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The regioselective benzoyloxylation process involves stirring a mixture of phenyl/benzyl-sulfanyl/selenylethanoate 1a-c with appropriate oxidant [benzoyl peroxide (BPO) 2a/ m-chloroperbenzoic acid (m-CPBA) 2b /bis(p-methoxybenzoyl peroxide) (BPM-BPO) 2c] under catalysis of hydrated copper acetate in toluene at 60 °C/RT. Regioselective C–H functionalization of esters 1a-c leads to benzoyloxy substituted phenyl/benzyl-sulfanyl/selenylbisesters 3a-g in good to excellent yields (80–95%). Variably substituted trans-3-phenylsulfanyl-β-lactams 4 employed for C3-H functionalization with BPO 2a generates diastereoisomeric mixture of trans- and cis-3-benzoyloxy-3-(phenylsulfanyl)-β-lactams 5 and 6 as major and minor isomers after efficient column chromatographic purification. The structural confirmation was done using IR, 1H NMR, 13C NMR, DEPT-90 NMR spectroscopic analysis and CHNS elemental analysis. In vitro antibacterial and antifungal evaluations on bisesters suggest benzylselenyl-bisester 3c as potent antimicrobial agent. Out of trans- and cis-3-benzoyloxy-β-lactams 5a and 6a, the trans isomer 5a was active against all microbial strains whereas the cis isomer 6a was completely inactive. [ABSTRACT FROM AUTHOR]

Excessive growth of microalgae results in the eutrophication and degradation of water quality in lakes and ponds. Microalgae have proved to be very beneficial for sustainable development. Its applications, starting from growth to death, make them a valuable resource for food and energy production. However, presence of microalgae in surface water sources affects water quality and its beneficial uses. The excess nutrient-rich agriculture runoff also adds problems to the downstream systems. Nutrient removal and microalgae harvesting are considered the most problematic task for improving water quality of surface waters. The main objective of the present study was to improve the water quality of nutrient rich waters using attached growth systems. The use of batch and continuous flow baffled attached growth systems has successfully improved the water quality and produced algal biomass that can be utilized or transformed into other valuable products. The performance of continuous flow baffled attached growth system was better and it improved the quality of oligotrophic, mesotrophic, eutrophic and hypereutrophic waters by removing>95% of TN and TP in 35, 63, 63 and 4 days, with biomass productivity of 2.69 ± 0.21, 3.17 ± 0.06, 3.61 ± 0.16 and 4.64 ± 0.3 gDWm−2 day−1, respectively. [ABSTRACT FROM AUTHOR]

Glacier velocity is an important parameter to characterize glacier dynamics and to derive ice thickness and mass balance. The 2-pass/3-pass Differential Synthetic Aperture Radar Interferometry (DInSAR) techniques are advantageous in estimating glacier movements. However, the 2-pass DInSAR requires an external Digital Elevation Model (DEM), whereas the 3-pass DInSAR does not. The 3-pass DInSAR technique is adopted with Sentinel-1A/1B to map the Svalbard glacier flow velocities. Furthermore, the effect of glacier surface elevation change on the 2-pass DInSAR results were revealed by comparing glacier velocity with the two time period topographic information. The coherence indicates that Sentinel-1A/1B has a high potential to infer operational glacier velocity using the 3-pass DInSAR method with an average atmospheric uncertainty of 0.24cm/day over the studied region. The precision of the 3-pass DInSAR is analyzed by comparing 2-pass derived line-of-sight (LOS) velocity on the same dates. The 3- pass DInSAR achieves high-resolution and detailed information. [ABSTRACT FROM AUTHOR]

0.7Bi0.99Nd0.01Fe0.99(Co, Ti)0.01O3-0.3CaTiO3 (BNFCTO-CaTO) composite was prepared by using sol-gel method. The structural formation was investigated using the XRD pattern, which confirms the presence of distorted rhombohedral structure with space group R3c and the superlattice orthorhombic crystal structure with space group Pbmn. The dielectric behavior of BNFCTO-CaTO has been studied within the frequency range of 100 Hz to 1 MHz from 300 K to 773 K. The sample shows dielectric dispersion in low frequency region that can be explained by Maxwell-Wagner type interfacial polarization. The temperature-dependent dielectric plot shows an anomaly near to magnetic transition temperature (TN) of BiFeO3 and shows relaxor behavior. Furthermore, BNFCTO-CaTO shows a strong magneto-dielectric coupling effect at room temperature. [ABSTRACT FROM AUTHOR]

