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Nominal grid data of geostationary remote sensing satellites are fundamental for generating the subsequent products. It can be obtained by normalized projection models, mainly based on the imaging mode. However, there are only definitions and primary descriptive equations for the normalized geostationary projection (NGP) model in the existing literature, while the corresponding imaging mode and the physical interpretation are missing, thus hindering the understanding of the produced nominal grid dataset as well as the subsequent products based on the grid. This paper first derived the imaging mode for NGP based on the limited literature. In addition, another new imaging mode was introduced and analyzed based on NGP. The corresponding projection model [nonstandard normalized geostationary projection (NNGP)] was proposed, which is entirely consistent with the situation of America’s Geostationary Operational Environmental Satellite-R Series (GOES-R) and Chinese Fengyun-4A (FY-4A). Furthermore, this paper proposed a novel nominal projection model for frame imaging, which is consistent with the imaging mode of China’s Gaofen-4. Finally, extensive experiments were designed to comparatively analyze the three nominal grids and demonstrate a detailed difference. By providing a theoretical basis for nominal grid selection, this research is highly significant for the efficient near-real-time production and further applications of geostationary images, as well as the conversion between different datasets resulting from different nominal grid data. In addition, our models are sufficiently tested during the on-orbit running of FY-4A, the first satellite of China’s second-generation three-axis stabilized geostationary meteorological satellite series. The algorithms provide the technical support for the high-precision image navigation and registration and play a significant role in robustly producing the meteorological data with similar quality to those from GOES-R. [ABSTRACT FROM AUTHOR]

One-minute rain rate exceeded for 0.01% of an average year (known as R0.01) is an important parameter, which is required in rain fade models for planning both satellite and terrestrial links, especially in the microwave and millimeter-wave bands. This paper presents the development of a new global model for estimating high-resolution R0.01. The model is developed using the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), ERA-Interim reanalyzed total precipitable water, and the cumulative distribution functions of the measured R0.01 via genetic algorithm. The newly proposed model performs better than the International Telecommunication Union Radio Sector (ITU-R) recommendation and widely used ITU-R P.837-6 model, particularly over the tropical regions, where ITU-R P.837-6 strongly underestimates R0.01. Overall, when compared to ITU-R P.837-6, the estimated R0.01 by the proposed model is improved by ~15%. Furthermore, the proposed model can provide R0.01 with a much higher spatial resolution (~5 km) as compared to ITU-R P.837-6 (~110 km). Another key advantage of the proposed model over ITU-R P.837-6 is that the inputs required for this model are readily available from meteorological database as well as from space-based observing system. This paper also investigated the long-term (1981–2016) trends in R0.01. The analysis revealed significant increasing trends in R0.01 over most parts of the globe, India and northern part of Southern America exhibited the strongest ($\sim 0.5\,\,\text {mm}\cdot \text {h}^{-1}\cdot $ year−1) increasing trends. It is anticipated that R0.01 obtained with the newly proposed model will have large implications in rain attenuation modeling for planning both terrestrial and earth–satellite microwave links. [ABSTRACT FROM AUTHOR]

The conventional strategy to download images captured by satellites is to compress the data on board and then transmit them via the downlink. It often happens that the capacity of the downlink is too small to accommodate all the acquired data, so the images are trimmed and/or transmitted through lossy regimes. This paper introduces a coding system that increases the amount and quality of the downloaded imaging data. The main insight of this paper is to use both the uplink and the downlink to code the images. The uplink is employed to send reference information to the satellite so that the onboard coding system can achieve higher efficiency. This reference information is computed on the ground, possibly employing extensive data and computational resources. The proposed system is called dual link image coding. As it is devised in this paper, it is suitable for Earth observation satellites with polar orbits. Experimental results obtained for data sets acquired by the Landsat 8 satellite indicate significant coding gains with respect to conventional methods. [ABSTRACT FROM AUTHOR]

The advanced MEdium Resolution Spectral Imager (MERSI II) is a major instrument onboard the Chinese FengYun 3D satellite, which was launched in November 2017. Extensive measurements were performed during MERSI II prelaunch testing to ensure effective characterization for on-orbit calibration. This paper gives a brief overview of the prelaunch performance testing conducted for MERSI II, as well as its improvements in terms of instrument design compared to MERSI I. The prelaunch calibration methodology and radiometric performance are detailed, including dynamic range, signal-to-noise ratio, noise equivalent differential temperature, linearity, and response uniformity. The assessment results indicate that most bands perform effectively with mirror specification noncompliances in a few reflective solar bands (RSBs). In addition, investigation of the stability and uniformity of the spherical integrating source indicates that they have a critical impact on the performance assessment and prelaunch calibration of RSBs. The temperature-dependence features of thermal emissive bands’ performances are also discussed in terms of their sensitivity to the operating temperature of the focal plane assembly and instrument circumstance. This paper also shows that the self-stability of the calibration source and the representation of the assessment methods are important as they affect the results of instrument performance evaluation. [ABSTRACT FROM AUTHOR]

This paper examines the potential in extracting the instantaneous location of maritime moving targets using a passive multistatic radar with Global Navigation Satellite Systems (GNSS) as illuminators of opportunity and a single receiver. This paper presents a theoretical framework for the localization of a moving target from a set of bistatic range measurements. The algorithm and its predicted accuracy are presented. The localization is achieved by what is essentially a multilateration technique, which can be applied while the transmitting platform is also in motion. The algorithms and the accuracy predictions, as a function of the number of transmitters, have been experimentally confirmed via a dedicated experimental campaign, where two different maritime targets were detected by up to 12 GNSS satellites belonging to different satellite constellations (GPS, GLONASS, and Galileo) simultaneously. To the best of the author’s knowledge, these are the first results of their kind and on this scale not only for GNSS-based passive radar but also for multistatic radar in general. [ABSTRACT FROM AUTHOR]

Focusing on open forests and woodlands within the Injune Landscape Collaborative Project research area in central southeast Queensland, Australia, and using dual-pol (HH and HV) ALOS PALSAR repeat-pass InSAR data (temporal baseline of 92 days), this paper explores the detection of forest disturbance from the spaceborne repeat-pass InSAR correlation magnitude by developing a simple and efficient forest disturbance detection approach. In particular, a generic physical InSAR scattering model is derived by accounting for the forest disturbance information as well as the normal temporal decorrelation effects that are later compensated for using the modified Random Volume over Ground model. Based on the generic model, a quantitative indicator of forest disturbance is retrieved, namely, disturbance index that varies from 0 (no disturbance) to 1 (complete deforestation). This index is compared with that identified using a time series of Landsat sensor data over a selective logging area and has a relative root mean square error of 13% at a spatial resolution of 0.8 ha. This paper highlights the use of the co-pol InSAR correlation magnitude for forest disturbance detection, which serves as a complimentary application to using the cross-pol counterpart for forest height inversion in a companion work. Given the global availability of this type of data (e.g., Japanese Aerospace Exploration Agency’s ALOS-1/2 and NASA-ISRO’s NISAR), the method is anticipated to contribute to the range of tools being developed for large-scale forest disturbance assessment and monitoring. [ABSTRACT FROM AUTHOR]

