Mars Colour Camera: The payload characterization/calibration and data analysis from Earth imaging phase

Abstract

Mars Colour Camera (MCC) on-board Mars Orbiter Mission is considered the 'eye' of the mission, taking photographs (imageries) of the surfacial features on Mars, and the cloud and dust around it. MCC is an important contextual camera for other non-imaging sensors like MSM, TIS, LAP, etc. The camera has been designed, characterized, calibrated and qualified at the Space Applications Centre, ISRO, Ahmedabad by a team of professional engineers and scientists. It has been miniaturized, ruggedized and space-qualified to match the weight and power budget of the mission. During Earth orbit phase, the images returned by the camera have been analysed qualitatively and quantitatively. The results show that MCC has been working as expected in terms of radiometry, geometry and application potential to discern various morphological features. The present article discusses these facts in detail.

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*For correspondence. ( e-ma il: arya_as@sac.isro.gov.in)
Mars Colour Camera: the payload
characterization/calibration and data analysis
from Earth imaging phase
A. S. Arya*, S. S. Sarkar, A. R. Srinivas, S. Manthira Moorthi,
Vishnukumar D. Patel, Rimjhim B. Singh, R. P. Rajasekhar, Sampa Roy,
Indranil Misra, Sukamal Kr. Paul, Dhrupesh Shah, Kamlesh Patel,
Rajdeep K. Gambhir, U. S. H. Rao, Amul Patel, Jalshri Desai, Rahul Dev,
Ajay K. Prashar, Hiren Rambhia, Ranjan Parnami, Harish Seth, K. R. Murali,
Rishi Kaushik, Deepak Patidar, Nilesh Soni, Prakash Chauhan,
D. R. M. Samudraiah and A. S. Kiran Kumar
Space Applications Centre, India n Space Research Or ganisation, Ahmedaba d 380 015, India
Mars Colour Camera (MCC) on-board Mars Orbiter
Mission is considered the ‘eye’ of the mission, taking
photographs (imageries) of the surfacial features on
Mars, and the cloud and dust around it. MCC is an
important contextual camera for other non-imaging
sensors like MSM, TIS, LAP, etc. The camera has
been designed, characterized, calibrated and qualified
at the Space Applications Centre, ISRO, Ahmedabad
by a team of professional engineers and scientists. It
has been miniaturized, ruggedized and space-qualified
to match the weight and power budget of the mission.
During Earth orbit phase, the images returned by the
camera have been analysed qualitatively and quantita-
tively. The results show that MCC has been working
as expected in terms of radiometry, geometry and
application potential to discern various morphological
features. The present article discusses these facts in
detail.
Keywords: Detector, Earth imaging phase, payload,
Mars colour camera.
Introduction
MARS Colour Camera (MCC) is a medium-resolution
camera, with RGB Bayer pattern detector. It is a ‘true
colour’ (offering a natural colour rendition, i.e. colours in
the image appear the same way as in the object) camera
flown on-board Mars Orbiter Mission (MOM). MCC has
been designed to return images of Mars, its Moons (Pho-
bos and Deimos) and oth er celestial objects in natural
colour. It is also designed to meet the following scientific
objectives:
(1) To map various morphological featur es on Mars
with varying resolution and scales using the unique
elliptical orbit.
(2) To map the geological setting around sites of meth-
ane emission source, if any.
(3) To provide context information for other science
payloads.
MCC is designed to image the complete Mar s disk with a
spatial resolution of nearly 4 km from an altitude of
80,000 km and localized scenes at higher spatial resolu-
tion of nearly 19 m from 370 km. It can provide a synop-
tic view of the full globe from the orbital altitudes
ranging from 63,000 to about 80,000 km around Mars1.
Figure 1 shows a photograph of the MCC payload
developed at Space Applications Centre (SAC), ISRO,
Ahmedabad and Figure 2 gives the instantaneous geomet-
ric field-of-view (IGFOV) and coverage (field-of-view,
FOV) of the camera from various orbital heights.
Figure 1. Mars C olour Camer a (MCC) payloa d.

