retrieval of columnar water vapour over land from backscattered solar radiation using the medium...

Retrieval of columnar water vapour over land from backscattered solar

radiation using the Medium Resolution Imaging Spectrometer

Ralf Bennartz*, Jurgen Fischer

Institut fur Weltraumwissenschaften, Freie Universitat Berlin, Berlin, Germany

Received 4 April 2000; accepted 13 March 2001

Abstract

We describe a new algorithm to derive columnar water vapour under cloud-free conditions over land from backscattered solar radiation

in the near-infrared. The algorithm will be used in ESA’s Medium Resolution Imaging Spectrometer (MERIS) ground processor. It is based

on radiative transfer simulations, where the radiance ratio between the MERIS channels 15 (900 nm) and 14 (885 nm) is used in an

inversion procedure based on regressions. The theoretical accuracy of the algorithm is about 1.7 kg/m2. We discuss and quantify possible

error sources using radiative transfer simulations. These error sources are variable aerosol optical thickness, type, and vertical distribution,

spectral variations in land surface reflectance, deviations between the actual and nominal bandsetting of the MERIS, sensor noise, variations

in surface pressure, and temperature variations. For validation, we re-calibrate the algorithm to the bandsettings of the Modular

Optoelectronical Scanner (MOS), which has been flown on the Indian IRS platform since 1996. Comparisons of the retrieved water vapour

path (WVP) with colocated radiosoundings for 239 cases in the period 1996–1999 show a RMSE of 2.49 kg/m2 with a BIAS component

of 0.04 kg/m2. D 2001 Elsevier Science Inc. All rights reserved.

1. Introduction

Water vapour is the most important and, at the same time,

highly variable greenhouse gas in the atmosphere. Obser-

vations of its spatial and temporal variations are thus not

only a major objective in climate research, but also of

importance for numerical weather prediction.

Many remote sensing techniques have been developed to

derive the water vapour path (WVP) or its vertical distribu-

tion. Over water surfaces, passive microwave remote sens-

ing techniques provide accurate measures of WVP

(Schlussel & Emery, 1990). Over land surfaces, conven-

tional techniques, such as radiosoundings, and infrared

remote sensing techniques have been established.

In recent years, two additional remote sensing methods to

retrieve water vapour have become available. The first

method is based on the Global Positioning System (GPS).

It allows derivation of the WVP as well as its vertical

distribution from the path delay of the GPS signals. While

still under research, this technique provides accurate meas-

ures of water vapour column amount (Borbas, 1998;

Rocken, Van Hove, & Ware, 1997).

The second method is based on backscattered solar

radiation in the near-infrared. It will become increasingly

utilized with the availability of new instruments. The Euro-

pean Space Agency (ESA) will launch the ENVIronmental

SATellite (ENVISAT) in 2001. This platform will be equip-

ped with the Medium Resolution Imaging Spectrometer

(MERIS), whose main dedication is ocean colour remote

sensing. Besides this, MERIS will have two channels in the

near-infrared, which allow for the derivation of atmospheric

water vapour. Their center wavelengths will be 885 and 900

nm, with ± 5 nm halfwidth value. While the channel at 885

nm is practically absorption-free, the channel at 900 nm is

located at the shortwave edge of the rst water vapour

absorption band. Other instruments, such as the Moderate

Resolution Imaging Spectroradiometer (MODIS) or the

POLarization and Directionality of the Earth’s Reflectance

(POLDER) onboard the ADvanced Earth Observing Satellite

2 (ADEOS 2), will have similar bandsettings and capabilities.

The overall achievable accuracy of the water vapour

retrieval from backscattered solar radiation has not yet been

determined. Recently, Vesperini, et al. (1999) found an

0034-4257/01/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved.

PII: S0034 -4257 (01 )00218 -8

* Corresponding author. Department of Physics and Astronomy,

University of Kansas, 1082 Malott Hall, Lawrence, KS 66045, USA.

Tel.: +1-785-864-3949; fax: +1-785-864-5262.

E-mail address: [email protected] (R. Bennartz).

www.elsevier.com/locate/rse

Remote Sensing of Environment 78 (2001) 274–283

RMS deviation of about 3.1 kg/m2, including a significant

negative bias for a global intercomparison of POLDER data

with radiosoundings over cloud-free land surfaces. In con-

trast, Tahl and v. Schoenermark (1998) found an accuracy

of 1.6 kg/m2, with zero bias for retrievals from the MOS

compared to radiosoundings. The latter study only covered

middle Europe.

