Remote Sensing of Particulate Pollution over the Indian
Subcontinent using VHRR onboard KALPANA-1
Research Proposal
Submitted to
ISRO-IIT Bombay Space Technology Cell
for consideration for funding
by
Virendra Sethi
Professor
Centre for Environmental Science & Engineering
Indian Institute of Technology Bombay
and
K.V.S.Badarinath
Scientist
Atmospheric Division
National Remote Sensing Centre
Indian Space Research Organisation
Department of Space, Govt. of India.
September 2009
1
01. Project Summary
Goal of the study is to demonstrate the viability of monitoring particulate air quality over
the Indian subcontinent using geostationary satellite sensors.
An algorithm will be
developed to retrieve aerosol optical depth (AOD) from Very High Resolution
Radiometer (VHRR) onboard an Indian geostationary satellite. The retrieved AOD will
be validated with that from ISRO-GBP ARFI network of stations, Aerosol Robotic
Network (AERONET) and other polar orbiting sensors such as Moderate Resolution
Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer
(MISR). Using AOD, as proxy for particulate loading, long range transports of natural as
well as anthropogenic aerosols over Indian subcontinent will be studied.
The
spatiotemporal correlations between the retrieved AOD and ground level fine particulate
matter mass concentration will also be investigated.
Key Words: Aerosol Optical Depth, Geostationary satellite sensor, particulate matter, air
quality management.
02. Origin of the proposal
This proposal originated from the need for continues spatial monitoring of particulate
pollution over India. As of now, to the best of our knowledge, no study has been reported
of retrieving AOD from an Indian geostationary satellite sensor thereby limiting its
application for particulate air quality monitoring over the region. Among 11 Indian inservice geostationary satellites, three of them (INSAT 2E, INSAT 3 A and KALPANA-1)
are equipped with VHRR sensor which could be utilized for PM remote sensing. This
forms the basis of the current proposal. Background to the above is described briefly in
the following paragraphs.
Aerosols, the ever present and highly-varying constituents of our atmosphere, have a
significant impact on human life beyond the climate element. Epidemiological studies
provide evidence of chronic health effects of atmospheric particulates or “particulate
2
matter (PM)” and these studies rely on spatial heterogeneity in PM concentrations to
estimate their effects (Pope and Dockery, 2006)1. Although traditional fixed site air
quality monitoring stations provide data on the characteristics and concentration levels of
PM at a particular place and time, they lack spatiotemporal representation of atmospheric
particulates.
Promisingly, recent advancements in satellite remote sensing provide an opportunity to
augment the often sparse ground based PM measurements with better spatiotemporal
representation. Aerosol remote sensing community uses aerosol optical depth (AOD) as a
surrogate parameter for PM loading over any region. Studies have been reported
regarding the AOD retrieval from various satellite sensors, most of which are polar
orbiting or low earth orbiting (LEO) sensors. An exception to this is the GOES aerosol/
smoke product (GASP) provided by US National Oceanic and Atmospheric
Administration (NOAA), National Environmental Satellite Data and Information Service
(NESDIS). GASP AOD is retrieved over US, every 30 minutes from Geostationary
Operational Environmental Satellite (GOES) platforms. Geostationary platforms has the
potential to provide aerosol measurements with good temporal resolution than polar
orbiting sensors whose retrievals are limited to twice per day , as by the morning and
afternoon passes of the orbiting satellites. The frequent observations of the same area
available from geostationary earth orbit (GEO) sensors, makes them useful for air quality
monitoring although it has limited spectral resolutions compared to LEO sensors. Remote
sensing using GEO sensors permit tracking the rapid movement of pollution from strong
sources (eg: biomass burning, dust storms) and also monitor diurnal variations in
atmospheric aerosol distribution over a particular region.
03. Definition of the problem
The proposed project will retrieve and validate AOD from VHRR sensor onboard
KALAPNA-1. The AOD data with high temporal resolution (~ 30 minutes) will provide
information about the particulate pollution trends and patterns over Indian subcontinent.
