Calibration Assessment of INSAT Satellite Infrared Data Using NOAA Satellite Data

 

Gagandeep Kaur, Soma Sen Roy , A.K.Sharma Junior Research Fellow, IMD Scientist-D, IMD Scientist- F, IMD

INTRODUCTION India Meteorological Department is receiving and archiving satellite

meteorological data from INSAT series since 1984.

Data is already archived on DLTs and resolutions of these data are 2.75 km/2km in visible, 11km/8km in infrared channels for INSAT-1/INSAT-2 series respectively.

The normalized calibration technique is attempted in order to re-calibrate the Kalpana-1 Infrared data and remove the effect of the temporal non-linearity of sensor response due to degradation of the sensor based on ISCCP (International satellite cloud climatology project) procedure over the Indian Ocean.

Table- KALPANA-1 Satellite SpecificationPayload Channels Bandwidth Resolution

VHRR (Very High Resolution Radiometer)

VisibleInfraredWater vapour

0.55µm-0.75µm10.5µm-12.5µm 5.7µm-7.1µm

2km x 2km8km x 8km8km X 8km

DRT ( Data Relay Transponder) ----- ----- ------

DATA AND METHODOLOGY Study Covers the months MAY, JUNE, JULY, OCTOBER, NOVEMBER and

DECEMBER of 2009. Taking 8-10 passes of NOAA of each month. Four automated steps to satellite Inter-calibration ::

Data collection: Study period: MAY, JUNE, JULY, OCTOBER, NOVEMBER,

DECEMBER 2009. Study region: ± 10º NS latitude of the equator; 64º-84º longitude east

Primary data for inter-calibration :: kalpana-1 Geostationary satellite VHRR Infrared brightness temperatures

Polar Orbiting Satellite used :: NOAA-19 AVHRR channel-4 brightness temperatures, data accessed through INCOIS, Hyderabad and its collection based on

currently operational satellite series status of imager AVHRR ISCCP survey on data availability

Satellite / Imager Channel Bandwidth Resolution Data Format

Kalpana-1/ VHRR Infrared 10.5µm - 12.5µm

8km X 8km H5

NOAA / AVHRR 4 10.3µm - 11.3µm

1km X 1km Hdf4

Table: Satellite Specifications for the study

….Contd

The impact of spectral differences between the IR win channels is important to consider when comparing brightness temperatures to those from AVHRR.

a) b)

Fig a) Shows the spectral response function of NOAA-AVHRR channel-4 and b) Kalpana-1 Infrared respectively.

Study area of interest : The strategy is to find the measurements by the polar orbiting satellite that are

concurrent and collocated with those from a geostationary satellite.

The near-simultaneous nadir observations with homogeneous scenes from NOAA and Kalpana-1 imagers are spatially collocated.

…Contd

Domain of the earth for the study ::: ±10º NS latitude of the equator, 64º- 84º longitude east

Time differences between observations should

be less than 30min.

Collocation Criteria ::

Study Region selected for inter-calibration

process

.

Constraint Criteria

Simultaneity Observational Time difference < 30min

View geometry Area of domain [10⁰S/10 N and ⁰64 E/84 E] of the earth selected ⁰ ⁰which matches with NOAA satellite.

Collocation criteria

Nearest neighbour distance technique is used to perform linear search iteratively to find closest point for Kalpana-1 points (latitude-longitude) in NOAA AVHRR data with maximum outer threshold to be 20 km. The least distance point would be selected for collocation.

…Contd

Table : data processing constraints

Nearest Neighbour distance Technique performs the linear search iteratively to find the

closest point in the LEO satellite observations with the maximum outer threshold taken as 20 km.

Nearest Neighbour Formula used ::

Where d is Euclidean distance; LatK , LonK , LatN and LonN are the latitude and longitude of Kalpana-1 and NOAA respectively.

For every fifth point of Kalpana-1 (latitude-longitude) in NOAA data, distance is calculated using above formula and simultaneously changing the threshold while iterating, if and only if distance is lesser than outer threshold (i.e. 20km).

The least distance point of NOAA AVHRR would be the best fitted collocation point for Kalpana-1 (latitude-longitude).

22 110110 NKNK LonLonLatLatd

…Contd

Calculation :: Linear Least Square equation is used to find coefficients (slope and intercept) .

