Qualifying IMG tropical spectra for clear sky

Journal of Quantitative Spectroscopy &Radiative Transfer 77 (2003) 131–148

www.elsevier.com/locate/jqsrt

Qualifying IMG tropical spectra for clear sky

G. Masielloa, C. Seriob; ∗, H. ShimodacaIMAAA=CNR, Tito Scalo, Pz, Italy

bINFM, Gruppo Collegato di Potenza, Sezione di Napoli, C.da Macchia Romania, Potenza, ItalycEarth Observation Research Center, NASDA Tokyo, Japan

Received 15 March 2002; accepted 22 May 2002

Abstract

The problem of cloud detection for the Interferometric Monitoring of Greenhouse Gases spectrometer hasbeen addressed by considering a set of thresholding tests which takes full advantage of the high spectralresolution of the sensor. The methodology has been applied to a case study consisting of spectra recordedin the tropics on sea surface, although the scheme may be easily extended to other latitudes. The algorithmis very e4cient because it uses only the observed spectrum and no on-line radiative transfer calculation isneeded. Based on this cloud detection scheme a set of clear-sky tropical spectra have been identi7ed to beused by the scienti7c community for further studies such as retrieval of atmospheric properties and highspectral resolution radiative transfer modeling.? 2002 Elsevier Science Ltd. All rights reserved.

Keywords: Infrared; Atmosphere; Clouds; Radiative transfer

1. Introduction

The Interferometric Monitoring of Greenhouse Gases (IMG) [1] has =own on board on theJapanese Advanced Earth Observing Satellite (ADEOS) from October 1996 to June 1997. The instru-ment is a Fourier Transform Spectrometer which observes Earth’s emission spectrum at nadir viewin three spectral bands from 3.3 to 16 �m with a spectral sampling of 0:05 cm−1 and an apodizedspectral resolution of 0:1 cm−1 [1]. The IMG data are of good spectral coverage of the Earth fulldisk and therefore provide new insights into remote sensing of atmospheric parameters (see, e.g.,[2–6]). However, one problem which is still hampering the full exploitation of IMG observations isthe absence of information about cloud contamination in the sensor footprint.

∗ Corresponding author. DIFA, University of Basilicata, C.da Macchia Romania, 85100 Potenza, Italy. Tel.: +39-0971-427-261; fax: +39-0971-427-271.E-mail address: [email protected] (C. Serio).

0022-4073/02/$ - see front matter ? 2002 Elsevier Science Ltd. All rights reserved.PII: S 0022-4073(02)00083-3

132 G. Masiello et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 77 (2003) 131–148

The Earth Observation Research Center (EORC) has developed a scene identi7cation procedurefor IMG which uses the Ocean Color and Temperature Scanner (OCTS) [7], an imager which hasbeen a companion instrument of IMG aboard ADEOS. The IMG footprint is co-located with thescene imaged through the OCTS visible channels. The OCTS co-location for the IMG observationsis available at the EORC IMG home page: http://www.eorc.nasda.go.jp/AtmChem/IMG/.

One limitation of the above procedure is that it is only available for day time and the presenceof clouds has to be based on visual inspection of the OCTS imaged scene, which while powerful,since the human eye is quite able to capture cloud texture, is nevertheless a process subjective andtherefore not immune from errors.

Our primary objectives are to develop an IMG stand alone day–night cloud detection scheme, rely-ing only on IMG observations, and to provide a well quali7ed set of clear-sky IMG spectra to be usedfor future studies concerning retrieval of atmospheric properties, high spectral resolution radiativetransfer modeling, assessment of cloud detection schemes for high spectral resolution infrared sensorssuch as the American Advanced Infrared Radiometer Sounder (AIRS) [8] and the European InfraredAdvanced Sounding Interferometer (IASI) [9]. A MATLAB code which implements the cloud de-tection scheme and the set of spectra used in this analysis are available from the authors on request.

To attain the above goals we have devised a cloud detection scheme which incorporates theso-called hs-method developed in [10,11] and three new thresholding tests which are particularlysensitive to low and thin clouds. A validation data set of clear-sky IMG spectra has been build upby using colocated OCTS imagery. This validation set has been then used to evaluate the performanceof the cloud detection scheme.

The scheme relies mostly on spectral channels in the atmospheric window and is, therefore,primarily intended for IMG Band 3, which covers the wave number range from 600 to 2000 cm−1.For the sake of brevity, we limit here to consider a case study consisting of sea surface tropical

spectra. The extension of the methodology to other latitudes is straightforward and the cloud detectionmethodology can be used for any nadir view high spectral resolution infrared sensors.