Within the present article, we describe a WVP retrieval

algorithm designed for MERIS, which is currently being

implemented in ESA’s ENVISAT ground segment. The

algorithm is based on regressions performed on simulated

data. The approach is described in detail in Section 2. With a

view on the aforementioned uncertainties in determining the

absolute accuracy of such algorithms, Section 3 extensively

deals with the relative impact of different possible error

sources. In Section 4, we provide a first validation of the

algorithm, which has been applied to MOS data and

compared to radiosoundings.

2. Dataset and method

The algorithm for water vapour retrieval has been

designed using forward radiative transfer simulations on

vertical profiles of all relevant atmospheric parameters.

The results of the simulations have been inverted to infer

WVP. Subsequently, we describe the input data and the

method in detail.

2.1. Input data

2.1.1. Vertical profiles

Algorithm development starts with the selection of

proper vertical profiles of atmospheric properties, which in

our case were taken from radiosoundings. These were taken

from the Global Telecommunication System (GTS), from

which we have the complete dataset between July 1987 and

July 1988 available. This dataset consists of roughly

500,000 radiosonde profiles over land, which were reduced

to 300 radiosoundings applying the following criteria:

� The radiosounding had to consist of at least 15 levels.� Radiosounding top height had to be above 10,000 m.� Automatic tests for pressure, temperature, and water

vapour were carried out to identify erroneous

measurements in the data. These could, for example,

be temperatures above 330 K, high supersaturations,

or positive vertical pressure gradients.� The cloud fraction had to be available from coinciding

surface data and had to be less than or equal to 50%.

This threshold excludes completely cloudy atmos-

pheres, while the range of variability of atmospheric

water vapour in partly cloudy conditions is covered.

This test was passed by about 5% of the data, which were

then further reduced so that surface pressure and water

vapour showed maximum variations in the final dataset of

300 radiosoundings (see Fig. 1). These radiosoundings were

then extrapolated to 100 km altitude using a smooth, linear

transition from the actually measured data to the US Stand-

ard Atmosphere, which was used aloft. The above procedure

ensures that the selected 300 radiosoundings cover the

variability of the actually observed temperature and water

vapour profiles. Note that neither the entire radiosounding

dataset nor the selection of the 300 radiosoundings neces-

sarily resembles the global distribution of water vapour

profiles, simply because the global distribution of radio-

soundings is biased towards the densely populated areas.

However, Fig. 1 shows that we do cover the entire range of

possible climates without using unrealistic profiles.

2.1.2. Aerosol optical parameters

Aerosol types, vertical distribution, and optical depth

were varied randomly. Aerosol models were taken from

Bolle et al. (1986). For each simulation, three different

aerosol types were considered, namely stratospheric, tropo-

spheric background, and continental aerosol. Stratospheric

aerosol was placed in the upper atmosphere between 15 and

20 km, tropospheric background aerosol was distributed in

the middle and lower atmosphere between 2 and 10 km, and

the continental aerosol was placed in the altitude range from

0 to 2 km. The optical depth of all three atmospheric

constituents was allowed to vary randomly within a broad

range of variability, as shown in Table 1. While over

extremely dark or bright surfaces variations in aerosol

optical depth may cause significant variations in the

retrieval, the particular choice of aerosol models does not

impose large variations (see Section 3.2.1 for details).

2.1.3. Spectral surface reflectance

The absolute value and spectral behavior of the surface

reflectance influence the water vapour retrieval. For the

present simulations, reflectance measurements of different

targets with high spectral resolution were taken, whereby

the reflectance varied between 10% and 90% (Bowker, et al.,

1985). We grouped the database in three categories, namely

vegetation, snow, and soil. The relative occurrence of these

categories within the simulations was chosen to roughly fit

their actual global distribution (57% vegetation, 33% bare

soils, 10% snow). For each simulation, we first selected the

category and then a spectrum was randomly selected from

all spectra within the category. We further constrained the

combinations in a way that snow surfaces were rejected if

the surface temperature exceeded 283 K, thus preventing

snow surfaces to be simulated in warm atmospheres.

