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A Fourier Transform Infrared (FTIR) spectrometer has been operational since March 2014, at National Remote Sensing Centre (NRSC), Shadnagar, which is a suburban site of central India. Direct solar radiation spectra recorded by the FTIR during clear sky days at a high spectral resolution of 0.01 cm-1 were analysed using the line-by-line radiative transfer algorithm (LBLRTA) to retrieve the initial results of column-averaged concentrations of methane (XCH4) and nitrous oxide (XN2O) in the atmosphere. Error residuals between the measured and estimated atmospheric transmissions are in the range of 0.40-0.80% for CH4 and 0.20-0.50% for N2O, respectively. The maximum (minimum) XCH4 and XN2O were, respectively, 1.88 ± 0.04 ppm (1.67 ± 0.02 ppm) and 316.15 ± 5.85 ppb (278.92 ± 4.02 ppb). Columnar concentrations of CH4 and N2O retrieved from the Infrared Atmospheric Sounding Interferometer (IASI) were compared against retrieved XCH4 and XN2O using the ground FTIR. The observed differences between the two data sets lie between -0.06 ppm and +0.10 ppm for CH4 and between +32 ppb and -10 ppb for N2O. [ABSTRACT FROM AUTHOR]

The Kashmir earthquake of 2005 triggered numerous landslides in inaccessible areas of the western Himalayas, which could be mapped using satellite remote sensing. The largest recorded seismicity-induced landslide dammed a river, which resulted in the formation of a stream in the toe region and created two reservoirs that pose an enormous threat in the event of a landslide dam breach. Using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data sets corresponding to the pre- and post-earthquake period and derived digital elevation models, landslide-induced lakes were monitored. The aerial extent, depth profile and volume of both the reservoirs were determined. This study has demonstrated the utility of ASTER data in providing valuable information that is critical for hazard mitigation in case of a landslide dam breach. [ABSTRACT FROM PUBLISHER]

High-resolution soil moisture holds the key to improving weather forecast, drought monitoring and hydrological modelling. Therefore, the present study investigates the potential of the temperature/vegetation dryness index (TVDI) from the MODIS to assess soil moisture status in sub-humid parts of India (western Uttar Pradesh). The TVDI was calculated by parameterizing the normalized difference vegetation index-surface temperature space from 8 day MODIS reflectance and surface temperature products. Correlation and regression analysis was carried out to relate the TVDI against in-situ measured soil moisture during early (April) and peak (October) stages of growth in sugarcane crop. Spatio-temporal patterns in the TVDI shows that northern areas had more surface wetness compared to southern areas. The results further reveal that a significantly strong and negative relationship exists between the TVDI and in-situ soil moisture, particularly when vegetation cover is sparse. The dryness index was also found satisfactory to capture the temporal variation in the surface moisture status in terms of antecedent precipitation index. [ABSTRACT FROM AUTHOR]

A simulated canopy reflectance dataset for a total of six channels in visible, near-infrared (NIR) and shortwave-infrared (SWIR) region, corresponding to Landsat Thematic Mapper (TM) was generated using the PROSAIL (PROSPECT+SAIL) model and a range of Leaf Area Index (LAI), soil backgrounds, leaf chlorophyll, leaf inclination and viewing geometry inputs. This dataset was used to develop and evaluate approaches for LAI estimation, namely, standard two-band nonlinear empirical vegetation index (VI)-LAI formulation (using Normalized Difference Vegetation Index/simple ratio (NDVI/SR)) and a multi-band principal component inversion (PCI) approach. The analysis indicated that the multi-band PCI approach had a smaller rms error (RMSE=0.380) than the NDVI and SR approaches (RMSE=2.28, 0.88), for an independently generated test dataset. [ABSTRACT FROM AUTHOR]