The paper is organized as follows. Section 2 describes the set of IMG data we have used andgives information about the IMG instrument in general. Section 3 describes the basic cloud detectionmethodology. Section 4 compares the results of the cloud detection scheme to that obtained throughOCTS colocation. Conclusion are given in Section 5.

2. The IMG data set

The IMG instrument is a Michelson interferometer which is operated with three infrared detectorscovering the spectral ranges or bands:

• Band 1: 3.3–4:3 �m,• Band 2: 4.3–5:0 �m,• Band 3: 5.0–16:7 �m.

Each detector has its own 7eld of view of 0:6◦, which yields a ground footprint of about 8 km2.The detector for Band 2 is on the optical axis so that it looks directly at satellite nadir position, withthe other two footprints side by side. The IMG Field of View and the relative position of the threefootprints is shown in Fig. 1. The 7gure also shows a complete observation cycle of IMG whichlasts 110 s during which the sensor acquires six diLerent observations ≈ 86 km apart. Each series

http://www.eorc.nasda.go.jp/AtmChem/IMG/

G. Masiello et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 77 (2003) 131–148 133

Fig. 1. Field of view geometry of IMG showing the footprint of the three bands (adapted by IMG Technical Reportavailable at web page http://www.eorc.nasda.go.jp/AtmChem/IMG/).

of six spectra represent a fundamental unit of data produced by the IMG data processing team andwill be referred to as an observation sequence. Band 1 has never operated during the operationallife of IMG, so that data are available only for Bands 2 and 3.

Because of the peculiar IMG Field Of View geometry, an independent cloud detection schemefor each band should be devised. However, thresholding tests for cloud detection rely mostly onatmospheric window channels which are mostly located in Band 3. For this reason, the scheme wepresent in this paper is intended only for Band 3. This should be kept in mind in the remaining ofthis paper, mostly when looking at the results presented in Section 4.

The data used in this analysis have been calibrated with the calibration program version 005 and006 (e.g., http://www.eorc.nasda.go.jp/AtmChem/IMG/). The spectra have been then apodized with aGaussian function of half width at half height of 0:25 cm−1 and resampled at a rate M�=0:25 cm−1.By this way the new data 7t the spectral characteristics of the IASI spectrometer [9] and can beused for analysis concerning such an instrument. The spectra have been recorded within the tropicalbelt and cover the period December ’96 to June ’97. Their geoghraphic location is shown in themap of Fig. 2. Each observation sequence is provided with the corresponding OCTS image showingearth location of the three IMG footprints. An example is shown in Fig. 3.

3. The cloud detection scheme

The cloud detection scheme is based on a series of four tests which are here described.

3.1. The hs-method

The hs-method has been developed in [10,11] and it was derived to take into account the highspectral quality which will become soon available with new generation infrared sensors such asAIRS and IASI [8,9], of which IMG has been a precursor.




−180 −120 −60 0 60 120 180−90

−60

−30

0

30

60

90

Longitude (degrees)

Latit

ude

(deg

rees

)

Fig. 2. Map of the IMG tropical sequences used in this analysis. Fig. 3. Example of OCTSframes co-located with theIMG footprint. The brightestbox refers to the IMG Band1, the box in the middle isthe footprint of Band 2 and7nally the third box refers toIMG Band 3. The sequenceof observations is 1–6 in thetop to bottom order.

Basically, the scheme exploits the unique spectral signature of sea surface in the window regionand needs a reference spectrum. In atmospheric window regions radiance spectra are quite sensitiveto the emissivity of the underlying emitting surface. Clear-sky spectra are sensitive of course to theemissivity of the land or sea, whereas cloudy spectra are mostly dependent on the cloud spectralsignature. Keeping this in mind, attention has been focused to some suitable atmospheric window(the atmospheric window 800–900 cm−1 has been considered to develop the hs scheme), and anindex which is very sensitive to emissivity while being quite insensitive to other possible interferingparameters, such as temperature of the underlying emitting surface and water vapor concentration, hasbeen de7ned. The index uses the observed spectrum and a suitable clear-sky (synthetic or measured)spectrum. We refer to [10,11] for any details on how to properly devise such an index and limit thediscussion to the relevant de7nitions.