2.2. Transmission calculations

The high-resolution atmospheric transmission was calcu-

lated for all layers of each radiosounding on basis of the

HITRAN’96 dataset (Rothman et al., 1998). Note that the

HITRAN line parameters for water vapour above

R. Bennartz, J. Fischer / Remote Sensing of Environment 78 (2001) 274–283 275

8000 cm � 1 have been updated in November 1999. This

update affected about two thirds of the lines in the relevant

spectral region. The results presented here were derived

from the updated database. We used a spectral resolution of

0.15 cm � 1, which translates to roughly 0.01 nm wave-

length resolution at 900 nm. The small impact of a further

increase in spectral resolution can be seen in Fig. 2, where

we plotted the transmission in MERIS channel 15

(900 ± 5 nm) for a 1-km-thick layer with 290 K temperature,

70% relative humidity, and absorber masses 1, 2, and 5,

which correspond to zenith angels of 0�, 60�, and 78�,respectively. In particular, compared to the pressure depend-

ency of the total absorption, the impact of spectral sampling

is uncritical. To demonstrate the maximum effect of pressure

broadening, in Fig. 2, pressure varies between 500 (roughly

5000 m) and 1000 hPa (surface level), while temperature

and humidity are kept fixed at comparably high levels. In

reality, temperatures of 290 K with relative humidities of

70% are unlikely to occur at 5000 m altitude. Nevertheless,

the results shown in Fig. 2 imply a nontrivial impact of

variable pressure-broadening on the total absorption. Espe-

cially for high plateaus where surface pressure is low,

systematic deviations between actual and retrieved water

vapour may occur. We did the same analysis for temper-

ature- and self-broadening (Rothman et al., 1998) and found

no significant variations in absorption. Due to the limited

areal extent of the validation dataset used in this study (see

Section 4), we were not able to statistically assess the impact

of pressure-broadening on actually observed data within this

study. To fit the broad band transmission of the considered

MOS and MERIS channels, the k-distribution method of

Bennartz and Fischer (2000) was used.

2.3. Radiative transfer simulations

The radiative transfer code MOMO (Matrix Operator

MOdel; Fell & Fischer, 1994; Fischer & Grassl, 1984) has

Fig. 1. Radiosounding dataset used for algorithm development. The upper panels show the complete global dataset with less than 50% cloud cover. The lower

panels show the 300 radiosoundings, which were selected from the total dataset.

Table 1

Variations in aerosol parameters for the three different aerosol types used in

this investigation

Type

Minimum

optimum depth

Maximum

optimum depth

Stratospheric 0.005 0.010

Tropospheric background 0.010 0.090

Continental 0.030 0.230

The values refer to a reference wavelength of 550 nm.

R. Bennartz, J. Fischer / Remote Sensing of Environment 78 (2001) 274–283276

been used to simulate the radiance for the MERIS chan-

nels. The Matrix Operator Method (Plass, et al., 1973)

offers the possibility of (a) combination of layers of any

given optical properties, (b) very fast calculation even in

the case of optically thick layers with highly anisotropic

phase functions, (c) choice of any desired spectral surface

reflectivity, and (d) the calculation of upwelling and

downwelling radiance within and on top of the atmosphere

for all layer boundaries.

Scattering and absorption processes due to aerosols are

represented by appropriate scattering and extinction coef-

ficients and the corresponding scattering phase function

derived from Lorentz–Mie theory. Air molecules are small

compared to the wavelength of the incoming sunlight. Thus,

molecular scattering can be described with Rayleigh theory.

2.4. Water vapour retrieval

The water vapour retrieval algorithm is based on the

work of Fischer (1988) and Bartsch and Fischer (1997). The

general algorithm approach is to relate the columnar water

vapour content to the ratio of MERIS channels 14 and 15,

located at 885 and 900 nm, respectively. The general form

of the retrieval algorithm is:

