Search

Based on theory, this research investigated the relationship between the penetration depth of electromagnetic waves and the conductivity of sea water based on daily parameters of temperature and salinity in Caspian Sea with low salinity and Oman Sea with high salinity. The data was received from the oceanographic center over a period of one year and the values are calculated daily and the results show that the wave range is more sensitive to salinity. The range decreases with the increase of the frequency, so that at the frequency of 1000 khz, the range for the Caspian Sea and Oman reached about 2 and 5 cm, respectively. The range in the low frequency of 100 Hz has increased significantly and its value for the Caspian Sea is 1.51 m in March (T=11.76 C0, P=7.59psu) and for the Oman Sea it is 16.22 m in January (T=22 C0 , P=36.5 psu) was obtained. Also, the sensitivity of wave range with temperature in the Caspian Sea is more than that of the Oman Sea. This amount of range was seen at minimum and maximum temperatures of 9.74 and 24.30 degrees Celsius for the Caspian Sea, respectively 34 and 45.2 meters, and for the Oman Sea at temperatures of 22 and 32.2 degrees Celsius equal to 19.57 and 22 meters. The penetration depth of radar waves in the Caspian Sea can be three times higher than in the Oman Sea due to its low salinity. Since the range decreases with the increase in salinity, therefore, taking into account the salinity of the effluent of desalination facilities in the range of 74.8psu, the range of waves with a frequency of 100Hz has decreased sharply from 19.7m to 4m, which can be the cause of its tracking in the sea.

Satellite sensors are one of the most important means of collecting real-time geospatial information. Due to their characteristics such as large spatial coverage and strong capability for dynamic monitoring, they are widely used in the observation of real-time flood situation information for flood situational awareness and response. Selecting the optimum sensor is vital when multiple sensors exist. Presently, sensor selection predominantly hinges on human experience and various quantitative and qualitative evaluation methods. Yet, these methods lack optimization considering the flood's spatiotemporal characteristics, such as different flood phases and geographical environmental factors. Consequently, they may inaccurately evaluate and select the inappropriate sensor. To address this issue, an innovative observation capability evaluation model (OCEM) is proposed to quantitatively pre-evaluate the performance of flood-water-observation-oriented satellite sensors. The OCEM selects and formulates various flood-water-observation-related capability factors and supports dynamic weight assignment considering the spatiotemporal characteristics of the flood event. An experiment involving three consecutive flood phase observation tasks was conducted. The results demonstrated the flexibility and effectiveness of the OCEM in pre-evaluating the observation capability of various satellite sensors across those tasks, accounting for the spatiotemporal characteristics of different flood phases. Additionally, qualitative and quantitative comparisons with related methods further affirmed the superiority of the OCEM. In general, the OCEM has provided a "measuring table" to optimize the selection and planning of sensors in flood management departments for acquiring real-time flood information. [ABSTRACT FROM AUTHOR]

Satellite sensors are one of the most important means of collecting real-time geospatial information. Due to their characteristics such as large spatial coverage and strong capability for dynamic monitoring, they are widely used in the observation of real-time flood situation information for flood situational awareness and response. Selecting the optimum sensor is vital when multiple sensors exist. Presently, sensor selection predominantly hinges on human experience and various quantitative and qualitative evaluation methods. Yet, these methods lack optimization considering the flood’s spatiotemporal characteristics, such as different flood phases and geographical environmental factors. Consequently, they may inaccurately evaluate and select the inappropriate sensor. To address this issue, an innovative observation capability evaluation model (OCEM) is proposed to quantitatively pre-evaluate the performance of flood-water-observation-oriented satellite sensors. The OCEM selects and formulates various flood-water-observation-related capability factors and supports dynamic weight assignment considering the spatiotemporal characteristics of the flood event. An experiment involving three consecutive flood phase observation tasks was conducted. The results demonstrated the flexibility and effectiveness of the OCEM in pre-evaluating the observation capability of various satellite sensors across those tasks, accounting for the spatiotemporal characteristics of different flood phases. Additionally, qualitative and quantitative comparisons with related methods further affirmed the superiority of the OCEM. In general, the OCEM has provided a “measuring table” to optimize the selection and planning of sensors in flood management departments for acquiring real-time flood information.

Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We disaggregated monthly GFED3 emissions during 2003–2009 to a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) active fire observations. Daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of burning in savannas. These patterns were consistent with earlier field and modeling work characterizing fire behavior dynamics in different ecosystems. On diurnal timescales, our analysis of the GOES WF_ABBA active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top-down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from multiple satellite sensors to improve daily emissions estimates.

Changes in the color of sea water caused by algal bloom has continuously risen in recent decades. This increase is due to the harmful effects of human activities that allow sea water to change from a normal to an abnormal state. Satellite sensors are the most accessible, cost-effective means of monitoring. They have been allocated to a large number of marine science studies. The MODIS satellite data that examines changes in color of the sea has been used in this study. In the Persian Gulf and Oman Sea, red tide phenomenon began October 2008 and continued until May 2009 with a high intensity that covered large areas. The results showed that the use of different algorithms and necessary corrections could be achieved in the concentration and size of red tide by color changes of the sea water, marine and oceanic detected by satellite sensors, and extent of changes found.

The study analyzed temporal variations of Ozone Monitoring Instrument (OMI)-observed NO2 columns, interregional correlation, and comparison between NO2 columns and NOx emissions during the period from 2006 to 2015. Regarding the trend of the NO2 columns, the linear lines were classified into four groups: (1) ‘upward and downward’ over six defined geographic regions in central-east Asia; (2) ‘downward’ over Guangzhou, Japan, and Taiwan; (3) ‘stagnant’ over South Korea; and (4) ‘upward’ over North Korea, Mongolia, Qinghai, and Northwestern Pacific ocean. In particular, the levels of NO2 columns in 2015 returned to those in 2006 over most of the polluted regions in China. Quantitatively, their relative changes in 2015 compared to 2006 were approximately 10%. From the interregional correlation analysis, it was found that unlike positive relationships between the polluted areas, the different variations of monthly NO2 columns led to negative relationships in Mongolia and Qinghai. Regarding the comparison between NO2 columns and NOx emission, the NOx emissions from the Copernicus Atmosphere Monitoring Service (CAMS) and Clean Air Policy Support System (CAPSS) inventories did not follow the year-to-year variations of NO2 columns over the polluted regions. In addition, the weekly effect was only clearly shown in South Korea, Japan, and Taiwan, indicating that the amounts of NOx emissions are significantly contributed to by the transportation sector.

Overshooting Tops (OTs) are associated with deep convective events and can be an indicator of severe weather conditions. In particular they are linked to strong updraughts, heavy rain, and have the potential to form dense ice clouds which pose a hazard to aviation. In this article, the Infrared Window Texture (IWT) method is used to detect OTs in a 5-year series of observations from the Spinning Enhanced Visible and InfraRed Imager aboard the Meteosat Second Generation series of geostationary satellites. This dataset shows that substantial differences in time-of-formation and cloud temperature exist between OTs formed in different locations. In particular, it is notable that OTs formed over land tend to be more frequent and form later in the day than those over the ocean. Oceanic OTs, meanwhile, are shown often to be colder than land-based OTs. The conclusions drawn from this dataset match well with data from other sources, such as the Tropical Rainfall Measurement Mission. A severe storm over South Africa is examined both to demonstrate the workings of the method and to highlight both strengths and weaknesses of the IWT method. [ABSTRACT FROM AUTHOR]

