[1] We present new polarimetric radar data for the surface of the north pole of the Moon acquired with the Mini-SAR experiment onboard India's Chandrayaan-1 spacecraft. Between mid-February and mid-April, 2009, Mini-SAR mapped more than 95% of the areas polewards of 80°latitude at a resolution of 150 meters. The north polar region displays backscatter properties typical for the Moon, with circular polarization ratio (CPR) values in the range of 0.1-0.3, increasing to over 1.0 for young primary impact craters. These higher CPR values likely reflect surface roughness associated with these fresh features. In contrast, some craters in this region show elevated CPR in their interiors, but not exterior to their rims. Almost all of these features are in permanent sun shadow and correlate with proposed locations of polar ice modeled on the basis of Lunar Prospector neutron data. These relations are consistent with deposits of water ice in these craters.

The current study investigates the potential of multi-temporal RADARSAT ScanSAR Narrow Beam B (SCNB) data to monitor rice crop growth and condition where special emphasis was given to the signature analysis of the crop. The study area is located in the Baleshwar and Bhadrak districts of Orissa. The temporal variations of radar backscatter of all land-cover classes were analysed as a function of time. The analysis of the Synthetic Aperture Radar (SAR) backscatter coefficient (s 0 ) of rice crop shows significant temporal behaviour and a large dynamic range during its growth period, which is due to the interaction of microwave radiation with the crop canopy, increasing from the transplanting stage to the reproductive stage. This temporal variation of SAR backscatter clearly differentiates rice fields from other land-cover classes.Separability studies among different class pairs carried out using t-test and Bhattacharya distance show that all the rice classes are separable from each other except the early rice, which was mixed with far sides of hills and shadow. Knowledge-based decision rule classifier based on the temporal evaluation of SAR backscatter was attempted to classify rice and non-rice areas, and achieved more than 98% accuracy in the case of rice class. The results are promising and confirm the possibility of operational use of RADARSAT data for rice crop growth monitoring.

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