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MCC payload
The design and development of MCC was a challenging
task. MCC payload was designed and realized with the
constraint of low power (< 4 W), smaller size and weight
(<1.5 kg) and extremely short time for realization (within
a year). It was designed with the available materials/
components and with commercial off-th e-shelf (COTS)
components wherever new components were required.
COTS components were adequately ruggedized and thor-
oughly tested to establish their space worthiness. The
high level of miniaturization in terms of size and weight
was achieved for all major sub-systems like optics, detec-
tor head assembly (DHA), camera structure and camera
electronics (CE) and the same was translated to integrated
payload level (details given subsequently in the text).
Concurrent engineering practices were extensively
followed. Three models were developed. ‘Verification
model’ was developed for demonstration of proof of con-
cept. ‘Flight model like’ and ‘flight model’ were devel-
oped identical to each other and were subjected to
qualification and acceptance level tests respectively. The
development of these models ran almost parallel with
feedback from one model incorporated onto the other and
verified quickly. Figure 3 shows the CAD simulation of
the MCC payload.
MCC used a multi-element lens assembly for collecting
the incident radiation from Mar s and focusing on the
detector. A COTS lens assembly having focal length of
105 mm with f-number of 4.0, diagonal field-of-view
(4.4) and spectral range (400–700 nm) was selected for
MCC based on the performance parameters and mission
requirements of smaller size and weight. The lens was
customized through in-house development to bring down
its mass and size. It was qualified at subsystem level to
establish its suitability for space use by subjecting it to
specified environmental conditions (temperature excur-
sions in vacuum and vibration loads). Figure 4 shows the
COTS lens and the ruggedized flight model MCC lens
Figure 2. C overage by MCC from di ffere nt or bital heights.
with more than 50% mass reduction. This was made pos-
sible by employing new materials like Al 6061-T6 alloys
which used a novel technique of mounting th e lenses in
the barrels using stress-free lock rings aided by elasto-
meric joints separated 120 apart on their radial periphery.
An IR cut-off filter (with an average transmission of
95% from 400 to 700 nm with a sharp cut-off at 735 nm)
mounted on a precisely designed and machined stress-
free mounting using flexures was placed in front of the
detector to limit out-of-band response beyond the red
region (>700 nm) for obtaining colour images with high
fidelity. Figure 5 shows the IR cut-off filter assembly for
MCC. Detector head assembly (DHA) (Figure 6) consists
of a commercial high-speed snapshot colour CMOS im-
age sensor with a pixel size of 5.5 m. It is an area array
having red (R), green (G) and blue (B) organic filters
deposited on top of it in the form of RGGB Bayer pat-
tern2. The detector is an active pixel sensor and incorpo-
rates most of front electronics, in it including ADC.
The detector and pr ocessing card were mounted on a
low-mass and scooped Al alloy structure (Figure 7). This
reduced the hardware complexity, improved feasibility in
electrical interconnection and significantly r educed the
mass eliminating the possibility of str ess on the dissimilar
metallic joints. The incoming panchromatic ph otons are
converted to electrons at pixel level having either ‘R’,
‘G’ or ‘B’ filter (according to RGB Bayer pattern laid
down on the top of the pixel) and photodiode. Subse-
quently, photo-generated electrons are converted to volt-
age using pixel-level charge to voltage amplifiers. These
signals are digitized at column-level analog to digital
converters (ADCs) using row and column-level multi-
plexers and decoders3. Like th e optics, the detector
underwent the entire process of ruggedization and quali-
fication for development of flight model. Detector head
assembly incorporated necessary electrical, mechanical
Figure 3. C AD simulation of MCC payload.