Let S1(�) and S2(�) be the observed and reference spectrum, respectively. Then, the 7rst operationinvolved to compute the hs index is to transform into brightness temperature (BT) the two spectra:

Ti(�) = B−1(Si(�)); (1)

where i= 1; 2 and B is the blackbody Planck function, S(�) and T (�) are respectively the radiance(e.g. units of W=m2 cm−1 sr) and the BT spectrum, respectively. After BT conversion, T1(�) andT2(�) are standardized through the operation

Hi(�) =Ti(�)− 〈Ti〉

si(2)

with i = 1; 2 and 〈Ti〉 and si the mean and standard deviation of Ti(�), respectively. The mean andstandard deviation are considered with respect to the wave number �. Furthermore, the correlationand cross-correlation of the couple (T1(�); T2(�)) are now computed according to

ri(j) =ci(j)ci(0)

(3)

and

r12(j) =c12(j)√c1(0)c2(0)

(4)

respectively, where j is the lag. The covariance and cross-covariance functions, ci and c12, respec-tively, are obtained by

ci(j) =1N

N−j∑k=1

Hi(k) · Hi(j + k) (5)

and

c12(j) =1N

N−j∑k=1

H1(k) · H2(j + k) (6)

with i= 1; 2; as usual, we have written H (kM�) =H (k), with M� being the sampling rate. Finally,the homomorphic degree of the two spectra is de7ned through the index:

hs=

∑NLj=1 |r1(j)− r2(j)|∑Nl

j=1 |r12(j)|; (7)

where NL is the number of lags for which the various correlation functions have been computed.Normalization of hs to the coherence function r12 will enhance diLerences in shape, resulting ina very e4cient test to detect inhomogeneities. The index hs will be zero for spectra, which areperfectly homomorphic, that possess homogenous features with clear-sky spectra, whereas it willtend to in7nity for incoherent functions.

The characteristic of the index above is that it is based on covariance functions and thereforetakes full advantage of the noise-averaging properties of correlation (see [10]). The use of correlationfunctions counts many applications to the identi7cation of atomic=molecular emission or absorptionfeatures (e.g. [12]).


800 810 820 830 840 850 860 870 880 890 900270

275

280

285

290

295

300

BT

spe

ctru

m (

K)

800 810 820 830 840 850 860 870 880 890 900270

275

280

285

290

295

300

BT

spe

ctru

m (

K)

wave number (cm−1)

(a)

(b)

Fig. 4. IMG reference spectra used in the hs-test. Panel (a) shows the relatively dry reference spectrum, panel (b) showsthe relatively wet reference spectrum.

To develop a cloud detection scheme on the basis of the hs index, we need to choose

• a suitable cut-oL point for NL,• a suitable reference spectrum,• a suitable threshold, hso, so that the presence of cloud is identi7ed when the computed hs isgreater than hso.

For NL we have experienced that NL = 100 is enough to retain all the important characteristicspresent in the covariance functions.

For the reference spectrum we have chosen two IMG tropical spectra, well quali7ed for clear skyon the basis of OCTS images. This choice, instead of a synthetic spectrum, minimizes biases due toforward modeling limitations. The two spectra are shown in Fig. 4 and corresponds to a wet and hotatmosphere and a relatively cold and dry atmosphere, respectively. The use of two reference spectragives higher =exibility to the method. Although, hs works on the basis of standardized spectra,spectra with very diLerent water concentration content could be diLerent in shape because, e.g., ofthe spectral signature of water vapor continuum.

In practice for a given observation we compute two values of hs, the 7rst one corresponding tothe dry reference spectrum and the second to the wet reference spectrum. Let hsd and hsw the dryand wet index, respectively. The test is passed if

min(hsw; hsd)¡hso: (8)


899.5 899.6 899.7 899.8 899.9 900 900.1 900.2 900.3 900.4 900.5295

296

297

298

299

300

BT

spe

ctru

m

1167.5 1167.6 1167.7 1167.8 1167.9 1168 1168.1 1168.2 1168.3 1168.4 1168.5295

296

297

298

299

300

BT

spe

ctru

m

800 850 900 950 1000 1050 1100 1150 12000.95

0.96

0.97

0.98

0.99


Em

issi

vity

σ=900 cm−1

σ=1168 cm−1

(a)

(b)

(c)

Fig. 5. (a) Example of IMG spectrum showing the super window 899.5–900:5 cm−1; (b) the super window 1167.5–1168:5 cm−1 is now shown; (c) sea surface spectral emissivity curve showing the position of the two super windows.

Finally, based on our previous experience with the hs method (e.g. [10,11]) a valuable thresholdhas been proved to be hso = 0:3. It is here important to stress that a suitable threshold may dependon the spectral resolution and type of apodization applied to the spectra.