W ¼ k0 þ k1logðRÞ þ k2log2ðRÞ; ð1Þ

R ¼ L15

L14; ð2Þ

where W is the column amount of water vapour, and R is the

ratio between L14 and L15 (Eq. (2)), which are the radiances

measured in MERIS channels 14 and 15, and k0, k1 and k2are regression constants. This simple model is based on the

assumption that a logarithmic relation between the absorber

mass and extinction exists. It therefore reflects Lambert’s

law for an idealized nonscattering atmosphere, unsaturated

absorption, and monochromatic radiation. Since none of

these preconditions is perfectly given, an empirical quad-

ratic correction term is introduced in Eq. (1). It has to be

outlined that the model described in Eq. (1) has been chosen

because of its simplicity and its physical motivation. The

relation between W and the radiance ratio may be fitted by

several other nonlinear functions with similar results in

terms of the retrieval error. The regression coefficients are

derived by inverting results of the radiative transfer

simulations and depend on observation geometry.

3. Error analysis

3.1. Impact of MERIS instrument characteristics

3.1.1. Signal-to-noise ratio (SNR)

To evaluate the algorithm’s sensitivity to variable SNR,

we derived the regression on the simulation dataset for

different SNRs and calculated the respective RMSE with

respect to the simulation dataset. This was done by adding

different levels of a synthetic Gaussian noise to the simu-

lation dataset and then calculating the RMSE for all differ-

ent noise levels. As shown in Fig. 3, the theoretical retrieval

accuracy is almost constant at 1.6 kg/m2. According to the

MERIS specification, an actual SNR better than 200 can be

expected, so that the impact of MERIS’ SNR is expected to

be negligible.

Fig. 2. Atmospheric transmission of MERIS channel 15 (900 ± 5 nm) for a

tropical atmosphere (WVP= 41 kg/m2). The abscissa gives the spectral

resolution at which the transmission was calculated. The different curves

depict different relative absorber masses and different surface pressure levels.

Fig. 3. Dependence of the algorithm’s retrieval uncertainty on the SNR of

the MERIS. The curve represents an average over all MERIS

observation geometries.


3.1.2. Spectral misregistration

Due to the impact of the system optics and other

distorting effects, the actual center position of each MERIS

channel is slightly distorted from the nominal center posi-

tion. Since the MERIS swath is covered by five independent

CCD cameras, this distortion depends on the camera and on

the relative viewing angle within each camera’s field of

view. The spectral misregistration of all five MERIS flight

model cameras (FM1–FM5) has been measured and pub-

lished by ESA (1998).

The MERIS channels with relevance to water vapour

retrieval are the channels 14 and 15, located at 890 and

900 nm, respectively. The spectral misregistrations according

to ESA (1998) are plotted in Fig. 4 for both channels. Note

that since 1998, the center wavelength of MERIS channel 14

has changed from 890 to 885 nm. This change was made in

response to the subsequently presented results of the impact

of spectral misregistration on channel 14 and the recommen-

dation of the authors. Therefore, and only in this section, we

add the respective wavelength of channel 14 in brackets

behind the channel definition. In all other sections,

channel 14 refers to the current channel setting of 885 nm.

For cameras FM1, FM2, and FM4, the average spectral

misregistration is positive with values of about 0.4 nm,

while for cameras FM3 and FM5 on average, a negative

spectral misregistration is observed. The variability within

the field of view of each camera is for all cameras less than

0.3 nm and shows for each camera a similar dependency on

incidence angle at both channels. The overall variability of

the center position of channels 14 (890 nm) and 15 is within

a range of ± 0.6 nm around the nominal center position.

The impact of such spectral misregistration on the aver-

aged atmospheric transmission for channels 14 (890 nm) and

15 is depicted in Fig. 5. Channel 14 (890 nm) exhibits

variations in the range of 3–5% transmission, dependent on

absorber mass, while the more strongly absorbing channel 15

Fig. 4. Actual spectral position of MERIS channels 14 and 15 as a function of camera (FM1–FM5) and incidence angle in the respective camera after

ESA (1998).

Fig. 5. Atmospheric transmission of MERIS channels 14 and 15 (nominal position 890 and 900 nm) as a function of the spectral misregistration for a tropical

standard atmosphere.


is almost not influenced by spectralmisregistration (see Fig. 5,

right). This is due to the fact that channel 15 is centered on the

absorption band at 900 nm, which is surrounded by the more

weakly absorbing regions at 895 and 905 nm. A shift of the

center wavelength of channel 15 results therefore in an

increase of absorption at one side and a compensating

decrease at the other. For channel 14 (890 nm), a shift of

the center wavelength to higher wavelengths results in an

increase of absorption without any compensation. Since the

variation in transmission due to spectral misregistration of

channel 14 (890 nm) would yield systematic deviations in the

water vapour estimates obtained for the different cameras of

MERIS, we proposed to ESA a slight shift of the center

position of channel 14 to 885 nm, which is far enough from

the 900-nm absorption band to guarantee stable, absorption-

free observations for all possible spectral misregistrations at

channel 14.