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Figure 4. C ommercial o ff the shel f len s (620 g) and MCC FM lens (310 g).
Figure 5. I nfrar ed cut-off filter assembly.
Figure 6. MCC detector head assembly.
and thermal interfaces. Figure 6 shows a ph otograph of
the actual realized FM detector head assembly.
The design and development of camera electronics
(CE) was based on the system and detector r equirements
Figure 7. D etector head assembly/ mount/filter.
of 16 programmable exposure controls, high-speed detec-
tor operation (52.5 MHz) and low-noise detector bias
generation (<1 mV), while taking into account the re-
quirements of miniaturization (low weight (~0.4 kg) and
raw power (~3 W)) and usage of available space-grade
components to meet the realization schedule in th e short-
est possible time. The miniaturization and performance
requirements of camera electronics were met by selecting
state-of-the-art space-grade components, field program-
mable gate array (FPGA) for logic implementation, low
drop-out (LDO) voltage regulators, compact hybrid DC–
DC modules, integrating electronics functions near the
focal plan e, usage of micro-D connectors, multi-layer
PCBs, etc. The CE consists of three major functional
blocks – the detector proximity electronics (DPE) which
generates the necessary low noise bias voltages and clock
signals for the detector; the logic and control electronics
(LCE) which generates the required clocks for detector
operation, interfaces with the base-band data handling
(BDH) and tele-command (TC) of spacecraft (S/C) bus,
etc. and the power supply electronics (PSE) which takes
the raw power from S/C and pr ovides low-noise (<5 mV
PARD (periodic and random deviation)) regulated power

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Figure 8. a, MCC FM LC E car d; b, MCC FM PSE card.
Figure 9. Light-weight EOM structure.
lines to the payload. Figure 8 a and b shows the actual re-
alized FM LCE and FM PSE cards respectively. The CE
incorporates exposure control logic to facilitate matching
detector dynamic range with intended scene dynamic
range.
The electro optic module (EOM) structure was desig-
ned and analysed with the objective of keeping the pack-
ages and the overall payload light weight and compact
while ensuring adequate structural stiffness, electrical
shielding and thermal stability to withstand the specified
environmental loads and meet the performance requir e-
ments. The EOM structur e was design ed to take envi-
ronmental loads like dynamic vibration, shock and
temperature excursions during the orbiting period. The
structure has been optimized for thermo-structural stability
(20 g RMS for structural and –40C to 60C for thermal)
for minimum deformation (change of distance between
lens-mounting plane and DHA-mounting plane) of the
order of 20 m. The structure was machined from
Al 6061-T6 alloy solid block into an ultra-light weight
structure (mass less than 200 g) with alignment accuracy
better than 10 m (parallelism between lens and detector
mounting planes and perpendicularity of these planes
with respect to structure base). Figure 9 shows the real-
ized EOM. The achieved fundamental mode is higher
than 400 Hz and survived the temperature excursions
(0–40C) without any deformation higher than 0.01 m.
Payload checkout system for MCC con sisted of four
subsystems: spacecraft interface simulator (SIS), payload
status indicator (PSI), payload data acquisition system
(PDAS), and application software (AS). SIS generated all
the commands required for the operation of the payload
Table 1 . Salient features and performance specifica tions o f M
ars
Colou r Camera
Para meter Value
S/C altitude (k m) 372  80000 (elliptical orbit)
Resolu tion (m) 19.5 @ periapsis
Frame size (km) 40  40 @ peria psis
Full Mars disc fr om 6 3,000 km to
apoapsi s
Spectral region (m) 0.4–0.7
Frame time 1s (frame selecti on at 1, 8 or 15 sec
period b y ground comma nding)
Exposu re time Total 16 ground programma ble
exposures ra nging from 34 s to
490 ms
Data volume/frame (Mb) 40
System MTF @ 46 LP/ mm (%) >21 (specification > 15%)
SNR @ near saturation >95 (specification > 50)
Size ( mm3) 346  128  113 (EOM + LCE )
122  105  26 ( PSE)
Mass ( kg) 1.27 (Goal < 1 .5kg)
Raw power (W) 3.0 (Goal < 4W)

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Figure 10 . Screenshot of MCC user -interface module.
and control signals in the absence of actual satellite.
PDAS acquired the data from the payload using in-house
developed PCI-based data acquisition card (DAQ),
formatted the same and make them available on network.
Application softwar e consisted of cor e libraries deve-
loped in C/C++, configuration data, parameter computa-
tion and analysis tools, supporting scripts, user interface
(UI) for data visualization and test results display. PSI re-
ceived and processed all the analog and digital telemetry
information from the payload for health monitoring.
Figure 10 shows the screen-shot of UI module of applica-
tion software system. Table 1 gives the salient features
and performance specifications of MCC.
Integrated payload characterization
MCC system was optimized to produce best optical and
electrical performance for all three bands. MCC payload
was characterized in terms of various performance para-
meters like modulation transfer function (MTF), payload
alignment and its stability, effective focal length and

