A Review of Atmospheric CorrectionTechniques for Hyperspectral Remote Sensingof Land Surfaces and Ocean Color

7/31/2019 A Review of Atmospheric CorrectionTechniques for Hyperspectral Remote Sensingof Land Surfaces and Ocean Color

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A Review of Atmospheric CorrectionTechniques for Hyperspectral Remote Sensing

of Land Surfaces and Ocean ColorBo-Cai Gao

1,*, Curtiss O. Davis

2, and Alexander F. H. Goetz

3

1Remote Sensing Division, Naval Research Laboratory, Washington, DC, USA;

*gao @nrl.navy.mil

2College of Oceanic and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA3Department of Geological Sciences, University of Colorado at Boulder, Boulder, CO, USA

Abstract The concept of imaging spectrometry, or

hyperspectral imaging, was originated from the NASA JetPropulsion Laboratory in the early 1980s. Different types ofimaging spectrometers have been built. Scientific data havebeen collected with these instruments from aircraft and

satellite platforms. Because imaging spectrometer data

contain absorption and scattering effects from atmosphericgases and aerosols, the atmospheric effects must be removedin order to use the data for quantitative remote sensing of

land surfaces and ocean color. Over the years, the

atmospheric correction algorithms have evolved from theearlier empirical line method and flat field method to morerecent methods based on rigorous radiative transfermodeling. We will give an overview of hyperspectral

atmospheric correction algorithms developed during the past

two decades. Issues related to spectral smoothing will bediscussed. Suggestions for improvements to the presentatmospheric correction algorithms will be given.

I. INTRODUCTION

Imaging spectrometers acquire images in manycontiguous spectral channels such that for each picture

element (pixel) a complete reflectance or emittance

spectrum can be derived from the wavelength region

covered [1], [2]. During the past two decades, different

types of imaging spectrometers have been built.

Hyperspectral imaging data have been collected with these

instruments from aircraft and satellite platforms. The solar

radiation on the Sun-surface-sensor ray path is subject to

absorption and scattering by the atmosphere and the

surface. Major atmospheric water vapor bands centered at

approximately 0.94, 1.14, 1.38 and 1.88 m, the oxygen

band at 0.76 m, and the carbon dioxide band near 2.08

m are present. Approximately half of the 0.4-2.5 mspectral region is affected by atmospheric gas absorptions.

The shorter wavelength region below 1 m is also affected

by molecular and aerosol scattering.

There are now growing interests in hyperspectral remote

sensing for research and applications in a variety of fields,

including geology, agriculture, forestry, coastal and inland

water studies, environment hazards assessment, and urban

studies. In order to study surface properties using imaging

spectrometer data, accurate removal of atmospheric

absorption and scattering effects is required. There is a

great need for correction of atmospheric effects and

conversion of radiances measured by the sensors to

reflectances of surface materials.

Since the mid-1980s, atmospheric correction algorithmshave evolved from the earlier empirical line method and

flat field method to more recent methods based on rigorous

radiative transfer modeling. In this extended abstract, we

present an overview of hyperspectral atmospheric

correction algorithms developed during the past two

decades.

II. ATMOSPHERIC CORRECTION APPROACHES

A. Scene-Based Empirical Approaches

During the mid-1980s, several scene-based empirical

approaches were developed to remove atmospheric effects

from hyperspectral imaging data for the derivation of

relative surface reflectance spectra. The Internal Average

Reflectance (IAR) approach of Kruse [3] calculates the

average spectrum of a scene. The spectrum of any pixel in

the scene is then divided by the average spectrum to

estimate the relative reflectance spectrum for the pixel.

This approach is mostly applicable for imaging data

acquired over arid areas without vegetation. The flat field

correction approach [4] assumes that there is an area in the

scene that has spectrally neutral reflectances, i.e., the

spectrum has little variation with wavelength. The mean

spectrum of the flat field is then used for the derivation

of relative reflectance spectra of other pixels in the scene.Both the IAR approach and the flat field approach do not

need any field measurements of reflectance spectra of

surface targets. The derived relative reflectance spectra

often have absorption features that are not present in

reflectance spectra of comparable materials measured in

the field or laboratory [5].

The empirical line approach [6] requires field-

measurements of reflectance spectra for at least one bright

U.S. Government work not protected by U.S. copyright


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target and one dark target. The imaging spectrometer data

over the surface targets are linearly regressed against the

field-measured reflectance spectra to derive the gain and

offset curves. The gain and offset curves are then applied

to the whole image for the derivation of surface

reflectances for the entire scene. This method produces

spectra that are most comparable to reflectance spectrameasured in the fields or laboratories [7].

It should be pointed out that absolute radiometric

calibrations of hyperspectral imaging data are not required

when using these empirical approaches for the estimates of

relative surface reflectances. However, the hyperspectral

imaging system should remain stable during data

acquisitions.