Addressing critical earth science questions often requires time scales beyond the life of any single satellite sensor. Overlap between satellite-based datasets allows for the quantification of continuity (and discrepancies) between sensors. Toward that end, collocated matchups between Sea-viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imager Radiometer Suite (VIIRS) water clarity data from the Gulf of Mexico were analyzed at simultaneous, daily, and monthly time scales. Simultaneous data indicated strong agreement between sensors, with unbiased percent difference (UPD) generally less than 10% for both SeaWiFS/MODIS and VIIRS/MODIS matchups, with no apparent temporal trends. Spatially, UPD was highest near frontal boundaries and at high sensor zenith angles, while bias showed nearshore/offshore trends. UPD and bias statistics did not diminish for daily matchups; however, large degradation was seen for comparisons of monthly means between sensors, particularly SeaWiFS/MODIS matchups. Data coverage represented an important factor contributing to uncertainties in monthly mean data, as higher UPD was observed when fewer valid satellite measurements were recorded. Requiring a minimum of 15 samples per pixel per month minimizes the uncertainties in monthly mean products, with UPD between satellites roughly equivalent to that for simultaneous matchups. Overall, these findings demonstrate high consistency between three satellite instruments for most locations, while several “hot spots” of inconsistency are also revealed, which should be avoided in time-series studies. The findings also highlight the need to quantify uncertainties in often-used satellite products (particularly monthly mean composites) as well as the need to have a sufficient number of observations to assure the fidelity of monthly means. [ABSTRACT FROM PUBLISHER]

Litter is acknowledged to affect microwave remote sensing data over forests, and accurate quantification of litter radiative properties is essential for proper retrieval of forest soil water content and forest canopy biophysical characteristics from satellite or airborne sensors. Furthermore, detailed characterization of forest soil organic horizons is of paramount importance for ecological studies and for carbon cycle and global changerelated studies. Ultrawideband ground-penetrating radar (GPR) data acquired above beech forest litter were used to analyze the effects of litter on the backscattering and investigate the ability of GPR to quantitatively characterize the properties of the forest floor. Radar measurements were performed over different litter configurations (i.e., single layers of recently fallen litter (OL) or of fragmented litter (OF), and combined OL-OF layers) and thicknesses. Radar data were processed through full-wave inversion. Over the considered frequency range (0.8-4.0 GHz), attenuation of the radar signal increased as both frequency and litter thickness increase, due to scattering and dielectric losses. These effects were accounted for in the radar model through frequency dependence of litter apparent electrical conductivity. Good agreement (RMSE = 9.5 × 10-3 m) was observed between estimated and measured litter thicknesses. Radar data inversions provided reliable estimates of litter electromagnetic properties, with relative dielectric permittivity values around 1.2 and 3.9 for OL and OF litters, respectively, and corresponding values of 0.032 and 0.080 Sm-1 for litter apparent electrical conductivity at 4.0 GHz. These results show great promise for the use of GPR for noninvasive characterization of forest litter. [ABSTRACT FROM PUBLISHER]

This paper reviews the advanced differential interferometry synthetic aperture radar (A-DInSAR) techniques, with two major components in focus. First is the basic concepts, synthetic aperture radar (SAR) data sources and the different algorithms documented in the literature, primarily focusing on persistent scatterers. In the second part, the techniques are compared in order to establish more linkage in terms of the variability of their applications, strength and validation of the interpreted results. Also, current issues in sensor and algorithm development are discussed. The study identified six existing A-DInSAR algorithms used for monitoring various deformation types. Generally, reports of their performance indicate that all the techniques are capable of measuring deformation phenomena at varying spatial resolution with high level of accuracy. However, their usability in suburban and vegetated areas yields poor results, compared to urbanized areas, due to inadequate permanent features that could provide sufficient coherent point targets. Meanwhile, there is continuous development in sensors and algorithms to expand the applicability domain of the technology for a wide range of deformable surfaces and displacement patterns with higher precision. On the sensor side, most of the latest SAR sensors employ longer wavelength (X and P bands) to increase the penetrating power of the signal and two other sensors (ALOS-2 PALSA-2 and SENTINEL-1) are scheduled to be launched in 2013. Researchers are investigating the possibility of using single-pass sensors with different look angles for SAR data collection. With these, it is expected that more data will be available for various applications. Algorithms such as corner reflector interferometry SAR, along track interferometry, liqui-InSAR, and squeeSAR are emerging to increase reliable estimation of deformation from different surfaces. [ABSTRACT FROM PUBLISHER]

