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This paper presents a novel approach to automatically detect and count cars in different aerial images, which can be satellite or unmanned aerial vehicle (UAV) images. Variations in satellite and/or UAV data make it particularly challenging to have a robust method that works properly on a variety of images. A solution based on the bag-of-words (BoW) model is explored in this paper due to its invariance characteristic and highly stable performance in object/scene categorization. Different from categorization tasks, vehicle detection needs to localize the positions of cars in images. To make BoW suitable for this purpose, we extensively improve the methodology in three aspects, namely, by introducing a recently proposed feature representation, i.e., the local steering kernel descriptor, adding spatial structure constraints, and developing an orientation aware scanning mechanism to produce detection with “one-window-one-car” results. Experiments are conducted on various aerial images with large variations, which consist of data from two public databases, e.g., the Overhead Imagery Research Data Set and Vehicle Detection in Aerial Imagery, as well as other satellite and UAV images. The results demonstrate the effectiveness and robustness of the proposed method. Compared with existing techniques, the proposed method is applicable to a wider range of aerial images. [ABSTRACT FROM AUTHOR]

Since the beginning of the altimeter mission TOPEX/Poseidon (T/P), followed by Jason-1 and Jason-2 on similar orbits, and many other missions on different orbits (ERS, EnviSat, etc.), mean sea level (MSL) products became essential for the comprehension of global ocean circulation. Since early in the T/P mission, a suspicious signal, having a period of near 59 days and amplitude of roughly 5 mm, was apparent in the Global MSL record. Compared with the 4–5-mm amplitude of the annual signal, the 59-day signal has understandably attracted attention. Moreover, the same signal has been subsequently detected in Jason-1 and later in Jason-2 MSLs. In 2010, the Ocean Surface Topography Science Team (OSTST) concluded this signal as the aliasing of a higher frequency error inherited from the tide model correction: the semi-diurnal wave S2. The source of this error was mainly attributed to T/P measurements, which were assimilated in ocean tide models. When these models are used in the computation of T/P MSL, most of the error cancels. However, this error is communicated to Jason-1 and Jason-2 MSLs. In order to gather and publish the OSTST analyses on this matter, this paper first attempts to list the myriad possibilities for the puzzling 59-day error in MSL. Then, this paper goes deeper into the description of the main contributor to this list: the tide models error. Indeed, since 2010, considerable efforts have been undertaken within the ocean tide community in order to correct ocean tide S2-waves from this error, particularly in the Goddard Ocean Tide (GOT) and finite element solution (FES) latest versions. Comparing several GOT and FES versions and a pure hydrodynamic tide model, this paper assesses, quantifies, and describes a reduction of the MSL 59-day error thanks to the latest releases. These analyses also confirm that a large part of this error has its origins in the T/P mission and has contaminated ocean tide solutions and Jason-1 and Jason-2 MSLs. They also suggest that ocean tide is not the only possible vector. Jason-1 and Jason-2 MSLs contain additional 59-day error—though to a lesser extent—that may either come from the measurements themselves or from another vector. [ABSTRACT FROM AUTHOR]

This paper addresses the performance in the retrieval of 3-D mean deformation maps by exploiting simultaneous or quasi-simultaneous squinted synthetic aperture radar (SAR) interferometric acquisitions in a repeat-pass scenario. In multisatellite or multibeam low earth observation missions, the availability of two (or more) lines of sight (LOSs) allows the simultaneous acquisition of SAR images with different squint angles, hence improving the sensitivity to the north–south component of the deformation. Due to the simultaneity of the acquisitions, the troposphere will be highly correlated and, therefore, will tend to cancel out when performing the differential measurement between the interferograms obtained with the different LOSs, hence resulting in a practically troposphere-free estimation of the along-track deformation measurement. In practice, however, the atmospheric noise in the differential measurement will increase for increasing angular separations. This paper expounds the mathematical framework to derive the performance by properly considering the correlation of the atmospheric delays between the simultaneous acquisitions. To that aim, the hybrid Cramér–Rao bound is exploited making use of the autocorrelation function of the troposphere. Some performance examples are presented in the frame of future spaceborne SAR missions at C and L band. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to interpreting the satellite attitude based on inverse synthetic aperture radar (ISAR) images. In the conventional viewpoint, quadratic and higher order phase terms of ISAR imagery are regarded as negative factors causing the defocusing phenomenon. In this paper, we introduce how to apply quadratic phase coefficients to estimate target attitude from the ISAR imagery. A geometric projection model of ISAR imaging is built according to radar line of sight, and an explicit expression is also derived to connect target attitude parameters and the image defocusing property. With the accommodation of Broyden–Fletcher–Goldfarb–Shanno algorithm, spatial-variant quadratic phase coefficients together with attitude parameters are determined by an image contrast maximization. We also extend the proposed algorithm to multistatic ISAR applications, where the quadratic phase information lying in simultaneous multistatic ISAR images can be mined to enhance the performance of target attitude estimation. Experimental results illustrate the feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Monitoring water quality with classical methods is not an easy task. Remote sensing with wide coverage and multiple temporal monitoring is the best solution for surface water quality monitoring. This paper demonstrates the determination of surface water quality parameters by using principal component analysis (PCA) data fusion and mining techniques with the aid of Landsat 8 OLI (L8 OLI), Sentinel 2A (S2A), and Göktürk-2 (GK2) satellite sensors. Chlorophyll-a, dissolved oxygen, total suspended solids, Secchi disk depth, total dissolved substance, and pH were the parameters selected for surface water quality analysis. High spectral resolution of L8 OLI/S2A images and the high spatial resolution of GK2 images were fused and analyzed by a suite of data mining models to provide more reliable images with both high spatial and temporal resolutions. Surface water quality parameters calculated by PCA-based response surface regression (RSR) method were compared with results obtained from multiple linear regression (MLR), artificial neural network (ANN), and support vector machines (SVMs) data mining methods. The performance of the data mining models derived using only multispectral band data and PCA fused data were quantified using four statistical indices; such as mean-square error (MSE), root MSE, mean absolute error, and coefficient of determination (R2). The analysis confirmed that the PCA-based RSR method is superior to MLR, ANN, and SVM data mining models to accurately estimate water quality parameters in lakes. [ABSTRACT FROM AUTHOR]