In this paper, the synthetic aperture radar (SAR) calibration for low-frequency missions by means of stable point targets is presented. Calibration at low frequency involves the absolute radiometric calibration, the antenna pattern and pointing characterization and validation, and the distortion system parameters' estimation. The use of traditional instrumentation, such as a polarimetric active radar calibrator, a corner reflector, or an active transponder, may be costly and can reduce the time the instrument is used for operational acquisitions. The purpose of this paper is to evaluate the potentiality in calibration of point targets for which the radar cross section and the time stability have been characterized. Given a calibration site, once that a set of the stable point targets have been detected by the analysis of an interferometric stack of SAR acquisitions, they may be used as passive calibrators for the validation of radiometry, elevation antenna pattern, and pointing estimation. We show that, although less targets are expected to be found in P- or L- band than in C- or X-band, a sufficient amount (about 250 targets per acquisition) can provide an accuracy in antenna pattern estimation of about 0.04 dB, if the target accuracy is 0.1 dB at 1◝. [ABSTRACT FROM AUTHOR]

This paper describes a new model of the seawater dielectric constant as a function of salinity and temperature at L-band. The model function is developed by fitting the accurate measurement data made at 1.413 GHz to a third-order polynomial. The purpose of this study is to provide an accurate model for earth-observing satellites to retrieve seawater salinities from remote sensing data. In this paper, the development of the model function is introduced along with an analysis of the goodness of fit. The model function is then compared with the model functions of Klein-Swift and Meissner-Wentz. Finally, the comparison is made between the retrieved salinity from the satellite data with the in situ data measured by Argo floats. [ABSTRACT FROM PUBLISHER]

Despite the great success of correlation filter-based trackers in visual tracking, it is questionable whether they can still perform on the satellite video data, acquired by a satellite or space station very high above the earth. The difficulty lies in that the targets usually occupy only a few pixels compared with the image size of over one million pixels and almost melt into the similar background. Since correlation filter models strongly depend on the quality of features and the spatial layout of the tracked object, they would probably fail on satellite video tracking tasks. In this paper, we propose a hybrid kernel correlation filter (HKCF) tracker employing two complementary features adaptively in a ridge regression framework. One feature is the optical flow that can detect variation pixels of the target. The other one is the histogram of oriented gradient that can capture the contour and texture information in the target, and an adaptive fusion strategy is proposed to employ the strengths of both features in different satellite videos. Quantitative evaluations are performed on six real satellite video data sets. The results show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames/s. [ABSTRACT FROM AUTHOR]

Current global leaf area index (LAI) products are generally produced from single-temporal satellite observations acquired by a single sensor. These LAI products are usually spatiotemporally discontinuous and inaccurate for some vegetation types in many areas, which limit the applications of these LAI products to the understanding of land dynamics. In this paper, a new data assimilation method was proposed to estimate multiscale and temporally continuous LAI values from multi-sensor time-series satellite observations with different spatial resolutions. An ensemble multiscale tree (EnMsT) was used to establish the conversion relationships between different spatial resolution LAI values, and dynamic models of the LAI at different spatial scales were constructed to evolve LAI at the corresponding spatial scales over time. At each time step, a multiscale Kalman filter (MKF) was introduced to fuse the predicted LAI values from the dynamic models at different spatial scales and to construct a forecasted EnMsT. When satellite observations were available, an ensemble multiscale filter (EnMsF) technique was applied to update the LAI values at each node of the EnMsT. The method was applied to estimate temporally continuous multiscale LAI values from the time series of Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) surface reflectance data and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data at several sites with different vegetation types. The estimated multiscale LAI values were compared with the MODIS and GEOV2 LAI products, and the reference LAI values at the corresponding scales aggregated from the high-resolution LAI surface images. The estimated LAI values with the finest spatial resolution were also validated by ground measurements from the selected sites. The results show that the new method is able to simultaneously estimate temporally continuous multiscale LAI values by assimilating satellite observations with different spatial resolutions, and the estimated multiscale LAI values are well consistent with the reference LAI values at the corresponding scales over the selected sites. The root-mean-square error (RMSE) and coefficient of determination of the retrieved LAI values at the finest spatial scale against the ground measurements over the selected sites are 0.539 and 0.788, respectively. [ABSTRACT FROM AUTHOR]

Fine spatial details of vegetation growth are usually lost in leaf area index (LAI) products obtained from coarse spatial resolution satellite sensors. This may bring uncertainties in ecosystem process models, which usually require LAI products with fine spatiotemporal resolutions. Successful downscaling of LAI dynamics to fine spatial resolution is very important for meeting the demands of these models. Hence, a multiscale multisensor approach using the ensemble Kalman smoother (EnKS) technique is proposed in this paper. The LAI dynamics at a coarser spatial resolution are incorporated as prior information into the remotely sensed observations for time series LAI estimation at a finer spatial resolution. Downscaled LAI dynamics are evaluated based on spatial distribution and temporal trajectory. The results indicate the assimilated LAI to be in good agreement with the reference values at the different spatial scales. For example, the coefficient of determination ($R^{2}$) between the reference values and fine-resolution LAI results retrieved by the proposed approach is 0.71 with a root-mean-square-error (RMSE) value of 0.65 on Julian day 185 at the Agro site. The method has proved to be effective for downscaling LAI dynamics, which improves the spatiotemporal patterns of fine-resolution LAI retrievals with respect to earlier methods. [ABSTRACT FROM AUTHOR]

First launched in 1972, the Landsat satellite sensors have provided the longest continuous record of high-quality images of the earth’s surface that are used in both civilian and military applications. The Landsat Multispectral Scanner System (MSS) sensor was onboard Landsat-1 through Landsat-5. In fact, the MSS sensors provide the only systematic global multispectral space-based imagery of the earth’s surface from 1972 to 1982. This paper focuses on the radiometric calibration update of the Landsats 1–5 MSS sensors. The radiometric calibration was performed in both radiance- and reflectance-based scales through the cross-calibration approach. Simultaneous or near-simultaneous image collections were available for MSS sensor pairs and used for the cross-calibration. The estimated uncertainties for this calibration update exhibit progressively decreasing calibration accuracy, ranging from 5.1% for MSS-5 to 8.8% for MSS-1 in the green spectral band, for example. Finally, the new radiometric calibration coefficients were validated through the use of pseudo-invariant calibration sites (PICS). The temporal Top of the Atmosphere (TOA) radiance and TOA reflectance over the Sonoran Desert were plotted with the purpose of verifying the lifetime radiometric stability of MSS sensors. With the previous calibration, the agreement between the measurements of TOA reflectance over the Sonoran Desert was around 7.5% for all spectral bands. With the calibration update implemented in this study, the agreement between MSS sensors is 3.6%, 2.9%, 3.5%, and 5.9% for the green, red, near-infrared response (NIR-1), and NIR-2 spectral bands, respectively. This study ties all the Landsat legacy instruments from Landsat-1 MSS through Landsat-8 OLI to a consistent radiometric scale. [ABSTRACT FROM AUTHOR]

