Estimates of aerosol absorption over India using multi-satellite retrieval

Aerosol absorption is poorly quantified because of the lack of adequate measurements. It has been shown that the Ozone Monitoring Instrument (OMI) aboard EOS-Aura and the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard EOS-Aqua, which fly in formation as part of the A-train, provide an excellent opportunity to improve the accuracy of aerosol retrievals. Here, we follow a multi-satellite approach to estimate the regional distribution of aerosol absorption over continental India for the first time. Annually and regionally averaged aerosol single-scattering albedo over the Indian landmass is estimated as 0.94 +/- 0.03. Our study demonstrates the potential of multi-satellite data analysis to improve the accuracy of retrieval of aerosol absorption over land.

Surface freshwater storage and variability in the Amazon basin from multi-satellite observations, 1993-2007

The amount of water stored and moving through the surface water bodies of large river basins (river, floodplains, wetlands) plays a major role in the global water and biochemical cycles and is a critical parameter for water resources management. However, the spatiotemporal variations of these freshwater reservoirs are still widely unknown at the global scale. Here, we propose a hypsographic curve approach to estimate surface freshwater storage variations over the Amazon basin combining surface water extent from a multi-satellite-technique with topographic data from the Global Digital Elevation Model (GDEM) from Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Monthly surface water storage variations for 1993-2007 are presented, showing a strong seasonal and interannual variability, and are evaluated against in situ river discharge and precipitation. The basin-scale mean annual amplitude of similar to 1200 km(3) is in the range of previous estimates and contributes to about half of the Gravity Recovery And Climate Experiment (GRACE) total water storage variations. For the first time, we map the surface water volume anomaly during the extreme droughts of 1997 (October-November) and 2005 (September-October) and found that during these dry events the water stored in the river and floodplains of the Amazon basin was, respectively, similar to 230 (similar to 40%) and 210 (similar to 50%) km(3) below the 1993-2007 average. This new 15 year data set of surface water volume represents an unprecedented source of information for future hydrological or climate modeling of the Amazon. It is also a first step toward the development of such database at the global scale.

Accurate point matching based on multi-objective Genetic Algorithm for multi-sensor satellite imagery

This paper investigates a novel approach for point matching of multi-sensor satellite imagery. The feature (corner) points extracted using an improved version of the Harris Corner Detector (HCD) is matched using multi-objective optimization based on a Genetic Algorithm (GA). An objective switching approach to optimization that incorporates an angle criterion, distance condition and point matching condition in the multi-objective fitness function is applied to match corresponding corner-points between the reference image and the sensed image. The matched points obtained in this way are used to align the sensed image with a reference image by applying an affine transformation. From the results obtained, the performance of the image registration is evaluated and compared with existing methods, namely Nearest Neighbor-Random SAmple Consensus (NN-Ran-SAC) and multi-objective Discrete Particle Swarm Optimization (DPSO). From the performed experiments it can be concluded that the proposed approach is an accurate method for registration of multi-sensor satellite imagery. (C) 2014 Elsevier Inc. All rights reserved.

Multi-Temporal Satellite Image Analysis Using Gene Expression Programming

This paper discusses an approach for river mapping and flood evaluation to aid multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation to extract water covered region. Analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images is applied in two stages: before flood and during flood. For these images the extraction of water region utilizes spectral information for image classification and spatial information for image segmentation. Multi-temporal MODIS images from ``normal'' (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as artificial neural networks and gene expression programming to separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water region. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification and region-based segmentation is an accurate and reliable for the extraction of water-covered region.

An assessment of terrestrial water storage, rainfall and river discharge over Northern India from satellite data

Terrestrial water storage (TWS) plays a key role in the global water cycle and is highly influenced by climate variability and human activities. In this study, monthly TWS, rainfall and Ganga-Brahmaputra river discharge (GBRD) are analysed over India for the period of 2003-12 using remote sensing satellite data. The spatial pattern of mean TWS shows a decrease over a large and populous region of Northern India comprising the foothills of the Himalayas, the Indo-Gangetic Plains and North East India. Over this region, the mean monthly TWS exhibits a pronounced seasonal cycle and a large interannual variability, highly correlated with rainfall and GBRD variations (r > 0.8) with a lag time of 2 months and 1 month respectively. The time series of monthly TWS shows a consistent and statistically significant decrease of about 1 cm year(-1) over Northern India, which is not associated with changes in rainfall and GBRD. This recent change in TWS suggests a possible impact of rapid industrialization, urbanization and increase in population on land water resources. Our analysis highlights the potential of the Earth-observation satellite data for hydrological applications.

