RESEARCH ARTICLE

Formulation of Time Series Vegetation Index from IndianGeostationary Satellite and Comparisonwith Global Product

Rahul Nigam & Bimal Kumar Bhattacharya &

Keshav R. Gunjal & N. Padmanabhan & N. K. Patel

Received: 24 October 2010 /Accepted: 6 May 2011 /Published online: 25 June 2011# Indian Society of Remote Sensing 2011

Abstract To study impact of climate change onvegetation time series vegetation index has a vitalrole to know the behaviour of vegetation dynamicsover a time period. INSAT 3A CCD (Charged CoupleDevice) is the only geostationary sensor to acquireregular coverage of Asia continent at 1 km×1 kmspatial resolution with high temporal frequency (half-an-hour). A formulation of surface reflectances in red,near infrared (NIR), short wave infrared (SWIR) andNDVI from INSAT 3A CCD has been defined andintegrated in the operational chain. The atmosphericcorrection of at-sensor reflectances using SMAC(Simple Model for Atmospheric Correction) modelimproved the NDVI by 5–40% and also increased itsdynamic range. The temporal dynamics of 16-dayNDVI composite at 0500 GMT for a growing year(June 2008–March 2009) showed matching profileswith reference to global products (MODIS TERRA)over known land targets. The root mean squaredeviation (RMSD) between the two was 0.14 with

correlation coefficient (r) 0.84 from 200 paired data-sets. This inter-sensor cross-correlation would help inNDVI calibration to add continuity in long termNDVI database for climate change studies.

Keywords NDVI . Geostationary . INSAT. Asia

Introduction

The influence of land-use change and landscapedynamics play a vital role in land atmosphere interactionand hence on climate change (Pielke et al. 2002).Vegetation index plays a major role in defining landuse change dynamics and is being used in differentdynamic vegetation model (Jarlan et al. 2008) embed-ded in climate models. The vegetation indices arederived by combining spectral data in different wavebands. The spectral indices are typically a sum,difference, ratio or other linear combinations ofreflectance factor or radiance observation from two ormore wavelength intervals (Wiegand et al. 1991). Thetime series vegetation index data provide a tool tostudy past conditions, monitor current conditions (vanLeeuwen et al. 2006), and prepare future trends.Comparison of present vegetation records with pastlong-term averages have been widely used to studyecosystem monitoring, and help to evaluate the impactof rising global temperature and CO2 levels (Nemani etal. 2003). These vegetation indices have been founduseful for vegetation density (Wiegand et al. 1979;

J Indian Soc Remote Sens (March 2012) 40(1):1–9DOI 10.1007/s12524-011-0122-2

R. Nigam (*) :B. K. Bhattacharya :K. R. Gunjal :N. K. PatelAgriculture, Terrestrial Biosphere and Hydrology Group(EPSA) Space Applications Centre (ISRO),Ahmedabad 380 015, Indiae-mail: rahulnigam@sac.isro.gov.in

N. PadmanabhanData Products Software Group,(SIPA) Space Applications Centre (ISRO),Ahmedabad 380 015, India

Spanner et al. 1990), green leaf density, photosynthesisactive biomass (Tucker 1979; Wiegand et al. 1991)absorbed photosynthetically active radiation and usedin climate modeling.

The “Normalized Difference Vegetation Index”(NDVI) is widely used for vegetation growth moni-toring (van Leeuwen et al. 2006). The NDVI issuccessful as a vegetation measure and it is suffi-ciently stable to permit meaningful comparisons ofseasonal and inter-annual changes in vegetation growthand activity. The strength of the NDVI is in its ratioingconcept, which reduces many forms of multiplicativenoise (illumination differences, cloud shadows, atmo-spheric attenuation, certain topographic variations)present in multiple bands (Huete et al. 2002). NDVIwas originally used as a measure of green biomass(Tucker et al. 1986). It has a strong theoretical basis asa measure of the fractional absorbed photosyntheticactive radiation (fAPAR) by the canopy (Sellers 1985;Fensholt et al. 2004). The NDVI relates reflectance (orradiance) in the red range and in the NIR range tovegetation variables such as leaf area index (LAI)(Wang et al. 2005; Hasegawa et al. 2010), crop cover(Wardlow and Egbert 2008), and vegetation phenology(Zhang et al. 2003; Sakamoto et al. 2005). NDVI hasbeen used to monitor and estimate primary productivity(Field et al. 1995; Matsushita and Tamura 2002) aswell as its assimilation in regional numerical weathermodels give better weather forecasting (Dutta et al.2009). It is sensitive to low chlorophyll contents, tolow fraction of vegetation cover and, as a result, to lowlevel of absorbed photosynthetic active solar radiation.For land surfaces dominated by vegetation, the NDVIvalues normally range from 0.1 to 0.8 during thegrowth season, the higher values being associated withgreater density of vegetation and greenness of the plantcanopy. Atmospheric effects, such as Rayleigh scatter-ing frommolecules, Mie scattering by aerosols, gaseousabsorption by atmospheric constituents and sub-pixel-sized clouds, all tend to increase the value of red withrespect to NIR and reduce the values of the computedvegetation indices. The quantitative estimation of mostof the biophysical parameters (LAI and fAPAR) usingsatellite based optical remote sensing requires timeseries NDVI data as model inputs. Daily and timecomposite NDVI series from moderate (1 km) to coarse(>1 km) resolution sensor data can provide ‘fullresolution’ of vegetation growth cycle than a singledate high resolution optical data with low repeativity.