Salt marsh vegetation extent and zonation are often controlled by bottom up factors determined in part by the frequency and duration of tidal inundation. Tidal inundation during remote-sensing mapping of salt marsh resources can alter the resulting image classification. The degree of this impact on mapping with very high resolution (VHR) imagery has yet to be determined. This paper utilizes topobathymetric light detection and ranging (LiDAR) data and bathtub models of a tidal stage at 5 cm intervals from mean low water (MLW) to mean high water (MHW) and determines the impact of tidal variation in salt marsh mapping within Jamaica Bay, NY, USA. Tidal inundation models were compared with the Worldview-2 and Quickbird-2 imageries acquired at a range of tidal stages. The modeled inundation of normalized difference vegetation index and smooth cordgrass (S. alterniflora) maps was compared from MLW to MHW. This paper finds that at 0.6 m above MLW, only 3.5% of S. alterniflora is inundated. This paper demonstrates a modeling approach integrating VHR satellite remote-sensing data and topobathymetric LiDAR data to address tidal variation in salt marsh mapping. The incremental modeling of the tidal stage is important for understanding areas most at risk from sea level rise and informs management decisions in accordance with this. [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]

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]

In this paper, the authors investigate the statistical matching of the panchromatic (Pan) image to the multispectral (MS) bands, also known as the histogram matching, for the two main classes of pansharpening methods, i.e., those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods. Also, hybrid methods combining CS with MRA, like the widespread additive wavelet luminance proportional (AWLP), are investigated. It is shown that all spectral, spatial, and hybrid methods must perform a dynamics matching of the enhancing Pan to the individual MS bands for MRA or a combination of them (the component that shall be substituted) for CS. For hybrid methods, the problem is more complex and both types of histogram matching may be suitable. Such an intersensor balance may be either explicit or implicitly performed by the detail-injection model, e.g., the popular projective and multiplicative injection models. An experimental setup exploiting IKONOS and WorldView-2 data sets demonstrates that a correct histogram matching is the key to attain extra performance from established methods. As a first result of this paper, the AWLP method has been revisited and its performance significantly improved by simply performing the histogram matching of Pan to the individual MS bands, rather than to the intensity component, thereby losing the original proportionality feature. [ABSTRACT FROM PUBLISHER]

This paper contributes to the predictive understanding of satellite precipitation estimation errors over complex terrain, which is fundamental to the development of error models for improving hydrological applications. This paper focuses on the Trentino-Alto Adige region of the eastern Italian Alps. Rainfall observations over a 10-year period (2000–2009) from a dense rain gauge network in the region are used as reference precipitation. A number of satellite precipitation error properties (probability of detection, false alarm rates, missed events, spatial correlation of the error, and hit biases) are investigated in terms of seasonality, satellite algorithm, rainfall intensity, gauge density, and temporal resolution dependencies. These error parameters are typically used in error models (e.g., SREM2D) and provide the basis for enhancing error scheme development. Three widely used satellite-based precipitation products are employed: 1) the Climate Prediction Center morphing product; 2) the precipitation estimation from remotely sensed imagery using artificial neural networks; and 3) the Tropical Rainfall Measuring Mission multisatellite precipitation analysis 3B42 near-real-time product. The three products show similar performances, with larger errors during the warm season, characterized by convective storms, and less variability in the cold season, characterized by more organized stratiform systems. Lower biases are depicted at the daily scale with respect to the 3-hourly resolution. The SREM2D error model has the ability to correct the satellite precipitation products, even though attention is needed for potential systematic errors when applying the calibrated model to independent periods or regions. [ABSTRACT FROM PUBLISHER]

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]

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]

The orbit of the tropical rainfall measuring mission (TRMM) satellite was boosted from 350 to 402.5 km in August 2001 to extend its lifetime by conserving fuel. Since the timing between transmission and reception by the precipitation radar (PR) onboard the TRMM satellite was a fixed constant for measurement from an original altitude of 350 km, the PR encountered a mismatch between the transmitting and receiving beams after the TRMM orbit boost. Although the PR algorithm in TRMM Version 7 employs a correction for the beam mismatch, its error remains as an underestimation of the precipitation estimates near surface in the second half of the swath. This paper aims to mitigate the beam-mismatch correction error in Version 7. The beam-mismatch correction needs to estimate the radar echo that would be measured in the virtual intermediate beam between the beam in question and the previous adjacent beam. The beam-mismatch correction developed in this paper assumes that the surface and precipitation echoes change linearly in the horizontal direction in parallel to the surface between the two beams. The new correction is tested with observational data, indicating that the method improves the accuracy of the correction at off-nadir angles. Statistics of the surface normalized radar cross sections and the bright band peak intensities at off-nadir angles are improved using the method. The asymmetric bias of the precipitation estimates with respect to the scan angle in Version 7 is mitigated by 95.9% over ocean and 72.5% over land with the new correction. [ABSTRACT FROM AUTHOR]

This paper addresses the exploitation of global navigation satellite systems as opportunistic sources for the joint detection and localization of vessels at sea in a passive multistatic radar system. A single receiver mounted on a proper platform (e.g., a moored buoy) can collect the signals emitted by multiple navigation satellites and reflected from ship targets of interest. This paper puts forward a single-stage approach to jointly detect and localize the ship targets by making use of long integration times (tens of seconds) and properly exploiting the spatial diversity offered by such a configuration. A proper strategy is defined to form a long-time and multistatic range and Doppler (RD) map, where the total target power can be reinforced with respect to, in turn, the case in which the RD map is obtained over a short dwell and the case in which a single transmitter is employed. The exploitation of both the long integration time and the multiple transmitters can greatly enhance the performance of the system, allowing counteracting the low-power budget provided by the considered sources representing the main bottleneck of this technology. Moreover, the proposed single-stage approach can reach superior detection performance than a conventional two-stage process where peripheral decisions are taken at each bistatic link and subsequently the localization is achieved by multilateration methods. Theoretical and simulated performance analysis is proposed and also validated by means of experimental results considering Galileo transmitters and different types of targets of opportunity in different scenarios. Obtained results prove the effectiveness of the proposed method to provide detection and localization of ship targets of interest. [ABSTRACT FROM AUTHOR]