Combining data sets of satellite-retrieved products for basin-scale water balance study: 2. Evaluation on the Mississippi Basin and closure correction model

In this study, we applied the integration methodology developed in the companion paper by Aires (2014) by using real satellite observations over the Mississippi Basin. The methodology provides basin-scale estimates of the four water budget components (precipitation P, evapotranspiration E, water storage change Delta S, and runoff R) in a two-step process: the Simple Weighting (SW) integration and a Postprocessing Filtering (PF) that imposes the water budget closure. A comparison with in situ observations of P and E demonstrated that PF improved the estimation of both components. A Closure Correction Model (CCM) has been derived from the integrated product (SW+PF) that allows to correct each observation data set independently, unlike the SW+PF method which requires simultaneous estimates of the four components. The CCM allows to standardize the various data sets for each component and highly decrease the budget residual (P - E - Delta S - R). As a direct application, the CCM was combined with the water budget equation to reconstruct missing values in any component. Results of a Monte Carlo experiment with synthetic gaps demonstrated the good performances of the method, except for the runoff data that has a variability of the same order of magnitude as the budget residual. Similarly, we proposed a reconstruction of Delta S between 1990 and 2002 where no Gravity Recovery and Climate Experiment data are available. Unlike most of the studies dealing with the water budget closure at the basin scale, only satellite observations and in situ runoff measurements are used. Consequently, the integrated data sets are model independent and can be used for model calibration or validation.

GPU-based normalized cuts for road extraction using satellite imagery

This paper presents a GPU implementation of normalized cuts for road extraction problem using panchromatic satellite imagery. The roads have been extracted in three stages namely pre-processing, image segmentation and post-processing. Initially, the image is pre-processed to improve the tolerance by reducing the clutter (that mostly represents the buildings, vegetation,. and fallow regions). The road regions are then extracted using the normalized cuts algorithm. Normalized cuts algorithm is a graph-based partitioning `approach whose focus lies in extracting the global impression (perceptual grouping) of an image rather than local features. For the segmented image, post-processing is carried out using morphological operations - erosion and dilation. Finally, the road extracted image is overlaid on the original image. Here, a GPGPU (General Purpose Graphical Processing Unit) approach has been adopted to implement the same algorithm on the GPU for fast processing. A performance comparison of this proposed GPU implementation of normalized cuts algorithm with the earlier algorithm (CPU implementation) is presented. From the results, we conclude that the computational improvement in terms of time as the size of image increases for the proposed GPU implementation of normalized cuts. Also, a qualitative and quantitative assessment of the segmentation results has been projected.

Surface Freshwater Storage Variations in the Orinoco Floodplains Using Multi-Satellite Observations

Variations in surface water extent and storage are poorly characterized from regional to global scales. In this study, a multi-satellite approach is proposed to estimate the water stored in the floodplains of the Orinoco Basin at a monthly time-scale using remotely-sensed observations of surface water from the Global Inundation Extent Multi-Satellite (GIEMS) and stages from Envisat radar altimetry. Surface water storage variations over 2003-2007 exhibit large interannual variability and a strong seasonal signal, peaking during summer, and associated with the flood pulse. The volume of surface water storage in the Orinoco Basin was highly correlated with the river discharge at Ciudad Bolivar (R = 0.95), the closest station to the mouth where discharge was estimated, although discharge lagged one month behind storage. The correlation remained high (R = 0.73) after removing seasonal effects. Mean annual variations in surface water volume represented similar to 170 km(3), contributing to similar to 45% of the Gravity Recovery and Climate Experiment (GRACE)-derived total water storage variations and representing similar to 13% of the total volume of water that flowed out of the Orinoco Basin to the Atlantic Ocean.