On May 31, 2003, the scan line corrector (SLC) of the Enhanced Thematic Mapper Plus (ETM+) on-board the Landsat-7 satellite failed, resulting in strips of data lost in all ETM+ images acquired since then. In this paper, we proposed a novel inpainting algorithm for recovering the ETM+ SLC-off images. The two slopes of the boundaries of each missing stripe were extracted through the Hough transform, ignoring the slope of the edge of the strip that overlaps the edge of the image. An adaptive dictionary was then developed and trained using ETM+ SLC-on images acquired before May 31, 2003 so that the physical characteristics and geometric features of the ground coverage of the data-missing strips can be considered during recovery. To make the algorithm computationally efficient, data-missing strips were repaired along their slope directions by using the logdet $\left ({\cdot }\right)$ low-rank nonconvex model along with the dictionary. The algorithm was tested using the simulated ETM+ SLC-off images created from a multiband ETM+ SLC-on image file and compared to the high accuracy low-rank tensor completion (HaLRTC), logDet, and tensor nuclear norm (TNN) algorithms. The results show that the ETM+ images restored using the new algorithm have lower RMSE, higher PSNR and structure similarity (SSIM) values, and better visualization. These results indicate that the new algorithm performs better than the other three algorithms and can efficiently and accurately restore the data-missing stripes. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept, develop the theory, and demonstrate the advantages of fully focused coherent processing of pulse echoes from a nadir-looking pulse-limited radar altimeter. This process, similar to synthetic aperture radar (SAR) imaging systems, reduces the along-track resolution down to the theoretical limit equal to half the antenna length. We call this the fully focused SAR (FF-SAR) altimetry processing. The technique is directly applicable to SAR altimetry missions such as CryoSat-2, Sentinel-3, or Sentinel-6/Jason-CS. The footprint of an FF-SAR altimeter measurement is a narrow strip on the surface, which is pulse limited across track and SAR focused along track. Despite the asymmetry of the altimeter footprint, the fully focused technique may be useful for applications in which one needs to separate specific targets within highly heterogeneous scenes, such as in the case of sea ice lead detection, hydrology, and coastal altimetry applications. In addition, over rough homogeneous surfaces, such as the ocean or ice sheets, the improved multilooking capability of FF-SAR leads to a significant increase in the effective number of looks with respect to the delay/Doppler processing, resulting in better geophysical parameter estimation. The proposed processing technique was verified by processing full bit rate CryoSat-2 SAR mode data over the Svalbard transponder. Hydrology, sea ice, and open ocean applications are also demonstrated in this paper, showing the improvement of this technique with respect to conventional and delay/Doppler altimetry. [ABSTRACT FROM PUBLISHER]

The panchromatic and multispectral (PMS) sensor is a high spatial resolution sensor aboard the GF-1 satellite launched on April 26, 2013. This paper focuses on the cross-calibration of the PMS sensor using Terra/Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat 8/Operational Land Imager (OLI). Two matched-image adjustment factors (MIAFs) are used in the cross-calibration which are the radiance MIAF and reflectance MIAF. Two test sites are chosen as the regions of interest. One is the Dunhuang test site, which has been used for the vicarious calibration of Chinese satellites since later 1990s. The other is the Golmud test site, which is a new site with no ground measured data available. The results show that both the Dunhuang and Golmud test sites can be used for cross-calibration. This paper reveals that the cross-calibration of the PMS sensor using OLI is better than using MODIS, as the calibration coefficient difference between the two test sites with OLI is smaller than that with MODIS. The uncertainty analysis results show that the uncertainty of cross-calibration using OLI is 5%–7% when the ground data are not available. [ABSTRACT FROM PUBLISHER]

Land-cover mapping over urban areas using Landsat imagery has attracted considerable attention in recent years as it can promptly and accurately reflect the biophysical composition status of the urban landscape and allow further applications such as urban planning and risk management. However, due to the large diversity across different urban landscapes, adequate training sample collection for urban area mapping is both challenging and time-consuming. In this paper, we propose a novel unsupervised sample collection method for mapping urban areas using Landsat imagery. Specifically, the idea is to select reliable, representative, and diverse training samples from the images in a two-stage and iterative manner, based on a set of spectral indices (vegetation, impervious surface, soil, water). To validate the effectiveness and robustness of the proposed method, a synthetic data set was designed and a series of Landsat images over 39 representative cities from different biomes across the world was employed. The effectiveness of the proposed algorithm was quantitatively validated by assessing the quality of the automatically collected samples and the accuracy of the mapping results. In terms of the mapping performance, the proposed automatic approach can achieve a comparable mapping accuracy to supervised classification with manually collected samples. On the basis of the freely accessed Landsat data, the proposed approach demonstrates a promising potential for automatic large-scale (i.e., global) mapping over urban areas. [ABSTRACT FROM AUTHOR]

The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth’s surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods. [ABSTRACT FROM AUTHOR]

The airborne Operation IceBridge and spaceborne CryoSat-2 missions observe polar sea ice at different spatial and temporal scales as well as with different sensor suites. Comparison of data products from IceBridge and CryoSat-2 is complicated by the fact that they use different geophysical corrections: reference ellipsoid, geoid model, tide model, and atmospheric corrections to derive surface elevation and sea-ice freeboard. In this paper, we compare sea-ice surface elevation and freeboard using eight coincident CryoSat-2, Airborne Topographic Mapper (ATM), and Land, Vegetation, and Ice Sensor (LVIS) observations with direct IceBridge underflights of CryoSat-2 ground tracks. We apply identical geophysical corrections to CryoSat-2 and IceBridge data to eliminate elevation biases due to these differences and focus on differences due to retracker performance. The IceBridge ATM and LVIS elevation and freeboard and Snow Radar snow depth data sets are averaged to each CryoSat-2 footprint for comparison. With snow depth measurements, we are able to compare elevations and freeboards at the snow/ice interface for five different CryoSat-2 retrackers (ESA, GSFCv1, AWI, JPL, and GSFCv2) and IceBridge. The overall mean of freeboard differences between GSFCv2, ESA, AWI, JPL retrackers, and ATM are in agreement within 0.05 m. However, the five different CryoSat-2 retrackers show distinct differences in mean elevation over leads and over floes. This suggests that the physical interpretation of the different retrackers needs to be considered depending on usage, for example, elevations from CryoSat-2 retrackers need to be carefully calibrated before comparing with elevation from other satellites for long-term surface elevation trends. [ABSTRACT FROM AUTHOR]

Recent studies demonstrate the power of applying satellite imagery in combination with artificial intelligence (AI) methods to advance the accuracy of forecasting ungauged river network water levels and discharge for early flood warning and management. In predicting river water levels and discharge time series, one of the most common sources of error with AI forecasting algorithms is the input imitation defect. When the input imitation defect occurs, regression methods simply present the input variables as output. In this paper, the input imitation defect is minimized by first introducing the two concepts of vertical error and horizontal error. Subsequently, upstream imagery information is combined with previous lags to propose a new procedure for predicting future satellite signals accurately and with the lowest possible input imitation defect. To accomplish this, the brightness temperature received by the Advanced Microwave Scanning Radiometer is used as a proxy of river discharge. The proposed method (PM) is finally compared with the simple linear regression and three well-known AI methods, i.e., multilayer perceptron, extreme learning machines, and radial basis function. The study outcome indicates that the PM results are more trustworthy and realistic. [ABSTRACT FROM AUTHOR]