The accurate estimation of within-footprint slope is very important for measuring earth’s surface characteristics using satellite light detection and ranging (LiDAR) data. Several models have previously been proposed for slope estimation; however, these models have limitations in either accuracy or applicability. Therefore, the main purpose of this paper is to explore the influence of various factors (e.g., ground vertical extent, footprint size, footprint shape, and footprint orientation) on slope estimation to better estimate the within-footprint slope using large-footprint waveform LiDAR data. The results indicated that the absolute slope error due to the coupling effect of ground vertical extent and footprint size increased with an increase in the ratio of ground vertical extent and footprint size, while the relative slope error had an opposite trend. The slope error caused by footprint shape was relatively low when the footprint eccentricity was small. However, the slope error due to footprint shape grew rapidly when the footprint eccentricity became larger; thus, it is essential to fully take into account the influence of footprint shape on within-footprint slope estimation. In addition, the results suggest that the slope error changed regularly based on the intersection angle between footprint orientation and terrain aspect. This paper also provided guidance for the determination of an easy and practical model for within-footprint slope estimation. The determination of best model is dependent on the value of intersection angle. Once the intersection value is given, the best model can easily be determined. Using the best model, the within-footprint terrain slope can be estimated with high accuracy. [ABSTRACT FROM AUTHOR]

The first Visible Infrared Imaging Radiometer Suite (VIIRS) is onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. As a primary sensor, it collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in the spectral regions from visible (VIS) to long-wave infrared. NASA's National Polar-orbiting Partnership (NPP) VIIRS Characterization Support Team has been actively involved in the VIIRS radiometric and geometric calibration to support its Science Team Principal Investigators for their independent quality assessment of VIIRS Environmental Data Records. This paper presents the performance assessment of the radiometric calibration stability of the VIIRS VIS and NIR spectral bands using measurements from SNPP VIIRS and Aqua MODIS simultaneous nadir overpasses and over the invariant surface targets at the Libya-4 desert and Antarctic Dome Concordia snow sites. The VIIRS sensor data records (SDRs) used in this paper are reprocessed by the NASA SNPP Land Product Evaluation and Analysis Tool Element. This paper shows that the reprocessed VIIRS SDRs have been consistently calibrated from the beginning of the mission, and the calibration stability is similar to or better than MODIS. Results from different approaches indicate that the calibrations of the VIIRS VIS and NIR spectral bands are maintained to be stable to within 1% over the first three-year mission. The absolute calibration differences between VIIRS and MODIS are within 2%, with an exception for the 0.865- \mu\mboxm band, after correction of their spectral response differences. [ABSTRACT FROM AUTHOR]

In spaceborne synthetic aperture radar, the sliding spotlight mode can acquire high resolution and large azimuth scene size simultaneously. However, when the resolution is very high and the azimuth scene size is large, the traditional hyperbolic range model (HRM) is inaccurate and the variation of the equivalent velocity in azimuth dimension cannot be ignored. Thus, the traditional imaging algorithms based on HRM are no longer available. For this problem, this paper proposes an equivalent acceleration range model, which can precisely take into account the spaceborne curved orbit. Then, velocity scaling algorithm based on this new range model is proposed to meet the needs of very high resolution and large azimuth scene size. The results of the simulation validate the effectiveness of the new range model and the imaging algorithm. [ABSTRACT FROM PUBLISHER]

This paper compares the retrieval of significant wave height (SWH) from reflected signals of opportunity in the L-band, S-band, and Ku-band. The fundamental observation is the time series of the interferometric complex field (ICF) of the reflected signal. A known relationship between the coherence time of the ICF time series (width of the ICF autocorrelation function) and SWH of the ocean is used for this retrieval. This relationship is applied to data recorded at three frequencies in the S-band, L-band, and Ku-band. The accuracy obtained for S-band and L-band were on the order of 0.4 m, but this model saturated around the SWH value of 3 m. A secondary algorithm used spectral bandwidth to estimate SWH. This model showed linear dependence, with the error also around 0.4 m. The ICF coherence time for Ku-band signals showed little sensitivity to SWH. Furthermore, the effect of fully developed sea, wind, and wave directions on SWH retrieval is analyzed. [ABSTRACT FROM PUBLISHER]

Since the SEASAT mission in 1978, satellite-based synthetic aperture radar (SAR) images have been used to study oceanic internal waves. Internal waves become visible in SAR images because their orbital currents modulate the surface roughness. While this leads to an accurate spatial representation of internal wave patterns, the complexity of the imaging mechanism makes it difficult to derive actual currents and internal wave amplitudes from SAR signatures. We demonstrate in this paper how a more robust parameter retrieval is possible with along-track interferometric SAR (InSAR) data that resolve amplitudes and temporal phase changes of the backscattered signal together, the latter of which are directly related to the scatterers' line-of-sight velocities. Our example data set, which was acquired by TerraSAR-X in Dual Receive Antenna mode at Dongsha (South China Sea), exhibits strong signatures of internal waves in the interferogram amplitude and phase. We use a simple internal soliton parameterization and a numerical radar imaging model to find a plausible combination of internal wave parameters, which leads to good agreement between simulated and observed signatures. Testing the sensitivity of radar amplitude and phase signatures to various parameters, we show that along-track InSAR data should generally permit more accurate and less ambiguous internal wave parameter retrievals than conventional SAR images. [ABSTRACT FROM PUBLISHER]

The conventional spaced-receiver approach uses amplitude scintillations to estimate equatorial ionospheric irregularity drift velocities. This approach is less applicable at high latitudes where there is a lack of substantial amplitude scintillations. This paper presents a method to estimate ionosphere irregularity horizontal drift velocities based on GPS signal carrier phase measurements. Joint time–frequency analysis of the carrier phase measurements using an adaptive periodogram technique generates time-varying spectrograms of ionospheric irregularity-induced phase fluctuations. Cross correlation of the spectrograms between antenna pairs provides time lag information on propagating radio signals through the same ionospheric structure. The time lag information is combined with known positions of the receiver array, satellite orbits, and assumed irregularity altitude to infer ionospheric irregularity horizontal drift velocity. This paper presents the methodology and demonstrates its feasibility using data collected by a GPS receiver array at Gakona, Alaska. The potential error sources of this method are also analyzed. [ABSTRACT FROM PUBLISHER]

