High spectral (10 nm) and radiometric (16 bits) resolutions of IRS-P3:MOS-B coupled with moderate spatial resolution (188 m) of IRS-P3:WiFS provide unique solutions to many problems related to sustainable management of ecosystems. While the high spatial resolutions of IRS-1C PAN and IRS-1C LISS-3 help in identifying the structural attributes of the biosphere, a synthetic product of MOS-B and WiFS offers immense potential to address several crucial issues including improved classification accuracy in heterogeneous land covers, environmental stress, improved vegetation signal-to-noise ratio, etc. In this paper, the operational issues such as multisensor calibration and validation, registration and merging of multisensor data from different platforms, identification of red edge using IRS-P3:MOS-B data, resolving subpixel heterogeneity, scale anomalies and uncertainty in spectral estimates of biophysical variables are discussed. With the integration of parameters sensitive to atmospheric scattering and soil background reflectance into NDVI derived from the synthetic image, the spectral index called soil adjusted and atmospheric resistant vegetation index (SARVI) has been found to be more sensitive to biophysical variables such as leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR). It has also reduced, up to certain extent, the uncertainty related to the spectral measurements of bio-physical variables. Further study, in this regard, aims at evaluating the changes in entropy with the fusion of high spectral, radiometric, spatial, and temporal data.
['V. Jayaraman', 'S.K. Srivastava', 'D. Kumaran Raju', 'U. R. Rao']
Total solution approach using IRS-1C and IRS-P3 data