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.

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.

Micro-satellite angular rate estimation using second-order sliding mode observer

Development of computationally efficient and accurate attitude rate estimation algorithm using low-cost commercially available star sensor arrays and processing unit for micro-satellite mission is presented. Our design reduces the computational load of least square (LS)-based rate estimation method while maintaining the same accuracy compared to other rate estimation approaches. Furthermore, rate estimation accuracy is improved by using recently developed fast and accurate second-order sliding mode observer (SOSMO) scheme. It also gives robust estimation in the presence of modeling uncertainties, unknown disturbances, and measurement noise. Simulation study shows that rate estimation accuracy achieved by our LS-based method is comparable with other methods for a typical commercially available star sensor array. The robustness analysis of SOSMO with respect to measurement noise is also presented in this paper. Simulation test bench for a practical scenario of satellite rate estimation uses moment-of-inertia variation and environmental disturbances affecting a typical micro-satellite at 500km circular orbit. Comparison studies of SOSMO with 1-SMO and pseudo-linear Kalman filter show that satisfactory estimation accuracy is achieved by SOSMO.

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.

Diagnóstico comparativo de mastitis en leche bovina, por tres pruebas de campo: california mastitis test (CMT), acidez (NaOH), y reductasa, en los municipios de la Concordia, San Rafael del Norte y San Sebastián de Yali, Jinotega

Con el propósito de evaluar tres técnicas para la determinación de Mastitis, (California Mastitis Test (CMT), Hidróxido de Sodio (NaHO) y Azul de Metíleno (AM)), en vacas de doble propósito, en 2000 se efectuó un estudio en los municipios de La Concordia, San Rafael de Norte y San Sebastián de Yalí, del Departamento de Jinotega. Se utilizaron Fincas de Referencia atendidas por el entonces Ministerio de Agricultura y Ganadería (MAG), de las cuales se seleccionaron catorce (14) fincas privadas y dos Cooperativas, para un total de 16 fincas que representaron el 53.3 % del total de fincas. De éstas se muestrearon 398 vacas en producción de diversas razas (Pardo suizo, Brahman y cruces entre Pardo suizo, Criollo y Holstein con Cebú y otros) de diversas lactaciones (número parto}. Las muestras de cada cuarto de la vaca, se tomaron a la hora del ordeño, entre 5:00 y 7:00a.m. Los resultados de CMT e NaHO, (+ ó -), se anotaron en campo al momento de prueba. En la prueba de AM, se hicieron 4 lecturas: 1) al momento de prueba, 2) a los 15 minutos, 3) a una hora y, 4) a tres horas después de la prueba. Se obtuvo un total de 396 datos de 4 cuartos en cada técnica, para un total de 4,752 observaciones. La información se analizó mediante una prueba de Chí Cuadrado, con el Procedimiento CATMOD, del Statistical Análisis System (SAS), Versión para PC 6.03, NC. Las variables de clasificación en el análisis fueron Razas (1-3), Números de Parto (1-6) y Meses de lactación (1-9). De las técnicas, se obtuvo el 46.2, 4.6 y 15.3 % de pruebas positivas con CMT, NaOH y AM, respectivamente; la CMT resultó más efectiva en la determinación de mastitis. De las Razas estudiadas, el Brahmán presentó un 45.2% de afectación por mastitis, mayor que Pardo suizo y cruces indefinidos, con 30.6 y 24.2 %, respectivamente. A partir del tercer parto, se incrementa el nivel de infestación de esta enfermedad, y durante la lactación, en los meses 4, 5,8 y 9.

Adaptacion de cuatro lineas de alfalfa forrajera (Medicago sativa L.) a las condiciones climaticas de la Hacienda Las Mercedes, campo y vivero