This paper proposed a 3-D surface deformation retrieval algorithm for synthetic aperture radar interferometry (InSAR) based on a Global Navigation Satellite System (GNSS) as a transmitter and a fixed receiver (GNSS-based InSAR), where simultaneous multiple GNSS transmitters and a repeat-pass concept were adopted. This paper consists of three parts. First, the interferometric phase model under repeat-pass concept was established for both general and bistatic permanent scatterer (PS) cases in the GNSS-based InSAR. Second, the 3-D deformation retrieval algorithm was presented, and the position dilution of precision was analytically derived to evaluate the performance of measured 3-D deformation. Third, using a designed transponder onboard displacement device as the bistatic PS, the feasibility of 3-D deformation retrieval using GNSS-based InSAR was confirmed by repeat-pass experiments, where four simultaneously available Beidou-2 Inclined Geosynchronous Orbit satellites were used as transmitters, and 16 repeat-pass data sets were collected. Using the proposed algorithm, the final experimental results suggested that the GNSS-based InSAR could obtain deformation estimations with better accuracy than at 5 mm in all three directions. Thus, huge potential exists for applications such as landslide prediction and infrastructure safety monitoring. [ABSTRACT FROM AUTHOR]

Meteosat Third Generation (MTG) is the next generation of European meteorological geostationary satellites, set to be launched in 2021. Besides ensuring continuity with Meteosat Second Generation imagery mission, the new series will feature new instruments, such as the Lightning Imager (LI), a high-speed optical detector providing near real-time lightning detection capabilities over Europe and Africa. The instrument will register events on pixels, where a lightning pulse generates a transient in the acquired radiance. In parallel, signal variations due to a number of unwanted sources, e.g., acquisition noise or jitter movement, are expected to produce false events. The challenge for on-board and on-ground processing is, thus, to discard as many false events as possible while keeping a majority of the true lightning events. This paper discusses a chain of algorithms that can be used by the LI for the detection of lightning and for the filtering of false events. Some of these algorithms have been developed in the framework of internal research and simulations conducted by the MTG team at the European Space Agency on an in-house LI simulator and therefore will not necessarily reflect the ultimate operational processing chain. The application of the chain on a representative scenario shows that 99.5% of the false events can be eliminated while keeping 83.6% of the true events, before generating the LI higher level data products. Machine learning techniques have also been studied as an alternative for on-ground event processing, and preliminary results indicate promising potential. [ABSTRACT FROM AUTHOR]

Due to the very high orbit, the apparent features of geosynchronous synthetic aperture radar (GEOSAR) are the curved trajectory and long integration time, which can lead to severe coupling between the azimuth and the range directions and, therefore, complicates the resolution evaluation. The traditional analytical approach based on the 2-D division may produce large resolution error, and the numerical approach may suffer from huge computation burden. Therefore, an analytical resolution evaluation approach for GEOSAR based on the local feature of the ambiguity function is studied in this paper. The proposed approach is validated with simulation data to be of high efficiency and accuracy. In addition, the proposed approach is also demonstrated to be capable of evaluating the resolution for other complex platforms, and of evaluating the 3-D resolution of a SAR system. [ABSTRACT FROM AUTHOR]

This paper analyzes the large positive bias of sea surface temperature (SST) retrievals of selected remotely sensed algorithms recorded during the simultaneous occurrence of upwelling and atmospheric subsidence along the coastal waters of Rio de Janeiro, Brazil. The optimal estimator (OE) for retrieving SST and the multichannel (MCSST) and nonlinear (NLSST) estimators are compared using Advanced Very High Resolution Radiometer-3 data. The in situ SST (SSTbuoy) data set used to validate the remotely sensed SST retrievals was collected from five moored buoys (four in the open sea and one in coastal waters). The principal results of this paper are as follows. First, the sensitivity analyses show that OE is quite susceptible to the first-guess SST rather than to the humidity profiles. Second, the comparison between the SSTOE and 365 cloud-free SSTbuoy measurements in open sea waters presents an root mean squared error (RMSE), bias, and standard deviation (STD) with the intervals of [0.5, 0.6], [−0.51, 0.13], and [0.27, 0.48], respectively. Third, the MCSST, NLSST, and OE SST produce a positive bias that can reach 5 K during simultaneous upwelling and atmospheric subsidence in coastal waters. Such unexpected errors are due to low SST values and water vapor compression in the lower layer of the atmosphere related to a temperature inversion. Fourth, an alternative approach using SSTbuoy obtained on the previous day as a first guess instead of the climatological SST significantly improves the errors (SSTOE–SSTbuoy) by reducing RMSE, bias, and STD by 58% (from 3.30 to 1.39 K), 73% (from 3.00 to 0.80 K), and 19% (from 1.38 to 1.12 K), respectively. [ABSTRACT FROM PUBLISHER]

The validation of in-orbit instrument performance requires both stability in calibration source and also calibration corrections to compensate for instrument changes. Unlike internal calibrations, the Moon offers an external source whose signal variance is predictable and nondegrading. This paper describes a method of validation using lunar observations scanning near full moon by the Clouds and the Earth's Radiant Energy System (CERES) Flight Model (FM)-1 and FM-2 aboard the Terra satellite, FM-3 and FM-4 aboard the Aqua satellite, and, as of 2012, FM-5 aboard Suomi National Polar-orbiting Partnership. Given the stability of the source, adjustments within the data set are based entirely on removing orbital effects. Lunar observations were found to require a consistent data set spanning at least two to three years in length to examine instrument stability due to the final step when lunar libration effects are addressed. Initial results show a 20% annual variability in the data set. Using this method, however, results show trends per data channel of 1.0% per decade or less for FM-1 through FM-4. Results for FM-5 are not included in this paper because a sufficient data record has not yet been collected. [ABSTRACT FROM PUBLISHER]