3.1.3. Impact of variable observation geometry

Fig. 6 shows the RMSE of the retrieval plotted on the

MERIS swath for a consecutive set of ENVISAT orbits. The

product exhibits stable estimates for the vast majority of

possible observation geometries (RMSE between 1.5 and

1.7 kg/m2). Only for very high latitudes and thus very high

solar zenith angles (Q8� 70�) a significant decrease of

accuracy can be observed.

3.2. Impact of environmental parameters

3.2.1. Variable aerosol and surface reflectance

As the water vapour retrieval is formulated in terms of

radiance ratios, any common factor in both channels cancels

when doing the ratio. This holds, in general, for temporal

variations in the solar constant and under the presumption of

a nonscattering atmosphere, as well for the impact of

variable surface reflectance. However, if a large part of the

signal received at the satellite originates from volume

scattering in the atmosphere, both channels may react differ-

ently and the radiance ratio may be affected as well. The

most significant amount of scattering in the near-infrared in

a cloud-free atmosphere occurs from aerosol in cases where

the surface reflectance is low. This is especially the case over

water surfaces, where the signal at the satellite is almost

entirely due to aerosol backscatter.

In order to examine the impact of aerosol and its

interaction with surface reflectance, we carried out a set

of radiative transfer simulations, where surface reflectance

and aerosol loading were systematically varied (see Fig. 7).

The aerosol optical thickness was varied between 0.045

and 0.6, and one can see that the largest impact occurs at

either very high or very low surface reflectances. At

intermediate surface reflectances, typical for vegetated

surfaces, the impact of aerosol is only weak. In general,

if surfaces with reflectance less than 8% are excluded

(water), the induced variation does not significantly

exceed ± 5% (see Fig. 7).

3.2.2. Spectral variations of surface reflectance

In general, the surface reflectance depends on wave-

length in a nonlinear way. Variations are due to spatially

and temporally highly variable parameters such as chlor-

ophyll absorption, plant cellular reflectance (‘red edge’),

Fig. 6. Accuracy of water vapour retrieval projected on the MERIS swath

for 1 day of ENVISAT orbits. The observation geometry corresponds to

Julian day 80.

Fig. 7. Retrieved water vapour path for a mid-latitude summer

atmosphere for 450 simulations with variable aerosol loading as a

function of surface reflectance. Each dot represents one simulation. The

spread of points at a given surface reflectance shows the variations

caused by different aerosol optical thickness (aerosol optical thickness

was varied between 0.045 and 0.6). The thick line gives the actual water

vapour path of a mid-latitude standard atmosphere and the thin lines give

an error range of ± 5%.


refractive index discontinuities of plant cellular constitu-

ents, absorption by iron-rich soils, and absorption by

water or ice constituents (Bowker et al., 1985; Gao,

et al., 1993). However, within the range of 880–910 nm,

the spectral variation of surface reflectance is considerably

small. Since variations in surface reflectance for optically

thin atmospheres directly result in variations of the meas-

ured signal, even these small variations may cause sig-

nificant systematic deviations of retrieved water vapour

content. This problem has been addressed in the literature

in different ways. Some algorithms use two window

channels that bracket the absorption channel (Tahl &

v. Schoenermark, 1998). These techniques rely on the

usage of a second window channel, which results in at

least one window channel being farther apart from the

absorption channel, so that spectral variations of surface

reflectance might be stronger. Another technique uses two

channels located at the same center wavelength, but with

variable width (Frouin, et al., 1990). While this technique

is insensitive to linear spectral variations in surface

reflectance, both channels are influenced by water vapour

absorption and thus, the dynamic range of the observed

signal is reduced. The approach of the MERIS tries to

combine advantages of both techniques. MERIS channels

14 (reference) and 15 (absorption) are located at 885 and

900 nm with a respective spectral width of 10 nm, so that

firstly, the reference channel is not influenced by water

vapour absorption. Secondly, both channels are only 15 nm

apart and hence, spectral variations of surface reflectance

are in general small. To illustrate the impact of such close

band settings, Fig. 8 compares histograms of the ratio

between surface reflectances in the reference and the

absorption channel for MOS and MERIS. The data used

for these histograms were taken from the aforementioned

surface reflectance dataset given by Bowker et al. (1985).