Various glass compositions were prepared in a glassy system [25BaCO3-(10-x) ZrO2-20TiO2-45SiO2-xCrO3] (x = 0, 2, 4, and 6) using melt-quenching technique. XRD patterns of glass and glass ceramics showed amorphous and crystalline nature. Density of glass and glass ceramics decreased from 3.4553 to 3.4381 g/cm3 and 3.7485 to 3.1977 g/cm3 with increasing concentration of CrO3. DTA of the glasses revealed a shift in base line which suggests a change in specific heat and attributed to the glass transition temperature, Tg. FTIR and Raman spectroscopic analysis indicated that an increasing doping of CrO3 changes structural network by increasing number of nonbridging oxygens (NBO) within glassy matrix. These results revealed that bands toward low frequency side endorsed to the vibration of Ba2+ metal cation, bending vibration of Si-O-Si and bonding formation of ZrO2. Concentration of CrO3, changed the microsture significantly and hence, a denser, fully developed mixed morphology was noticed with spherical grains of ZrO2, irregular shape of bazirite (BaZr0.97Ti0.03Si3O9) phase and TiO2 grains. Glass ceramic sample BZTS6C971 CrO3 (x = 6) retains high value of dielectric constant, ~4555 due to space charge polarization. Hence, these glass ceramics might be useful for diverse applications such as in optical devices, electronic circuits, and multilayer ceramic capacitors. [ABSTRACT FROM AUTHOR]

Monitoring the dynamics of snow and glaciers in the Indian Himalaya has always attracted the attention of the remote sensing community. The snow water equivalent (SWE) represents the amount of water contained in the snowpack, and it is a product of snow depth (SD) and snow density ( ρ s ). The estimation of SD at high spatial and temporal resolution is still a challenge, especially in rugged mountainous terrain, like the Himalayas. In this paper, bistatic TerraSAR-X quad-polarization data is used to retrieve the SD using the Polarimetric SAR Interferometry (PolInSAR) coherences. The PolInSAR coherence at hv polarization shows a linear relationship with SD, which is verified with the field data. This relationship is utilized for the retrieval of SD using the proposed algorithm. Spatially variable ρ s is used to retrieve the SWE. The coefficient of determination ( R 2 ) values of the retrieved SD on 8 January 2016 and 19 January 2016 are 0.62 and 0.80, respectively. Likewise, it is 0.61 and 0.72 for retrieved SWE. The root mean square error (RMSE) in SD on 8 January 2016 and 19 January 2016 are 7.00 cm and 5.48 cm, respectively; for SWE, it is 2.02 cm and 1.48 cm, respectively. [ABSTRACT FROM AUTHOR]

The studies on snow depth comprise a crucial area of research in the Indian Himalayas, where the seasonal snow cover primarily drives the rivers and significant water resources. In this paper, the initial estimates of the line of sight displacement obtained using differential interferometric phase in VV and VH polarizations using Sentinel-1 bi-temporal dual polarimetric SAR data corresponding to snow covered and snow free land cover, are improved by applying bias corrections for the snow phase and for residual errors in displacement derived from the corrected snow phase. The bias for the snow phase is computed from the observed phase in VV and VH polarizations for the snow free area and the bias for the residual errors is computed by observing the stationary pixels identified in the snow free area using a digital elevation model. The snow depth is computed as a weighted sum of the corrected displacements in the VV and VH polarization, with the weights derived using the local incidence angle. The snow depth results based on the proposed approach was evaluated with respect to field measurements and a coefficient of determination of 0.628 was observed with an improvement of ~0.4 as compared to the displacement observed in the VV and VH channel using the conventional method. [ABSTRACT FROM AUTHOR]