Con el objeto de evaluar el comportamiento adaptativo de cuatro líneas de alfalfa (Medicago sativa L.) en condiciones de campo y de vivero, se realizó un estudio en la Hacienda Las Mercedes, propiedad de la Universidad Nacional Agraria, ubicada en la ciudad de Managua Km 11 carretera norte, , entrada al CARNIC 2 km al lago. Teniendo su ubicación geográfica en un cuadrante con las siguientes coordenadas: 12°10'14"a 12"08'05" en latitud Norte y 86.10'22" a 86"09'44" longitud Oeste. El estudio se realizó en dos fuses 1) de campo y 2) en vivero. En ambos se determinó el grado de adaptación de cuatro líneas de alfalfa (Medicago sativa L.), tres procedentes de Texas-EE UU (8L418, 105916 y 9818) y una de Sébaco-Nicaragua (l3-A50) donde se ha establecido por más de tres años. El ensayo de campo se estableció en un área que anteriormente fue utilizada para la siembra de sorgo forrajero y el de vivero se realizó en el vivero de la UNA, en la misma finca. Se consideró cada una de las líneas como tratamiento. El Diseño experimental usado para ambas fases fue de bloques completos al azar (BCA), con 4 repeticiones. En campo con parcelas experimentales fueron de 4 m2 (2 m x 2 m), para un área total de 120m2 Se sembraron 6 surcos a una distancia de 30 cm entre surco y 14 plantas por surcos distanciados a 15 cm. Se realizó análisis de varianza utilizando programa SAS versión 99, cuando se encontró diferencias significativas o altamente significativas para tratamientos se realizaron pruebas de medias según Duncan. El terreno se preparó de forma convencional, con una chapea inicial, un paso de arado y gradeo de forma mecanizada, posteriormente se realizó la estructuración del diseño de campo. Las variables evaluadas según las condiciones de campo fueron: germinación, altura de la plantas, daños por plaga y enfermedades y ramificación, en las condiciones de vivero fueron: germinación, sobrevivencia, altura (cm), daños por plagas y enfermedades. Como resultado se obtuvo que 3 de las líneas presentaron buena germinación en condiciones de campo y vivero siendo la de mejor comportamiento la línea 13A-50 con un promedio del 97%. Para altura la línea 9818 presentó el mejor comportamiento en condiciones de campo con rangos de 48cm - 58 cm manteniendo superioridad durante el estudio en comparación con el resto de las líneas evaluadas. En daños por plagas la línea 8L418 la de menor afectación, y la más afectada fue la línea l3A-50. En daños por enfermedades la línea 9818 obtuvo los mayores daños en los niveles de moderado a muy grave y la línea l3A-50 fue la de menor incidencia. Todas las líneas presentaron una ramificación media de 30%, siendo la línea 105916 la de mejor comportamiento. En vivero la línea l3A-50 presentó la mejor altura. La línea 9818 fue la de mejor adaptabilidad en condiciones de campo, seguida de la línea BA-50. Pero en condiciones de vivero la línea 13A-50 fue la de mejor adaptabilidad, seguida de la línea 8L418.en resumen la línea l3A-50 fue la de mejor comportamiento adaptativo.

Evaluación de diferentes alternativas quimicas y no quimicas para el manejo de patógenos de suelo en fase de semillero y campo en el cultivo del tomate (Licopersicum esculentum L.)

Con el fin de evaluar y seleccionar las mejores opciones para manejo de patógenos del suelo, que sean rentables económicamente para los productores y que preserven la calidad del medio ambiente y la salud humana, se realizó el presente estudio durante época de primera de 1995, en la finca del productor Pablo Maltéz, San Isidro de la Cruz Verde, Managua del 5 de junio al 7 de septiembre de 1995. El experimento se dividió en dos fases: de semillero y campo. En la fase de semillero, se evaluó el efecto de: Cal, Ceniza, Mezcla de cal y ceniza, Agua hirviendo, Vitavax-300 y un testigo sin aplicación, sobre la pudrición radical y del tallo, usando las variedades UC-82 y VF-134. Las variables a medir fueron: Porcentaje de incidencia de plantas enfermas por "Damping off” y el efecto de éstas alternativas sobre patógenos del suelo. En la fase de campo, se evaluó el efecto que tienen la mezcla de cal y ceniza, vitavax-300 y un testigo sin aplicación. Se evaluó la variedad UC-82 y se tornó la variable: Porcentaje de incidencia de plantas enfermas por pudrición radical y del tallo. Los resultados indican, que en la fase de semillero el Agua hirviendo fue el mejor tratamiento por ejercer buen efecto sobre patógenos del suelo en ambas variedades, en cambio Cal (460 g) y Ceniza (115 g) realizaron mejor efecto en la variedad VF-134, seguido por Mezcla de cal y ceniza, que resultó muy efectivo en el manejo de patógenos en la variedad UC-82. En fase de campo la mezcla de cal y ceniza fue el mejor tratamiento por ejercer buen control sobre la incidencia de pudrición radical y del tallo.