Remote sensing data assimilation using coupled radiative transfer models
W. Verhoef, and H. Bach. Physics and Chemistry of the Earth, Parts A/B/C, 28 (1-3):
3--13(2003)
Abstract
This paper discusses data assimilation of biophysical parameters retrieved from optical remote sensing images in land surface process models by means of image simulation and model inversion. Two different approaches are presented. The first is based on model inversion of atmospherically corrected Landsat TM surface reflectance images and assimilation of the retrieved parameters in a crop growth model. In the second approach top-of-atmosphere (TOA) hyperspectral radiance images have been simulated for the future ESA mission SPECTRA. In this case only the simulation of the images has been executed in order to demonstrate the feasibility of this task with existing software running on a PC. The radiative transfer models that have been used are PROSPECT (leaf level), GeoSAIL (canopy level) and MODTRAN4 (atmosphere). Coupling of this chain of models to land use information of the area can be used to generate TOA radiance images. Comparison of simulated images with actual remote sensing data can be applied to retrieve biophysical parameters and in turn these can be employed to update process models of crop growth.
Description
ScienceDirect - Physics and Chemistry of the Earth, Parts A/B/C : Remote sensing data assimilation using coupled radiative transfer models
%0 Journal Article
%1 BachVerhoef_assim
%A Verhoef, Wout
%A Bach, Heike
%B Applications of Quantitative Remote Sensing to Hydrology
%D 2003
%J Physics and Chemistry of the Earth, Parts A/B/C
%K BRDF assimilation brdf canopy crops modeling optical reflectance remotesensing satellite uncertainty vegetation yield
%N 1-3
%P 3--13
%T Remote sensing data assimilation using coupled radiative transfer models
%U http://www.sciencedirect.com/science/article/B6X1W-483SMNY-2/1/5f4b4b961c42f18766c513d42b64aca9
%V 28
%X This paper discusses data assimilation of biophysical parameters retrieved from optical remote sensing images in land surface process models by means of image simulation and model inversion. Two different approaches are presented. The first is based on model inversion of atmospherically corrected Landsat TM surface reflectance images and assimilation of the retrieved parameters in a crop growth model. In the second approach top-of-atmosphere (TOA) hyperspectral radiance images have been simulated for the future ESA mission SPECTRA. In this case only the simulation of the images has been executed in order to demonstrate the feasibility of this task with existing software running on a PC. The radiative transfer models that have been used are PROSPECT (leaf level), GeoSAIL (canopy level) and MODTRAN4 (atmosphere). Coupling of this chain of models to land use information of the area can be used to generate TOA radiance images. Comparison of simulated images with actual remote sensing data can be applied to retrieve biophysical parameters and in turn these can be employed to update process models of crop growth.
@article{BachVerhoef_assim,
abstract = {This paper discusses data assimilation of biophysical parameters retrieved from optical remote sensing images in land surface process models by means of image simulation and model inversion. Two different approaches are presented. The first is based on model inversion of atmospherically corrected Landsat TM surface reflectance images and assimilation of the retrieved parameters in a crop growth model. In the second approach top-of-atmosphere (TOA) hyperspectral radiance images have been simulated for the future ESA mission SPECTRA. In this case only the simulation of the images has been executed in order to demonstrate the feasibility of this task with existing software running on a PC. The radiative transfer models that have been used are PROSPECT (leaf level), GeoSAIL (canopy level) and MODTRAN4 (atmosphere). Coupling of this chain of models to land use information of the area can be used to generate TOA radiance images. Comparison of simulated images with actual remote sensing data can be applied to retrieve biophysical parameters and in turn these can be employed to update process models of crop growth.},
added-at = {2008-05-15T12:28:50.000+0200},
author = {Verhoef, Wout and Bach, Heike},
biburl = {https://www.bibsonomy.org/bibtex/22d2a5c414376052290c3f31a7e292dcf/jgomezdans},
booktitle = {Applications of Quantitative Remote Sensing to Hydrology},
description = {ScienceDirect - Physics and Chemistry of the Earth, Parts A/B/C : Remote sensing data assimilation using coupled radiative transfer models},
interhash = {451c23b7633be82d029f4733ab69f8db},
intrahash = {2d2a5c414376052290c3f31a7e292dcf},
journal = {Physics and Chemistry of the Earth, Parts A/B/C},
keywords = {BRDF assimilation brdf canopy crops modeling optical reflectance remotesensing satellite uncertainty vegetation yield},
number = {1-3},
pages = {3--13},
timestamp = {2008-05-15T12:28:50.000+0200},
title = {Remote sensing data assimilation using coupled radiative transfer models},
url = {http://www.sciencedirect.com/science/article/B6X1W-483SMNY-2/1/5f4b4b961c42f18766c513d42b64aca9},
volume = 28,
year = 2003
}