Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data

Guanter, Luis; Estellés, V.; Moreno, José

doi:10.1016/j.rse.2006.12.005

Cited by 80 publications

(61 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[10] As in the case of MERIS, the fluorescence retrieval is coupled to an accurate atmospheric correction algorithm [Guanter et al, 2007a]. It is specifically designed for ultra- …”

Section: Results From Casi-1500 Datamentioning

confidence: 99%

Estimation of solar‐induced vegetation fluorescence from space measurements

Guanter

Alonso

Gómez-Chova

et al. 2007

Geophysical Research Letters

131

106

View full text Add to dashboard Cite

[1] A characteristic spectral emission is observed in vegetation chlorophyll under excitation by solar radiation. This emission, known as solar-induced chlorophyll fluorescence, occurs in the red and near infra-red spectral regions. In this paper a new methodology for the estimation of solar-induced chlorophyll fluorescence from spaceborne and airborne sensors is presented. The fluorescence signal is included in an atmospheric radiative transfer scheme so that chlorophyll fluorescence and surface reflectance are retrieved consistently from the measured at-sensor radiance. This methodology is tested on images acquired by the

show abstract

“…[10] As in the case of MERIS, the fluorescence retrieval is coupled to an accurate atmospheric correction algorithm [Guanter et al, 2007a]. It is specifically designed for ultra- …”

Section: Results From Casi-1500 Datamentioning

confidence: 99%

Estimation of solar‐induced vegetation fluorescence from space measurements

Guanter

Alonso

Gómez-Chova

et al. 2007

Geophysical Research Letters

131

106

View full text Add to dashboard Cite

show abstract

“…This work ends with recommendations towards a new generation of spectral indices. [45,46]. Finally, a calibration dataset was prepared, which refers to the centre pixel of each ESU and corresponding LCC and LAI values.…”

Section: Assessment and Mapping Applicationsmentioning

confidence: 99%

On the Semi-Automatic Retrieval of Biophysical Parameters Based on Spectral Index Optimization

et al. 2014

View full text Add to dashboard Cite

Regression models based on spectral indices are typically empirical formulae enabling the mapping of biophysical parameters derived from Earth Observation (EO) data. Due to its empirical nature, it remains nevertheless uncertain to what extent a selected regression model is the most appropriate one, until all band combinations and curve fitting functions are assessed. This paper describes the application of a Spectral Index (SI) assessment toolbox in the Automated Radiative Transfer Models Operator (ARTMO) package. ARTMO enables semi-automatic retrieval and mapping of biophysical parameters from optical remote sensing observations. The SI toolbox facilitates the assessment of biophysical parameter retrieval accuracy of established as well as new and generic SIs. For instance, based on the SI formulation used, all possible band combinations of formulations with up to ten bands can be defined and evaluated. Several options are available in the SI assessment: calibration/validation data partitioning, the addition of noise and the definition of curve fitting models. To illustrate its functioning, all two-band combinations according to simple ratio (SR) and normalized difference (ND) formulations as well as various fitting functions (linear, exponential, power, logarithmic, polynomial) functions led to similar best correlations, though spatial differences emerged when applying the functions to HyMap imagery. We suggest that a systematic SI assessment is a strong requirement in the quality assurance approach for accurate biophysical parameter retrieval.

show abstract

“…The aerosol optical thickness at 550 nm (AOT@550 nm) is widely used to describe the aerosol loading [26][27][28][29][30]. The physically based atmospheric correction algorithm CHRIS@CRI needs, as input, the aerosol AOD at 550 nm, the water vapour and ozone columnar content, and a standard or user-defined aerosol type, defined by its size distribution function (SD) and the real (mr) and imaginary (mi) part of the complex refractive index.…”

Section: Atmospheric Correctionmentioning

confidence: 99%

“…The magnitude of aerosol effects in remotely sensed data mainly depends on aerosol type and amount, observation geometry, and the wavelength [2]. Several studies have shown the crucial role of aerosol optical thickness at 550 nm in atmospheric transfer modelling [17][18][19][20][21][22][23][24] and on the atmospheric correction of multispectral and hyperspectral data for ocean and land properties retrieval [25][26][27][28][29][30][31]. The combination of satellite hyperspectral data, ground measurements, and model-based analysis allows complete information datasets for accurate surface reflectance retrieval [32].…”

Section: Introductionmentioning

confidence: 99%

Effect of the Aerosol Model Assumption on the Atmospheric Correction over Land: Case Studies with CHRIS/PROBA Hyperspectral Images over Benelux

et al. 2015

View full text Add to dashboard Cite

Surface reflectance has a central role in the analysis of land surface for a broad variety of Earth System studies. An accurate atmospheric correction, obtained by an appropriate selection of aerosol model, is the first requirement for reliable surface reflectance estimation. In the aerosol model, the type is defined by the physical and chemical properties, while the loading is usually described by the optical thickness at 550 nm. The aim of this work is to evaluate the radiative impact of the aerosol model on the surface reflectance obtained from Compact High Resolution Imaging Spectrometer (CHRIS) hyperspectral data over land by using the specifically developed algorithm CHRIS Atmospherically Corrected Reflectance OPEN ACCESSRemote Sens. 2015, 7 8392 Imagery (CHRIS@CRI) based on the 6SV radiative transfer model. We employed five different aerosol models: one provided by the AERONET inversion products (used as reference), three standard aerosol models in 6SV, and one obtained from the output of the GEOS-Chem global chemistry-transport model (CTM). The results obtained for the two case studies selected over Benelux show that in the absence of AERONET data on the scene, the best performing aerosol model is the one derived from CTM output.

show abstract

Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data

Cited by 80 publications

References 24 publications

Estimation of solar‐induced vegetation fluorescence from space measurements

Estimation of solar‐induced vegetation fluorescence from space measurements

On the Semi-Automatic Retrieval of Biophysical Parameters Based on Spectral Index Optimization

Effect of the Aerosol Model Assumption on the Atmospheric Correction over Land: Case Studies with CHRIS/PROBA Hyperspectral Images over Benelux

Contact Info

Product

Resources

About