This paper presents a framework for using remote sensing imagery and image processing techniques to analyze the interactions among tropical depressions (TDs) and typhoons. The purpose is to properly understand and predict this phenomenon, in order to reduce their influence on the regions where they pass by. Improvements on the understanding of the interactions among TDs and typhoons are obtained and presented. They carry the potential to improve forecast of severe weather system. In this paper, we analyzed the physical responses of TDs on two typhoons Tembin and Bolaven (2012), including TD's appearance, development, interaction, and mergence. According to the Central Weather Bureau of Taiwan, Tembin and Bolaven were formed about 1550 km apart. In general, the Fujiwhara effect happens when two storms are within a distance of 1400 km. Does the TD have impacts on the typhoons, and could we also call this impact as Fujiwhara effect? The 3-D profiles of the weather systems are reconstructed using the multifunctional transport satellite (MTSAT) infrared cloud images by the image reconstruction technique (IRT). The 3-D profiles of typhoons were implemented for investigating the in-depth distribution of the cloud top from a surface cloud image. The main parameters, which have been analyzed in this paper, are distance, height difference, rotation, and sizes of the two TDs and the two typhoons. The results showed that two TDs occurred between typhoons Tembin and Bolaven, which interacted with each other through upward convection and attraction between two typhoons and the TDs, respectively. The mergence of the two TDs caused the typhoons to start rotating and generate strengthened power. The findings of the TD effects on two typhoons represent a new aspect of the interaction between TD and typhoon. [ABSTRACT FROM PUBLISHER]

This paper presents a reliable technology for GPS/Global'naya Navigatsionnaya Sputnikovaya Sistema (GLONASS) surveying under difficult observational conditions, i.e., in forest environments. The technology is based on the rapid static and Real-Time Kinematic (RTK) methods. Test surveys for the developed technology were carried out on a point in a dense forest, with a high degree of difficulty for satellite access. The coordinates of the point were determined by three GPS/GLONASS receivers positioned in a line on a special base, which were separated by a fixed distance of 0.5 m. The test surveys were conducted using Topcon HiPer Pro receivers with 10-Hz RTK positioning and gathering data at a sampling rate of 10 Hz. Practical tests for the presented technology showed that rapid static surveys based on three rover GPS/GLONASS receivers were a quite efficient technology in forest environments (unlike the RTK) and allowed for reliable centimeter accuracy of the determined positions to be obtained in 5-min observational sessions. [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]

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]

Estimation of the surface elevation change of the Greenland Ice Sheet (GrIS) is essential for understanding its response to recent and future climate change. Laser measurements from the NASA’s Ice, Cloud, and land Elevation Satellite (ICESat) created altimetric surveys of GrIS surface elevations over the 2003–2009 operational period of the mission. This paper compares four change detection methods using Release 634 ICESat laser altimetry data: repeat tracks (RTs), crossovers (XOs), overlapping footprints (OFPs), and triangulated irregular networks (TINs). All four methods begin with a consistently edited data set and yield estimates of volumetric loss of ice from the GrIS ranging from −193 to −269 km3/yr. Using a uniform approach for quantifying uncertainties, we find that volume change rates at the drainage system scale from the four methods can be reconciled within 1- $\sigma $ uncertainties in just 5 of 19 drainage systems. Ice-sheet-wide volume change estimates from the four methods cannot be reconciled within 1- $\sigma $ uncertainties. Our volume change estimates lie within the range of previously published estimates, highlighting that the choice of method plays a dominant role in the scatter of volume change estimates. We find that for much of the GrIS, the OFP and TIN methods yield the lowest volume change uncertainties because of their superior spatial distribution of elevation change rate estimates. However, the RT and XO methods offer inherent advantages, and the future work to combine the elevation change detection methods to produce better estimates is warranted. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a feature-tracking algorithm for sea ice drift retrieval from a pair of sequential satellite synthetic aperture radar (SAR) images. The method is based on feature tracking comprising feature detection, description, and matching steps. The approach exploits the benefits of nonlinear multiscale image representations using accelerated-KAZE (A-KAZE) features, a method that detects and describes image features in an anisotropic scale space. We evaluated several state-of-the-art feature-based algorithms, including A-KAZE, Scale Invariant Feature Transform (SIFT), and a very fast feature extractor that computes binary descriptors known as Oriented FAST and Rotated BRIEF (ORB) on dual polarized Sentinel-1A C-SAR extra wide swath mode data over the Arctic. The A-KAZE approach outperforms both ORB and SIFT up to an order of magnitude in ice drift. The experimental results showed high relevance of the proposed algorithm for retrieval of ice drift at subkilometre resolution from a pair of SAR images with 100-m pixel size. From this paper, we found that feature tracking using nonlinear scale-spaces is preferable due to its high efficiency against noise with respect to image features compared with other existing feature tracking alternatives that make use of Gaussian or linear scale spaces. [ABSTRACT FROM PUBLISHER]

The availability of multiangular information from the NASA Multi-angle Imaging SpectroRadiometer (MISR) instrument provides an excellent opportunity for the characterization of surface anisotropy, which can be used for improving surface albedo estimation. However, the MISR data have been reported with large uncertainties and data gaps due to inaccurate aerosol estimation and/or cloud masking limiting its otherwise broader applications. To mitigate these issues, two approaches were proposed to estimate land surface albedo directly from surface reflectance (LSA_sfc) and Top-of-Atmosphere reflectance (LSA_toa), respectively. As a further development of the traditional albedo algorithms, this is the first attempt to simultaneously utilize multispectral and multiangular information in surface albedo estimation without any prior constraining information. Validations at AmeriFlux sites show that the proposed algorithms can achieve accuracies similar to that of the MISR product with respective bias and RMSE of 0.004 and 0.032 for LSA_sfc and 0.005 and 0.032 for LSA_toa algorithms. We found that the LSA_toa algorithm can significantly reduce data gaps and provide accurate surface albedo retrievals with two to three times more valid data than the current MISR product. In addition, these approaches can be easily applied to other optical sensors to produce accurate and gap-free clear-sky surface albedo estimations. The results of this paper also highlight the importance of having two to three simultaneous observations with sufficient angular sampling, which can improve albedo accuracy and reduce data gaps. [ABSTRACT FROM PUBLISHER]