Construction of Singer Subgroup Orbit Codes Based on Cyclic Difference Sets

A recent approach for the construction of constant dimension subspace codes, designed for error correction in random networks, is to consider the codes as orbits of suitable subgroups of the general linear group. In particular, a cyclic orbit code is the orbit of a cyclic subgroup. Hence a possible method to construct large cyclic orbit codes with a given minimum subspace distance is to select a subspace such that the orbit of the Singer subgroup satisfies the distance constraint. In this paper we propose a method where some basic properties of difference sets are employed to select such a subspace, thereby providing a systematic way of constructing cyclic orbit codes with specified parameters. We also present an explicit example of such a construction.

Investigation of orographic features influencing spatial distribution of rainfall over the Western Ghats of India using satellite data

Using remotely sensed Tropical Rainfall Measuring Mission (TRMM) 3B42 rainfall and topographic data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM), the impact of oroghraphical aspects such as topography, spatial variability of elevation and altitude of apexes are examined to investigate capacious summer monsoon rainfall over the Western Ghats (WG) of India. TRMM 3B42 v7 rainfall data is validated with Indian Meteorological Department (IMD) gridded rainfall data at 0.5 degrees resolution over the WG. The analysis of spatial pattern of monsoon rainfall with orography of the WG ascertains that the grade of orographic precipitation depends mainly on topography of the mountain barrier followed by steepness of windward side slope and altitude of the mountain. Longer and broader, i.e. cascaded topography, elevated summits and gradually increasing slopes impel the enhancement in precipitation. Comparing topography of various states of the WG, it has been observed that windward side of Karnataka receives intense rainfall in the WG during summer monsoon. It has been observed that the rainfall is enhanced before the peak of the mountain and confined up to the height about 800m over the WG. In addition to this, the spatial distribution of heavy and very heavy rainfall events in the last 14 years has also been explored. Heavy and very heavy rain events on this hilly terrain are categorized with a threshold of precipitation (R) in the range 150>R>120mmday(-1) and exceeding 150mmday(-1) using probability distribution of TRMM 3B42 v7 rainfall. The areas which are prone to heavy precipitation are identified. The study would help policy makers to manage the hazard scenario and, to improve weather predictions on mountainous terrain of the WG.

The X-ray photoelectron spectroscopy and high-temperature structural studies of Zn1-xNixO/NiO two-phase composites

Detailed investigation of the chemical states and local atomic environment of Ni and Zn in the two-phase composites of Zn1-xNixO/NiO was reported. The X-ray photoelectron spectra of both Ni-2p and Zn-2p revealed the existence of a doublet with spin-orbit splitting approximate to 17.9 and 23.2eV, respectively confirming the divalent oxidation state of both Ni and Zn. However, the samples fabricated under oxygen-rich conditions exhibit significant difference in the binding energy approximate to 18.75eV between the 2p3/2 and 2p1/2 states of Ni. The shift in the satellite peaks of Ni-2p with increasing the Ni composition x within the Zn1-xNixO/NiO matrix signifies the attenuation of nonlocal screening because of reduced site occupancy of two adjacent Zn ions. The temperature dependence of X-ray diffraction analysis reveals a large distortion in the axial-rhombohedral angle for oxygen-rich NiO. Conversely, no significant distortion was noticed in the NiO system present as a secondary phase within Zn1-xNixO. Nevertheless, the unit-cell volume of both wurtzite h.c.p. Zn1-xNixO and f.c.c. NiO exhibits an anomalous behavior between 150 and 300 degrees C. The origin of such unusual change in the unit-cell volume was discussed in terms of oxygen stoichiometry.

Observation of correlated spin-orbit order in a strongly anisotropic quantum wire system

Quantum wires with spin-orbit coupling provide a unique opportunity to simultaneously control the coupling strength and the screened Coulomb interactions where new exotic phases of matter can be explored. Here we report on the observation of an exotic spin-orbit density wave in Pb-atomic wires on Si(557) surfaces by mapping out the evolution of the modulated spin-texture at various conditions with spin-and angle-resolved photoelectron spectroscopy. The results are independently quantified by surface transport measurements. The spin polarization, coherence length, spin dephasing rate and the associated quasiparticle gap decrease simultaneously as the screened Coulomb interaction decreases with increasing excess coverage, providing a new mechanism for generating and manipulating a spin-orbit entanglement effect via electronic interaction. Despite clear evidence of spontaneous spin-rotation symmetry breaking and modulation of spin-momentum structure as a function of excess coverage, the average spin polarization over the Brillouin zone vanishes, indicating that time-reversal symmetry is intact as theoretically predicted.