3.2. Super window channel test

The hs-test has proved to be e4cient for moderate and thick cloudiness. However, it may befooled by thin clouds [11] such as high cirrus. An additional test, which may in part overcomethis problem is based on super window channels, that is spectral channels which are only slightlyaLected by gas line absorption.

Two of these super windows are shown in Fig. 5. They cover the spectral ranges 899.5–900:5 cm−1

and 1167.5–1168:5 cm−1, respectively and with central wave number �w1 = 900 cm−1 and �w2 =1168 cm−1, respectively. Let 〈T (�w1)〉 and 〈T (�w2)〉 be the BT values integrated over the two superwindows, respectively:

〈T (�wi)〉= 1�ui − �li

∫ �ui

�li

T (�) d� (9)

with i = 1; 2; T (�) the BT spectrum and �u and �l the upper and lower limit of the window,respectively. The idea, which goes back to Inoue [13], is to base a suitable test on the diLerence

MTw = 〈T (�w1)〉 − 〈T (�w2)〉: (10)


The location of the central wave number �w1 and �w2 over the seawater spectral emissivity curve[14] is shown in Fig. 5. We see that �w1 has been chosen in such way to correspond to the maximumof the emissivity curve in the spectral range 800–1200 cm−1. Because of this choice, we expect theabove BT diLerence to be positive in clear-sky conditions, over sea surface. However, because ofthe spectral dependence of the water vapor continuum, the sign is normally reversed and only fordry conditions we have that the diLerence is positive.

The diLerence temperature de7ned in Eq. (10) measures the slope of the spectrum in the atmo-spheric window. This slope is very sensitive to the presence of ice particles such as those presentin cirrus clouds (e.g. [15]). It is here to be stressed that conventional BT diLerence methods todetect or classify clouds are mainly based on the couple of window channels located at 830 and900 cm−1 (see, e.g. [15]), whereas we use the couple 900 and 1168 cm−1. This is because we wantto convey in the scheme additional spectral information coming from the segment of the atmosphericwindow in between 1100 and 1200 cm−1. The window 800–900 cm−1 has been already used in thehs-method, so that additional tests based on this window would add no fresh information to thecloud detection scheme.

The range of possible clear-sky values for MTw has been investigated by computing syntheticspectra for tropical air masses. To this end we have used a representative set of tropical temperatureand moisture pro7les, collected by NOAA=NESDIS from radiosonde ascent in the years 1988 and1989. Based on these calculations we have that for clear sky

�1¡MTw¡�2 (11)

with �1 = −2 K and �2 = 1:6 K. This range is used to test IMG tropical observations for clearsky. If the computed MTw does not meet the above limits, the corresponding spectrum is =aggedcloudy.

3.3. The 791 cm−1 CO2 split window test

A further test for low cloudiness has been devised by exploiting the property of the weak CO2

Q-branch at 791 cm−1. CO2 absorption yields a very well de7ned, sharp spectral feature centeredat 791:75 cm−1 (see Fig. 6) in between of a window region with weak water vapor lines on theright-hand side. Because the CO2 mixing ratio is constant with altitude, an elementary calculationshows that the optical depth at the center of the CO2 line is proportional to the ground level pressure.However, the proportionality is altered because of water vapor continuum absorption whose strengthdepends on the H2O concentration itself which is highly variable in time and space. This eLect canbe highly reduced by diLerentiating the BT at the center of the line with a suitable BT outside theband, giving the split window diLerence (see Fig. 6)

MT� = T (�o)− T (�i); (12)

where �i = 791:75 cm−1 is the in-band wave number and �o = 790:5 cm−1 is the out-of-band wavenumber. One limitation of this method is that the constant of proportionality depends on the temper-ature pro7le itself. Nevertheless, for clear-sky MT� assumes values which are typically greater thanthose characterizing cloudy conditions.


788 789 790 791 792 793 794 795260

265

270

275

280

285

290

295

300

BT

spe

ctru

m (

K)


Fig. 6. Example of IMG spectrum showing the CO2 Q-branch at 791 cm−1. The depth of the CO2 line de7nes the quantityMT� which is the basic ingredient of the test discussed in Section 3.3.