Satellite remote sensing has accumulated decades of normalized difference vegetation index (NDVI) data. For long-term environmental studies, multisensor NDVI discrepancies should be initially corrected via intersensor NDVI calibration. This paper proposes a generalized model and its simplified form for the calibration by incorporating sensor-, atmosphere-, and observational geometry-related parameters into an analytical function. The models are first validated using the tandem Landsat-5 Thematic Mapper and Earth Observing One (EO-1) Advanced Land Imager (ALI) NDVIs and then used to evaluate current intercalibration methods. The results show that surface reflectance is the most critical factor for linear intercalibrations followed by water vapor. While other atmospheric parameters as well as solar zenith angle have minor effects on linear intercalibrations. The strong nonlinear relationships between the spectral band adjustment factors and NDVI may explain why quadratic functions are used for NDVI intercalibration. Although quadratic intercalibrations are generally more accurate than linear intercalibrations, they may still perform poorly when water vapor exhibits significant spatial variations. In addition, the uncertainties associated with current NDVI intercalibrations are discussed based on the proposed models. This paper furthers the understanding of multisensor NDVI intercalibration and provides insight into intercalibration of other remote sensing indices. [ABSTRACT FROM AUTHOR]

GPS-based water vapor tomography has been proved to be a cost-effective means of obtaining spatial and temporal distribution of atmospheric water vapor. In previous studies, the tomography height is empirically selected without considering the actual characteristics of the local water vapor distribution, and most existing studies only consider the signals passing from the top boundary of the tomography area. Therefore, the observed signals coming out from the side face of the tomography area are excluded as ineffective information, which not only reduces the utilization rate of signals used but also decreases the number of voxels crossed by rays. This becomes the research point of this paper, which studies the possibility of selecting a reasonable tomography boundary and using signals passing from the side face of the tomography area. This paper first tries to determine the tomography height based on the local atmospheric physical property using many years of radiosonde data, and 8 km is selected as the tomography boundary in Hong Kong. The second part focuses on superimposing the signals penetrating from the side face of the tomography area to tomography modeling by introducing a scale factor that is able to determine the water vapor content of each signal with the part that belongs in the tomography area. Finally, a tomography experiment is carried out based on data provided by the Satellite Positioning Reference Station Network (SatRef) in Hong Kong to validate the proposed method. Experimental result demonstrates that the utilization rate of the signal used and the number of voxels crossed by rays are both increased by 30.32% and 12.62%, respectively. The comparison of tomographic integrated water vapor (IWV) derived from different schemes with that from radiosonde and ECMWF data shows that the RMS error of the proposed method (4.1 and 5.1 mm) is smaller than that of the previous method (5.1 and 5.6 mm). In addition, the tomographic water vapor densities derived from different schemes is also compared with those of by radiosonde and ECMWF; the statistical result over the experimental period shows that the proposed method has an average RMS error of 1.23 and 2.12 g/m3, respectively, which is superior to the previous method at 1.60 and 2.43 g/m3, respectively. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel framework for fusing multi-temporal, multispectral satellite images and OpenStreetMap (OSM) data for the classification of local climate zones (LCZs). Feature stacking is the most commonly used method of data fusion but does not consider the heterogeneity of multimodal optical images and OSM data, which becomes its main drawback. The proposed framework processes two data sources separately and then combines them at the model level through two fusion models (the landuse fusion model and building fusion model) that aim to fuse optical images with landuse and buildings layers of OSM data, respectively. In addition, a new approach to detecting building incompleteness of OSM data is proposed. The proposed framework was trained and tested using the data from the 2017 IEEE GRSS Data Fusion Contest and further validated on one additional test (AT) set containing test samples that are manually labeled in Munich and New York. The experimental results have indicated that compared with the feature stacking-based baseline framework, the proposed framework is effective in fusing optical images with OSM data for the classification of LCZs with high generalization capability on a large scale. The classification accuracy of the proposed framework outperforms the baseline framework by more than 6% and 2% while testing on the test set of 2017 IEEE GRSS Data Fusion Contest and the AT set, respectively. In addition, the proposed framework is less sensitive to spectral diversities of optical satellite images and thus achieves more stable classification performance than the state-of-the-art frameworks. [ABSTRACT FROM AUTHOR]

Satellite video target tracking is a new topic in the remote sensing field, which refers to tracking moving objects of interest from satellite video in real time. The target of interest usually occupies only a few pixels in a satellite video image, even when the train is long. Thus, satellite video target tracking still faces new challenges compared with traditional visual tracking, including the detection of low-resolution targets, features with less representation, and targets with an extremely similar background. Little research has been done on satellite video target tracking, and little is known about whether or not the existing tracking algorithms can still work on the satellite video data. This paper, for the first time, intensively investigated 13 typical trackers in traditional visual tracking. The experimental results suggest that most of the state-of-the-art tracking algorithms mainly rely on luminance, color features, or convolutional features, and they fail to track satellite video targets due to their inadequate representation features. To overcome this difficulty, we propose a velocity correlation filter (VCF) algorithm, which employs both a velocity feature and an inertia mechanism (IM) to construct a specific kernel correlation filter for the satellite video target tracking. The velocity feature has a high discriminative ability to detect moving targets in satellite videos, and the IM can prevent model drift adaptively. Experimental results on three real satellite video data sets show that the VCF outperforms state-of-the-art tracking methods with regard to precision and success plots while running at over 100 frames per second. [ABSTRACT FROM AUTHOR]

Urban black and odorous water (BOW) is a serious global environmental problem. Since these waters are often narrow rivers or small ponds, the detection of BOW waters using traditional satellite data and algorithms is limited both by a lack of spatial resolution and by imperfect retrieval algorithms. In this paper, we used the Chinese high-resolution remote sensing satellite Gaofen-2 (GF-2, 0.8 m). The atmospheric correction showed that the mean absolute percentage error of the derived remote sensing reflectance ($R_{\mathrm {rs}}$) in visible bands is 25.19%. We first measured $R_{\mathrm {rs}}$ spectra of two classes of BOW [BOW with high concentrations of iron (II) sulfide, i.e., BOW1 and BOW with high concentrations of total suspended matter, i.e., BOW2] and ordinary water in Shenyang. Then, in situ $R_{\mathrm {rs}}$ data were converted into $R_{\mathrm {rs}}$ corresponding to the wide GF-2 bands using the spectral response functions. We used the converted $R_{\mathrm {rs}}$ data to calculate several band combinations, including the baseline height, [ $R_{\mathrm {rs}}$ (green) $- R_{\mathrm {rs}}$ (red))/($R_{\mathrm {rs}}$ (green) $+ R_{\mathrm {rs}}$ (red)], and the color purity on a Commission Internationale de L’Eclairage (CIE) chromaticity diagram. The color purity was found to be the best index to extract BOW from ordinary water. Then, $R_{\mathrm {rs}}$ (645) was applied to categorize BOW into BOW1 and BOW2. We applied the algorithm to two synchronous GF-2 images. The recognition accuracy of BOW2 and ordinary water are both 100%. The extracted river water type near Weishanhu Road was BOW1, which agreed well with ground truth. The algorithm was further applied to other GF-2 data for Shenyang and Beijing. [ABSTRACT FROM AUTHOR]