While for the MERIS variations are in the range of ± 1%,

MOS shows a roughly five times higher variability. In

moderately scattering atmospheres, where the received

signal is governed by surface reflectance, this ratio

directly translates to variations in the observed radiance

ratio. The impact of such spectral variations in surface

reflectance on the retrieval can be seen in Fig. 9. Calculations

are done in a similar way as for Fig. 7, but in addition,

surface reflectance in channel 14 was changed by 1% with

respect to the surface reflectance of channel 15. It can be

Fig. 8. Histogram of ratios between reference and absorption channel surface reflectances for MERIS (channels at 900/885 nm) and MOS (channels at

945/870 nm) for about 150 different surface types. The surface reflectance data were taken from Bowker et al. (1985). Note the different scales of the

respective ordinates.

Fig. 9. As Fig. 7, but including ± 1% deviation between the surface

reflectance in the window and absorption channel. The upper set of dots is

derived for an increase of the surface reflectance in the window channel by

1%; the lower set for a corresponding decrease of 1%.


seen that the resulting variability exceeds the variability due

to aerosol.

4. Validation

4.1. Re-calibration of the algorithm to MOS data

For validation, we re-calibrated the MERIS algorithm to

the MOS channel settings. Since the MOS sensor has a

swath width of approximately 200 km, maximum off-nadir

angles are ± 7.5�. This corresponds to an only 0.9% longer

optical path at the scan edge than at nadir view. We therefore

neglect the dependence of the radiation field on observer

zenith angle and thus on azimuth and regard MOS as only

nadir-looking. In Table 2, we give the coefficients we

derived for Eq. (1) for MOS. The radiance ratio in Eq. (1)

for MOS was redefined as (Eq. (3)):

R ¼ L945

L870 þ ð930�870Þð1010�870Þ ðL1010 � L870Þ

; ð3Þ

where L denotes an observed radiance at the wavelength

indicated by the subscript. The denominator is a weighted

average between the two MOS window channels at 870

and 1010 nm to obtain an interpolation for a fictional

window channel at 930 nm and thus 15 nm apart from

the MOS absorption channel at 945 nm. The fraction in

the denominator weights the two window channels

according to their relative spectral distance from 930 nm.

Note that this constellation was not chosen to optimize the

MOS retrieval, but to simulate the spectral difference

between the MERIS channels. If we had interpolated

the window channels exactly to 945 nm, we would

have resembled the Continuum Interpolated Band Ratio

(CIBR) technique as applied to MOS data by Tahl and

v. Schoenermark (1998).

Table 2 also gives the RMSEs with respect to the

model data on which the regression coefficients were

derived. Except for very high solar zenith angles (con-

sistent with the above discussed results for the MERIS

channel settings), theoretical accuracy is between 1.66 and

1.73 kg/m2.

4.2. Comparison with radiosoundings

We applied the re-calibrated algorithm to MOS over-

passes over central Europe between 1996 and 1999. Radio-

soundings were available for the complete period from the

GTS. The combined radiosounding MOS dataset was man-

ually screened according to the following criteria:

� The radiosounding had to be within a range of

± 200 km from the MOS subsatellite track and

hence, the closest MOS observation must be 100 km

apart at maximum.� A maximum time difference of ± 3 h between MOS

overpass and radiosounding was allowed.� The MOS scene close to the radiosounding had to be

at least about 50% cloud-free, where clouds were

masked from multispectral MOS imagery by eye.

Only the cloud-free areas of the MOS images were

used for the subsequent intercomparison.

We found, in total, 239 match-ups between MOS data

and radiosoundings. The MOS data were averaged over all

cloud-free pixels within less than 200 km distance from the

radiosounding. A scatterplot between the MOS data and

radiosoundings is presented in Fig. 10. The RMS deviation

between MOS and radiosounding is 2.49 kg/m2 and the bias

is 0.04 kg/m2.

