Is there an abnormal enhancement of atmospheric aerosol before the 2008 Wenchuan earthquake?

There are extensive reports of ionospheric disturbances before the great 2008 Wenchuan earthquake, which are possibly explained by seismogenic electric field hypotheses linked with the aerosols injected in atmosphere. This paper attempts to investigate the possible change of atmospheric aerosol optical depth (AOD) associated with this earthquake by using MODIS data from both Terra and Aqua satellites. The result shows a clear enhancement of AOD along the Longmenshan faults 7 days before the quake, which is 1 day and 4 days earlier than the reported negative and positive ionospheric disturbances, respectively, and is 1 day earlier than or quasi-synchronism with other reported atmospheric anomalies including air temperature, outgoing longwave radiation and relative humidity. Particularly, the spatial distribution of AOD enhancement is very local and it is correlated well with the active faults and surface ruptures. We suggest that this unique enhancement could be associated with the Lithosphere–Atmosphere–Ionosphere coupling process during the preparation of the Wenchuan earthquake.     

Satellite derived aerosol optical depth climatology over Bangalore,India

Climatological aerosol optical depths (AOD) over Bangalore, India have been examined to bring out the temporal heterogeneity in columnar aerosol characteristics. AOD values at 550 nm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA’s Terra and Aqua satellites, for the period of 2002–2011 have been analyzed (independently) for the purpose. Frequency distributions of the AOD values are examined to infer the monthly mean values. Monthly and seasonal variations of AOD are investigated in the light of regional synoptic meteorology. Climatological monthly and seasonal mean Terra and Aqua AOD values exhibited similar temporal variation patterns. Monthly mean AOD values increased from January, peaks during May and thereafter (except for a secondary peak during July) fall off to reach a minimum during December. Monsoon season recorded the highest climatological seasonal mean AOD, while winter season recorded the lowest. AOD values show an overall increasing trend on a yearly basis, which was found mainly due to sustained increase in the seasonal averaged AOD during summer. The results obtained in the present study are compared with that of the earlier studies over the same location and also with AOD over various other Indian locations. Finally, the radiative and climatic impacts are discussed.     

Validation of space-born Moderate Resolution Imaging Spectroradiometer remote sensors aerosol products using application of ground-based Multi-wavelength Radiometer

The measurements of aerosol optical properties were carried out during April 2006 to March 2011 over Mohal (31.9°N, 77.12°E) in the northwestern Indian Himalaya, using the application of ground-based Multi-wavelength Radiometer (MWR) and space-born Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensors. The average (±standard deviation) values of aerosol optical depth (AOD) at 500 nm, Ångström exponent and turbidity coefficient during the entire measurement period were 0.25 ± 0.09, 1.15 ± 0.42 and 0.12 ± 0.06 respectively. About 86% AOD values retrieved from MODIS remote sensor were found within an uncertainty limit (Δτ_pλ = ±0.05 ± 0.15τ_pλ). In general, the MWR derived AOD values were higher than that of MODIS retrieval with absolute difference ∼0.02. During the entire period of measurement space-born MODIS remote sensor and ground-based MWR observation showed good correspondence with significant correlation coefficient ∼0.78 and root mean square difference ∼0.06. For daily observations the relative difference between these two estimates stood less than 9%. However, satellite-based and ground-based observation showed good correspondence, but further efforts still needed to eliminate systematic errors in the existing MODIS algorithm.     