Edge states, spin transport, and impurity-induced local density of states in spin-orbit coupled graphene

We study graphene, which has both spin-orbit coupling (SOC), taken to be of the Kane-Mele form, and a Zeeman field induced due to proximity to a ferromagnetic material. We show that a zigzag interface of graphene having SOC with its pristine counterpart hosts robust chiral edge modes in spite of the gapless nature of the pristine graphene; such modes do not occur for armchair interfaces. Next we study the change in the local density of states (LDOS) due to the presence of an impurity in graphene with SOC and Zeeman field, and demonstrate that the Fourier transform of the LDOS close to the Dirac points can act as a measure of the strength of the spin-orbit coupling; in addition, for a specific distribution of impurity atoms, the LDOS is controlled by a destructive interference effect of graphene electrons which is a direct consequence of their Dirac nature. Finally, we study transport across junctions, which separates spin-orbit coupled graphene with Kane-Mele and Rashba terms from pristine graphene both in the presence and absence of a Zeeman field. We demonstrate that such junctions are generally spin active, namely, they can rotate the spin so that an incident electron that is spin polarized along some direction has a finite probability of being transmitted with the opposite spin. This leads to a finite, electrically controllable, spin current in such graphene junctions. We discuss possible experiments that can probe our theoretical predictions.

Anthropogenic aerosol fraction over the Indian region: model simulations versus multi-satellite data analysis

Anthropogenic aerosols play a crucial role in our environment, climate, and health. Assessment of spatial and temporal variation in anthropogenic aerosols is essential to determine their impact. Aerosols are of natural and anthropogenic origin and together constitute a composite aerosol system. Information about either component needs elimination of the other from the composite aerosol system. In the present work we estimated the anthropogenic aerosol fraction (AF) over the Indian region following two different approaches and inter-compared the estimates. We espouse multi-satellite data analysis and model simulations (using the CHIMERE Chemical transport model) to derive natural aerosol distribution, which was subsequently used to estimate AF over the Indian subcontinent. These two approaches are significantly different from each other. Natural aerosol satellite-derived information was extracted in terms of optical depth while model simulations yielded mass concentration. Anthropogenic aerosol fraction distribution was studied over two periods in 2008: premonsoon (March-May) and winter (November-February) in regard to the known distinct seasonality in aerosol loading and type over the Indian region. Although both techniques have derived the same property, considerable differences were noted in temporal and spatial distribution. Satellite retrieval of AF showed maximum values during the pre-monsoon and summer months while lowest values were observed in winter. On the other hand, model simulations showed the highest concentration of AF in winter and the lowest during pre-monsoon and summer months. Both techniques provided an annual average AF of comparable magnitude (similar to 0.43 +/- 0.06 from the satellite and similar to 0.48 +/- 0.19 from the model). For winter months the model-estimated AF was similar to 0.62 +/- 0.09, significantly higher than that (0.39 +/- 0.05) estimated from the satellite, while during pre-monsoon months satellite-estimated AF was similar to 0.46 +/- 0.06 and the model simulation estimation similar to 0.53 +/- 0.14. Preliminary results from this work indicate that model-simulated results are nearer to the actual variation as compared to satellite estimation in view of general seasonal variation in aerosol concentrations.

A Novel Approach for Multispectral Satellite Image Classification Based on the Bat Algorithm

Among the multiple advantages and applications of remote sensing, one of the most important uses is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this letter, we propose a novel bat algorithm (BA)-based clustering approach for solving crop type classification problems using a multispectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multispectral satellite image and one benchmark data set from the University of California, Irvine (UCI) repository are used to demonstrate the robustness of the proposed algorithm. The performance of the BA is compared with two other nature-inspired metaheuristic techniques, namely, genetic algorithm and particle swarm optimization. The performance is also compared with the existing hybrid approach such as the BA with K-means. From the results obtained, it can be concluded that the BA can be successfully applied to solve crop type classification problems.