribution function
8
background dusty
8
reflectance forward-scatter
8
ocean reflectance
8
forward-scatter direction
8

Similar Publications

Excessive total suspended matter (TSM) concentrations can exert a considerable impact on the growth of aquatic organisms in fishponds, representing a significant risk to aquaculture health. This study revised existing unified models using empirical data to develop an optimized TSM retrieval model tailored for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) (R = 0.69, RMSE = 7.

View Article and Find Full Text PDF

Satellite observations from the Clouds and the Earth's Radiant Energy System show that Earth's energy imbalance has doubled from 0.5 ± 0.2 Wm during the first 10 years of this century to 1.

View Article and Find Full Text PDF

Black carbon, or soot, significantly contributes to atmospheric light absorption due to its low single scattering albedo (SSA). This study investigates the impact of soot's hygroscopic restructuring on satellite remote sensing, focusing on radiative forcing, top-of-atmosphere (TOA) reflectance, and aerosol optical depth (AOD) retrievals. We characterized soot aging using relative humidity (RH) growth factor functions and modeled fresh and aging soot aggregates using a cluster-cluster aggregation algorithm.

View Article and Find Full Text PDF
Article Synopsis
  • - This study analyzes the impact of road construction on water quality, focusing on the E18 Arendal-Tvedestrand highway in Norway, by using Remote Sensing data from Sentinel-1 and Sentinel-2 to monitor water turbidity from 2017 to 2021.
  • - Sentinel-2's Top of Atmosphere data, corrected using the MAIN algorithm, was found to be effective in estimating water turbidity levels, with findings showing a significant correlation between the corrected data and ground-based observations.
  • - Results show that road construction activities can lead to increased turbidity in nearby water bodies, highlighting the potential of Remote Sensing tools in cloud platforms like Google Earth Engine for managing water quality during such projects.
View Article and Find Full Text PDF

Land Surface Temperature (LST) is a crucial parameter in studies of urban heat islands, climate change, evapotranspiration, hydrological cycles, and vegetation monitoring. However, conventional satellite-based approaches for LST retrieval often require additional data like land surface emissivity (LSE). Meanwhile, traditional machine learning (ML) techniques face challenges in acquiring representative training data and leveraging data from varied sources effectively.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!