Retrieval and validation of aerosol optical depth using GF-1 WFV cameras data

Conventional AOD (Aerosol Optical Depth) retrieval is restricted to the global and regional scale due to the limited spatial resolution of satellites. This does not allow for aerosol monitoring at the city level. The Chinese GF-1 Wide Field of View (WFV) sensors have sufficiently fine resolution as a data source for AOD retrieval with fine spatial resolution and a 4-day revisit time. In this study, principles similar to those in the Deep Blue (DB) and Dark Target (DT) algorithms were used to retrieve AOD at 100 m spatial resolution from GF-1 WFV images supported by Moderate Resolution Imaging Spectraradiometer (MODIS) surface reflectance (SR) products (MOD09A1). The derived GF-1 WFV AOD were compared with a combination of MOD04_3K DT AOD and MOD04_L2 DB AOD (MODIS AOD) to find that they yield reasonable Spearman correlations (R_S > 0.82) over Taiwan and Beijing. The derived GF-1 WFV AOD were also validated against Aerosol Robotic Network (AERONET) AOD; the Spearman correlation values were R_S = 0.911 in Beijing and R_S = 0.858 in Taiwan.     

A surface reflectance correction model to improve the retrieval of MISR aerosol optical depth supported by MODIS data

Combined use of different satellite sensors are known to improve retrievals of aerosol optical depth (AOD). In this study, we propose a new method for retrieving Multi-angle Imaging SpectroRadiometer (MISR) AOD data supported by Moderate Resolution Imaging Spectroradiometer (MODIS) data in Jiangsu Province, China, over the period of 2016–2018 using MODIS L1B, bidirectional reflectance distribution function (BRDF), MISR 1B2T, and ground-measured AOD data. This method is based on the surface reflectance determined by the MODIS V5.2 algorithm. Through the observation angle and spectral conversion between different sensors, the MISR AOD can be obtained. The correlation coefficient (R) and root-mean-square error (RMSE) between the retrieved MISR and ground-measured AOD data varied between different seasons. The accuracy of the MISR AOD retrieval was notably improved after correcting the MISR surface reflectance. Therefore, the method proposed in this study is feasible for the retrieval of MISR AOD supported by MODIS data, and will be applicable to atmospheric environmental monitoring over large areas in the future.     

Clouds and Earth Radiant Energy System (CERES), a review: Past,present and future

The Clouds and Earth Radiant Energy System (CERES) project’s objectives are to measure the reflected solar radiance (shortwave) and Earth-emitted (longwave) radiances and from these measurements to compute the shortwave and longwave radiation fluxes at the top of the atmosphere (TOA) and the surface and radiation divergence within the atmosphere. The fluxes at TOA are to be retrieved to an accuracy of 2%. Improved bidirectional reflectance distribution functions (BRDFs) have been developed to compute the fluxes at TOA from the measured radiances with errors reduced from ERBE by a factor of two or more. Instruments aboard the Terra and Aqua spacecraft provide sampling at four local times. In order to further reduce temporal sampling errors, data are used from the geostationary meteorological satellites to account for changes of scenes between observations by the CERES radiometers.     

Seasonal variability of atmospheric aerosol over the North Indian region during 2005–2009

The Indo-Gangetic basin (IGB) extends 2000 km in length along NW–SE and has 400 km width, in the north the basin is bounded by towering Himalaya. High aerosol optical depth (AOD) is observed over the IGB throughout the year. The Himalaya restricts the transport of aerosols across Tibet and China. We have used ground based Kanpur and Gandhi College Aerosol Robotic Network (AERONET) stations and Multiangle Imaging SpectroRadiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) Terra level-3 AOD products for the years 2005–2009 to study the variability of aerosol over the Indo-Gangetic (IG) plains. An increase in both satellite-derived as well as ground observed aerosol loading during 2005–2009 has been found over major cities located in the IG plains. The correlation coefficients between AERONET and MISR data are found to be 0.70, 0.36 0.82, in contrast the correlation coefficients between AERONET and MODIS 0.49, 0.68, and 0.43, respectively during summer, winter and monsoon seasons. The AOD estimation using MISR is found to be close to AERONET data during summer and monsoon seasons, in contrast MODIS estimation is better during winter season.     

