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

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.

[1]  B. Rock,et al.  Measurement of leaf relative water content by infrared reflectance , 1987 .

[2]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[3]  M. E. Bauer,et al.  Relation of agronomic and multispectral reflectance characteristics of spring wheat canopies , 1983 .

[4]  Ajai,et al.  Oilseed crop discrimination : selection of optimum bands and role of middle infrared , 1995 .

[5]  Philip H. Swain,et al.  Remote Sensing: The Quantitative Approach , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  E. B. Knipling.  Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation , 1970 .

[7]  C. Tucker.  Remote sensing of leaf water content in the near infrared , 1980 .

[8]  D. Riaño,et al.  Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment , 2002 .

[9]  Jai Singh Parihar,et al.  Remote sensing based crop inventory: A review of Indian experience , 2002 .

[10]  S. Tarantola,et al.  Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .

[11]  J. Campbell.  Introduction to remote sensing , 1987 .

[12]  R. M. Hoffer,et al.  Feature Selection Methodologies Using Simulated Thematic Mapper Data , 1983 .

[13]  G. Carter.  Ratios of leaf reflectances in narrow wavebands as indicators of plant stress , 1994 .

[14]  Evaluation of spectral bands and spatial resolution of LISS II and LISS III sensors on-board IRS satellites for crop identification , 1998 .

[15]  Sushma Panigrahy,et al.  Role of middle infrared bands of Landsat thematic mapper in determining the classification accuracy of rice , 1992 .

[16]  Frédéric Baret,et al.  Complementarity of middle-infrared with visible and near-infrared reflectance for monitoring wheat canopies☆ , 1988 .

[17]  D. S. Ruhal,et al.  Comparative performance of thematic mapper middle-infrared bands in crop discrimination , 1996 .

[18]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .