In this study, we investigated the potential improvement of land-use/land-cover (LU/ LC) classification using multidate backscatter intensity as well as interferometric coherence images derived from Advanced Land Observing Satellite phased array L-band synthetic aperture radar data. Four interferometric synthetic aperture radar data pairs in horizontal-horizontal polarizations were processed to obtain backscatter intensity and coherence images. From the analysis of these images, it was observed that backscatter values alone are not sufficient to separate certain LU/LC classes, e.g. forest and mining areas, due to similarities in the associated scattering mechanisms producing similar backscatter values. However, the temporal coherence values from these LU/LC features were found to be distinctly different. Supervised classifications using maximum-likelihood distance were performed with various combinations of data (three-date backscatter intensity and two-date backscatter intensity with corresponding coherence data) to generate LU/LC maps of the study area. The comparison of classification accuracies obtained for different combinations of data indicates that the classification accuracy is improved by adding coherence information to the backscatter intensity data compared to using the multidate backscatter intensity data alone. Thus, the analysis of backscatter intensity along with coherence is a better alternative than using backscatter intensity alone to improve the accuracy in LU/LC classification.

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Abstract-In the present study evaluation of L-band SAR data at different polarization combinations in linear, circular as well as hybrid polarimetric imaging modes for crop and other landuse classifications has been carried out. Full-polarimetric radar data contains all the scattering information for any arbitrary polarization state, hence data of any combination of transmit and receive polarizations can be synthesized, mathematically from full-polarimetric data. Circular and various modes of hybrid polarimetric data (where the transmitter polarization is either circular or orientated at 45 • , called π/4 and the receivers are at horizontal and vertical polarizations with respect to the radar line of sight) were synthesized (simulated) from ALOS-PALSAR fullpolarimetric data of 14th December 2008 over central state farm central latitude and longitude 29 • 15 N/75 • 43 E and bounds for northwest corner is 29 • 24 N/75 • 37 E and southeast corner is 29 • 07 N/75 • 48 E in Hisar, Haryana (India) Supervised classification was conducted for crops and few other landuse classes based on ground truth measurements using maximum-likelihood distance measures derived from the complex Wishart distribution of SAR data at various polarization combinations. It has been observed that linear fullpolarimetric data showed maximum classification accuracy (92%) followed by circular-full (89%) and circular-dual polarimetric data (87%), which was followed by hybrid polarimetric data (73-75%) and then linear dual polarimetric data (63-71%). Among the linear dual polarimetric data, co-polarization complex data showed better classification accuracy than the cross-polarization data. Also multidate single polarization SAR data over central state farm during rabi (winter) season was analyzed and it was observed that single date fullpolarimetric SAR data produced equally good classification result as the multi-date single polarization SAR data.

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Forest stand biomass serves as an effective indicator for monitoring REDD (reducing emissions from deforestation and forest degradation). Optical remote sensing data have been widely used to derive forest biophysical parameters inspite of their poor sensitivity towards the forest properties. Microwave remote sensing provides a better alternative owing to its inherent ability to penetrate the forest vegetation. This study aims at developing optimal regression models for retrieving forest above-ground bole biomass (AGBB) utilising optical data from Landsat TM and microwave data from L-band of ALOS PALSAR data over Indian subcontinental tropical deciduous mixed forests located in Munger (Bihar, India). Spatial biomass models were developed. The results using Landsat TM showed poor correlation (R 2 = 0.295 and RMSE = 35 t/ha) when compared to HH polarized L-band SAR (R 2 = 0.868 and RMSE = 16.06 t/ha). However, the prediction model performed even better when both the optical and SAR were used simultaneously (R 2 = 0.892 and RMSE = 14.08 t/ha). The addition of TM metrics has positively contributed in improving PALSAR estimates of forest biomass. Hence, the study recommends the combined use of both optical and SAR sensors for better assessment of stand biomass with significant contribution towards operational forestry.