Characteristics of recent dust storms over the Indian region using real time multi-satellite observations from the direct broadcast receiving system at IMD

In this study, observations from microwave satellites, visible and infrared instruments have been analyzed to detect dust storm over north and north–west part of India during 18–23 March 2012. This study investigated the approach to utilize the multi satellite data of Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the Terra and Aqua satellite and the Advanced Microwave Sounding Unit (AMSU) on-board NOAA satellite to study the characteristics of dust storms from real time direct broadcast (DB) receiving system installed at three places of India Meteorological Department (IMD). The dust storm detection is based on the infrared brightness temperature (BT) difference between channels at 11 and 12 μm and polarized BT difference between two channels of 89 and 23.8 GHz. It is found that the significant differences between the BT of channel 89 and 23.8 can be used as a discriminator of identifying dust storm. The Total Ozone Mapping Spectroradiometer (TOMS) Aerosol Index (AI) and AMSU-A 23 GHz channel BT from NOAA satellite over the north and north-west part of India have also been analyzed. The result indicated the characteristic behavior between BT and AI during the different phases of the dust storm. Finally, the occurrence of dust outbreaks has also been validated with sky radiometer of IMD, which confirms the presence of a dust storm over the Indian region. Further, the findings of the study and its approaches apply to the other dust storm cases which occurred during the months of April and June 2012. The integrated approach suggested the potential to use high resolution data of microwave as well as thermal–infrared using multi-satellite observations from real time direct broadcast system for the detection of severe, moderate or weak dust storms very well. The approach is found to be promising for operational application.     

Satellite and ground detection of very dense smoke clouds produced on the islands of the Paraná river delta that affected a large region in Central Argentina

Intense fires were produced on the Paraná river delta islands, Argentina, during most part of 2008, by a combination of an exceptionally dry period and the farmers’ use of a fire land-cleaning technique. In April 2008, those fires significantly affected the nearby regions and their inhabitants, from Rosario city to Buenos Aires mega-city. In this work we present satellite as well as ground Aerosol Optical Depth (AOD) at 550 nm data obtained during the propagation of pollution clouds to the central zone of Argentina. The highest value (1.18) was registered at Buenos Aires by atmospheric remote sensing, using the satellite instrument MODIS/Terra on April 18th 2008 at 10:35 local time (= UT − 3 h). On the same day, ground air quality detectors also measured in this city the highest Total Suspended Particle (TSP) value of the month, 2.02 mg/m³. The AOD(550) daily variation at Rosario Astronomical Observatory, which is located near the Paraná riverside, was derived by combining solar ultraviolet erythemal irradiance data (measured with a YES biometre) with model calculations. On April 25th 2008, from 12:00 to 15:30 local time, a rather high and constant AOD(550) value was registered, with a mean value of (0.90 ± 0.21). Cities located on the side of the Rosario–Buenos Aires highway (San Nicolás, Baradero and San Pedro) were also affected, showing a mean AOD(550) between the Rosario and Buenos Aires values. The particulate matter was collected with gridded samplers placed on the Paraná river islands as well as at the Rosario Observatory. They were analysed with a Scanning Electron Microscope (SEM) and mainly showed a biological origin. Even if normally large particles travel small distances from the source, organic aerosol in the range of 40–100 μm and complex asymmetric structures were registered several kilometres away from the aerosol sources on the islands. Another event of intense UV index attenuation (98.6%) occurred on September 18th 2008, due to very dense smoke clouds that extended over the Rosario area for several hours. The clouds were driven away from the fires by East–northeast and East–southeast winds. The minimum value of this index measured around noon allows to derive a maximum AOD(550)_max = (3.65 ± 0.90) at 12:45 local time. Soot clouds extended over the Paraná river, transporting Burned Biomass Debris (BBD) that deposited on Rosario. In particular, burned leaves and small branches with dimensions of 1–20 cm were collected. The mean (BBD) particles deposited on the ground from 7:00 to 19:00 local time were (0.92 ± 0.20) BBD/(m² h).     

Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data

Land surface temperature (LST) is an important factor in global change studies, heat balance and as control for climate change. A comparative study of LST over parts of the Singhbhum Shear Zone in India was undertaken using various emissivity and temperature retrieval algorithms applied on visible and near infrared (VNIR), and thermal infrared (TIR) bands of high resolution Landsat-7 ETM+ imagery. LST results obtained from satellite data of October 26, 2001 and November 2, 2001 through various algorithms were validated with ground measurements collected during satellite overpass. In addition, LST products of MODIS and ASTER were compared with Landsat-7 ETM+ and ground truth data to explore the possibility of using multi-sensor approach in LST monitoring. An image-based dark object subtraction (DOS3) algorithm, which is yet to be tested for LST retrieval, was applied on VNIR bands to obtain atmospheric corrected surface reflectance images. Normalized difference vegetation index (NDVI) was estimated from VNIR reflectance image. Various surface emissivity retrieval algorithms based on NDVI and vegetation proportion were applied to ascertain emissivities of the various land cover categories in the study area in the spectral range of 10.4–12.5 μm. A minimum emissivity value of about 0.95 was observed over the reflective rock body with a maximum of about 0.99 over dense forest. A strong correlation was established between Landsat ETM+ reflectance band 3 and emissivity. Single channel based algorithms were adopted for surface radiance and brightness temperature. Finally, emissivity correction was applied on ‘brightness temperature’ to obtain LST. Estimated LST values obtained from various algorithms were compared with field ground measurements for different land cover categories. LST values obtained after using Valor’s emissivity and single channel equations were best correlated with ground truth temperature. Minimum LST is observed over dense forest as about 26 °C and maximum LST is observed over rock body of about 38 °C. The estimated LST showed that rock bodies, bare soils and built-up areas exhibit higher surface temperatures, while water bodies, agricultural croplands and dense vegetations have lower surface temperatures during the daytime. The accuracy of the estimated LST was within ±2 °C. LST comparison of ASTER and MODIS with Landsat has a maximum difference of 2 °C. Strong correlation was found between LST and spectral radiance of band 6 of Landsat-7 ETM+. Result corroborates the fact that surface temperatures over land use/land cover types are greatly influenced by the amount of vegetation present.     

The use of MODIS 250 m bands to improve the MODIS 1 km ocean color atmospheric correction algorithm in turbid water

“Clear water” is a scale-dependent concept, so it is more likely to successfully find the “clear water” from images with smaller scale than that with larger scale data. In this study, an optimal spectral relationship of moderate-resolution imaging spectroradiometer (MODIS) 250 m and 1 km resolution data at near-infrared bands (OSRLM) is constructed for converting pseudo “clear water” reflectance at 859 nm to those at 748 and 869 nm. According to scale effects, the satellite-observed pseudo “clear water” reflectance is greater than 5.18%, larger than that derived from OSRLM model. An atmospheric correction model for MODIS 1km data using pseudo “clear water” reflectance of MODIS 250 m data (ACMM) was developed for improving the performance of traditional “clear water” atmospheric correction model (CWAC). The model validation results indicate that ACMM model has a better performance than CWAC model. By comparison, the uncertainty decreases by 19.18% in the use of ACMM model over CWAC model for deriving water-leaving reflectance in Taihu Lake, China. This uncertainty is significantly reduced in water-leaving reflectance estimation due to partial removal of scale effects on “clear water”. These findings imply that satellite-derived aerosol scattering contribution at smaller scale usually has a better performance than that at larger scale.     