1
Pope, C.A.III, Dockery, D.W., 2006. Health effects of fine particulate air pollution: Lines that connect.
Journal of the Air and Waste Management Association 56, 709-742
3
The correlation analysis between VHRR AOD and ground-level fine particulate matter
mass concentrations will be carried out to investigate how better AOD could be used as
surrogate for PM pollution over India for better air quality forecasts and management.
04. Objectives
The research is intended to
(a) Retrieve AOD from VHRR sensor onboard an Indian geostationary satellite.
(b) Validate the retrieved AOD with AOD from other ground & space based sensors.
(c) Investigate spatiotemporal distribution of fine particulate matter over the Indian
subcontinent using VHRR AOD data.
To accomplish this, following tasks are delineated:
(a) Calibration of VHRR sensor.
(b) Retrieval of surface reflectance from calibrated radiance values.
(c) Retrieval of optical depth from radiative transfer models with suitable aerosol
models using a look-up table approach.
(d) Comparison of retrieved AOD with AOD from other ground or space based
sensors such as AERONET, MODIS and MISR.
(e) Building a statistical regression model to understand the correlations between
AOD and fine particulate mass concentration over the Indian subcontinent.
05. International Status & National Status
International Status
The success of AVHRR, GOES, and Landsat instruments, which were primarily designed
for surface and meteorological observations, in observing aerosols initiated the
application of satellite remote sensing in air quality research in 1970s (Carlson and
4
Wendling2, 1977; Fraser3, 1976). Recent advancements in remote sensing technology
and use of new satellite sensors dedicated for monitoring aerosols and trace gases, has
made satellite remote sensing a promising tool for air quality management (Hoff and
Christopher4, 2009).
Aerosol optical depth, a dimensionless measure of scattering and absorption of light by
aerosols over the total vertical column of the atmosphere, has been used to quantify air
borne particulate matter over specific regions. Currently, attempts to relate AOD with air
quality are being addressed by the research community. Retrieval of AOD from satellite
sensors has had a long history. The first retrieval of aerosol optical depth over land used
GOES observations over eastern United States (Fraser et al.5, 1984).
Landsat and
AVHRR data were also used in some early studies for retrieving AOD (Tanre’ et al.6,
1988, Holben et al.7, 1992), but with high uncertainty, mainly because these instruments
were not targeted for such atmospheric measurements.
In recent years, dedicated space borne sensors have emerged for aerosol remote sensing,
accelerated with advancements in the sensor instrumentation and retrieval methods. Most
of these sensors are intended for global scale remote sensing and therefore put in polar
orbits. Apart from atmospheric scientists, the data from these sensors have been of
interest also to air quality research community. Many researchers have reported use of
2
Carlson, T.N. and Wendling, P., (1977). Reflected radiance measured by NOAA 3 VHRR as a function of
optical depth for Saharan dust. Journal of Applied Meteorology 16, 1368–1371.
3
Fraser, R.S., (1976). Satellite measurement of mass of Sahara dust in the atmosphere. Applied Optics 15,
2471–2479.
4
Hoff, R. and Christopher, S., (2009). Critical Review: Remote sensing of particulate pollution from space
: Have we reached the promised land ?.J. Air & Waste Manage. Assoc. 59, 645-675.
5
Fraser, R.S., Kaufman, Y.J. and Mahoney, R.L., (1984). Satellite measurements of aerosol mass and
transport. Atmospheric Environment 18, 2577–2584.
6
Tanre´ , D., Deschamps, P.Y., Devaux, C. and Herman, M., (1988). Estimation of Saharan aerosol optical
thickness from blurring effects in thematic mapper data. Journal of Geophysical Research 93, 15955–
15964.
7
Holben, B.N., Vermote, E., Kaufman, Y.J., Tanre´ , D. and Kalb, V., (1992). Aerosol retrieval over land
from AVHRR data – application for atmospheric correction. IEEE Transactions on Geoscience and Remote
Sensing 30, 212–222.