The normalized difference vegetation index(NDVI) product is now-a-days regularly availablefrom observations in red and near infrared bands inlarge – view global polar orbiting sensors such as:SPOT-VGT, MODIS TERRA and AQUA, NOAAAVHRR at spatial resolutions varying from 250 m to8 km. These are available maximum twice per day ondaily or time composite basis. The NDVI generated atmultiple times in a day from geostationary satellitesensors provide opportunity to get more cloud freeNDVI as compared to once or twice overpasses in aday by polar orbiting large view sensor (Fensholt etal. 2006). The effects due to orbital drift as in NOAAAVHRR will be least because of constant viewinggeometry of geostationary sensors with respect toearth targets. Moreover, the diurnal behaviour ofnarrow band surface reflectances is ideal to studyBRDF (Bi-directional Reflectance DistributionFunction) characteristics of similar homogeneousland targets having similar sensor viewing conditions.This again helps further correct NDVI through surfaceBRDF modeling. No other existing geostationarysatellite missions in the world except INSAT 3A CCD(1 km) of India and Feng Yung of China and MSGSEVIRI (3 km) have payloads that take multipleobservations per day in narrow spectral optical bands(red, NIR and SWIR) at 1 km X 1 km spatial resolution.In INSAT 3ACCD payload was specifically designed tomonitor vegetation and snow cover conditions overAsian region regularly. The preliminary documentationto estimate NDVI from CCD has been done byBhattacharya and Pandya (2007). The present studywas undertaken to do further investigation on atmo-spheric corrected NDVI with the following objectives:

(i) Formulation of INSAT 3A CCD NDVI to maketime series vegetation index from Indian geosta-tionary satellite

(ii) Comparison of formulated CCD NDVI withglobal available MODIS NDVI product fordifferent land cover types

Methodology

The radiances reaching satellite sensors from earthsurface are generally influenced mainly by threefactors viz. (i) sun-sensor viewing geometry, (ii)atmospheric noises, adjacency and (iii) BRDF effects.

2 J Indian Soc Remote Sens (March 2012) 40(1):1–9

To overcome the effects of these stated factorscorrections are needed to make at sensor spectralreflectances (Ri) into surface spectral reflectances.These corrections broadly defined as:

Level 1: Generation of angular normalizedatmospherically corrected surfacespectral reflectances

Level 2: Level 1+adjacency effect correctionLevel 3: Level 2+BRDF correction

Level 1 correction has three different components

A. Rayleigh scatteringB. Gaseous absorption (ozone, water vapour,

CO2)C. Aerosol scattering and absorption

Post-Launch Vicarious Calibration of BandRadiances

The electronic performance of CCD sensor elementsgenerally is degraded due to space weathering. Thecross-calibration has been carried out with highresolution (56 m) IRS-P6 AWiFS sensor with equalatmospheric perturbations for red and NIR bands.Three dates spread over December (2008), February(2009) and March (2009) for both INSAT 3A CCDand AWiFS having same overpass time (0500 GMT)were chosen for cross-calibration. Top of Atmosphere(TOA) band radiances from six different land coverssuch as: agriculture, forest, snow, bare soil, waterbody and cloud were used for recalibration.

Cloud Filtering

The optical properties of clouds showed that itsreflectances in red, NIR or cloud albedo in broadvisible band become high and even more than 90%.But SWIR band reflectances are less in presence ofwater clouds due to higher absorption. Three criteriawere fixed for cirrus (high level), alto (medium level)and cumulus (low level) clouds based on several CCDscenes. The first two criteria are only based on TOAreflectance thresholds in red and NIR bands due topresence of more of ice cloud. In third criteria, SWIRTOA reflectance threshold was introduced in additionto red and NIR reflectances due to increasingpresence of water clouds. Further processing wascarried out only in cloud free pixels.