Y = m * X + c; or

BTNOAA = m X BTKALPANA-1 + c

Where “m” and “c” are:

Where N is the total number of observations; X is the Kalpana-1 Infrared brightness temperatures and Y is the NOAA AVHRR channel-4 brightness temperatures.

Kalpana-1 data is re-calibrated to get the new corrected brightness temperatures for the Infrared channel data.

NEWBTKALPANA-1=m x OLDBTKALPANA-1 + c

22

)2

)(

))(()((

XXN

XYXXYc22 )(

))((

XXN

YXXYNm

…Contd

Calculating the Confidence of limit using two-tailed test . For this “z” score is computed and state the Confidence of Limit.

Where and µ is the mean of brightness temperatures of Kalpana-1 VHRR Infrared ( 10.5 µm-12.5µm) and NOAA AVHRR channel-4 ( 10.3µm-11.3µm) data respectively; σ is the total standard deviation; N is the total number of observations of collocated points.

σA is the standard deviation of Kalpana-1 Infrared brightness temperatures; σB is the standard deviation of NOAA AVHRR Channel-4 brightness temperatures. NA is the total no. of observations of Kalpana-1 data; NB is the total number of observations of NOAA data.

Results :: Linear Least Square Coefficients are calculated for three cases:

For whole defined region [ ±10º NS LATITUDE OF THE EQUATOR, 64º-84º LONGITUDE EAST ]

For those brightness temperatures of Kalpana-1 less than 273.15 K i.e. cloudy region

For those brightness temperatures of Kalpana-1 greater than 273.15 K i.e. Clear sky.

/

Xz

N

2 2A B

A BN NN

X

RESULTS Table; Shows the slope and intercept of Kalpana-1 data for months MAY, JUNE, JULY,

OCTOBER, NOVEMBER AND DECEMBER, 2009.MONTH SLOPE INTERCEPT

FOR WHOLE DEFINED REGION [ ±10º NS LATITUDE OF THE EQUATOR, 64º-84º LONGITUDE EAST ]

MAY 1.112289 -34.505653JUNE 1.030094 -11.937085JULY 1.054203 -18.024540

OCTOBER 1.088709 -30.246927NOVEMBER 1.026153 -10.706260DECEMBER 1.090758 -27.551078

FOR KALPANA-1 INFRARED BRIGHTNESS TEMPERATURES LESS THAN 273.15 K [ CLOUDY REGION ]

MAY 0.970036 1.604305JUNE 0.916543 15.543576JULY 0.932155 11.682330

OCTOBER 0.899116 17.598706NOVEMBER 0.963910 4.140101DECEMBER 1.000548 -5.557144

FOR KALPANA-1 INFRARED BRIGHTNESS TEMPERATURES GREATER THAN 273.15 K [ CLEAR SKY ]

MAY 1.341694 -101.628705JUNE 1.240224 -71.393918JULY 1.197678 -58.537575

OCTOBER 1.441522 -132.770388NOVEMBER 1.258093 -77.290598DECEMBER 1.243498 -71.774777

Table; Shows the Mean, Standard Deviation and Confidence of limit for Kalpana-1 infrared and NOAA AVHRR channel-4 Data for the following months of 2009.

MONTH MEAN OF KALPANA-1 DATA

MEAN OF NOAA DATA

S.D. OF KALPANA-1

S.D OF NOAA Z score Confidence of limit ( % )

FOR WHOLE DEFINED REGION [ ±10º NS LATITUDE OF THE EQUATOR, 64º-84º LONGITUDE EAST ]MAY 285.422796 282.967031 17.187106 21.257833 18.203339 99.8

JUNE 267.117325 261.835939 23.350019 23.320393 22.186203 99.9

JULY 269.153805 265.718339 22.469510 26.443518 19.059516 99.8

OCTOBER 281.866291 276.623321 18.490257 23.36236 30.885194 100

NOVEMBER 263.754661 259.946475 27.158275 30.417195 17.569263 99.8

DECEMBER 279.199373 276.987894 21.521850 25.828927 12.981307 99.7

FOR BRIGHTNESS TEMPERATURES OF KALPANA-1 DATA LESS THAN 273.15 K [ CLOUDY REGION ]