As in the previous section, based on synthetic calculation we have that MT� for clear sky assumesvalues greater than 15 K, which gives us the following criterion to be satis7ed by IMG clear-skyobservations:

MT�¿ �3 = 15 K: (13)

3.4. The spatial consistency and coherency test

As it has been shown in Section 2, each IMG observation sequence consists of six spectra whichare 86 km apart and spatially distributed, approximately, on a latitudinal line of 6×86 km=516 km.On such a distance and over sea surface (at tropics), the clear-sky dynamic is expected to be verylow. Sharp diLerences from one spectrum to the next are therefore unlikely and constitute a clue forcloudy conditions. To trap this possible situation we have devised a test which is applied at eachIMG sequence at a time.

After passing the three thresholding tests above on a given sequence, let us suppose that j spectra(j¿ 1) have been =agged clear. At this point the maximum of MT� (Eq. (12)) is computed. LetMmax be the maximum, then the following additional clear sky criterion is set up:

Mmax −MT�(j)¡�4: (14)


The value of �4 has been found by trial and error by experiencing with a few clear-sky IMGsequences. We have found that the above diLerence is in the range 0:7–1 K. For the work hereshown, the value �4 = 0:9 K has been used.

4. Cloud detection scheme evaluation

As pointed out in the introduction section, the OCTS imager provides independent informationfor cloud discrimination in the IMG footprint. Using this information we can build up a set of IMGclear-sky spectra which can be used as a validation data set for our methodology. This approach hasbeen taken in this study, although it turned out to be a very di4cult task.

First, many OCTS images do not have enough contrast to clearly detect clear sky, which limitsthe number of cases to be included in the validation data set.

Second, and mostly important, many IMG observations detected clear through OCTS, exhibitsspurious spectral feature which are likely to be the results of phase mismatching in the calibrationprocess. The spectra had, therefore, to be checked once at a time, even in presence of well contrastedOCTS images. An example of an IMG sequence showing spurious spectral feature is illustrated inFig. 7. Although the OCTS image shows no presence of clouds for Observations 1 and 2, the

800 1000 1200 1400 1600 1800 20000

0.05

0.1


Spe

ctru

m (

Wat

t/m2 −

cm −1

− sr)

Obs. 1Obs. 2Obs. 3Obs. 4Obs. 5Obs. 6

Fig. 7. Example of a sequence of IMG spectra showing phase mismatching calibration. The right-hand part of the 7gureshows the six OCTS frames with the position of the IMG footprints (observation 1 is at the top, 6 at the bottom). TheIMG sequence is # 329130.


840 845 850 855 860 865 870 875 8800.08

0.09

0.1

0.11

0.12

0.13

0.14

Spe

ctru

m (

Wat

t/m2 −c

m−1

−sr)


Obs. 1Obs. 2Obs. 3Obs. 4Obs. 5Obs. 6

Fig. 8. Further example of IMG spectra showing anomalies in the atmospheric region window. The anomaly here manifestsas short pulses mostly evident in between the range 855–860 cm−1. The right-hand part of the 7gure shows the six OCTSframes with the position of the IMG footprints (observation 1 is at the top, 6 at the bottom). The IMG sequence is #334124.

sequence exhibits spurious spectral structure in the atmospheric window which are, for this case,surely due to phase mismatching.

In other cases the spurious spectral features appear in a form of short pulses which distort thenormal spectral structure expected for clear sky in the atmospheric window. An example is shownin Fig. 8.

In the end, after a tedious and long scrutiny we sorted 166 OCTS clear-sky spectra and 196OCTS cloudy spectra which form our validation data set. The clear and cloudy validation dataset are shown in Tables 1 and 2, respectively. The cloudy set encompasses various kind of cloudcontamination. Thick clouds, open and closed cloud cell formations, strati7ed clouds are present inthe set. Cloudiness partly contaminating the IMG footprint for Band 3 was induced, as well.

The four tests described in the previous section have been implemented in such a way to form anAND logic structure, that is they have to be simultaneously satis7ed for the given IMG sequence tobe declared clear. Keeping this in mind, the validation data set has been passed through the clouddetection scheme.