Accurate global observations from space are critical for global climate change study. However, atmospheric temperature trend derived from spaceborne microwave instruments remains a subject of debate, due mainly to the uncertainty in characterizing the long-term drift of instrument calibration. Thus, a highly stable target with a well-known microwave radiation is required to evaluate the long-term calibration stability. This paper develops a new model to simulate the lunar emission at microwave frequencies, and the model is then used for monitoring the stability of the Advanced Technology Microwave Sounder (ATMS) onboard Suomi NPP satellite. It is shown that the ATMS cold space view of lunar radiation agrees well with the model simulation during the past five years and this instrument is capable of serving the reference instrument for atmospheric temperature trending studies, and connecting the previous generation of microwave sounders from NOAA-15 to the future Joint Polar Satellite System Microwave Sounder onboard NOAA-20 satellite. [ABSTRACT FROM AUTHOR]

The Landsat program plays an important role in providing continuous long-term multispectral moderate resolution observations of the earth’s surface. There is an interest by the community to improve the temporal sampling in future generations of the sensors. One way to achieve higher sampling is to collect imagery with a wider swath. For future instruments using multilayer dielectric filters for band selection, this has the further implication that light may enter the filters on the detectors at a higher angle of incidence, which will shift the center wavelength of the spectral bandpasses. Through simulation of a forest environment this paper explored the impact of this effect on measured spectral radiance and the normalized vegetation difference index. The effect is quantified both for angles of incidence seen in the current Landsat-8 Operational Land Imager as well as for pushbroom designs with up to 13° off-axis imaging. Results indicate the effect will be significant compared to instrument noise and comparable to the limit of current requirements on filter manufacturing tolerances. [ABSTRACT FROM AUTHOR]

Satellite-observed surface temperatures over heterogeneous urban pixels are generally determined by viewing and illumination angles, as well as by 3-D geometries and component temperature distributions of urban surfaces. This paper focuses on developing a Geometric model to simulate Urban Thermal Anisotropy considering overlaps of shadows on the ground for simplified neighborhoods (GUTA-osg). A Boolean model and projections for buildings are used to calculate the average fractions of urban components in a field of view. Simulation results derived from GUTA-osg are compared with those from the 3-D discrete anisotropic radiative transfer model. At view and solar zenith angles lower than 50°, GUTA-osg can be applied to urban scenes of $h/w <2.0$ (building-height-to-spacing ratio) with confidence where errors of the estimated fraction of walls and shaded ground are less than 0.1. As $h/w$ increases, GUTA-osg performs well when applied to urban scenes with smaller building plan area index values (e.g., $\lambda _{p} < 0.3$ for $h/w = 2.0$ –4.0). GUTA-osg accurately simulates the anisotropy of airborne measured temperatures, with $R^{2} > 0.85$ and root mean square errors < 0.8 K. Biases of the model can result from: 1) the neglect of shadows on walls cast by adjacent buildings and 2) model simplifications, including constant building heights and random distributions of building location and orientation. GUTA-osg uses urban morphologic indexes as inputs, and coefficient values can be derived by fitting against multiangular thermal infrared observations. The model can be used to adjust the directional temperature to a common viewing geometry. [ABSTRACT FROM AUTHOR]

Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i.e., the data usability is greatly reduced. In this paper, a novel method of missing information reconstruction in remote sensing images is proposed. The unified spatial–temporal–spectral framework based on a deep convolutional neural network (CNN) employs a unified deep CNN combined with spatial–temporal–spectral supplementary information. In addition, to address the fact that most methods can only deal with a single missing information reconstruction task, the proposed approach can solve three typical missing information reconstruction tasks: 1) dead lines in Aqua Moderate Resolution Imaging Spectroradiometer band 6; 2) the Landsat Enhanced Thematic Mapper Plus scan line corrector-off problem; and 3) thick cloud removal. It should be noted that the proposed model can use multisource data (spatial, spectral, and temporal) as the input of the unified framework. The results of both simulated and real-data experiments demonstrate that the proposed model exhibits high effectiveness in the three missing information reconstruction tasks listed above. [ABSTRACT FROM AUTHOR]

This paper proposes a novel contextual method for classification of polarimetric synthetic aperture radar data. The method combines support vector machine (SVM) and Wishart classifiers to benefit from both parametric and nonparametric methods. This method computes the energy function of a Markov random field (MRF) in the neighborhoods of the pixel using Wishart distribution. It then relates the Markovian energydifference function to the SVM classifier. Therefore, the salt-and-pepper effect on the classified map is reduced using a contextual classifier. Moreover, to achieve the full advantage of spatial information, texture features are added into the contextual classification. Texture features are extracted from SPAN images and are added to the SVM classifier. In this paper, two Radarsat-2 polarimetric images acquired in the leaf-off and leaf-on seasons are used from a forest area. Efficient multitemporal information is exploited using composite kernels in SVM. Comparison of the proposed algorithm with the Wishart, Wishart-MRF, SVM, and SVM with composite kernel classifiers shows a 21.72%, 16.17%, 11.29%, and 8.19% improvement in overall accuracy, respectively. Moreover, incorporating texture features into classification results significant increase in the average accuracy in forest species compared with the use of only polarimetric features. [ABSTRACT FROM PUBLISHER]

The eigendecompostion of the coherency matrix and the related parameters, i.e., entropy $H$, $\alpha$, and anisotropy $A$, are effective and popular tools for the analysis and quantitative estimation of the physical parameters of polarimetric synthetic aperture radar images. However, the speckle noise constitutes the main obstacle that hinders these goals and should be filtered. In this paper, based on studies obtained from extensive simulated data sets, we tried to determine how this noise is transmitted to sample eigendecomposition parameters. The dependence between means and variances of sample parameters leads to building their speckle models and to the definition of a bias elimination procedure. We found that sample eigenvalues were affected by multiplicative noise, whereas sample entropy and sample anisotropy were affected by a mixture of multiplicative and additive noise sources. In addition to its versatility, independence to knowledge of the equivalent number of looks and high ability to bias compensation, the proposed bias suppression technique reduced the variance of noise. Simulated and real data as well as the existing theories are used for validation in this paper. [ABSTRACT FROM PUBLISHER]

In this paper, we present a method for the multitemporal and contextual classification of georeferenced optical remote sensing images acquired at different epochs and having different geometrical resolutions. The method is based on Conditional Random Fields (CRFs) for contextual classification. The CRF model is expanded by temporal interaction terms that link neighboring epochs via transition probabilities between different classes. In order to be able to deal with data of different resolution, the class structure at different epochs may vary with the resolution. The goal of the multitemporal classification is an improved classification performance at all individual epochs, but also the detection of land-cover changes, possibly using lower resolution data. This paper also contains a comparison of the performance of different models for the interaction potentials. Results are given for two different test sites in Germany, where Ikonos, RapidEye, and Landsat images are available. Our results show that the multitemporal classification does indeed increase the overall accuracy of all epochs compared to a monotemporal classification and to a state-of-the-art multitemporal classification method, and that it is feasible to detect changes in lower resolution images. [ABSTRACT FROM PUBLISHER]

Ground surface temperature (GST) measurements are scarce in Tibetan plateau (TP), whereas the satellite observations and numerical weather model outputs are good alternatives to fill the spatial gaps among ground stations. However, the evaluation of different temperature products is challenging due to the distinct temporal and spatial dimensions in their acquisition methods. This paper intended to develop an evaluation framework for comparing the performances of various temperature data, including the Advanced Along-Track Scanning Radiometer (AATSR) satellite land surface temperature (LST) data, the high Asia refined (HAR) analysis numerical outputs, and the GST. In the proposed framework, we introduce a diurnal temperature cycle model and an aggregated weighted method to solve the temporal and spatial mismatch problem between different data sets. The results over TP show that the evaluation framework solves the temporal and spatial matching among different data sets. AATSR LST and HAR outputs are consistent regardless of the heterogeneous and weather conditions at Linzhi site indicating that the fully homogeneous land surface conditions are not the only way for the satellite/simulation validations. Our results suggest that the proposed framework of time normalization and spatial aggregation method is appropriate for evaluating satellite thermal infrared retrieved data sets and numerical simulations even when the proper ground measurements are insufficient. Since it performs well in the high elevation and complex land surface-conditioned TP region, it will be easy to be adopted in the other regions with a variety of data sets. [ABSTRACT FROM AUTHOR]

The Soil Moisture Active Passive (SMAP) satellite mission launched in January 2015 provides worldwide soil moisture (SM) monitoring based on L-band brightness temperature ($T_{B}^{\mathrm{ p}}$) measurements at horizontal ($\text{T}_{B}^{\mathrm{ H}}$) and vertical ($T_{B}^{\mathrm{ V}}$) polarizations. This paper presents a performance assessment of SMAP soil emission model and SM retrieval algorithms for a Tibetan desert ecosystem. It is found that the SMAP emission model largely underestimates the SMAP measured $T_{B}^{\mathrm{ H}}$ (≈ 15 K), and the $T_{B}^{\mathrm{ V}}$ is underestimated during dry-down episodes. A cold bias is noted for the SMAP effective temperature due to underestimation of soil temperature, leading to the $T_{B}^{\mathrm{ p}}$ underestimation (>5 K). The remaining $T_{B}^{\mathrm{ H}}$ underestimation is found to be related to the surface roughness parameterization that underestimates its effect on modulating the $T_{B}^{\mathrm{ p}}$ measurements. Further, the topography and uncertainty of soil information are found to have minor impacts on the $T_{B}^{\mathrm{ p}}$ simulations. The SMAP baseline SM products produced by single-channel algorithm (SCA) using the $T_{B}^{\mathrm{ V}}$ measurements capture the measured SM dynamics well, while an underestimation is noted for the dry-down periods because of $T_{B}^{\mathrm{ V}}$ underestimation. The products based on the SCA with $T_{B}^{\mathrm{ H}}$ measurements underestimate the SM due to underestimation of $T_{B}^{\mathrm{ H}}$ , and the dual-channel algorithm overestimates the SM. After implementing a new surface roughness parameterization and improving the soil temperature and texture information, the deficiencies noted above in $T_{B}^{\mathrm{ p}}$ simulation and SM retrieval are greatly resolved. This indicates that the SMAP SM retrievals can be enhanced by improving both surface roughness and adopted soil temperature and texture information for Tibetan desert ecosystem. [ABSTRACT FROM AUTHOR]

For most Earth observation applications, passive microwave radiometry from the geostationary orbit requires prohibitively large apertures for conventional single-satellite platforms. This paper proposes a novel interferometric technique capable of synthesizing these apertures using satellite formation flight. The significance of such concept is in its capacity to synthesize microwave apertures of conceptually unconstrained size in space for the first time. The technique is implemented in two formation flight configurations: a formation of a single full-sized satellite with microsatellites and a formation of several full-sized satellites. Practical advantages and challenges of these configurations are explored by applying them to geostationary atmospheric sounding at 53 GHz, the lowest sounding frequency considered for future sounder concepts Geostationary Atmospheric Sounder, GeoSTAR, and Geostationary Interferometric Microwave Sounder. The two configurations produce apertures of 14.4 and 28.8 m, respectively, and a spatial resolution of 16.7 and 8.3 km, respectively, from the geostationary orbit. The performance of these interferometers is simulated, and the challenges identified are threefold. First, intersatellite ranging in micrometer-level precision is required. Second, the extremely sparse design suggests that further innovation is necessary to improve radiometric resolution. Third, the presence of long baselines suggests extreme decorrelation effects are expected. While the first requirement has already been demonstrated on ground, the other two remain for future research. This technique can be implemented at arbitrary microwave frequencies and arbitrary circular orbits, meaning it can also be applied to other geostationary applications, or to achieve unprecedented spatial resolution from lower orbits, or to extend the accessible frequencies into lower frequency radio waves. [ABSTRACT FROM AUTHOR]

In this paper, a new pansharpening method is proposed by constructing a set of multiscale geometric support tensor filters (MGSTFs). First, a least-square ridgelet support tensor machine is developed to derive a series of MGSTFs. Then the source images are formulated as tensors and filtered by MGSTFs to capture geometric and salient features of images. These features are then fused at each scale and direction to obtain the fused products. The distortions can be reduced by exploring the tensor formulation of multispectral data and endowing the filters’ directionality to capture the geometric details of images. Some experiments are carried out on several groups of QuickBird and GeoEye-1 images, and the results show that our proposed method can simultaneously reduce spectral distortions and preserve spatial details in the fused image. [ABSTRACT FROM AUTHOR]