Atmospheric Correction

The atmospheric noises such as molecular (Rayleigh)and aerosol (Mie) scattering along with gaseousabsorption corrected by using simple model foratmospheric correction (SMAC) with wide viewsensor coefficients (Rahman and Dedieu 1994).That has been successfully used for large viewsatellite sensors such as NOAA AVHRR, METEOSATetc. The generalized functional form of SMACmodel is

rðqs; qv;ΔfÞ ¼ tgðqs;qvÞfraðqs; qv;ΔfÞþ ½e�t=ms þ tdðqsÞ�

� ½rce�t=mv þ retdðqvÞ�1� reS

g ð1Þ

with

TðqÞ ¼ e�t=m þ tdðqÞ ð2Þwhere θ=θs or θv

ρ* TOA spectral reflectance at satellite sensorlevel

ρc spectral surface reflectanceθs sun zenith angleθv view zenith angleΔf relative azimuth between sun and sensortg total gaseous transmissionρa atmospheric reflectance which depends on

optical properties of air molecule, aerosol,and sun-sensor viewing geometry.

τ atmospheric optical depth (et=msandet=mvbeing the direct atmospherictransmission) in sun (θs) and view (θv)directions

td(θ) atmospheric diffuse transmittancesS spherical albedo of the atmosphere1� reS taken in account multiple scattering

between surface and the atmosphere

This atmospheric correction scheme uses 1st

order correction for additive and multiplicativeatmospheric noises with the assumption that surfaceis lambertian. This scheme is simple to implementand tested against 5S atmospheric radiative transfercode (Tanre et al. 1990) and is thus increasingly usedfor generating surface reflectances from TOA radi-ances globally. Along with sun-sensor angular

J Indian Soc Remote Sens (March 2012) 40(1):1–9 3

geometry, this requires atmospheric inputs such ascolumnar ozone, precipitable water and aerosoloptical depth (AOD) at 0.55 μm. The database ondaytime mean of five years’ (2002–2006) ozone,precipitable water and aerosol optical depth at 0.55μm from MODIS TERRA (0530 GMT) and AQUA(0800 GMT) was prepared through interpolation ofMODIS eight-day atmospheric products (1°X 1°)equivalent to CCD resolution. These were furtherused as inputs to SMAC model to compute surfacereflectances.

NDVI Product Formatting

The computation of NDVI was carried out in cloudfree pixels using surface reflectances in CCD red andNIR bands. The NDVI was scaled to binary (8-bit)format with offset=110 and gain=0.01. The endproduct contains NDVI in h5 format that containscloud free NDVI, surface band reflectances as well asfiles for angular geometry for the geographical bound(44.5°–105.3°E, 9.8°S–45.5°N) known as Asia mer-cator region.

Satellite Data and Statistical Evaluation

To validate INSAT 3ACCDNDVI with available globalMODIS TERRA NDVI the data from May 2008 toMarch 2009 has been used. The pixel of MODISTERRA within 1 km grid cell were averaged to createone pair of MODIS TERRA and INSAT 3A CCDNDVI. The NDVI values were randomly taken out fromdifferent known natural targets like agriculture, forest,desert and snow to cover all type of land cover. The rootmean square error (RMSD) and mean absolute deviation(MAD) has been computed by following formulae

RMSD ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

P

i½ðPiÞ � ðOiÞ�2

N

v

u

u

t

MAD ¼P

iABS½ðPiÞ � ðOiÞ�

N

Where Pi – NDVICCD at ith case

Oi=NDVIMODIS at ith case

N=number of paired datasets

0

10000

20000

30000

40000

50000

60000

70000

100000

80000

60000

40000

20000

0-50 -42 -34 -26 -19 -11 -3 5 13 21 29 37 45

-0.2 0.1 0.4 0.7

Uncorrected NDVI

% Change in NDVI

-0.2 0.1 0.4 0.7

Corrected NDVI

Fre

qu

ency

Fre

qu

ency

0

10000

20000

30000

40000

50000

60000

70000

Fre

qu

ency

(a) (b)

(c)

Fig. 1 Comparison of atmospherically corrected NDVI with uncorrected NDVI for a particular day

4 J Indian Soc Remote Sens (March 2012) 40(1):1–9

Result and Discussion

Effect of Atmospheric Correction on CCD NDVI

Atmospherically corrected NDVI has been generatedby applying Rayleigh and Mie corrections usingaverage atmospheric conditions through SMAC mod-el. The corrected NDVI was then compared withatmospherically uncorrected NDVI over Indian sub-continent. It was found that for a particular clear day,the NDVI range increased from −0.2 to 0.6 inuncorrected one to −0.2 to 0.7 atmosphericallycorrected one. The frequency distribution of uncor-rected, corrected NDVI and percent difference be-tween them are shown in Fig. 1a, b and c, respectively.Through respective difference of corrected and un-corrected NDVI stated above ranged from −25 to 45,but majority of pixels under different land cover typesshowed positive difference between 5 to 40%.