5
AOD data from these polar orbiting sensors for particulate air quality monitoring at
regional and local scales (Wang and Christopher8, 2003; Engel-Cox et al.9, 2004; Liu et
al.10, 2005; Van Donkelaar et al.11,2006; Gupta et al.12, 2006; Pelletier et al.13, 2007; Liu
et al.14, 2007 a, b). However, the data from polar orbiting satellites are inherently limited
in temporal resolution. Frequent observation of the same area is important for air quality
monitoring since it permits tracking rapid transport of pollution from one region to the
other. Several researchers have therefore, attempted retrieval of AOD from geostationary
satellite sensors for possible air quality applications. Most of these studies utilized data
from GOES series and are limited to United States and near by regions because of the
viewing geometry of GOES platforms (Fraser et al.15, 1984, Knapp and Vonder Haar 16,
8
Wang, J. and Christopher, S.A., (2003). Intercomparison between satellite-derived aerosol optical
thickness and PM2.5 mass: implications for air quality studies. Geophysical Research Letters 30 (21), 2095.
9
Engel-Cox, J.A., Holloman, C.H., Coutant, B.W. and Hoff, R.M., (2004). Qualitative and quantitative
evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmospheric
Environment 38, 2495-2509.
10
Liu, Y., Sarnat, J.A., Kilaru, V., Jacob, D.J. and Koutrakis, P., (2005). Estimating ground-level PM2.5 in
the eastern United States using satellite remote sensing. Environmental Science & Technology 39, 3269–
3278.
11
Van Donkelaar, A., Martin, R.V. and Park, R.J., (2006) Estimating ground-level PM2.5 using aerosol
optical depth determined from satellite remote sensing. J. Geophys. Res., 111, D21201,
doi:10.1029/2005JD006996.
12
Gupta, P., Christopher, S.A., Wang, J., Gehrig, R., Lee, Y. and Kumar, N., (2006). Satellite remote
sensing of particulate matter and air quality assessment over global cities. Atmospheric Environment 40,
5880–5892.
13
Pelletier, B., Santer, R. and Vidot, J., (2007). Retrieving of particulate matter from optical measuremets:
A semiparametric approach. J. Geophys. Res., 112, D06208, doi:10.1029/2005JD006737.
14
Liu, Y., Kahn, R. and Koutrakis, P., (2007a). Estimating PM2.5 component concentrations and size
distributions using satellite retrieved fractional aerosol optical depth: part I – method development. Journal
of the Air & Waste Management Association 57, 1351–1359.
Liu, Y., Kahn, R., Turquety, S., Yantosca, R.M. and Koutrakis, P., (2007b). Estimating PM2.5 component
concentrations and size distributions using satellite retrieved fractional aerosol optical depth: part II –a case
study. Journal of the Air & Waste Management Association 57, 1360–1369.
15
Fraser, R.S., Kaufman, Y.J. and Mahoney, R.L., (1984). Satellite measurements of aerosol mass and
transport. Atmospheric Environment 18, 2577–2584.
6
2002; Knapp et al.17, 2005; Christopher et al.18, 2002; Prados et al.19 , 2007; Paciorek et
al.20, 2008). Some studies have also been reported from Europe which utilized the data
from Meteosat series (Pinty et al.21,2000 (a, b); Brindley and Ignatov 22,2006; Popp et al.
23
,2006). No attempts have been made so far to retrieve AOD from Indian geostationary
sensors and the data over the region are therefore limited for potential real-time air
quality applications.
16
Knapp, K.R. and Vonder Haar, T.H., (2002). Aerosol optical depth retrieval from GOES-8: Uncertainty
study and retrieval validation over South America. Journal of Geophysical Research 107, NO. D7,4055,
10.1029/2001JD000505.
17
Knapp, K.R., Frouin, R., Kondragunta, S. and Prados, A., (2005). Toward aerosol optical depth retrievals
over land from GOES visible radiances: determining surface reflectance. International Journal of Remote
Sensing, 26: 18, 4097-4116.