This work presents an effective hybrid classification approach for feature extraction from fused images of two different satellite sensors. Wavelet transform function was used to fuse the panchromatic Cartosat-I and multispectral Landsat ETM+ sensor's images which could preserve both the spatial and spectral components of the original images. Multi-resolution segmentations based on homogeneity criterion formed the basis for the hybrid approach which uses supervised fuzzy NN approach of classifier in conjunction with knowledge classification system. Gaussian fuzzy membership function was defined on an optimal set of object features such as the Normalized Difference Vegetation Index, band mean, area, shape index and brightness derived from the segmented image objects for class description. Based on our kappa index analysis evaluation, the hybrid approach provides significantly better performance than its other counterparts such as artificial neural network, maximum likelihood classifier and support vector machine in terms of classification accuracy. [ABSTRACT FROM AUTHOR]

The high number of forest fires occurring every year constitutes one of the major degradation factors of ecosystems, especially in the Mediterranean region. The ability of satellite sensors to cover wide areas makes them valuable tools for the prevention, detection and mapping of wildfires and fire related properties of the ecosystems. However, increased spatial resolution, coupled with a lower number of available spectral channels, makes image analysis difficult. Thus, users need to employ additional image analysis techniques in order to achieve their objectives. The aim of this work was to assess to what extent potential fuels can be discriminated within different image analysis techniques and satellite imagery products using the indirect mapping method. The main hypothesis was that very high-resolution data, coupled with object-oriented image analysis (OOIA), would improve the ability to differentiate among potential fuels. To test this hypothesis, fuel types were extracted from high and very high spatial resolution satellite imagery using object-oriented and pixel-based image analyses, respectively. The general conclusion of this study was that the use of object-oriented image analysis not only produces more accurate results, but also allows differentiation among the largest number of potential fuels. Object-oriented analysis allows better discrimination of potential fuels using very high-resolution imagery and the indirect mapping method. Six different potential fuels were classified using the object-oriented approach compared with only four when using pixel-based image analysis. The overall accuracy achieved reached approximately 80%. [ABSTRACT FROM PUBLISHER]

The study analyzed temporal variations of Ozone Monitoring Instrument (OMI)-observed NO2 columns, interregional correlation, and comparison between NO2 columns and NOx emissions during the period from 2006 to 2015. Regarding the trend of the NO2 columns, the linear lines were classified into four groups: (1) 'upward and downward' over six defined geographic regions in central-east Asia; (2) 'downward' over Guangzhou, Japan, and Taiwan; (3) 'stagnant' over South Korea; and (4) 'upward' over North Korea, Mongolia, Qinghai, and Northwestern Pacific ocean. In particular, the levels of NO2 columns in 2015 returned to those in 2006 over most of the polluted regions in China. Quantitatively, their relative changes in 2015 compared to 2006 were approximately 10%. From the interregional correlation analysis, it was found that unlike positive relationships between the polluted areas, the different variations of monthly NO2 columns led to negative relationships in Mongolia and Qinghai. Regarding the comparison between NO2 columns and NOx emission, the NOx emissions from the Copernicus Atmosphere Monitoring Service (CAMS) and Clean Air Policy Support System (CAPSS) inventories did not follow the year-to-year variations of NO2 columns over the polluted regions. In addition, the weekly effect was only clearly shown in South Korea, Japan, and Taiwan, indicating that the amounts of NOx emissions are significantly contributed to by the transportation sector. [ABSTRACT FROM AUTHOR]