Analysis of Spatio-Temporal Dynamics of CCDNDVI

The 16-day NDVI composite has been computed fromdaily NDVI to analyze the spatio-temporal dynamics ofNDVI for a growing season over different land covercategories from June 2008 to March 2009. In agricul-ture, NDVI showed quite high dynamics as compared todesert and forest, respectively. From the Fig. 2, it isquite evident that in Indo-gangetic plain NDVI showedhigh dynamics of intensive agricultural activities fromJune 2008 to March 2009. In Indo-gangetic plain,overall spatial NDVI was low during June but itincreased from July due to increase of vegetation coverwith progression of monsoon rainfall over Indianlandmass. The NDVI shows decreasing trend duringOctober with the maturity of kharif crops but againshows increasing trend in November and peak inFebruary due to progress of rabi crops.

Fig. 2 Spatio-temporal dynamics of CCD NDVI during a growing season

J Indian Soc Remote Sens (March 2012) 40(1):1–9 5

Validation

For the validation purpose, 16 day NDVI compositeat 0500 GMT satellite data were used from 26th May2008 to 30th March 2009 to cover dynamic range ofland cover type. The 16-day composite were preparedby extracting maximum NDVI value for each pixelfrom daily 16-day NDVI data. This will minimize thecloud interference on NDVI and to capture thephenological shift of vegetation. The MODIS TERRAcloud free NDVI were available as 16 day compositeat 250 m spatial resolution. These were then linearlyaggregated to target CCD grid resolution. The CCDNDVI and aggregated MODIS TERRA NDVI were

extracted over different land targets such as agricul-ture (a), forest (b) and desert (c). The temporalevolution of NDVI and rate of change of slope ofthe curve matches well throughout growing year withMODIS TERRA NDVI (Fig. 3). Temporal profileover agricultural target in Punjab (agriculture) typi-cally showed two peaks corresponding to growth ofKharif rice and winter wheat. In case of desert, NDVIvalues from both CCD and TERRA showed littlechange between 0.05 to 0.2 except small peak duringsouth west monsoon season. The NDVI profiles fromCCD and MODIS TERRA showed similar patternover forest target. The spikes in the monsoon monthscould be due to difference in detection technology in

Agriculture : Punjab(30.50 N, 76.40 E)

0

0.2

0.4

0.6

0.8

1

ND

VI

0

0.2

0.4

0.6

0.8

1

ND

VI

MODIS TERRA

INSAT 3A CCD

(a)

Rajasthan : Desert(26.60 N, 70.90 E)

(b)

Evergreen Forest : Western Ghat (18.50 N, 73.10 E)

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Number of 16 days interval starting from 1 June 2008

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Number of 16 days interval starting from 1 June 2008

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Number of 16 days interval starting from 1 June 2008

ND

VI

MODIS TERRA

INSAT 3A CCD

MODIS TERRA

INSAT 3A CCD

(c)

Fig. 3 Comparison oftemporal profiles ofCCD and TERRA.Comparison for differentland cover types

6 J Indian Soc Remote Sens (March 2012) 40(1):1–9

cloud contaminated pixels for two sensors. In MODISboth optical and thermal bands used for cloud screening,but only optical band data is used for cloud screening inCCD. The overall RMSD of 0.14 with a correlation of0.84 (n=200) was obtained as shown in Fig. 4. MODISshow overall high NDVI value over INSAT 3A CCD.This may due to difference in spectral and spatialresolution of sensor itself along with difference inatmospheric correction schemes. Miura et al. (2008)also reported high MODIS NDVI as compared toASTER derived NDVI with mean difference of 0.031from same (TERRA) platform having identical sun-target-view geometry for both the cases. In General,MODIS bands are much narrower in spectral band-widths than ASTER red and NIR bands. Likewise, thecentral wavelengths from two sensors differ. MODISred band completely avoids the red edge region(~ 680 nm), the ASTER counterpart extends to coverthat wavelength. The MODIS NIR band overlaps atthe longest wavelength portion of the ASTERcounterpart. These are the consequences of theMODIS band selection requirements to avoidFraunhofer lines and atmospheric absorption lines.Similarly, MODIS TERRA NDVI showed high NDVIwith mean bias (NDVIMODIS – NDVICCD) of 0.07 ascompared to CCD NDVI. In addition CCD NDVIcomputation does not explicitly consider complexmodeling of surface BRDF and adjacency effects asincorporated in MODIS.