In this paper, we propose two methods to optimize scattering mechanism vectors in polarimetric interferometric synthetic aperture radar (InSAR) dealing with high-resolution SAR data. The methods optimize an interferogram by focusing on the pixel-by-pixel variation of the scattering mechanisms. We show the efficacy of the methods using fully polarimetric interferometric radar data obtained from the Advanced Land Observing Satellite-2 PALSAR-2 to generate accurate digital elevation models successfully. Finally, we confirm the importance of considering the polarization in such InSAR systems by comparing parameters optimized in the proposed methods with those calculated from the well-known Pauli coherency matrix. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key surface feature parameter. Temperature and emissivity separation (TES) and split-window (SW) algorithms are two typical LST estimation algorithms that have been applied to a variety of sensors to generate LST products. The TES algorithm can synchronously obtain LST and emissivity, but it requires high accuracy for atmospheric correction of the thermal infrared (TIR) data and does not perform well for surfaces with low spectral emissivity contrast. On the contrary, the SW algorithm can retrieve LST without detailed atmospheric data because the linear or nonlinear combination of brightness temperatures in the two adjacent TIR channels can reduce the atmospheric effect; however, this algorithm requires prior accurate pixel emissivity. Combining the two algorithms can improve the accuracy of LST estimation because the emissivity calculated from the TES algorithm can be used in the SW algorithm, and the LST from the SW algorithm can then be applied to the TES algorithm as an initial value to refine emissivity and LST. This paper investigates the aforementioned hybrid algorithm using Chinese Gaofen-5 satellite data, which will provide four-channel data for TIR at 40 m for synchronously retrieving LST and emissivity. The results showed that the hybrid algorithm was less sensitive to instrument noise and atmospheric data error, and can obtain LST and emissivity with an error less than 1 K and 0.015, respectively, which is better than those obtained with the single TES or SW algorithm. Finally, the hybrid algorithm was tested in simulated image and ground-measured data, and obtained accurate results. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) reflective solar bands (RSBs) are calibrated on-orbit using a solar diffuser (SD) with its degradation tracked by an onboard SD stability monitor (SDSM). The SDSM has nine detectors with wavelengths from 0.41 to 0.94 \mu \textm . It is operated during each scheduled SD calibration event, making alternate observations of the sun and the SD. Due to erroneous design parameters, which led to the misalignment of the key elements in the SDSM, there are significant ripples in the sun view responses as the solar viewing angle changes. At the mission beginning, the effect of the ripples was eliminated by normalizing each SDSM detector response to the response of detector 9 (D9) at 0.94 \mu \textm , assuming that D9 had no degradation. However, D9 degradation increases over MODIS operation times. Degradation of up to 2% has recently been observed in D9 for the Terra MODIS. A newly implemented approach reduces the sun view ripples using a lookup table constructed using SDSM data carefully selected from a short period early in the mission lifetime. In this paper, we provide an overview of different approaches that have been applied over the years by the MODIS Characterization Support Team to track the on-orbit SD degradation. We evaluate the overall SD and SDSM on-orbit performance for both Terra and Aqua MODIS, as well as the impact on the MODIS RSB calibration uncertainty. [ABSTRACT FROM PUBLISHER]

The ScaRaB payload onboard the low-inclination Megha-Tropiques (MT) satellite has been making observations of radiative fluxes at the top of the atmosphere (TOA) for different local times (LTs) of the day over the tropics since October 2011. This provides a unique opportunity to investigate the diurnal variation of the regional instantaneous aerosol direct radiative effect efficiency (IADREE) at TOA, which is otherwise not possible using the available long-term satellite observations carried out using similar sensors onboard polar sun-synchronous satellites. In this paper, the diurnal variations of the IADREE over the Arabian Sea and the Atlantic Ocean during June–September, when both these regions are engulfed by large-scale mineral dust plumes transported from the adjoining deserts, are investigated using collocated multiyear (2012–2014) observations of the MT-ScaRaB measured shortwave fluxes and MODIS-derived aerosol optical depth. The estimates of the IADREE made using the MT-ScaRaB data at 13:30 LT are found to be in agreement with those derived from the Cloud and the Earth’s Radiant Energy System data at this LT, at which the latter observations are carried out. The IADREE derived from the MT-ScaRaB shows diurnal peak value of −53 ± 10 Wm $^{{{-2}}}\tau _{{{550}}}^{{{-1}}}$ and −40 ± 3 Wm $^{{{-2}}}\tau _{{{550}}}^{{{-1}}}$ at solar zenith angle of ~40° over the Arabian Sea and the Atlantic Ocean, respectively. Diurnal mean aerosol direct radiative effect efficiency at TOA during June–September is −22 ± 4.5 Wm $^{{{-2}}}\tau _{{{550}}}^{{{-1}}}$ over the Arabian Sea and −18 ± 3.6 Wm $^{{{-2}}}\tau _{{{550}}}^{{{-1}}}$ over the Atlantic Ocean. [ABSTRACT FROM PUBLISHER]

The Gaofen-5 (GF-5) satellite, the only satellite that provides the thermal infrared (TIR) sensor in the national high-resolution earth observation project of China, will observe earth surface at a spatial resolution of 40 m in four TIR channels. This paper aims at developing a new nonlinear, four-channel split-window (SW) algorithm to retrieve land surface temperature (LST) from GF-5 image. In the SW algorithm, its coefficients were obtained based on several subranges of atmospheric column water vapors (CWV) under various land surface conditions, in order to remove the atmospheric effect and improve the retrieval accuracy. Results showed that the new algorithm can obtain LST with root-mean-square errors of less than 1 K. Compared with previous two- and three-channel SW algorithms, the four-channel SW algorithm obtained better results in estimating LST, especially under moist atmospheres. Methods of estimating CWV and pixel emissivity were also conducted. The sensitive analysis of LST retrieval to instrument noise and uncertainty of pixel emissivity and water vapor demonstrated the good performance of the proposed algorithm. At last, the new SW algorithm was validated using ground-measured data at six sites, and some simulated images from airborne hyperspectral TIR data. [ABSTRACT FROM PUBLISHER]

The high-contrast edge of the Moon has been used for the on-orbit measurement of the modulation transfer function (MTF) of remote sensing instruments with a lunar observation capability. With the lunar edge as a target, the classical edge method is applied to the National Aeronautics and Space Administration's MODerate resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites. One of the major difficulties encountered during the calculation is that the spatial resolution of MODIS is too coarse to capture the fine structure of the edge spread function (ESF), which is required to calculate the MTF. To produce the MODIS ESF in high resolution, lunar images of a selected edge acquired by multiple instrument scans need to be superposed by aligning the edge positions accurately. In this paper, an algorithm is developed to perform the alignment, based on the lunar position data generated by the MODIS geolocation algorithm and recorded scan by scan. The positions of the lunar edges at the focal plane are calculated scan by scan, allowing the construction of a high-quality ESF for MTF derivation. The algorithm is applied to all MODIS bands with 250-m, 500-m, and 1-km spatial resolutions in both along-scan and along-track directions. The along-track MTF results are particularly valuable because the onboard SpectroRadiometric Calibration Assembly can only monitor the along-scan MTF. The trending results show that the along-track MTF of MODIS has been stable throughout the MODIS lifetime and is well above the design specification for all bands. The limitation of the algorithm is analyzed. The algorithm developed in this paper can be applied to other instruments with similar design features. [ABSTRACT FROM PUBLISHER]

The scaled complex Wishart distribution is a widely used model for multilook full polarimetric synthetic aperture radar data whose adequacy is attested in this paper. Classification, segmentation, and image analysis techniques that depend on this model are devised, and many of them employ some type of dissimilarity measure. In this paper, we derive analytic expressions for four stochastic distances between relaxed scaled complex Wishart distributions in their most general form and in important particular cases. Using these distances, inequalities are obtained that lead to new ways of deriving the Bartlett and revised Wishart distances. The expressiveness of the four analytic distances is assessed with respect to the variation of parameters. Such distances are then used for deriving new tests statistics, which are proved to have asymptotic chi-square distribution. Adopting the test size as a comparison criterion, a sensitivity study is performed by means of Monte Carlo experiments suggesting that the Bhattacharyya statistic outperforms all the others. The power of the tests is also assessed. Applications to actual data illustrate the discrimination and homogeneity identification capabilities of these distances. [ABSTRACT FROM AUTHOR]