MAY 253.105308 247.725652 14.782206 20.731505 20.165422 99.9

JUNE 248.335495 247.033631 17.937101 22.699057 27.677741 99.99

JULY 249.431608 244.191344 16.662626 20.787247 26.105294 99.99

OCTOBER 252.587819 244704372 15.634245 21.335309 25.101430 99.99

NOVEMBER 243.308866 238.667940 19.351656 23.063283 21.486689 99.9

DECEMBER 247.673295 242.251943 18.174880 24.147564 18.093209 99.8

FOR BRIGHTNES TEMPERATURES OF KALPANA-1 DATA GREATER THAN 273.15 K [ CLEAR SKY ]

MAY 292.496054 290.81155 5.796803 10.678828 25.444818 99.99

JUNE 286.408123 277.039373 6.349174 10.817519 23.172141 99.99

JULY 287.016145 285.215279 6.271998 11.890143 18.682010 99.8

OCTOBER 290.624766 286.171678 6.156178 13.302606 46.780036 100

NOVEMBER 288.639791 285.845157 7.171256 13.107259 23.107259 99.99

DECEMBER 290.347430 289.271782 5.870189 10.921981 14.714635 99.7

Scatterplot of Kalpana-1infrared brightness temperatures versus NOAA AVHRR channel-4 brightness temperatures for whole defined region;

MAY JUNE

JULY OCTOBER

NOVEMBER DECEMBER

Scatterplot Kalpana-1 infrared brightness temperatures versus NOAA AVHRR channel-4 brightness temperatures for CLOUDY REGION [ for those brightness temperatures of Kalpana-1 Infrared less than 273.15 K ] ;

MAY JUNE

JULY OCTOBER

NOVEMBER DECEMBER

Scatterplot Kalpana-1infrared brightness temperatures versus NOAA AVHRR channel-4 brightness temperatures for CLEAR SKY [ for those brightness temperatures of Kalpana-1 Infrared greater than 273.15 K ] ;

MAY JUNE

JULY OCTOBER

NOVEMBER DECEMBER

CAL-VAL TEST SITE

Gagandeep Kaur Manish Paliwal, A.K.Mitra

Junior Research Fellow Junior Research Fellow Scientist- C

A.K.Sharma

Scientist F

To establish a Calibration and Validation (CAL/VAL) site for

INSAT 3D satellite

AIM

Why Cal-Val Test Site is needed ??

To verify the post-launch radiometric calibration of satellite instruments.

To monitor instruments performance over time.

CAL/VAL TESTED SITES

The Following sites followed the key guidelines of QA4EO ( Quality Assurance framework for Earth Observation)

Railroad Valley Playa, NV, USA, North America Ivanpah, NV/CA, USA, North America LSpec Frenchman Flat, NV, USA, North America La Crau, France, Europe Dunhuang, Gobi Desert, Gansu Province, China, Asia Negev, Southern Israel, Asia Tuz Gölü, Central Anatolia, Turkey, Asia Dome C, Antarctica

SELECTION CRITERIA FOR TEST SITE

1. The site should have high spatial uniformity, relative to the pixel size, to minimize the effects of scaling radiometric data and atmospheric adjacency effects due to light scattered from outside the target region, to the size of the entire test site. Table for Earth target types that are often used for post launch ground look calibration of satellite instruments.

2. The site should have a surface reflectance greater than 0.3 in order to provide higher signal-to-noise ratio (SNR) and reduce uncertainties due to the atmospheric path radiance.

3. The surface properties of the site (reflectance factor, BRDF ( Bi-directional reflectance distribution function), should be temporally invariant. Otherwise, adequate accuracy would be obtained only if these properties were measured for every calibration. This implies that the site should have little or no vegetation.

4. The surface of the site should be horizontal and have nearly Lambertian reflectance.

5. The site should be located at high altitude (to minimize aerosol loading and the uncertainties due to unknown vertical distribution of aerosols), far from the ocean (to minimize the influence of atmospheric water vapour), and far from urban and industrial areas (to minimize anthropogenic aerosols).

6. The site should have cloud free and low precipitation characteristics. The low probability of cloud coverage also increases the probability of the satellite instruments imaging the test site at the time of overpass.