Geostationary meteosat second generation (MSG)SEVIRI derived cloud free daily averaged NDVI (3 km)compared with resampled daily MODIS TERRA/AQUA NDVI (250 m) by Fensholt et al. (2006). They

showed fairly good agreement in the dynamic rangewith a tendency to little higher MSG NDVI in thebeginning of the growing season (July-August) andlower towards the end (October-November). This hasbeen attributed to seasonal variation in solar azimuthangle, which influence observations from geostationaryplatform as compared to those from polar orbitingplatform having lesser swath. Therefore, present com-parison of CCD NDVI with MODIS TERRA showsgood agreement in connection with earlier findings.

NDVI product from INSAT 3A CCD has beenderived at 30 min interval and due to changing sunzenith and azimuth angles with reference to target, redand NIR reflectance get changed. It has been reflectedin diurnal pattern of NDVI for different targets asshown in Fig. 5. NDVI experienced minimum change

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

MODIS TERRA NDVI

INS

AT

3A

CC

D N

DV

I

r = 0.83, n = 200MAD = 0.10RMSD = 0.14

Fig. 4 Pooled comparison of INSAT 3A CCD NDVI withMODIS TERRA NDVI

0.3

0.4

0.5

0.1

0

0.2

0.3

0.1

0

0.2

0.3

830 930 1030 1130 1230 1330 1430 1530

830 930 1030 1130 1230 1330 1430 1530

ND

VI

Local time(hrs)

830 930 1030 1130 1230 1330 1430 1530

Local time(hrs)

Local time(hrs)

Himalayan Evergreen Forest

Desert

Vegetation

Fig. 5 Diurnal (1 hr) variation of NDVI from INSAT- 3A CCD(9th May’10)

J Indian Soc Remote Sens (March 2012) 40(1):1–9 7

during 11:30 to 14:30 LMT (Local Mean Time). This isbecause the daily NDVIs are generated primarily fromdata gathered around midday. Further studies fromINSAT 3A CCD used this time period for compostingof NDVI under different time domain. The similardiurnal NDVI pattern has been reported by Proud et al.(2010) for Meteosat second generation using similar(SMAC) atmospheric correction scheme.

Conclusions

The surface reflectances in red and NIR were used tocompute NDVI by applying simple model foratmospheric correction. The atmospherically cor-rected INSAT 3A CCD NDVI was further evaluatedand validated with global product to evaluate itsspatio-temporal profiles and its range over differentnatural targets. This study suggests that atmosphericcorrected CCD temporal NDVI profile follow thesame trend as globally available NDVI products in agrowing year for different vegetation systems andland cover types. The CCD NDVI showed fair goodcorrelation with global available MODIS TERRANDVI product. The prospects appear good for futureefforts to reprocess CCD data sets with a goal ofcontinuity generating of NDVI product to study landsurface changes with time. The NDVI generated atcontinental scale at high temporal resolution throughINSAT 3A CCD has made a way to produce NDVI atglobal level by clubbing different geostationary satellitedata. With the availability of time series operationalINSAT 3A CCD NDVI products, an accurate quantifi-cation of radiation and rainfall interception by canopyand their related response can be further evaluated.Moreover, the vegetation fraction, a derivative of NDVI,can also be assimilated into land surface process modulein weather forecasting schemes. These have a greatrelevance to ecological, hydological as well as meteo-rological applications. In addition, long term NDVI ofthe past, present and future can be generated throughinter-sensor calibration for climate change studies.

Acknowledgments The authors are extremely grateful toSatellite Meteorology Division, IMD, New Delhi for providingthe time series INSAT 3A CCD data from IMDPS in thepurview of IMD-ISRO project. The authors would like to thankDr. R.R. Navalgund, Director, Space Applications Centre(SAC), ISRO for his support given for this study. The authorswould also like to thank Shri. A.S. Kiran kumar, Associate

Director, SAC and Dr. J.S. Parihar, Deputy Director EPSA,SAC for their timely help during this study. The authors aregrateful to Dr Sushma Panigrahy, Group Director, Agriculture,Terrestrial Biosphere and Hydrology Group for her valuablesuggestions while carrying out the analysis.

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