To begin with we discuss the results obtained for the OCTS clear data set. We found that 146out of the total 166 passed the test. Twenty spectra were rejected as cloudy. This result was in partexpected since the validation data set was prepared by visual inspection of OCTS images, a process


Table 1List of IMG spectra classi7ed clear according to OCTS imagery

IMG ID# OBS# IMG ID# OBS# IMG ID# OBS# IMG ID# OBS#

165829 1 235125 5 331327 6 356228 3165829 2 289729 5 331328 2 356228 5167228 5 289729 6 331328 3 356228 6167228 6 291931 1 331328 6 356230 2167229 1 291931 3 331329 1 356230 4187925 3 292629 1 331329 2 356231 1189226 1 292629 2 331928 3 356231 2189226 2 292832 1 331928 4 356627 2189825 1 292832 6 331928 5 356731 1189825 2 293131 1 334125 1 356731 2189825 3 293131 6 334127 3 356831 4190626 2 293331 1 334127 4 357028 2190626 3 294032 4 334127 5 357028 4190626 4 294229 1 334127 6 357031 1190626 5 294831 1 334128 5 357627 4190626 6 294831 2 334128 6 357630 6209026 5 309626 2 334330 1 358028 2209525 1 309630 1 334330 5 372631 1210924 2 309630 2 334330 6 373024 1210924 4 309630 4 335330 1 373024 4210924 6 309630 5 335330 4 373231 5229826 6 309630 6 335330 5 373231 6230628 4 309631 1 335330 6 373629 2231427 2 309631 2 335331 1 373629 3231427 3 309631 3 335331 2 373629 5231427 4 310229 2 335930 3 373729 3231628 6 310229 3 335930 4 373829 1231726 1 310230 1 337228 4 373829 2231726 2 310430 4 337330 3 373829 4231726 3 310925 4 352928 3 373928 4231726 5 310929 5 353629 5 374030 4231726 6 325631 1 353629 6 374128 6231826 5 328730 4 353829 6 374431 5232431 3 329928 5 354029 1 374730 1232524 2 329928 6 354429 5 374730 2232524 5 330428 1 354730 1 375128 6233725 5 330428 2 354731 2 375130 5233726 5 330428 6 354731 4 375130 6233924 5 330430 1 354828 6 375227 3235124 5 330430 3 354829 1 375227 5235124 6 331031 1 354829 2 ∗∗ ∗∗235125 2 331327 5 356228 2 ∗∗ ∗∗


Table 2List of IMG spectra classi7ed cloudy according to OCTS imagery

IMG ID# Obs.# IMG ID# Obs.# IMG ID# Obs.# IMG ID# Obs.#

167228 2 294032 1 352930 3 358028 1167229 5 294032 2 352930 5 358028 3167229 6 294032 3 353030 3 358028 5187925 5 294229 5 353230 4 358028 6189226 3 294731 2 353230 5 372631 4189226 5 294731 3 353230 6 372631 5189227 1 294731 4 353231 1 372631 6189227 2 294731 5 353231 3 372930 1189227 5 294731 6 353231 4 372930 6189825 4 294831 3 353231 6 373024 2189825 5 294831 4 353629 1 373024 5209026 2 309626 6 353829 4 373024 6209026 6 309629 4 354029 3 373231 1209524 1 310230 2 354029 4 373231 2209524 6 310230 3 354029 5 373629 6209525 3 310230 5 354029 6 373828 3209525 4 310230 6 354030 4 373928 1209627 2 310231 2 354030 6 373930 1209829 2 310231 3 354428 5 373930 2209829 3 310231 4 354429 2 373930 3210924 5 310429 3 354730 4 373930 4230425 3 310431 4 354730 6 373930 5230425 4 325631 5 354828 3 373931 2230628 2 325631 6 354829 4 373931 4230628 3 328730 1 354829 6 374030 6231826 1 328730 2 355527 1 374031 1231826 3 330428 3 355527 3 374031 5231932 4 330428 4 355527 6 374127 3231932 5 330430 2 355828 1 374127 5231932 6 330430 4 355828 2 374127 6232431 6 330430 6 355828 3 374128 1232524 3 331031 4 355828 4 374128 2232524 4 331329 5 355828 6 374128 3233724 1 334330 3 356230 1 374128 4233724 2 334330 4 356230 3 374128 5233924 1 335930 6 356231 5 374328 3233924 6 337229 1 356231 6 374328 4235124 1 337229 2 356830 1 374328 5235124 2 337330 1 356830 4 374328 6235124 3 337330 2 356830 5 374431 3290531 1 337330 6 357027 5 374730 3290531 2 352928 4 357028 6 374730 4291931 6 352929 1 357031 6 374730 5293131 3 352929 2 357324 3 375128 1293131 4 352929 3 357324 4 375128 3293131 5 352929 4 357531 1 375128 4293331 3 352929 5 357531 2 395231 3293331 5 352929 6 357625 4 395231 4293331 6 352930 1 357625 5 395231 6


800 850 900 950 1000 1050 1100 1150 1200

260

280

300B

T s

pect

rum

(K

)