The current superresolution (SR) methods based on deep learning have shown remarkable comparative advantages but remain unsatisfactory in recovering the high-frequency edge details of the images in noise-contaminated imaging conditions, e.g., remote sensing satellite imaging. In this paper, we propose a generative adversarial network (GAN)-based edge-enhancement network (EEGAN) for robust satellite image SR reconstruction along with the adversarial learning strategy that is insensitive to noise. In particular, EEGAN consists of two main subnetworks: an ultradense subnetwork (UDSN) and an edge-enhancement subnetwork (EESN). In UDSN, a group of 2-D dense blocks is assembled for feature extraction and to obtain an intermediate high-resolution result that looks sharp but is eroded with artifacts and noises as previous GAN-based methods do. Then, EESN is constructed to extract and enhance the image contours by purifying the noise-contaminated components with mask processing. The recovered intermediate image and enhanced edges can be combined to generate the result that enjoys high credibility and clear contents. Extensive experiments on Kaggle Open Source Data set, Jilin-1 video satellite images, and Digitalglobe show superior reconstruction performance compared to the state-of-the-art SR approaches. [ABSTRACT FROM AUTHOR]

Clouds and accompanying shadows, which exist in optical remote sensing images with high possibility, can degrade or even completely occlude certain ground-cover information in images, limiting their applicabilities for Earth observation, change detection, or land-cover classification. In this paper, we aim to deal with cloud contamination problems with the objective of generating cloud-removed remote sensing images. Inspired by low-rank representation together with sparsity constraints, we propose a coarse-to-fine framework for cloud removal in the remote sensing image sequence. Leveraging on group-sparsity constraint, we first decompose the observed cloud image sequence of the same area into the low-rank component, group-sparse outliers, and sparse noise, corresponding to cloud-free land-covers, clouds (and accompanying shadows), and noise respectively. Subsequently, a discriminative robust principal component analysis (RPCA) algorithm is utilized to assign aggressive penalizing weights to the initially detected cloud pixels to facilitate cloud removal and scene restoration. Moreover, we incorporate geometrical transformation into a low-rank model to address the misalignment of the image sequence. Significantly superior to conventional cloud-removal methods, neither cloud-free reference image(s) nor additional operations of cloud and shadow detection are required in our method. Extensive experiments on both simulated data and real data demonstrate that our method works effectively, outperforming many state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for 3-D rain field reconstruction using satellite signals. It uses the estimated signal-to-noise ratio (SNR) at the ground receivers for low-earth orbit (LEO) satellites and, thus, indirectly estimates the path-integrated rain attenuation of the microwave communication links. A least-squares algorithm is employed to perform the 3-D tomographic reconstruction of the rain field. The proposed system model consists of an LEO satellite with a realistic overpass trajectory and multiple ground receivers with SNR estimators. Two synthetic rain events near the Great Barrier Reef in Australia are used to test the reconstruction outcome. Simulation results suggest that the reconstructed rain field has close agreement with the synthetic rain field. [ABSTRACT FROM AUTHOR]

This paper presents a new, simple, fast method to retrieve cloud altitude from measurements of light absorption in the strongest molecular band of oxygen (the A-band), centered at 761 nm, by the polarization and directionality of the earth’s reflectance sensor. First, we assess the validity of the method against synthetic spectra as a function of realistic cloud scenarios and satellite observation geometries. The retrieval error estimate amounts, on average, to less than 500 m. Second, the hurricane Ileana, overpassed the August 30, 2012, off the coasts west of Mexico and Southern California, is selected as test bed for the comparison of our retrievals against independent and nearly coincident cloud data inferred with the Moderate Resolution Imaging Spectroradiometer onboard Aqua and the Global Ozone Monitoring Experiment 2 onboard MetOp-A. We find that our approach can accurately reproduce the cloud height patterns, as long as the clouds are not thin cirrus. [ABSTRACT FROM AUTHOR]

The optical remote sensing images not only have to make a fundamental tradeoff between the spatial and spectral resolutions, but also are inevitable to be polluted by the clouds; however, the existing pansharpening methods mainly focus on the resolution enhancement of the optical remote sensing images without cloud contamination. How to fuse the cloud-contaminated images to achieve the joint resolution enhancement and cloud removal is a promising and challenging work. In this paper, a pansharpening method for the challenging cloud-contaminated very high-resolution remote sensing images is proposed. Furthermore, the cloud-contaminated conditions for the practical observations with all the thick clouds, the thin clouds, the haze, and the cloud shadows are comprehensively considered. In the proposed methods, a two-step fusion framework based on multisource and multitemporal observations is presented: 1) the thin clouds, the haze, and the light cloud shadows are proposed to be first jointly removed and 2) a variational-based integrated fusion model is then proposed to achieve the joint resolution enhancement and missing information reconstruction for the thick clouds and dark cloud shadows. Through the proposed fusion method, a promising cloud-free fused image with both high spatial and high spectral resolutions can be obtained. To comprehensively test and verify the proposed method, the experiments were implemented based on both the cloud-free and cloud-contaminated images, and a number of different remote sensing satellites including the IKONOS, the QuickBird, the Jilin (JL)-1, and the Deimos-2 images were utilized. The experimental results confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In the autumn and winter, aerosol is the important atmospheric pollutant over the Beijing–Tianjin–Hebei region. For monitoring aerosol in the autumn and winter, the lack of vegetation and the aging of MODIS sensor are two problems that needed to be solved. In this paper, after analyzing the characteristics of aerosol radiance in the red and shortwave infrared ($2.12~\mu \text{m}$) bands of MODIS, we develop a new algorithm for terrestrial aerosol with the assumption that the reflectance ratio between the red and $2.12~\mu \text{m}$ bands is invariant. With MODIS data over the Beijing–Tianjin–Hebei region from September 2016 to February 2017, the algorithm is applied to aerosol retrieval. The retrieved aerosol optical depth images show that our algorithm can retrieve aerosol over sparse vegetation, and the validation with the AERONET/PHOTONS Beijing site shows that the correlation is greater than 0.9% and 77% of the retrievals fall within the expected error. An error analysis shows that a 2% error in the proportion of the soot component can lead to 15% retrieval error, and over more than 60% of the surface area, the error from the changes in the ratio between the red and $2.12~\mu \text{m}$ bands can lead to retrieved errors less than 0.1. [ABSTRACT FROM AUTHOR]