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Figure 11 . Signal to noise ratio versus temperature.
Figure 12 . Radiometric linearity.
distortion measurement, ghost/backgr ound analysis, dark
noise, signal to noise ratio (SNR) at n ear saturation, etc.
MCC employed multiple levels of exposure settings
varying from 34 s to 490 ms to meet the imaging re-
quirements of various targets and varying illumination
conditions. Extensive radiometric calibration was carried
out using an integrating sphere (uniform illumination
source) to establish radiometric performance for various
exposure settings. Light transfer characteristics (LTC) of
all three bands was carried out with multiple exposure
modes and suitable radiance level to cover complete
dynamic range of the payload. Radiometric response for
each band was established. Figure 11 shows the SNR per-
formance at ~850 counts (10 bit digitization) for both
data chains (BDH systems – main and redundant) and for
different raw bus voltages (28, 35 and 42 V) during
thermo-vacuum test.
Figure 12 shows radiometric response (typical for the
green band) at exposure setting of 133 s for the MCC
payload. Colour reproduction capability of MCC was
determined largely by accurate spectral calibration of the
payload in the complete spectral range. The spectral
response measurement of MCC was carried out using a
monochromator source and standard spectro-radiometer
for the spectral range 300–1100 nm. The in-band meas-
urements were carried out in step size of 2 nm interval
and the out-of-band data were acquired at 10 nm interval.
The spectral response measurement was carried out to
cover all the zones of the detector array at the focal plane.
Figure 13 shows the spectral response of all three bands.
The geometric performance of MCC was carried out in a
detailed manner in the laboratory as well as thermovac
conditions. The performance was consistent. Figure 14
shows the MTF performance data. Radiometric, spectral
and geometric performance meets the requirements with a
comfortable margin.
Data product scheme
Level-1 product (calibrated data) generation involves
detector-wise photo response non-uniformity model cor-
rection as understood from pre-launch laboratory calibra-
tion exercises; line/pixel loss correction and tagging the
geographic coordinates to each pixel. An MCC image is a
Bayer filter mosaic, a colour filter array (CFA) for
arranging RGB colour filters on a square grid of photo
sensors. The demosaicing algorithm was developed to re-
construct a full colour image. The software pipeline to
produce minimum planetary data system (PDS) compli-
ance product in the active archive was developed, tested,
evaluated and readied at ISSDC, Bangalore. Software for
data products included reference datasets and utilities to
help ascertain radiometric and geometric accuracies, and
a tool to produce a high dynamic range colour image
from multiple MCC frames called bracketed exposures.

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Figure 13 . Relative spectral response of MCC.
Figure 14 . Modula tion transfer function at various temperatures (MCC) .
Earth imaging experiments
MOM was launched on 5 November 2013 and MCC
started imaging from 19 November 2013. Earth imaging
experiments (EIE) were conducted during the Earth orbit
phase (EOP) in order to assess the functional and per-
formance aspects of MCC and assess its application
potential vis-à-vis th e objectives envisaged. Three imag-
ing sessions on two different dates, viz. two sessions on
19 November 2013 and one on 23 November 2013 were
conducted. This included imaging from varying altitudes
(spatial resolution), illumination conditions, taking multi-
ple snapshots of a given area of interest (AOI), etc. in order
to view physiographic, morphological and other geologi-
cal details of our plan et so as to ascertain the expected
results from highly elliptical Mars orbit. The modes of
operation also ranged from mode-3, having integration
time of 0.4 ms to mode-13, having integration time of
0.133 ms for MCC.
The imaging sessions were chosen to get favourable
illumination and viewing geometry: During EIE, there
were five major objectives: (1) To image India for out-
reach purpose; (2) To image Earth from Mars Apoapsis
equivalent (about 60,000–70,000 km) altitude; (3) To