Glaciers have a high impact in the socio-economic sectors including water supply, energy production, flood and avalanches. A high precision digital elevation model (DEM) is required to monitor glaciers and to study various glacier processes. The present study deals with the qualitative and quantitative evaluation of the DEM generated from the bistatic TanDEM-X data by comparing it with GPS, Ice, Cloud, and land Elevation Satellite (ICESat) data and standard global DEMs such as Shuttle Radar Topography Mission (SRTM) and Advanced Space-borne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM). The study area consists of highly undulating glaciated terrain in western Himalaya, India. The results reveal that TanDEM-X is slightly better than SRTM both qualitatively and quantitatively, whereas ASTER GDEM showing maximum discrepancy among the three DEMs. The Root Mean Square Error (RMSE) of the TanDEM-X DEM with respect to GPS is 3.5 m at lower relief and 11.9 m at glaciated terrain, against 6.7 and 12.5 m for SRTM and 9.3 and 19.8 m for ASTER GDEM, respectively, for the same sites. On an average, for the whole study area, the RMSE of TanDEM-X is 7.9 m, SRTM is 9.3 m and ASTER GDM is 14.2 m. The RMSE of TanDEM-X, SRTM and ASTER GDEM with respect to ICESat are 16.3, 19.9 and 101.1 m, respectively. It is evident from the analysis that though SRTM is closer to TanDEM-X in terms of accuracy in the mountainous terrain, however, TanDEM-X will be more useful for studying glacier dynamics and topography. [ABSTRACT FROM AUTHOR]

Snow cover is an important parameter for hydrological modelling and climate change modelling. Various methods are available only for wet snow-cover mapping using conventional synthetic aperture radar (SAR) data. Total snow (wet + dry) cover mapping with SAR data is still a topical research area. Therefore, incoherent target decomposition theorems have been implemented on fully polarimetric SAR data to characterize the scattering of various targets. Further classification techniques – both unsupervised and supervised – have been applied for accurate mapping of total snow cover. For this purpose, Advanced Land Observing Satellite – phased array-type L-band SAR (ALOS–PALSAR) data (12 May 2007) have been analysed for snow classification of glaciated terrain in and around Badrinath region in Himalaya. An ALOS-Advanced Visible and Near Infrared Radiometer (AVNIR)-2 image (6 May 2007) was also used to provide assistance in the selection of different training classes. It has been found that the application of incoherent target decomposition theorems such as H/A/α and four-component scattering mechanism models are good for extracting the desired information of snow cover from fully polarimetric PALSAR data. Finally, based on these target decomposition theorems and the Wishart classifier, PALSAR data have been classified into snow or non-snow cover, and the user accuracy of snow classes was found to be better than the user accuracy of other classes. Hence, the application of incoherent target decomposition theorems with full polarimetric ALOS-PALSAR data is useful for snow-cover mapping. [ABSTRACT FROM PUBLISHER]

In recent years, there has been increased utilization of fully polarimetric synthetic aperture radar (POLSAR) data to study glaciated terrain features for glaciological and climate change modelling. This article is concerned with more accurate results and appropriate analysis of POLSAR data over a highly rugged glaciated area in Himalayan region. For this purpose, the modified Yamaguchi four-component scattering power decomposition (4-CSPD) method with a rotation concept of 3 × 3 coherency matrix [T] about line of sight is evaluated. It has been found that the modified Yamaguchi 4-CSPD method significantly improved the decomposition results as compared with the original 4-CSPD by minimizing the cross-polarized Horizontal-Vertical (HV) components. This modified 4-CSPD leads to enhancement in the double bounce scattering and surface scattering components and also avoids the overestimation problem in the volume scattering component as compared with the original 4-CSPD from the sloped terrain. The significant reductions of the negative power occurrence in the surface scattering (3.9%) and the double bounce scattering (19.7%) components have also been noticed as compared with the original 4-CSPD method over the glaciated area in this part of the Indian Himalaya. [ABSTRACT FROM PUBLISHER]