18
Christopher, S.A., Zhang, J. and Holben, B.N., (2002). GOES-8 and NOAA-14 AVHRR retrieval of
smoke aerosol optical thickness during SCAR-B. International Journal of Remote Sensing, 23: 22, 49314944.
19
Prados, A., Kondragunta, S., Ciren, P. and Knapp, K., (2007). GOES aerosol/smoke product (GASP)
over North America: comparisons to AERONET and MODIS observations. J. Geophys. Res. Atmos., 112,
D15201.
20
Paciorek, C.J., Liu, Y., Moreno-Macias, H. and Kondragunta, S., (2008). Spatiotemporal associations
between GOES aerosol optical depth retrievals and ground-level PM2.5. Environ. Sci. Technol., 42,58005806.
21
Pinty, B., Roveda, F., Verstraete, M.M., Gobron, N., Govaerts, Y., Martonchik, J.V., Diner, D.J. and
Kahn, R.A. (2000 a). Surface albedo retrieval from Meteosat, 1. Theory. J. Geophys. Res. 105 (D14),
18,099–18,112.
Pinty, B., Roveda, F., Verstraete, M.M., Gobron, N., Govaerts, Y., Martonchik, J.V., Diner, D.J. and Kahn,
R.A., (2000 b). Surface albedo retrieval from Meteosat, 2. Applications. J. Geophys. Res. 105 (D14),
18,113–18,134.
22
Brindley, H.E. and Ignatov,A., (2006). Retrieval of mineral aerosol optical depth and size information
from Meteosat Second Generation SEVIRI solar reflectance bands. Remote Sensing of Environment 102
(3-4), 344-363
23
Popp, C., Foppa. N., Hauser, A. and Wunderle, S., (2006). Aerosol optical depth retrieval over land using
Meteosat-8 SEVIRI data. Atmospheric Science Conference, ESA-ESRIN, Frascati, Italy, May 2006.
7
National Status
Limited numbers of studies have been reported from India, demonstrating the application
of satellite remote sensing for particulate air quality monitoring. Most of these studies
used AOD data products from polar orbiting sensors such as MODIS and MISR to
investigate spatial distribution of atmospheric particulates and the correlations between
their ground level mass concentrations and AOD (Di Girolamo et al.24, 2004; Prasad et
al.25, 2006; Kumar et al.26, 2007).
IRS P4 OCM (Ocean Color Monitor) seems to be the only Indian satellite sensor reported
so far to have been used for AOD retrieval by researchers (Dey and Singh 27, 2002; Das et
al.28, 2002). However these retrievals, being over ocean, have limited air quality
applications.
06. Importance of the Proposed Project in Indian/ISRO Context
Increasing trend in particulate pollution is of global concern due to its impact on
atmospheric processes and human life. Uncertainties in the sources and pollutant
transport evoke a need for continuous spatial monitoring of PM. Identifying this need
and understanding the benefits of geostationary orbit sensors for air quality monitoring,
countries like United States have already scheduled dedicated geostationary satellite
sensor missions as part of their popular GOES series. The new Advanced Baseline
24
Di Girolamo et al., (2004). Analysis of Multi-angle Imaging SpectroRadiometer (MISR) aerosol optical
depths over greater India during winter 2001–2004. Geophysical Research Letters, 31, L23115,
doi:10.1029/2004GL021273.
25
Prasad, A. K., Singh, R. P. and Kafatos, M., (2006). Influence of coal based thermal power plants on
aerosol optical properties in the Indo-Gangetic basin. Geophys Res Lett, 2006, 33(5): L05805.
26
Kumar, N., Chu, A. and Foster, A., (2007). An empirical relationship between PM2.5 and aerosol optical
depth in Delhi Metropolitan. Atmospheric Environment 41, 4492-4503.
27
Dey, S., and Singh, R.P., (2002). Retrieval of aerosol parameters using IRS P4 OCM data over Arabian
sea and the bay of Bengal. Current Science 83: 10, 1235-1240.