The detection of radio-frequency interference (RFI) continues to be an important problem for satellite-based microwave radiometers. This paper introduces two new probability-distribution-based techniques for computing the likelihood that a given brightness temperature observation contains an RFI signal. These methods extend the spectral difference method already being employed for the detection of RFI signals, and they allow for simultaneous observation-by-observation RFI detection of both land- and sea-based brightness temperature observations. This paper starts by laying out the theoretical groundwork for both techniques. It will then expand upon that groundwork to detail its practical application in analyzing WindSat brightness temperature observations. Finally, this paper compares the resulting probability indices from the detailed algorithms with spectral difference and principle component analysis indices computed from WindSat observations for various geographic regions. These comparisons will show effective RFI signal detection by these new techniques for RFI signal strengths as low as 4–5 K. [ABSTRACT FROM AUTHOR]

Intercalibration of satellite instruments is critical for detection and quantification of changes in the Earth's environment, weather forecasting, understanding climate processes, and monitoring climate and land cover change. These applications use data from many satellites; for the data to be interoperable, the instruments must be cross-calibrated. To meet the stringent needs of such applications, instruments must provide reliable, accurate, and consistent measurements over time. Robust techniques are required to ensure that observations from different instruments can be normalized to a common scale that the community agrees on. The long-term reliability of this process needs to be sustained in accordance with established reference standards and best practices. Furthermore, establishing physical meaning to the information through robust Système International d'unités traceable calibration and validation (Cal/Val) is essential to fully understand the parameters under observation. The processes of calibration, correction, stability monitoring, and quality assurance need to be underpinned and evidenced by comparison with “peer instruments” and, ideally, highly calibrated in-orbit reference instruments. Intercalibration between instruments is a central pillar of the Cal/Val strategies of many national and international satellite remote sensing organizations. Intercalibration techniques as outlined in this paper not only provide a practical means of identifying and correcting relative biases in radiometric calibration between instruments but also enable potential data gaps between measurement records in a critical time series to be bridged. Use of a robust set of internationally agreed upon and coordinated intercalibration techniques will lead to significant improvement in the consistency between satellite instruments and facilitate accurate monitoring of the Earth's climate at uncertainty levels needed to detect and attribute the mechanisms of change. This paper summarizes the state-of-the-art of postlaunch radiometric calibration of remote sensing satellite instruments through intercalibration. [ABSTRACT FROM PUBLISHER]

This paper introduces the bidirectional synthetic aperture radar (BiDi SAR) imaging mode, i.e., the simultaneous imaging of two directions by one antenna into one receiving channel, and presents short-term time series of images and interferograms acquired by the TerraSAR-X and TanDEM-X satellites. A comparison to alternative approaches for the acquisition of short-term time series is provided. The BiDi acquisition geometry is defined, and a TerraSAR-X BiDi antenna pattern is analyzed. BiDi raw data are simulated, sampled with different pulse repetition frequency values, and compared with real BiDi raw data. The spectral separation of simultaneously acquired forward- and backward-looking images is explained. This paper presents the image results of BiDi acquisitions with TerraSAR-X and TanDEM-X satellites flying with 20-km along-track separation. This pursuit configuration allowed for the acquisition of up to six short-term repeated images and up to three interferograms in a single pass. An overview of potential applications for the new BiDi SAR imaging mode concludes this paper. [ABSTRACT FROM PUBLISHER]

Since early 2012, the cross-track infrared sounder (CrIS) onboard the Suomi National Polar-Orbiting Partnership satellite has continually provided the hyperspectral infrared observations for profiling atmospheric temperature, moisture, and greenhouse gases. The CrIS radiance data are also directly assimilated into global numerical weather prediction models to improve the medium-range forecasts. These important applications require accurate CrIS calibration. Since CrIS radiometric accuracy depends on the accurate spectral calibration, it is important to reduce the spectral uncertainty and increase the calibration stability for weather and climate applications. In this paper, the accuracy of CrIS spectral calibration and its stability are assessed using the operational sensor data record (SDR) data generated by the interface data processing segment (IDPS). A spectral validation method is developed and applied to clear scene data over ocean from September 22, 2012, to April 19, 2016. It is shown that CrIS metrology laser wavelength varies within 3 ppm, as measured by the Neon calibration subsystem. While the current CrIS operational algorithm is designed to have a spectra error less than 2 ppm, the actual spectral errors are about 4 ppm due to the IDPS software bugs. A new correction method is applied to fix the bugs and to further improve CrIS spectral calibration. It is found that the CrIS spectral calibration accuracy is less than 1 ppm in both normal and full spectral resolution SDR data sets. [ABSTRACT FROM AUTHOR]

Inspired by the synthetic aperture radar (SAR) technique, a nadir radar altimeter concept called the “Delay/Doppler altimeter” or “SAR mode altimeter” provides better precision and resolution capabilities than conventional pulse-limited altimeters (i.e., low-resolution mode). This concept was initially carried on board the CryoSat-2 satellite, then used on Sentinel-3, initially for user requirements on ice or inland water monitoring. This paper addresses geophysical parameter retrieval from Delay/Doppler altimetry over ocean surfaces. For the inversion of geophysical parameters (sea surface height, significant wave height, and backscatter coefficient), we developed an inversion method based on the numerical computation of the radar power-return equation, including instrument design features, such as the range and azimuth impulse responses. To compare this technique with respect to conventional altimetry, we also generated reduced SAR (RDSAR) measurements from the same input data. Geophysical parameter retrieval from low- and high-resolution techniques was then performed for cross-comparison, demonstrating consistency for both techniques, but with a constant 3-cm bias. The proposed processing strategy was then validated using two years of CryoSat-2 data over oceans. The SAR mode provides significant benefits for the observation of small-scale signals (below 50 km) and performs as accurately as conventional altimetry for basin or global scales. The results demonstrate what is expected from the upcoming Sentinel-3 and Sentinel-6/Jason-CS missions. [ABSTRACT FROM PUBLISHER]

Remote sensing satellite sensors feature a tradeoff between the spatial, temporal, and spectral resolutions. In this paper, we propose an integrated framework for the spatio–temporal–spectral fusion of remote sensing images. There are two main advantages of the proposed integrated fusion framework: it can accomplish different kinds of fusion tasks, such as multiview spatial fusion, spatio–spectral fusion, and spatio–temporal fusion, based on a single unified model, and it can achieve the integrated fusion of multisource observations to obtain high spatio–temporal–spectral resolution images, without limitations on the number of remote sensing sensors. The proposed integrated fusion framework was comprehensively tested and verified in a variety of image fusion experiments. In the experiments, a number of different remote sensing satellites were utilized, including IKONOS, the Enhanced Thematic Mapper Plus (ETM+), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Hyperspectral Digital Imagery Collection Experiment (HYDICE), and Système Pour l' Observation de la Terre-5 (SPOT-5). The experimental results confirm the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper considers the exploitation of navigation satellite systems as opportunity transmitters for bistatic and multistatic synthetic aperture radar (SAR). The simultaneous availability of multiple satellites over a scene of interest at different viewing angles allows multistatic SAR acquisitions using a single receiver on or near the ground. The resulting spatial diversity could be used to drastically improve image resolution or to enhance image information space. To exploit the availability of multiple satellites, two data fusion approaches are here considered. In the former, point features of the single images obtained from different perspectives are extracted and then combined, whereas in the latter, a multistatic image is first obtained by combining the single channel data at the image level and then the point features are extracted. This is achieved by considering ad hoc CLEAN-like techniques. These techniques have been developed on both the analytical and simulation levels and experimentally verified with real GNSS-based SAR imagery. The techniques described here are not limited to GNSS-based SAR but may be applied to any multistatic SAR system. [ABSTRACT FROM AUTHOR]