Table 2

Coefficients for Eq. (1) re-calibrated to MOS data

Solar zenith angle [�] k0 [kg/m2] k1 [kg/m

2] k2 [kg/m2] RMS [kg/m2]

0.00 � 0.974 � 8.291 9.399 1.73

14.17 � 0.974 � 8.214 9.241 1.73

25.95 � 0.976 � 8.034 8.857 1.71

37.63 � 0.978 � 7.728 8.222 1.69

49.28 � 0.976 � 7.241 7.300 1.66

60.92 � 0.957 � 6.447 6.049 1.68

72.55 � 0.853 � 4.956 4.507 2.03

The angles are those used in the radiative transfer model (Gauss–Lobatto

integration). The RMS gives the root mean square deviation between the

simulation dataset and the regression fit. They deviate only slightly from

those given in the above discussion of the MERIS retrieval errors.

Fig. 10. Scatterplot of radiosonde-derived vs. MOS-retrieved water vapour

path. The thin line is the identity line.


The results shown in Fig. 10 are based on averages over

all cloud-free MOS pixels that are less than 200 km away

from the radiosounding station. In addition to the averaged

WVP, the standard deviation for the data used within each

scene gives a measure of the internal variability of the

retrieval. We found the average standard deviation within

the cloud-free parts of the MOS overpasses to be 1.25 kg/m2.

To assess the accuracy of the radiosoundings as well

as the impact of the time delay between the MOS

overpass and the radiosounding, we search our combined

MOS/radiosounding database for cases, where radiosound-

ings for 6 UTC and 12 UTC for the same station and day

were available. We found 103 such cases. The mean

absolute difference between the WVP derived for the 6

UTC and 12 UTC radiosoundings was 1.8 kg/m2. Obvi-

ously, it is not possible to subdivide this value into

differences caused by the time difference between con-

secutive radiosoundings and the random error associated

with each radiosounding.

5. Outlook

Within the course of this investigation, we describe and

evaluate the water vapour retrieval algorithm dedicated to

the MERIS. We show that over land surfaces, the algo-

rithm’s theoretical accuracy is in the order of 1.7 kg/m2,

dependent on the sensors SNR. For a set of 239 coinciding

MOS and radiosounding data, the RMS deviation between

MOS data and colocated radiosoundings was found to be

2.49 kg/m2 with a 0.04 kg/m2 bias component. While

some possible error sources could not be assessed due to

the limited comparison dataset available, these results are

encouraging as they are in the accuracy range of about

10% relative error for typical mid-latitude atmospheres. In

addition to the validation of the retrieval algorithm, we

may also conclude that the small bias component of the

retrieved water vapor path indicates that the line parame-

ters used for the simulations sufficiently describe the water

vapour absorption in the spectral region around 945 nm.

With its comparably high spatial resolution of 300 m at

nadir, MERIS data might therefore be a useful tool in

smallscale and mesoscale studies on land surfaces and on

evapotranspiration. Further, at a degraded resolution,

MERIS water vapour retrievals might be useful to gain

information about water vapour on a global scale, for

example, in the framework of data assimilation into numer-

ical weather prediction models, especially over sparsely

populated areas, where no information about water vapour

can be obtained otherwise.

In addition to these applications, the extended usage of

the MERIS water vapour channels over clouds (Albert, et al.,

2001) and (almost nonreflecting) ocean surfaces will allow

process studies to be performed on cloud/radiation and

aerosol/radiation interaction.

Acknowledgments

We would like to thank the colleagues at DLR-DFD for

providing the MOS data. This research has been funded in

the framework of the MERIS Application Project (MAPP),

which is supported by the German Federal Ministry for

Education and Research (BMBF) under contract no. 07

UFE 16/1.

References

Albert, P., Bennartz, R., & Fischer, J. (2001). Remote sensing of atmos-

pheric water vapor from backscattered sunlight in cloudy atmospheres.

Journal of Atmospheric and Oceanic Technology, 18 (6), 865–874.

Bartsch, B., & Fischer, J. (1997). Passive remote sensing of columnar water

vapour content over land surfaces, MPI Report No. 234, Hamburg

0937-1060, Germany.