28
Das, I., Mohan, M., Krishnamoorthy, K., (2002). Detection of marine aerosols with IRS-P4 Ocean
Colour Monitor. Journal of Earth System Science 111:4, 425-435.
8
Imager (ABI) which is expected to have aerosol remote sensing capabilities similar to
MODIS is scheduled to fly on GOES-R satellite around the year 2012 (Laszlo et al.29,
2008). India has also scheduled INSAT 3D for 2009 which will carry payloads similar to
GOES and is expected to retrieve aerosol parameters along with other geophysical
parameters. In this context, the project proposed has importance as it will initialize
utilization of Indian geostationary satellites for PM remote sensing over the Indian
subcontinent. The proposed project intends to develop a capacity for real-time satellite
remote sensing of air pollution in India. The high temporal resolution AOD data, which
will be available out of this project, will enable better analysis of episodic events and
their impact on air quality and health in the region. This research will be useful for the
future development of globally accessible and interoperable decision support systems, as
part of Global Earth Observation System of Systems (GEOSS) activity, which integrates
earth observation data from remote sensing platforms and ground measurements for
better air quality forecasts and management.
07. Review of Expertise Available with Proposed Investigating Group/Institution
Professor Virendra Sethi: To be added
Dr.K.V.S.Badarinath and his team at National Remote Sensing Centre, Hyderabad have
been working on atmospheric science related activity using satellite data and ground
based instrumentation. They have established facility for monitoring aerosol properties at
urban region of Hyderabad and conducted field experiments.
08. Work Plan/Methodology
The project is proposed for a period of 36 months. The methodology and time schedules
are discussed in the following sections.
29
Laszlo, I., Ciren, P., Liu, H., Kondragunta, S., Tarpley, J.D. and Goldberg, M.D., (2008). Remote sensing
of aerosol and radiation from geostationary satellites. Advances in Space Research 41, 1882-1893.
9
The proposed project has two aspects namely AOD retrieval from a geostationary sensor
and application of the same for particulate matter (PM) remote sensing over India. A
brief description of the methodology of these two aspects is given in the following subsections.
a) Retrieval of AOD from VHRR sensor
VHRR sensor has 3 spectral bands namely visible (0.55-0.75m), Thermal Infra Red
(10.5-12.5m) and Water Vapour band (5.7-7.1m). As the VHRR sensor lack an
onboard calibration system, the radiance data available from this sensor needs to be
calibrated before attempting retrieval of AOD. Various methods have been used in past
by researchers to calibrate such sensors which lack onboard calibration devices or
deteriorated in orbit in course of time (Rao et al.30, 1999). In this project a vicarious
calibration method similar to that described by Knapp and Vonder Haar 31 (2000) will be
followed to calibrate the VHRR visible band. The clear-sky observations over Indian
Ocean will be correlated to the theoretical reflectances computed by Second Simulation
of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model (Vermote et
al.32, 1997), from which a measure of sensor response will be derived. Once the
calibrated radiance value is available the next step will be to assess the surface
contribution to top of atmosphere (TOA) VHRR visible reflectance, using a compositing
method (Knapp et al.33, 2005). Composite reflectance method uses observations for a
given earth location over a number of days to determine source contribution. The
30
Rao, C. R. N., Chen, J., Sullivan, J. T. and Zhang, N., (1999). Post-launch calibration of meteorological
satellite sensors. Advances in Space Research 23:8, 1357-1365.
31
Knapp, K. R. and Vonder Haar, T. H., (2000). Calibration of the Eighth Geostationary Observational
Environmental Satellite (GOES-8) Imager Visible Sensor. Journal of Atmospheric and Oceanic
Technology 17,1639-1644.
32
Vermote, E. F., Tanré, D., Deuzé, J. L., Herman, M. and Morcrette, J.-J., (1997). Second simulation of
the satellite signal in the solar spectrum, 6S: An overview. IEEE Trans. Geosci. Remote Sens. 35, 675-686.