In this study, we have demonstrated the capability of full polarimetric ALOS/Phased Array L-band Synthetic Aperture Radar data for the characterization of the forests and deforestation in Cambodia, to support climate change mitigation policies of Reducing Emission from Deforestation and Forest Degradation (REDD). We have observed mean backscattering coefficient (σ°), entropy (H), alpha angle (α), anisotropy (A), pedestal height (PH), Radar Vegetation Index (RVI) and Freeman–Durden three-component decomposition parameters. The observations show that the forest types and deforested area are showing variable polarimetric and backscattering properties because of the structural difference. Evergreen forest is characterized by a high value of σ° HV (−12.96 dB) as compared with the deforested area (σ° HV=−22.2 dB). The value of polarimetric parameters such as entropy (0.93), RVI (0.91), PH (0.41) and Freeman–Durden volume scattering (0.43) is high for evergreen forest, whereas deforested area is characterized by the low values of entropy (0.36) and RVI (0.17). Based on these parameters, it is found that σ° HV, entropy, RVI and PH provide best results among other parameters. [ABSTRACT FROM PUBLISHER]

Glaciated terrain classification is important for hydrological and climate change modelling. For this purpose, fully polarimetric Advanced Land Observation Satellite-Phase Array L-band Synthetic Aperture Radar (ALOS-PALSAR) data has been used over Indian Himalayan glaciated region. PALSAR data has been analyzed based on the three and four component scattering decomposition methods for glaciated terrain classification. These methods have been applied on multi-looked 3 × 3 coherency matrix of ALOS-PALSAR data. The analysis of these methods shows that the Freeman and Durden three-component scattering power decomposition (3-CSPD) method has over estimation problem in volume backscattering component as compared to the Yamaguchi four-component scattering power decomposition (4-CSPD) method. After finding suitability of 4-CSPD method over Himalayan glaciated terrain, it has been combined with complex Wishart distribution for supervised classification of ALOS-PALSAR image. By this way, an overall accuracy has been found to be 93.38%. Even this procedure shows very high accuracy but discrimination between vegetation and glacier snow/ice classes was not properly done. To overcome this ambiguity, the probability difference between surface backscattering and volume backscattering has been introduced as further steps in classification procedure. [ABSTRACT FROM AUTHOR]

Radar remote sensing has great potential to determine the extent and properties of snow cover. Availability of space-borne sensor dual-polarization C-band data of environmental satellite- (ENVISAT-) advanced synthetic aperture radar (ASAR) can enhance the accuracy in measurement of snow physical parameters as compared with single polarization data measurement. This study shows the capability of C-band synthetic aperture radar (SAR) data for estimating dry snow density over snow covered rugged terrain in Himalayan region. The snow density is an important parameter for the snow hydrology and avalanche forecasting related studies. An algorithm has been developed for estimating snow density, based on snow volume scattering and snow-ground scattering components. The radar backscattering coefficients of both horizontal-horizontal (hh) and vertical-vertical (vv) polarization and incidence angle are used as inputs in the algorithm to provide the snow dielectric constant which can be used to derive snow density using Looyenga's semi-empirical formula. Comparison was made between snow density estimated from algorithm using ENVISAT-ASAR hh and vv polarization data and the measured field value. The mean absolute error between estimated and measured snow density was found to be 0.024 g/cm3. [ABSTRACT FROM AUTHOR]

The environmental satellite (ENVISAT) advanced synthetic aperture radar (ASAR) offers the opportunity for monitoring snow parameters with dual polarization and multi-incidence angle. Snow wetness is an important index for indicating snow avalanche, snowmelt runoff modelling, water supply for irrigation and hydropower stations, weather forecasts and understanding climate change. We used a first-order scattering model that includes both volume and air/snow surface scattering based on a developed inversion model to estimate snow dielectric constant, which can be further related for estimating snow wetness. Comparison with field measurement showed that the correlation coefficient for snow permittivity estimated from ASAR data was observed to be 0.8 at 95% confidence interval and model bias was observed as 2.42% by volume at 95% confidence interval. The comparison of ASAR-derived snow permittivity with ground measurements shows the average absolute error 2.5%. The snow wetness range varies from 0 to 15% by volume. [ABSTRACT FROM AUTHOR]