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Figure 15 . a, First image acqu ired by MCC on 1 9 N ovember 20 13. b, Another image on 23r d November 2 013 over Sahara desert.
Figure 16 . MTF estimation across land/o cean boundary.
Figure 17 . SNR of MCC in one mode for bands 1 a nd 3.
image from geo-stationary equivalent altitude
(36,000 km); (4) To image at a resolution of 1 km; (5) To
analyse and evaluate the data.
Qualitative analysis
The first photograph (Figure 15 a) was taken on 19 No-
vember 2013 (0820 UT) from an altitude of 67,975 km
with 3.5 km spatial resolution. It was the first MCC
image showing parts of Asia and Africa, including India.
The swath of the image was about 7240 km and it was
taken using 0.4 ms integration time. Three snapshots,
each shot per second, were taken. Another imaging ses-
sion over the Sahara desert was carried out on 23 No-
vember 2013 (0900 UT) from an altitude of 18,746 km
(Figure 15 b). The spatial resolution was 0.91 km. Visual
interpr etation of the image was carried out and many
Martian morphological analogues like barchans, longitu-
dinal sand dunes, parabolic dunes, volcanic rock outcrops
and aeolian corridors (streaks) could be clearly mapped.
In the first image (Figure 15 a), most of India could be
covered with minimal cloud cover. Th e four major ph ysi-
ographic zones of India, viz. Himalayan range (white
snow), the Indo-Gangetic plain (greyish), Thar desert
(beige colour) and the south ern peninsula (dark) were
picked up distinctly with textbook pr ecision by the
maiden image taken by MCC. The cyclone ‘Helen’ (white),
off the eastern coast, was picked up before its landfall.
Additionally, the dispersal pattern of the suspended sedi-
ments discharged by rivers into the Gulf of Khambhat
and Gulf of Kachchh was also seen in light blue colour,
off the Gujarat coast. Holy lake ‘Mansarovar’ was also
visible across the Himalayan snow peaks. Other features
in the image show parts of Sahara and Arabian deserts
(bright colour), Trans-Himalayan Tibetan plateau, fertile
Indus valley and a variety of cloud patterns.

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Figure 18 . Images showing ma tch between coa stal and l and bounda ry bef ore (left) and after (right) improve ment.
Figure 19 . Single frame (a) and three-fra me (b) high dynamic ra nge (HDR) ima ges.
Quantitative analysis
MCC data, acquired on 19 and 23 November 2013 were
analysed for the followin g aspects: (1) MTF measurement
(2) Signal-to-noise-ratio; (3) Dynamic range of the data
(in radiance domain); (4) Inter-sensor comparison.
Image-based MTF computation: MTF was estimated at
land/ocean boundary (Figure 16, indicated by dotted-
square off the Saurashtra coast, Gujarat). It measured
20.3%, which conforms to the pre-launch laboratory tests
of MCC.
Image-based signal-to-noise ratio computations: Sys-
tematic analysis of SNR was carried out separately for
various targets, viz. low albedo target (deep ocean) and
high albedo target (sand/cloud). Homogenous areas were
identified and SNR assessed using the procedure descri-
bed in the literature4,5. The assessed average SNR ranges
from 80 to 180 among different bands. SNR values show
that we can expect good discrimination of Martian fea-
tures. Figure 17 shows the typical SNR computed from
MCC image in mode 13. As expected from ground LTC
data, saturation count (DN) was found to be about 850.
Geometric performance evaluation: An elaborate exer-
cise was carried out to establish the geometric accuracy
of MCC datasets acquired over Earth bound phase. Ter-
restrial surface features were distinctly discernible by
direct visual analysis in MCC image. Geometric accuracy