Bennartz, R., & Fischer, J. (2000). A modified k-distribution approach

applied to narrow band water vapour and oxygen absorption estimates

in the near-infrared. Journal of Quantitative Spectroscopy and Radia-

tive Transfer, 66, 539–553.

Bolle, H.-J., Joseph, J. H., McCormick, M. P., Raschke, E., Pollack, J. B.,

& Spankuch, D. (1986). A preliminary cloudless standard atmosphere

for radiation computation. Technical Report. WCP-Report 112, WMO/

TD-No. 24

Borbas, E. (1998). Derivation of precipitable water from GPS data: an

application to meteorology. In: A. K. Richter (Ed.), Physics and chem-

istry of the earth, vol. 23, No. 1 (pp. 87–90). Maryland, USA: Elsevier.

Bowker, D. E., Davis, R. E., Myrick, D. L., Stacy, K., & Jones, W. T.

(1985). Spectral reflectances of natural targets for use in remote sensing

studies. NASA Reference publication 1139. Maryland, USA: NASA

Goddard Space Flight Center Greenbelt.

ESA, Meris spatial registration. (1998). ESA reference no. PO-TN-ESA-

ME-00732. ESA-ESTEC, Noordwijk, the Netherlands.

Fell, F., & Fischer, J. (1994). MOMO — The FU-Berlin Radiative Transfer

Model based on the Matrix Operator Method. Model Description and

User Manual. MAST-II, Technical Progress Report, Programme of the

European Commission Contract No. MAS2-CT92-0020.

Fischer, J. (1988). High-resolution spectroscopy for remote sensing of phys-

ical cloud properties and water vapour. In: J. Lenoble, & J.-F. Geleyn

(Eds.), Current problems in atmospheric radiation ( pp. 151–156).

Hampton, Virginia, USA: Deepak Publishing.

Fischer, J., & Grassl, H. (1984). Radiative transfer in an atmosphere–ocean

system: an azimuthally dependent matrix operator approach. Applied

Optics, 23, 1035–1039.

Frouin, R., Deschamps, P. Y., & Lecomte, P. (1990). Determination from

space of atmospheric total water vapour amounts by differential absorp-

tion near 940 nm: theory and airborne verification. Journal of Applied

Meteorology, 29, 448–460.

Gao, B.-C., Goetz, A. F. H., Westwater, E. R., Conel, J. E., & Green, R. O.

(1993). Possible near-IR channels for remote sensing precipitable water

vapour from geostationary satellite platforms. Journal of Applied Mete-

orology, 32, 1791–1801.

Plass, G. N., Kattawar, G. W., & Catchings, F. E. (1973). Matrix operator

theory of radiative transfer: 1. Rayleigh scattering. Applied Optics, 12,

314–329.

Rocken, C., Van Hove, T., &Ware, R. (1997). Near real-time GPS sensing of

atmospheric water vapor.Geophysical Research Letters, 24, 3221–3224.

Rothman, L., Rinsland, C., Goldman, A., Massie, S., Edwards, D., Flaud, J.,

Perrin, A., Camy-Peyret, C., Dana, V., Mandin, J., Schroeder, J.,

McCann, A., Gamache, R., Wattson, R., Yoshino, K., Chance, K., Jucks,

K., Brown, L., Nemtchinov, V., & Varanasi, P. (1998). The HITRAN

molecular spectroscopic database and HAWKS (HITRAN atmospheric


workstation): 1996 edition. Journal of Quantitative Spectroscopy and

Radiative Transfer, 60, 665–710.

Schlussel, P., & Emery, W. (1990). Atmospheric water vapor over ocean

from special sensor microwave/imager observations. International

Journal of Remote Sensing, 11, 753–766.

Tahl, S., & v. Schoenermark, M. (1998). Determination of the column water

vapour of the atmosphere using backscattered solar radiation measured

by the Modular Optoelectronical Scanner (MOS). International Journal

of Remote Sensing, 19, 3223–3236.

Vesperini, M., Breon, F.-M., & Tanre, D. (1999). Atmospheric water vapour

content from spaceborne POLDER measurements. IEEE Transactions

on Geoscience and Remote Sensing, 37, 1613–1619.


retrieval of columnar water vapour over land from backscattered solar radiation using the medium...

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