… Contd

RAINFALL CLIMATOLOGY OF THE PROPOSED TEST SITE - JAISALMER

RAINFALL CLIMATOLOGY OF THE SITE - JODHPUR

RAINFALL CLIMATOLOGY OF THE SITE - BIKANER

Satellite picture of Jaisalmer

NORMAL RAINFALL MAPS OF THE TEST SITE – JAISALMER

(Lat: 26.9 N ; Long: 70.9 E)

NORMAL RAINFALL OF THE TEST SITE - JAISALMER

NORMAL ANNUAL RAINFALL MAP OF THE TEST SITE - JAISALMER

VEGETATION INDEX

CLOUD FRACTION

AEROSOL OPTICAL DEPTH

AEROSOL SIZE

POST-LAUNCH CALIBRATION OF METEOROLOGICAL SATELLITE SENSORSC.R.Nagaraja Rae’, J. Chen*, J.T. Sullivan’, and N. Zhang*

NOAA/NESDIS Office of Research and Applications, Washington, D.C., 20233, USA

QSS Group Inc., 4500 Forbes Boulevard, Lanham, MD 20706, USA

TEST SITE SIZE ::

The size of the surface area to be sampled is selected depending on the imagers resolution (medium or high-resolution)

An area approximately equal to 3 sensor pixels should be considered a minimum with a further 2 pixels of similar terrain at its periphery. For example MERIS has a 300 m pixel size and so a site of around 1 x 1 km is ideal.

PORTABLE SPECTRO-RADIOMETER- Used to calculate Reflectance Factor of the test site surface.

SUN-PHOTOMETER- Used to determine aerosol optical depth and water-vapour content.

Instruments used for Tuz Golu test site :: ASD FieldSpec3 Spectroradiometer -

Measurement aims: To perform radiance/reflectance measurements over the selected target  area at different times with various sampling rates to determine the spatial/temporal uniformity and to obtain the radiometric characteristics of the site.

1. field portable

2. precision radiometer with a spectral range (350- 2500 nm)

3. designed to collect solar reflectance, radiance and irradiance measurements.

4. Spectral Resolution: 3 nm @ 700 nm, 10 nm @ 1400/ 2100 nm

INSTRUMENTS USED ON THE TEST SITE

6. Set-up time: Approximately 90 minutes to set-up and 15 minutes to be dismantled. Transportation. They weight totally 47 kg.

7. Power Requirements: 12VDC NiMh high current rechargeable battery pack is required. It can be used for 4-5 hours. Charging time is 8 hours.

Gonio Radiometric Spectrometer System (GRASS)

Measurement aims: To conduct a series of multi-angular spectral measurements of the surface, perform measurements at different solar zenith angles, perform measurements over the same surface on sequential days to determine the temporal stability, and perform diffuse irradiance measurements.

1. easily and quickly assembled in remote situations,

2. measures the Earth’s reflected sunlight over half a hemisphere, at 30-degree intervals i.e. 0°, 30°, 60°, 90°, 120°, 150°, 180°, on a series of seven arms.

3. Resolution: same as ASD radiometer; Set up time: about 2 hours to set-up, and similarly 2 hours to dismantle at the end of the day. The instrument is left assembled on site overnight to reduce construction time.

4. Power Requirements: To run the instrument requires some power – this is achieved through the use of the Petrol Generator.

… Contd

… Contd

Above Figure shows ASD FieldSpec3 Spectroradiometer

Above Figure shows Gonio Radiometric Spectrometer System (GRASS)

Microtops II Sunphotometer : Measurement aims: To determine the aerosol loading/water vapour

content and its temporal variability over the target area. The aerosol data will be used as an input to the Radiative Transfer Code (RTC) for the atmospheric correction of the satellite data.

1. hand-held instrument for measuring the direct solar radiance using a 2.5° viewing angle.

2. configured with five filters for the determination of aerosol optical thickness (AOT )

3. Resolution: 0.1 W/m2

4. Set-up: Location and time should be set up at the beginning of the measurements

5. Transportation: 10 x 20 x 4.3cm – 600 gram

6. Power Requirements: 4 x AA Alkaline batteries

As per the study and on above discussed criteria for the Calibration and Validation Test Site, it seems that JAISALMER (India) site (Lat: 26.9 N ; 70.9 E) have suitable properties to be a CAL-VAL site for INSAT-3D.

CONCLUSION

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