B. Radiative Transfer Modeling Approaches

Surface reflectance spectra can be derived from

hyperspectral imaging data using radiative transfer

modeling approaches. Gao et al. [8] first started thedevelopment of the Atmosphere Removal Algorithm

(ATREM) in the late 1980s. The method retrieves scaled

surface reflectance spectra assuming horizontal surfaces

having Lambertian reflectances from imaging spectrometer

data. In this method, the integrated water vapor amount on

a pixel by pixel basis is derived from the 0.94- and the

1.14-m water vapor absorption features. The transmission

spectrum of water vapor (H2O), carbon dioxide (CO

2),

ozone (O3), nitrous oxide (N

2O), carbon monoxide (CO),

methane (CH4), and oxygen (O

2) in the 0.42.5 m region

is simulated based on the derived water vapor value, the

solar and the observational geometry, and through use of

narrow band spectral models. The scattering effect due to

atmospheric molecules and aerosols is modeled with the

5S computer code. The AVIRIS radiances are divided by

solar irradiances above the atmosphere to obtain the

apparent reflectances. The scaled surface reflectances are

derived from the apparent reflectances using the simulated

atmospheric gaseous transmittances and the simulated

molecular and aerosol scattering data. If the slopes and

aspects of the surfaces are known, the scaled reflectances

can be converted to real reflectances.

The band model version of the ATREM code (Version

3.1) was widely distributed in the 1990s to the

hyperspectral research community through the Center forthe Study of Earth from Space (CSES), University of

Colorado at Boulder, Colorado. Major upgrades were

made to the ATREM code in the late 1990s and early

2000s. The band model is replaced with a line-by-line

atmospheric transmittance model [9] and the

HITRAN2000 line database. The 5S computer code is

replaced with the newer 6S code for modeling atmospheric

scattering effects. A module for modeling atmospheric

NO2

absorption effects in the 0.4 0.8 m spectral region

is also added.

There are now a number of atmospheric correction

algorithms for retrieving surface reflectances from

hyperspectral imaging data. They include, but not limited

to, the Atmosphere CORrection Now (ACRON), the Fast

Line-of-sight Atmospheric Analysis of SpectralHypercubes (FLAASH), the High-accuracy Atmospheric

Correction for Hyperspectral Data (HATCH) [10], and a

series of Atmospheric and Topographic Correction

(ATCOR) codes. Some of these codes include more

advanced features, such as spectral smoothing, topographic

correction, and adjacency effect correction. These features

are absent in the ATREM code. The atmospheric

correction algorithms described so far are mostly designed

for remote sensing of land surfaces.

There is a small research community interested in

hyperspectral remote sensing of ocean color. Because the

ocean surfaces are much darker than land surfaces and the

air/water interface is not Lambertian, very accuratemodeling of atmospheric absorption and scattering effects

and the specular surface reflection effects is required in

order to derive water leaving reflectances from

hyperspectral imaging data measured over water surfaces.

We have developed an atmospheric correction algorithm

nicknamed TAAFKA for hyperspectral remote sensing of

ocean color [11]. The algorithm uses lookup tables

generated with a vector radiative transfer code and a

spectral matching technique. Channels located at

wavelengths longer than 0.86 m where the water leaving

reflectances are close to zero have been used for the

derivation of information on atmospheric aerosols. The

aerosol information is then extracted back to the visible

based on aerosol models during the retrieval of water

leaving radiances. Quite reasonable results have been

obtained when applying the algorithm to process

hyperspectral imaging data acquired with the AVIRIS

instrument from an ER-2 aircraft and the Hyperion

instrument on the EO-1 satellite platform.

C. Hybrid Approaches

Researchers have used combinations [12], [13] of

radiative modeling approaches and empirical approaches

for the derivations of surface reflectances fromhyperspectral imaging data. For example, Clark et al. [14]

used a combination of ATREM and field spectral

measurements over a single ground calibration site. The

use of ATREM model allows improved atmospheric

corrections at elevations that are different from the

calibration site, and the ground calibration removes the

residual errors commonly associated with radiative transfer

models.



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III. DISCUSSIONS

The atmospheric NO2

transmittances near 0.4 m are

typically 0.985 to 0.995 in the Sun-surface-sensor path.

Over polluted airs with enhanced tropospheric NO2

concentrations, the NO2

transmittances near 0.4 m can be

smaller than 0.985. When retrieving surface reflectancesover darker targets, such as green vegetation and coastal

waters, with 0.4-m reflectances of approximately 0.02 to

0.03 using radiative transfer modeling approaches, the NO2

absorption effects should be included in the models.

Otherwise, errors on the orders of 10 to 20% can be

introduced in the retrieved surface reflectances near 0.4

m.

Some of the atmospheric correction algorithms have

built-in modules to smooth the output spectra on a pixel-

by-pixel basis in order to eliminate spikes in the derived

surface reflectance spectra. Such algorithms are not

suitable for use with the hybrid approach of Clark et al.

[14] for the derivation of surface reflectances, because acommon scaling factor for different pixels in a scene is no

longer present after the pixels being smoothed

individually. It should be pointed out that the smoothing

algorithms can introduce unrealistic broad absorption

features in the output spectra. The end users of the

algorithms with built-in smoothing modules should be

aware of the problem.

IV. SUMMARY

There are basically three types of atmospheric correction

approaches, i.e., the scene-based empirical approaches,

radiative transfer modeling approaches, and the hybrid

approaches, for atmospheric corrections of hyperspectral

imaging data. The radiative transfer modeling approaches

are sufficiently mature and can be used for routine

processing of hyperspectral imaging data. The hybrid

approach of Clark et al. [14] allows the derivation of

laboratory-like reflectance spectra from imaging

spectrometer data. There are still rooms for improvements

to radiative transfer models. Users of algorithms with

built-in smoothing modules should be careful about the

possibility of artificial broad absorption features present in

their retrieved surface reflectance spectra.

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A Review of Atmospheric CorrectionTechniques for Hyperspectral Remote Sensingof Land Surfaces and Ocean Color