A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape

Remotely sensed high spatial resolution thermal images are required for various applications in natural resource management. At present, availability of high spatial resolution (<200 m) thermal images are limited. The temporal resolution of such images is also low. Whereas, coarser spatial resolution (∼1000 m) thermal images with high revisiting capability (∼1 day) are freely available. To bridge this gap, present study attempts to downscale coarser spatial resolution thermal image to finer spatial resolution using relationships between land surface temperature (LST) and vegetation indices over a heterogeneous landscape of India. Five regression based models namely (i) Disaggregation of Radiometric Temperature (DisTrad), (ii) Temperature Sharpening (TsHARP), (iii) TsHARP with local variant, (iv) Least median square regression downscaling (LMS_DS) and (v) Pace regression downscaling (PR_DS) are applied to downscale LST of Landsat Thematic Mapper (TM) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) images. All the five models are first evaluated on Landsat image aggregated to 960 m resolution and downscaled to 480 m and 240 m resolution. The downscale accuracy is achieved using LMS_DS and PR_DS models at 240 m resolution at 0.61 °C and 0.75 °C respectively. MODIS data downscaled from 1000 m to 250 m spatial resolution results root mean square error (RMSE) of 1.43 °C and 1.62 °C for LMS_DS and PR_DS models, respectively. The LMS_DS model is less sensitive to outliers in heterogeneous landscape and provides higher accuracy when compared to other models. Downscaling model is found to be suitable for agricultural and vegetated landscapes up to a spatial resolution of 250 m but not applicable to water bodies, dry river bed sand sandy open areas.     

Sensitivity analysis for air temperature profile estimation methods around the tropopause using simulated Aqua/AIRS data

A sensitivity analysis is performed to investigate potential improvements to the accuracy of air temperature profile retrievals near the tropopause. A simple inversion method is employed to identify and remove redundant spectral channels from the retrievals using simulated data for the high-spectral resolution sounder AIRS (Atmospheric Infrared Sounder) on the Aqua satellite. Bayesian optimal theory and inverse technique are applied for the atmospheric temperature profile retrievals, and the 15 μm CO₂ absorption bands (620–750 cm⁻¹) are chosen for this study. Sequentially elimination of redundant channels is directly integrated into the inverse scheme for the temperature profile, in order to accurately retain the valuable channels and remove all the redundant channels, for accurate retrieval of the temperature profile. Also, the tropopause and troposphere are treated differently in the inverse scheme to improve the retrieval accuracy in the tropopause. Results of a sensitivity analysis based on this method, for the Tropical and Middle-Latitude Summer models simulated by MODTRAN4.0, show that the estimated accuracies are improved by 2 K around the tropopause, and are only changed by less than 0.2 K in the troposphere.     

Monitoring the South Atlantic Anomaly using ATSR instrument series

The South Atlantic Anomaly (SAA) has been monitored for 19 years using the Along Track Scanning Radiometer (ATSR) series of instruments onboard the ERS-1, ERS-2 and ENVISAT ESA satellites. The time evolution of the night-time particle induced noise in the short wavelength infrared (SWIR, 1.6 μm) and visible (VIS, 0.55 μm) channels of the ATSR instrument series have been analysed. The monthly location and extension of the SAA are inferred by fitting a two-dimensional, elliptical Gaussian function to the coordinates of the night-time hot spots detected over the SAA region. The location of the centre of the SAA is found to drift westwards with an average drift rate of about 0.24 deg/year and northward with an average drift rate of about 0.12 deg/year. Irregularities are found where the drift speed is inverted and the SAA moves eastward and southward. Results indicate that, as expected, the retrieved values of SAA’s strength and extension are anti-correlated with the solar activity expressed by the solar flux at 10.7 cm (F10.7). Finally, the peak-to-peak amplitude of the seasonal variation of the SAA strength, estimated from monthly VIS data, is found to be 30% of the average value with the annual to semiannual amplitude ratio of 1.38.     