33
Knapp, K.R., Frouin, R., Kondragunta, S. and Prados, A., (2005). Toward aerosol optical depth retrievals
over land from GOES visible radiances: determining surface reflectance. International Journal of Remote
Sensing, 26: 18, 4097-4116.
10
atmospheric effects such as aerosol extinction, Rayleigh scattering and gaseous
absorption will be removed from the composite using 6S radiative transfer model to
retrieve the surface reflectance. Aerosol Optical Depth will be then retrieved from TOA
and surface reflectance, using radiative transfer model with suitable aerosol models
(based on AERONET data). Cloud screening will be carried out using spectral and
spatial thresholds applied to the brightness temperature derived from the thermal band
data (Coakley and Bretherton34, 1982; Stowe et al.35, 1999). Validation of retrieved AOD
will be done by comparing it with ground based measurements, AERONET data and
AOD from polar orbiting sensors such as MODIS and MISR.
b) Analysis of spatiotemporal distribution of fine particulate matter over the Indian
subcontinent using VHRR data
The transport patterns of PM over the Indian subcontinent will be studied in this project
using the AOD retrieved from VHRR with newly developed algorithm. This investigation
is expected to provide insight into long range transports and inter-state transports of air
pollution over the region. This investigation will be carried out for period of one year
inorder to identify seasonal pollution hotspots in India. Some natural episodic events such
as sand storms and forest fires and their impact on the air quality of the region will be
better investigated using the high temporal resolution data from VHRR during the study
period. This project will also correlate the ground measurements of PM with geolocated
AOD values from VHRR.
In India, the data on ground level PM are collected using monitoring station networks
managed by Central Pollution Control Board (CPCB), State Pollution Control Boards
(SPCBs), and other networks operated by universities or research groups. The National
Ambient Monitoring Programme (NAMP) network operated under the guidelines of
34
Coakley, J. A. and Bretherton, F. P., (1982). Cloud cover from high resolution scanner data: Detecting
and allowing for partial fields of view. J. Geophys. Res. 87(C7), 4917– 4932.
35
Stowe, L.L., Davis, P.A. and McClain, E.P., (1999). Scientific basis and initial evaluation of the
CLAVR-1 global clear/cloud classification algorithm for the advanced very high resolution radiometer.
Journal of Atmospheric and Oceanic Technology 16, 656–681.
11
CPCB has 342 stations covering 127 cities/towns in 26 states and 4 Union Territories of
the country. Besides these 342 stations CPCB and SPCBs together have about 15-16
continuous monitoring stations. This project will make use some of these data to
understand spatiotemporal correlations between the retrieved VHRR AOD and groundlevel PM in India. Statistical regression models similar to that reported by researchers for
other parts of the world (Wang and Christopher36, 2003; Liu et al.37, 2005, Paciorek et
al.38, 2008) will be developed between AOD and ground-level PM.
36
Wang, J. and Christopher, S.A., (2003). Intercomparison between satellite-derived aerosol optical
thickness and PM2.5 mass: implications for air quality studies. Geophysical Research Letters 30 (21), 2095.
37
Liu, Y., Sarnat, J.A., Kilaru, V., Jacob, D.J. and Koutrakis, P., (2005). Estimating ground-level PM2.5 in
the eastern United States using satellite remote sensing. Environmental Science & Technology 39, 3269–
3278.
38
Paciorek, C.J., Liu, Y., Moreno-Macias, H. and Kondragunta, S., (2008). Spatiotemporal associations
between GOES aerosol optical depth retrievals and ground-level PM2.5. Environ. Sci. Technol., 42,58005806.