IMG # 167228 Obs. 6

800 850 900 950 1000 1050 1100 1150 1200

260

280

300

BT

spe

ctru

m (

K)

Clear Sky Reference Spectrum

900 950 1000 1050 1100 1150 1200298

299

300

301

302

BT

(K

)


referenceobservation

(a)

(b)

(c)

Fig. 9. IMG observation showing the signature of light cirrus contamination (panel a). The signature is evidenced by theslope of the straight line (shown in red) which connects the super window at 900 cm−1 to that at 1168 cm−1. The slopeis further evidenced by comparing the spectrum shown in panel (a) to the clear-sky reference spectrum shown in panel(b). The direct comparison between the two slopes is provided in panel (c).

which is not so e4cient for light clouds. Of the 20 rejected spectra, 13 exceeded the lower thresholdof the window channel test discussed in Section 3.2. The failure could well be the result of thincirrus contamination. This kind of contamination is hardly diagnosed on the basis of a visual checkof visible imagery alone. To illustrate the possibility for thin cirrus contamination, Fig. 9 showsthe spectrum slope for the observation 6 of the IMG sequence # 167228 for which we computedMTw = −2:89. The spectral signature of cirrus appears here to be quite evident when we observethe increasing linear trend visible in the atmospheric region. The comparison with a typical clearsky spectrum (also shown in Fig. 9) leads us to conclude that cirrus contamination is very likely tobe the case for this observation. For the other seven rejected spectra, 7ve were found slightly cloudcontaminated by a better check of the corresponding OCTS imagery. The remaining two were veryclose to the threshold values and no evident spectral signature of light cloud contamination couldbe evidenced by inspection of OCTS images and the spectra themselves. These two cases could becases for which the scheme simply failed.

For the validation cloudy data set, we found that the test diagnosed cloudy 195 out of the 196total spectra. This better coincidence was expected since the classi7cation of cloudy spectra by vi-sual inspection was easier because very well contrasted cloud scenes were privileged in the selectionprocess. The only observation which was falsely detected clear is the # 6 of the IMG sequence


800 820 840 860 880 900260

265

270

275

280

285

290

295

300

305IMG #353829


BT

Spe

ctru

m (

K)

obs1: cloudyobs2: cloudyobs3: cloudyobs4: cloudyobs5: clear obs6: clear

Cloud Test Results:

Fig. 10. Results of the cloud detection scheme for the IMG sequence # 353829. The left-hand side of the 7gure showsthe six spectra in the atmospheric window 800–900 cm−1 with the cloud detection results summarized in the legend. Theright-hand part of the 7gure shows the six OCTS frames with the position of the IMG footprints (observation 1 is at thetop, 6 at the bottom).

# 337330. For this case the test failed since the Band 3 footprint was partly contaminated byclouds.

Apart from this case, the ability of the scheme to detect partly cloudy contaminated IMG foot-prints was found excellent. As an example, Fig. 10 shows the results for the IMG sequence# 353829. It is seen that the observations 1 and 2 which are partly contaminated by cloudsare =agged cloudy upon passing the test. The observations 5 and 6 are correctly detected asclear.

Again, it is here important to stress that the cloud test uses only information from the IMG Band3, so that the results for the cloud test refers to that band. Unfortunately, Band 2 is not sensedthrough the same Field of View of Band 3, so that the results of the cloud detection cannot beextended to Band 2.

A further example of the cloud detection scheme is shown in Fig. 11. The ability of the test todetect the low cloud contamination for the footprint of observation 3 is here really impressive.

Finally, we want to show a case which highlights the good cloud penetration of IMG because ofits small size footprint. Fig. 12 shows a sequence of observations recorded over a compact cirrusformation. Nevertheless, IMG is able to penetrate a relatively small hole in the clouds with the 7fthobservation. This observation was =agged clear upon passing the test.


800 820 840 860 880 900260

265

270

275

280

285

290

295

300

305IMG #335330


BT

Spe

ctru

m (

K)

obs1: clear obs2: clear obs3: cloudyobs4: clear obs5: clear obs6: clear

Cloud Test Results

Fig. 11. As Fig. 10 but for the IMG sequence # 335330.

5. Conclusions

A cloud detection scheme for IMG has been described in this paper. The scheme is based on a setof thresholding tests which have been devised to take full advantage of the high spectral resolutionprovided by IMG. In the present version the scheme runs for sea surface only, although the extensionof the method to land surface is straightforward.

