Study on the utility of IRS-P6 AWiFS SWIR band for crop discrimination and classification

  • Rabindra K. Panigrahy
  • S. S. RayEmail author
  • S. Panigrahy
Short Note


This present study was conducted to find out the usefulness of SWIR (Short Wave Infra Red) band data in AWiFS (Advanced Wide Field Sensor) sensor of Resourcesat 1, for the discrimination of different Rabi season crops (rabi rice, groundnut and vegetables) and other vegetations of the undivided Cuttack district of Orissa state. Four dates multi-spectral AWiFS data during the period from 10 December 2003 to 2 May 2004 were used. The analysis was carried out using various multivariate statistics and classification approaches. Principal Component Analysis (PCA) and separability measures were used for selection of best bands for crop discrimination. The analysis showed that, for discrimination of the crops in the study area, NIR was found to be the best band, followed by SWIR and Red. The results of the supervised MXL classification showed that inclusion of SWIR band increased the overall accuracy and kappa coefficient. The ‘Three Band Ratio’ index, which incorporated Red, NIR and SWIR bands, showed improved discrimination in the multi-date dataset classification, compared to other SWIR based indices.


AWiFS SWIR band Crop discrimination Classification Three-band ratio index 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Alrichs JS and Bauer ME (1983) Relation of agronomic and multi-spectral reflectance characteristics of spring wheat canopies. Agronomy Journal 75: 987–993CrossRefGoogle Scholar
  2. Baret F, Guyot GT, Begue A, Maurial P and Podaire A (1988) Complementarity of middle-infrared with visible and near-infrared reflectance for monitoring wheat canopies. Rem Sens Environ 26: 213–225CrossRefGoogle Scholar
  3. Campbell JB (1989) Introduction to Remote Sensing. Taylor & Francis, London (2nd ed.) pp. 461Google Scholar
  4. Carter GA (1994) Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Int J Rem Sens 15: 697–703CrossRefGoogle Scholar
  5. Ceccato P, Flassee S, Tarantola S, Jacquemoud S and Gregoire JM (2001) Detecting vegetation leaf water content using reflectance in the optical domain. Rem Sens Environ 77: 22–33CrossRefGoogle Scholar
  6. Chuvieco E, Riano D, Agnado I and Cocero D (2002) Estimation of fuel moisture content from multitemporal analysis of Landsat thematic mapper reflectance data: applications in fire danger assessment. Int J Rem Sens 23(11): 2145–2162CrossRefGoogle Scholar
  7. Czaplaski RL (1992) Misclassifiation bias in area estimation. Photogramm Engineer and Rem Sens, 58: 189–192Google Scholar
  8. Dadhwal VK, Parihar JS, Medhavy TT, Rahul DS, Jarwal SD and Khera AP (1996) Comparative performance of thematic mapper middle-infrared bands in crop discrimination. Int J Rem Sens 17(9): 1727–1734CrossRefGoogle Scholar
  9. Dadhwal VK, Singh RP, Dutta S and Parihar JS (2002) Remote sensing based crop inventory: A review of Indian experience. Tropical Ecology 43(1): 107–122Google Scholar
  10. Dean ME and Hoffer RM (1983) Feature selection methodologies using simulated thematic mapper data. Symp. Machine Processing of Remotely Sensed Data, LARS, Purdue University, West Lafayette, Ind. pp 347–356Google Scholar
  11. Gao BC (1996) NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Rem Sens Environ 58: 257–266CrossRefGoogle Scholar
  12. Hunt ER, Rock BN and Nobel PS (1987) Measurement of leaf relative water content by infra-red reflectance. Rem Sens Environ 22: 429–435CrossRefGoogle Scholar
  13. Knipling EB (1970) Physical and physiological basis for the reflectance of visible and near-infrared radiation. Rem Sens Environ 1: 155–159CrossRefGoogle Scholar
  14. Manjunath KR, Kundu N and Panigrahy S (1998) Evaluation of spectral bands and spatial resolution of LISS II and LISS III sensors onboard IRS satellites for crop identification. J Indian Soc Rem Sens 26(4): 197–208CrossRefGoogle Scholar
  15. Panigrahy S and Parihar JS (1992) Role of middle infrared bands of Landsat thematic mapper in determining the classification accuracy of rice. Int J Rem Sens 13(15): 2943–2949CrossRefGoogle Scholar
  16. Sharma SA, Bhatt HP and Ajai (1995) Oilseed crop discrimination: selection of optimum bands and role of middle infrared. ISPRS J Photogramm & Rem Sens 50(5): 25–30CrossRefGoogle Scholar
  17. Swain PH and Davis SM (1978) Remote Sensing: The Quantitative Approach. McGraw Hill, New YorkGoogle Scholar
  18. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Rem Sens Environ 8: 127–150CrossRefGoogle Scholar
  19. Tucker CJ (1980) Remote Sensing of leaf water content in the near infrared. Rem Sens Environ 10: 23–32CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2009

Authors and Affiliations

  • Rabindra K. Panigrahy
    • 2
  • S. S. Ray
    • 1
    Email author
  • S. Panigrahy
    • 1
  1. 1.Agriculture, Forestry & Environment Group, RESASpace Applications Centre, ISROAhmedabadIndia
  2. 2.C-DACPuneIndia

Personalised recommendations