Analysis of INSAT 3A CCD NDVI data for crop monitoring in rabi season



Over the last 30 years, coarse resolution satellite sensors are being used routinely to monitor crops and other vegetation, to generate various estimates on their areal extent and to detect the impact of moisture stress on vegetation, at regional scales. Satellite derived Vegetation indices have been used extensively for varied geospatial applications in agriculture.  Vegetation indices show better sensitivity than individual band reflectance and hence are more preferred for crop vigor assessment, crop discrimination, crop monitoring etc. Vegetation indices also act as proxies to various crop bio-physical parameters.  NDVI,  the most popular index due to its simplicity in computation and  interpretation has been found to be a proven index for crop studies. As a result, several geospatial products of NDVI and its derivatives are available as free downloads from various sensors of moderate to coarse resolutions. But all these datasets are from polar orbiting satellite systems and are limited by inadequate temporal repitivity, leading to the presence of considerable extent of cloud infestation in the composite images. Geostationary satellites with constant viewing direction produce the images of higher geometric fidelity and their very high temporal frequency reduce the residual cloud content in the composite images.

Application potential of geostationary satellite data for crop monitoring is investigated in the current study. Indian Geostationary satellite INSAT 3A launched in 2003 provides spectral data in red, NIR and SWIR bands at 1km spatial resolution through its CCD sensor. It has a very high temporal receptivity (half an hour) with constant view direction.NDVI generated at multiple times in a day from INSAT provide opportunity to get more cloud free NDVI compared polar orbiting large swath satellites (Fensholtet al., 2006).


NDVI,remotesensing,crop-monitoring,geo-stationary satellites.

Full Text:



BROWN, J.C.; JEPSON, W.E.; KASTENS, J.H.; WARDLOW, B.; LOMAS, J.; PRICE, K. ,Multitemporal, moderate spatial resolution remote sensing of modern agricultural production and land modification in the Brazilian Amazon. GIScience and Remote Sensing, v.44, p.117 148, 2007.

BATISTA, T. T., SHIMABUKURO, Y. E., and LAWRENCE, W.T., 1997, The long term monitoring of vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR data, International Journal of Remote Sensing, 18, 3195-3210.

BENEDETTI, R., and ROSSINI, P., 1993, On the use of NDVI profiles as a tool for agricultural statistics – The case study of wheat yield estimate and forecast in Emilia Romanga, Remote Sensing of Environment, 45, 311-326.

BHATTACHARYA, B. K., NIGAM, R., DORJEE,N., PATEL, N.K. and PANIGRAHY, S., Normalized difference vegetation index-towards development of operational product from INSAT 3A CCD. National Symposium ISRS, 18-20 Dec. 2008

BHATTACHARYA B.K, MALLICK K, NIGAM R, PATEL N K,PADMANABAN N, MAHAMMAD SK S, RAMAKRISHNAN R and PARIHAR J S 2008.,A study on land surface radiation budget parameters using KALAPNA-1 VHRR and INSAT-3A CCD data for agrometeorological applications; ISRO Scientific Report ISRO/ASP/SAC/01/2008.

BANNARI, A., MORIN, D., and BONN, F., 1995, A Review of Vegetation Indices, Remote Sensing Reviews, 13, 95-120.


A.J.B., Estimating soybean crop areas using spectral temporal surfaces derived from MODIS images in MatoGrosso, Brazil. Pesquisa Agropecuária Brasileira, v.45, p.72 80, 2010.

FENSHOLT,R.,SANDHOLT.,STISEN,S. and TUCKER,C.(2006).Vegetation monitoring with the geostationary Meteosat Second Generation SEVIRI sensor. Remote Sensing of Environment,101,212-229.


C.E.P. Wavelet analysis of MODIS time series to detect expansion and intensification of row crop agriculture in Brazil. Remote Sensing of Environment, v.112, p.576 587, 2008.

TUCKER, C.J., AND CHOWDHARY, B.J., 1987, Satellite remote sensing of drought conditions,Remote Sensing of Environment, 23, pp.243-251

MURTHY, C.S., SESHASAI, M.V.R., BHANUJAKUMARI. V.,and ROY, P.S., 2007, Agricultural drought assessment at disaggregated level using AWiFS/WiFS data of Indian Remote Sensing satellites, Geocarto International, 22, pp.127-140

RUDORFF, C. de M.; RIZZI, R.; RUDORFF, B.F.T.; SUGAWARA, L.M.; VIEIRA, C.A.O. Superfícies de respostaespectro temporal de imagens do sensor MODIS para classificação de área de soja no Estado do Rio Grande do Sul. Ciência Rural, v.37, p.118 125, 2007.

TUCKER, C. J, TOWNSHEND, J.R.G., and GOFF, T.E. , 1985, African land covers classification using satellite data, Science, 227, pp.369-375.

VYAS S., RAHUL NIGAM, S. PANIGRAHY and J.S. PARIHAR, 2012, Monitoring crop progress area using INSAT-3A CCD NDVI data: A case study of rabi season in India, Proc. National Symposium on Space Technology for Food and Environmental Security, 5-7 Dec, 2012, New Delhi.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.