Estimation of vertically integrated water vapor in Hungary using MODIS imagery

Information about the amount and spatial structure of atmospheric water vapor is essential in understanding meteorology and the Earth environment. Space-borne remote sensing offers a relatively inexpensive method to estimate atmospheric water vapor in the form of integrated water vapor (IWV). The research activity reported in the present paper is based on the data acquired by the HRPT/MODIS (High Resolution Picture Transmission, MODerate resolution Imaging Spectroradiometer) receiving station established in Budapest (Hungary) by the Space Research Group of the Eötvös Loránd University. Integrated water vapor is estimated by the remotely sensed data of the MODIS instrument with different methods and also by the operational numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF). Radiosonde data are used to evaluate the accuracy of the different IWV fields though it has been pointed out that the in situ data also suffers from uncertainties. It was found that both the MODIS and the ECMWF based fields are of good accuracy. The satellite data represent finer scale spatial structures while the ECMWF data have a relatively poor spatial resolution. The high quality IWV fields have proved to be useful for radiative transfer studies such as the atmospheric correction of other satellite data from times different than the overpass times of satellites Terra/Aqua and the forecast times of the model data. For this purpose the temporal variability of IWV is scrutinized both using ECMWF and MODIS data. Taking advantage of Terra and Aqua overpasses, the mean rate of change of IWV estimated by the near infrared method was found to be 0.47 ± 0.45 kg m⁻² h⁻¹, while it was 0.13 ± 0.65 kg m⁻² h⁻¹ based on the infrared method. The numerical weather prediction model’s analysis data estimated −0.01 ± 0.13 kg m⁻² h⁻¹ for the mean growth rate, while using forecast data it was 0.24 ± 0.18 kg m⁻² h⁻¹. MODIS data should be used when available for the estimation of the IWV in other studies. If no satellite data are available, or available data are only from one overpass, ECMWF based IWV can be used. In this case the analysis fields (or the satellite field) should be used for temporal extrapolation but the rate of change should be calculated from the forecast data due to its higher temporal resolution.     

Evaluation of MODIS GPP over a complex ecosystem in East Asia: A case study at Gwangneung flux tower in Korea

Moderate Resolution Imaging Radiometer (MODIS) gross primary productivity (GPP) has been used widely to study the global carbon cycle associated with terrestrial ecosystems. The retrieval of the current MODIS productivity with a 1 × 1 km² resolution has limitations when presenting subgrid scale processes in terrestrial ecosystems, specifically when forests are located in mountainous areas, and shows heterogeneity in vegetation type due to intensive land use. Here, we evaluate MODIS GPP (MOD17) at Gwangneung deciduous forest KoFlux tower (deciduous forest; GDK) for 2006–2010 in Korea, where the forests comprise heterogeneous vegetation cover over complex terrain. The monthly MODIS GPP data overestimated the GDK measurements in a range of +15% to +34% and was fairly well correlated (R = 0.88) with the monthly variability at GDK during the growing season. In addition, the MODIS data partly represented the sharp GPP reduction during the Asian summer monsoon (June–September) when intensive precipitation considerably reduces solar radiation and disturbs the forest ecosystem. To examine the influence of subgrid scale heterogeneity on GPP estimates over the MODIS scale, the individual vegetation type and its area within a corresponding MODIS pixel were identified using a national forest type map (∼71-m spatial resolution), and the annual GPP in the same area as the MODIS pixel was estimated. This resulted in a slight reduction in the positive MODIS bias by ∼10%, with a high degree of uncertainty in the estimation. The MODIS discrepancy for GDK suggests further investigation is necessary to determine the MODIS errors associated with the site-specific aerodynamic and hydrological characteristics that are closely related to the mountainous topography. The accuracy of meteorological variables and the impact of the very cloudy conditions in East Asia also need to be assessed.     