The algorithm has been applied to a set of IMG tropical spectra and the results compared toOCTS imagery which gives independent information about cloudy contamination in the IMG foot-prints. The algorithm has proved to be very successful in diagnosing the presence of clouds andcan be reliably applied to other IMG sequences for which the OCTS co-location is not avail-able. Even in presence of OCTS co-location, the scheme may add additional information, valu-able to detect, e.g., thin cirrus cloud contamination which are not well contrasted in visibleimagery.

We have checked that the scheme may also detect the presence of spurious spectral features whichare present in IMG spectra because of phase mismatching in the calibration process. For this reason,it is highly recommended to pass IMG sequences through the test presented in this paper even inpresence of very well contrasted OCTS images. IMG spectra showing spurious spectral feature are,indeed, not unlikely, a point which should be kept in mind by people aiming at using IMG data,e.g., for retrieval or climatological studies.


800 820 840 860 880 900210

220

230

240

250

260

270

280

290

300IMG #355828


BT

Spe

ctru

m (

K)

obs1: cloudyobs2: cloudyobs3: cloudyobs4: cloudyobs5: clear obs6: cloudy

Cloud Test Results

Fig. 12. As Fig. 10 but for the IMG sequence # 355828.

Acknowledgements

Work supported by Italian Space Agency.

References

[1] Kobayashi H, Shimota A, Yoshigahara C, Yoshida I, Uehara Y, Kondo K. Satellite-borne high-resolution FTIR forlower atmosphere sounding and its evaluation. IEEE Trans Geosci Remote Sensing 1999;37(3):1496–507.

[2] Amato U, Cuomo V, DeFeis I, Romano F, Serio C, Kobayashi H. Inverting for geophysical parameters from IMGradiances. IEEE Trans Geosci Remote Sensing 1999;37(3):1620–32.

[3] Clerbaux C, Hadji-Lazaro J, Payan S, Camy-Peyret C, Megie G. Retrieval of CO columns from IMG=ADEOSspectra. IEEE Trans Geosci Remote Sensing 1999;37(3):1657–61.

[4] Lubrano AM, Serio C, Clough SA, Kobayashi H. Simultaneous inversion for temperature and water vapor fromIMG radiances. Geophys Res Lett 2000;27:2533–6.

[5] Hadji-Lazaro J, Clerbaux C, Couvert P, Chazette P, Boonne C. Cloud 7lter for CO retrieval from IMG infraredspectra using ECMWF temperatures and POLDER cloud data. Geophys Res Lett 2001;28:2397–400.

[6] Lubrano AM, Masiello G, Serio C, Matricardi M, Rizzi R. IMG evidence of chloro=uorocarbon absorption in theatmospheric window region 800–900 cm−1. JQSRT 2002;72(5):623–35.

[7] Shimoda M, Oaku H, Mitomi Y, Murakami H, Kawamura H. Satellite-borne high-resolution FTIR for loweratmosphere sounding and its evaluation. IEEE Trans Geosci Remote Sensing 1999;37(3):1484–95.


[8] Aumann HH, Strow L. AIRS, the 7rst hyper-spectral infrared sounder in support of operational weather forecasting.IEEE Aerospace Conference, Big Sky, 10–17 March 2001, p. 345–56.

[9] Cayla F-R. IASI infrared interferometer for operations and research. In: ChTedin A, Chahine MT, Scott NA, editors.High spectral resolution infrared remote sensing for earth’s weather and climate studies. NATO ASI Series, I9.Berlin: Springer, 1995. p. 9–19.

[10] Serio C, Lubrano AM, Romano F, Shimoda H. Cloud detection over sea-surface using auto-correlation functions ofup welling infrared spectra in the 800–900 cm−1 window region. Appl Opt 2000;29:3565–72.

[11] Masiello G, Matricardi M, Rizzi R, Serio C. Homomorphism between clear-sky and cloudy spectra in the 800–900 cm−1 window region. Appl Opt 2002;41(6):965–73.

[12] Beer R, Norton RH. Analysis of spectra using correlation functions. Appl Opt 1988;7:1255–61.[13] Inoue T. A cloud type classi7cation with NOAA-7 split window measurements. J Geophys Res 1987;92:3991–4000.[14] Masuda K, Takashima T, Takayama Y. Emissivity of pure and sea waters for the model sea surface in the infrared

window regions. Remote Sensing Environ 1988;24:313–29.[15] Stephens GL. Remote sensing of the lower atmosphere. Oxford, UK: Oxford University Press, 1994.

Qualifying IMG tropical spectra for clear sky

Documents

Transcript of Qualifying IMG tropical spectra for clear sky