Search results for " radiative transfer"
showing 5 items of 75 documents
Sensitivity of shortwave radiative fluxes to the vertical distribution of aerosol single scattering albedo in the presence of a desert dust layer
2010
Abstract The aim of this work is to quantify the sensitivity of shortwave radiative fluxes to changes in the vertical distribution of aerosol absorption, taken into account through the aerosol Single Scattering Albedo (SSA). The case study represents a real atmospheric situation with a desert dust layer (DDL) in the mid troposphere over an urban Boundary Layer (BL) observed at Rome on 20 June 2007. A moderately high aerosol optical depth (AOD), 0.292 at 550 nm, and low Angstrom exponent of 0.30 were measured. The observed case was reconstructed with a radiative transfer model, in which the SSA of the boundary layer aerosols was varied from that of a highly absorbing aerosol type (urban) to …
Analysis of thermal infrared data from the Digital Airborne Imaging Spectrometer
2001
Thermal infrared data of the Digital Airborne Imaging Spectrometer (DAIS), whose channels 74-79 are in the 8-13 w m waveband region, were analysed with the aim of recovering land surface temperature (LST). DAIS images were acquired over an experimental site where field and laboratory emissivity measurements were performed, and these were used to recover the LST from the six DAIS thermal channels. Atmospheric correction of DAIS data was calculated by means of a nearby radiosounding and a radiative transfer model. DAIS derived LSTs were compared with ground measurements of LST made coincidentally for a few test fields, the central DAIS channels yielding temperatures up to 10°C higher than gro…
An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning
2015
Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth’s surface and their interactions with vegetation and atmosphere. When it comes to studying vegetation properties, RTMs allows us to study light interception by plant canopies and are used in the retrieval of biophysical variables through model inversion. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. Emulators are advantageous in real practice because…
Global Estimation of Biophysical Variables from Google Earth Engine Platform
2018
This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estim…
Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data.
2022
The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically-based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C)…