Satellite measurements from microwave instruments have made a significant contribution to the skill of numerical weather forecasting, on both global and regional scales. A FAST microwave Emissivity Model (FASTEM), which was developed by the Met Office, U.K., has been widely utilized to compute the surface emitted radiation in forward calculations. However, the FASTEM model was developed for frequencies in the range of 20–60 GHz, and it is biased at higher and lower frequencies. Several critical components such as variable sea surface salinity and full Stokes vector have not been generally taken into account. In this paper, the effects of the permittivity models are investigated, and a new permittivity model is generated by using the measurements for fresh and salt water at frequencies between 1.4 and 410 GHz. A modified sea surface roughness model from Durden and Vesecky is applied to the detailed two-scale surface emissivity calculations. This ocean emissivity model at microwave is now being used in the Community Radiative Transfer Model, and it has resulted in some major improvements in microwave radiance simulations. This paper is a joint effort of the Met Office, U.K., and the Joint Center for the Satellite Data Assimilation, U.S. The model is called as FASTEM-4 in the Radiative Transfer for TIROS Operational Vertical Sounder model. [ABSTRACT FROM AUTHOR]

Ship detection is an important application of global monitoring of environment and security. In order to overcome the limitations by other systems, surveillance with satellite synthetic aperture radar (SAR) is used because of its possibility to provide ship detection at high resolution over wide swaths and in all weather conditions. A new X-band radar onboard the TerraSAR-X (TS-X) satellite gives access to spatial resolution as fine as 1 m. In this paper, first results on the combined use of TS-X ship detection, automatic identification system (AIS), and satellite AIS (SatAIS) is presented. The AIS system is an effective terrestrial method for tracking vessels in real time typically up to 40 km off the coast. SatAIS, as a space-based system, allows almost global coverage for monitoring of ships since not all ships operate their AIS and smaller ships are not equipped with AIS. The system is considered to be of cooperative nature. In this paper, the quality of TS-X images with respect to ship detection is evaluated, and a first assessment of its performance for ship detection is given. The velocity of a moving ship is estimated using complex TS-X data. As test cases, images were acquired over the North Sea, Baltic Sea, Atlantic Ocean, and Pacific Ocean in Stripmap mode with a resolution of 3 m at a coverage of 30 km \times 100 km. Simultaneous information on ship positions was available from TS-X and terrestrial as well as SatAIS. First results on the simultaneous superposition of SatAIS and high-resolution radar images are presented. [ABSTRACT FROM AUTHOR]

With the continuing increase in the number of images collected everyday from different sensors, the automated registration of multisensor/multispectral images has become a very important issue. This is particularly true when pre- and postevent image comparison is concerned: For this particular application, the requirement of obtaining the earliest possible postevent image imposes the use of data potentially possessing significantly different characteristics with respect to the pre-event image. Strongly inhomogeneous image pairs require robust automatic registration techniques, preferably based on resolutionindependent feature-based registration. In a previous paper, we proposed a mode-based feature-matching scheme mutated from the computer vision domain and adapted to pre- and postevent feature matching. Some of the weak points highlighted in that first version are addressed in this paper, where a new version of the method is proposed, which exploits a new piece of information, i.e., the adjacency between feature points, generally preserved across the disaster event. Extensive generation of synthetic cases allowed one to obtain significant feedback and, consequently, tune the algorithm. Three real cases of pre- and postevent feature matching on high-resolution satellite images are shown and discussed. [ABSTRACT FROM AUTHOR]

This paper describes the TerraSAR-X mission concept within the context of a public-private partnership (PPP) agreement between the German Aerospace Center (DLR) and the industry. It briefly describes the PPP concept as well as the overall project organization. This paper then gives an overview of the satellite design and the corresponding ground segment, as well as the main mission parameters. After a short introduction to the scientific and commercial exploitation scheme, this paper finally focuses on the mission accomplishments achieved so far during the ongoing mission. [ABSTRACT FROM AUTHOR]

In support of the Ice, Cloud, and land Elevation Satellite (ICESat)-II mission, this paper studies the bias in surface- elevation measurements caused by undetected thin clouds. The ICESat-II satellite may only have a 1064-nm single-channel lidar onboard. Less sensitive to clouds than the 532-nm channel, the 1064-nm channel tends to miss thin clouds. Previous studies have demonstrated that scattering by cloud particles increases photon-path length, thus resulting in biases in ice-sheet-elevation measurements from spaceborne lidars. This effect is referred to atmospheric path delay. This paper complements previous studies in! the following ways: First, atmospheric path delay is estimated over the ice sheets based on cloud statistics from the Geoscience Laser Altimeter System onboard ICESat and the Moderate Resolution Imaging Spectroradlometer (MODIS) onboard Terra Aqua. Second, the effect of cloud particle size and shape is studied with the state-of-the-art phase functions developed for MODIS cirrus- cloud microphysical model. Third, the contribution of various orders of scattering events to the path delay is studied, and analytical model of the first-order scattering contribution is developed. This paper focuses on the path delay as a function of telescope field of view (FOV). The results show that reducing telescope FOV can significantly reduce the expected path delay. As an I , ample, the average path delays for FOV : 167 µad (a 100-m-diameter circle on the surface) caused by thin undetected clouds by the 1064-nm channel over Greenland and East Antarctica illustrated. [ABSTRACT FROM AUTHOR]

Object extraction from either aerial or satellite images is a research focus in the field of image processing, where it is essential to determine whether pixels on an image should be classified as object or background. This is a difficult issue, particularly for those pixels which are uncertain for such a determination. This paper presents a newly developed object extraction method that is based on fuzzy topology. First, the idea that the concept object extraction is understood as a fuzzy set in fuzzy topological space is introduced, i.e., the fuzzy object in object extraction, which is composed of its interior and a portion of its boundary, in fuzzy topological space is introduced. Second, the segmentation of the fuzzy object into the interior and the boundary described. Two threshold values induced by the optimal threshold value are proposed for determining the interior, boundary, and exterior of the fuzzy object. Third, object extraction regarding the boundary is introduced, where the connectivity analysis is applied to determine whether the pixels within the boundary should classified as the object. Finally, as an example of application, the developed fuzzy-topology-based object extraction method is applied to boulder extraction from aerial images. The experimental results demonstrate that the newly proposed method provides better object extraction result, particularly to those boundary pixels which are difficult to determine if they should be classified as the object by the existing solutions. The main contribution this paper includes the following two points: 1) Theoretically, this paper introduces fuzzy topology into object extraction where the object and the image background are understood as fuzzy sets in fuzzy topological space, and 2) this paper newly proposes the connectivity analysis method for determining if the pixels within the object boundary should be classified as the object. The overall quality of object extraction is thus improved greatly. [ABSTRACT FROM AUTHOR]