12
09. Time Schedule
Time
(Months)
Activities
Milestones

Calibrated radiance
value from VHRR

Permanent equipments
procured

Validated Aerosol
Optical Depth from
VHRR
Analysis of spatiotemporal distributions of
PM over India

Identification of
episodic events in the
region
Development of regression models between
AOD and ground-level PM

Correlation coefficients
between VHRR AOD
and ground level PM

Recommendations for
future work
Literature Review
0-6
Calibration of VHRR Sensor
Deriving seasonal surface reflectance of the
region from VHRR data
7-21
Development of AOD retrieval algorithm
Validation of algorithm
22-33
34-36
Final report and publications
13
Budget
(in Rupees)
Item
1st Year
2nd Year
3rd Year
3,60,000.00
3,60,000.00
3,60,000.00
10,80,000.00
2. Travel
30,000.00
30,000.00
30,000.00
90,000.00
3. Contingency costs
50,000.00
50,000.00
50,000.00
1,50,000.00
*10,00,000.00
(3,00,000.00)
* 2,88,000.00
(1,48,000.00)
* 17,28,000.00
(8,88,000.00)
0.00
0.00
88,000.00
88,000.00
5,28,000.00
5,28,000.00
*10,00,000.00
(3,00,000.00)
* 4,64,000.00
(3,24,000.00)
* 27,84,000.00
(19,44,000.00)
1. Salaries/Wages
4. Equipments &
Maintenance
5. Overhead @ 20%
6. Grand Total
Total
10. Budget Estimates: Summary
* Microtops-II Sunphotometer (see section 10.3) is available with NRSC. NRSC permitting, we may
collaborate for use of this instrument for this project and in such case its procurement cost may be
deducted from estimated budget.
Section 10.1
A: Budget for Manpower
Designation &
number of persons
Research Engineer
(1no.)
Project staff (1no.)
Total
Budget
Monthly
emoluments
(in Rupees)
1st Year
2nd Year
3rd Year
Total
16,000.00
1,92,000.00
1,92,000.00
1,92,000.00
5,76,000.00
14,000.00
1,68,000.00
1,68,000.00
1,68,000.00
5,04,000.00
30,000.00
3,60,000.00
3,60,000.00
3,60,000.00 10,80,000.00
B: Justification for salaries & wages
14
Personnel with two to three years of research experience will be absorbed as research
engineer in this project and he/she will work full time on the project under the
supervision of PI. The fellowship will be given in case of research engineer having no
regular employment and is not drawing any other fellowship during the tenure of the
project. A fresh Post-graduate or equivalent will be taken as project staff in this project,
who will assist research engineer in collecting ground truths and programming works.
Section 10.2
A: Budget for other costs
Budget
Item
(in Rupees)
1st Year
2nd Year
3rd Year
Total
Travel
30,000.00
30,000.00
30,000.00
90,000.00
Contingency costs
50,000.00
50,000.00
50,000.00
1,50,000.00
Total
80,000.00
80,000.00
80,000.00
2,40,000.00
B: Justification for other costs
The research results will be presented in aerosol science, air quality and remote sensing
conferences in India. Traveling allowances will be used for such purposes. Also,
traveling allowances will cover the visits to partner institute as part of the project and also
any travel associated with collecting ground truths. Contingency provision will cover
expenses towards communication, postal and unforeseen expenses. The contingency head
will also provide for preparation of interim and final reports.
Section 10.3
A: Budget for permanent equipment
Sl No:
Generic name of the equipment
along with the make and product
Imported/
Indigenous
Estimated Cost
(Rs)
15
1
Model 540, Microtops II
Sunphotometer with 5 filters,
select from 340, 380, 440, 500,
675, 870, 936 & 1020nm, with
built-in Barometer/Altimeter and
GPS reciever
2
HP Workstation PC xw6600
Total
Imported
7,00,000.00
Indigenous
3,00,000.00
10,00,000.00
B: Justification for permanent equipment
Ground based measurements of AOD at different wavelengths will be made using the
hand held sun photometer. These AOD values are needed to compare retrieved VHRR
AOD and also for atmospheric corrections of the satellite data during retrieval of surface
reflectances. Workstation PC will facilitate high speed computations and image
processing.
16