Plasma electron temperature variability in lunar surface potential and in electric field under average solar wind conditions

The Moon is immersed in plasma environment. The most interesting challenge of the lunar plasma– field environment is that it is alternatively dominated by the extended but variable outer atmosphere of the Earth – the magnetosphere – and by the extended but highly variable solar atmosphere – the solar wind. Understanding the plasma environment and its interaction with the lunar surface will be beneficial to both manned and robotic surface exploration activities and to scientific investigations. Presented is a preliminary map of variations of lunar surface electric potential over the day side and night side using probe equations and a discussion on dust dynamics in this E-field structure using the data from Electron Reflectometer in Lunar Prospector spacecraft during 1998–1999. On the day side, potential is around 5 V and on the night side it reaches up to −82 V. On the night side region, only highly energetic electrons can overcome this large negative potential. The variation at electron temperature (T_e) strongly reflects in the surface potential. The potential reaches to a value of −82 V for T_e = 58 eV. Surface charging causes the electrostatic transport of charged dust grains. Dust grain size of 0.1 μm shows a levitation height of 4.92 m on lunar day side, 748 m on terminator region and 3.7 km on the night side. The radius of maximum sized grain to be lofted, R_max, peaks at the terminator region (R_max = 0.83 μm). At the transition region dust levitation is almost absent. This region is most suited for exploration activities as the region is free from hazards caused by lunar dust.     

Development and testing of a statistical texture model for land cover classification of the Black Sea region with MODIS imagery

A statistical model is proposed for analysis of the texture of land cover types for global and regional land cover classification by using texture features extracted by multiresolution image analysis techniques. It consists of four novel indices representing second-order texture, which are calculated after wavelet decomposition of an image and after texture extraction by a new approach that makes use of a four-pixel texture unit. The model was applied to four satellite images of the Black Sea region, obtained by Terra/MODIS and Aqua/MODIS at different spatial resolution. In single texture classification experiments, we used 15 subimages (50 × 50 pixels) of the selected classes of land covers that are present in the satellite images studied. These subimages were subjected to one-level and two-level decompositions by using orthonormal spline and Gabor-like spline wavelets. The texture indices were calculated and used as feature vectors in the supervised classification system with neural networks. The testing of the model was based on the use of two kinds of widely accepted statistical texture quantities: five texture features determined by the co-occurrence matrix (angular second moment, contrast, correlation, inverse difference moment, entropy), and four statistical texture features determined after the wavelet transformation (mean, standard deviation, energy, entropy). The supervised neural network classification was performed and the discrimination ability of the proposed texture indices was found comparable with that for the sets of five GLCM texture features and four wavelet-based texture features. The results obtained from the neural network classifier showed that the proposed texture model yielded an accuracy of 92.86% on average after orthonormal wavelet decomposition and 100% after Gabor-like wavelet decomposition for texture classification of the examined land cover types on satellite images.     

Atmosphere sounding by GPS radio occultation: First results from TanDEM-X and comparison with TerraSAR-X

On 21 June 2010 the TerraSAR-X satellite was joined by the TanDEM-X satellite. A Global Positioning System (GPS) radio occultation (RO) experiment using the twin satellites has been carried out to estimate the precision of GPS atmospheric soundings. For the Day Of Year (DOY) 330–336, 2011, we analyze phase and amplitude data recorded by GPS receivers separated by a few hundred meters in a low earth orbit and derive collocated atmospheric refractivity profiles. In the altitude range 10–20 km the standard deviation between TerraSAR-X and TanDEM-X refractivity does not exceed 0.15%. The standard deviation is rapidly increasing for lower and higher altitudes; close to the surface and at an altitude of 30 km the standard deviation reaches 0.8% and 0.5%, respectively. Systematic deviations between TerraSAR-X and TanDEM-X refractivity in the considered altitude range (0–30 km) are negligible. The results confirm the anticipated high precision of the GPS RO technique. However, the difference in the retrieved refractivity in the lower troposphere for different Open Loop (OL) signal tracking parameters, altered onboard TanDEM-X for DOY 49–55, 2012, calls for an in depth analysis. At the moment we can not exclude that a potential bias in the OL Doppler model introduces a bias in our retrieved refractivity at altitudes <8$<8$ km.