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Figure 20 . Histogram of red, blue and green bands (a) before and (b) after generation of HDR.
evaluation was carried out using ground control points
collected from ortho-rectified images acquired from
‘True Marble’ (NASA) and IRS-Resourcesat-2 AWIFS
datasets. After estimating the residual errors of orbit/
MCC images using reference data, additional corrections
were applied. Thereafter, the world coastal and land
boundary data were overlaid on MCC data which were
found to match (Figure 18). Accuracies achieved were of
the order of +/– 0.5 MCC pixel resolution at various alt i-
tudes.
Image-based radiance range of the data: Radiance
range is an important element of the datasets used for
evaluating the quality of the data. The radiance range
comprised of data from low-albedo targets which in this
case was deep ocean and high-albedo target which was
sand/cloud in all the three datasets. The data acquired by
the payload experienced varying viewing geometry and
solar illumination and hence the data were normalized for
both the effects. It was observed that radiance range
lay between 2 and 50 mW/cm2/sr/m for red band, 5
and 38 mW/cm2/sr/m for green band, and 7 and
28 mW/cm2/sr/m for blue bands. The saturation radi-
ance for the three bands was 54.71, 50.65 and
48.41 mW/cm2/sr/m respectively, for the modes selected
for operation. The data do not get saturated in this mode
of operation and the same is expected in Mars imaging
phase.
In order to check the radiance value and top of atmo-
sphere reflectance with the contemporary satellite, an
analysis was done by taking observations from geo-
stationary satellite INSAT 3A CCD red band only, as it is
the only band common in both the payloads. The data
were so selected such that the time of obser vation nearly
matches in both cases. As was done for MCC, normaliza-
tion for viewing geometry and solar illumination was also
done for INSAT 3A CCD datasets. MCC and CCD radi-
ance showed close approximation in values (R2 ~ 0.69).
High dynamic range data product: A specialized high
dynamic range (HDR) product was generated using three
frames imaged consecutively to enhance the land fea-
tures. The difference between the single-frame and three-
frame image was clearly visible (Figure 19 a and b
respectively). The same is represented by histograms of
red, blue and green bands before and after generation of
HDR (Figure 20 a and b respectively).
The surface features on land was enhanced in the HDR
image, e.g. Thar Desert, India.
Conclusion
The payload could be realized within the weight and
power budget of the mission. Three models of the payload
were made, characterized, calibrated and space-qualified
in a record period of one year. Indigenous miniaturization

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of the flight model of MCC brought down its mass by
50%. The MCC data generated during the Earth orbit
phase at different altitudes were of professional quality
and found to be mostly conformal with the calibration
values of LTC data generated during laboratory tests of
MCC. Visual interpretation of the MCC images could be
used to identify Martian analogous features on the Earth
surface with satisfactory quality. Post-launch perform-
ance of the payload has been excellent. The data pipeline
has been established. MCC is expected to give images of
desired quality during rest of the Mars mission, which
will help the scientific community to further understand
the static (morphological) Martian features and dynamic
processes (ice-cap changes, dust devils, etc.) during the
useful life of the mission.
1. Anon., Pre-shipment review document, Mars Colou r Camera, Docu-
ment N o. SAC-MO M-04-April 2013.
2. Hua , L. and Chen, H., A col or i nterpolation algorithm for Ba yer pa t-
tern digita l cameras based on green component s and color di ffere nce
space. Informati cs and Computing, IEEE Internationa l Conferenc e,
Shanghai, 10–12 December 2010, pp. 791–7 95.
3. El Gamal, A., CMOS ima ge sensors. IEEE Circu its Dev. Mag.,
2005 , 21, 6–20 .
4. Zha ng, L., Automatic digital surface model (DSM) g eneration from
linea r ar ray images. Ph D dissertation. Institute o f Geodesy and Pho-
togra mmetry, Zurich, Switzerland, 2005.
5. Baltsavias, E. P., Pateraki, M. and Zhang, L. Radiometric and geo-
metric evaluat ion of IKONO S geo images a nd t heir use for 3D
building mo deling. In Proceedings of Joint ISPRS Workshop on
High Resolution Mappi ng from Space 200 1, Hannover, Germany,
19–21 September 2001.
ACKNOWLEDGEMEN TS. W e thank Dr K. Radhakrishnan, Chair-
man, ISRO, for encou ragement and support and the ADCOS Commit-
tee, ISRO and peer reviewers of the payload system/su b-sy stem and
scientific studies for their useful suggestions and guideline s. We also
thank the entire team of Indian Spac e Science Data Centre (ISSDC) ,
Byalalu for QLD and L0 data products immediately after acquisition;
Regional Remote Sen sing Centre, Bengaluru, for support in pr oducing
the first-day qua lity images of MCC, and those who have been di rectly
or indirectly inv olved in making this study pos sible.
doi: 10.18 520/v109/i6/1076-1086