This paper presents a new method based on recent optimization technique to detect slow-moving landslides (<150m/year) in time series of displacement field generated by satellite images. Sparse optimization is applied simultaneously on the 3-D data set in space as well as in time. The proposed method takes into account the distinctive signal physical properties in space and time, by enforcing a sparse representation by blocks in space, but a continuing and monotonous representation in time of the landslides. As a result, we show that a mixed $\ell _{1,2}$ -norm is the most suitable norm for this detection problem, compared to pure $\ell _{1}$ -norm or $\ell _{2}$ -norm. Moreover, an outlier estimation step is included that sets apart the Gaussian noise from locally sparse processing errors in the data. The performance of this approach is tested by applying it both on synthetic data and on a time series of displacements fields over 16 dates in the Colca Valley, Peru. This detection presents commission and omission errors for landslides of 29% and 14%, respectively, using a medium resolution (10 m) data set of optical satellite images. It detects all important landslides, already known from field investigations. Moreover, it also points out other smaller or unknown landslides, increasing the existing slow-moving landslide inventory by +50%. [ABSTRACT FROM AUTHOR]

Atmospheric motion vectors (AMVs) estimation helps in better understanding of atmospheric dynamics and also plays a key role in weather forecasting. It has been a challenging task because of the nonrigid motion of clouds and cyclones. In this paper, a modified Weighted Ensemble Transform Kalman Filter-based data assimilation technique is proposed for accurate flow vector estimation at each pixel directly from satellite generated infrared images of clouds/cyclones. This method provides clear visualization of both local and global motion with spatial and temporal consistencies very efficiently even in the case of splitting and merging of clouds or over long tracks. One of the key abilities of proposed method is in forecasting applications and also for generating motion vectors in the absence of data in real scenarios, even without the usage of the existing complex weather models. Estimated AMVs are validated using state-of-the-art European Centre for Medium-range Weather Forecasting (ECMWF) analysis data, and cyclone tracks are validated using the Indian Meteorological Department (IMD) best track data. The results obtained demonstrate the efficacy of proposed method over other existing methods. [ABSTRACT FROM AUTHOR]

Satellite images are widely applied in humanitarian mapping that labels buildings, roads, and so on for humanitarian aid and economic development. However, the labeling now is mostly done by volunteers. In this paper, we utilize deep learning to solve humanitarian mapping tasks of a mobile software named MapSwipe. The current deep learning techniques, e.g., convolutional neural network (CNN), can recognize ground objects from satellite images but rely on numerous labels for training for each specific task. We solve this problem by fusing multiple freely accessible crowdsourced geographic data and propose an active learning-based CNN training framework named MC-CNN to deal with the quality issues of the labels extracted from these data, including incompleteness (e.g., some kinds of object are not labeled) and heterogeneity (e.g., different spatial granularities). The method is evaluated with building mapping in South Malawi and road mapping in Guinea with level-18 satellite images provided by Bing Map and volunteered geographic information from OpenStreetMap, MapSwipe, and OsmAnd. The results based on multiple metrics, including Precision, Recall, F1 Score, and area under the receiver operating characteristic curve, show that MC-CNN can fuse the crowdsourced labels for higher prediction performance and be successfully applied in MapSwipe for humanitarian mapping with 85% labor saved and an overall accuracy of 0.86 achieved. [ABSTRACT FROM AUTHOR]

GaoFen1 is the first high-resolution earth observation satellite built in China, and carries four wide field-of-view (WFV) cameras to achieve large-scale monitoring and mapping. However, the unstable attitude measurement accuracy of the satellite generally imparts low geopositioning accuracy and inconsistent geometric error in overlapping areas of WFV images. A feasible and effective large-scale planar block adjustment (PBA) method is presented that corrects the geometric errors of the vast WFV images integrally, further improving the geometric accuracy of these images. In addition, whether ground control points (GCPs) are needed, and the effect of different numbers of GCPs on PBA accuracy is also investigated. Two key technologies are used in this paper. First, a universal PBA error equation based on the virtual control points is presented to allow PBA with or without GCPs. Second, an adjustment method aided by a digital elevation model is adopted to overcome the weak convergence geometry among WFV images, further ensuring stable estimation of PBA. The effectiveness of the proposed method was verified by 664 WFV images covering most of mainland China. The satisfactory experimental results indicate that the method presented herein is reasonable and effective, but that a certain number of GCPs is needed to ensure the accuracy of large-scale PBA results for WFV images. [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]

The global navigation satellite system (GNSS) data are beneficial for sensing the earth’s ionosphere by virtue of their temporal continuity and spatial coverage. In recent years, GNSS (GPS, GLONASS, BDS, and GALILEO) has developed rapidly, with BDS and GALILEO offering position, navigation, and timing services and the ongoing modernization of GPS and GLONASS. The customary approach to sensing the ionosphere normally uses the dual- or multifrequency (DF) data provided by geodetic-grade receivers and consists of two sequential steps. The first step is retrieving ionospheric observables using the carrier-to-code leveling technique. In the second step, using the thin-layer ionospheric model, one can isolate the main vertical total electron content (VTEC) for ionosphere sensing as well as the by-product of the satellite differential code biases (SDCBs) and the receiver differential code biases. In this paper, we proposed a multi-GNSS single-frequency (SF) precise point positioning approach enabling the simultaneous retrieval of VTEC and SDCBs with low-cost receivers. The root mean square of the VTEC differences between the values retrieved from the multi-GNSS SF method and DF method is approximately 0.5 total electron content unit in each system. [ABSTRACT FROM AUTHOR]

Data heterogeneity can pose a great challenge to process and systematically fuse low-level data from different modalities with no recourse to heuristics and manual adjustments and refinements. In this paper, a new methodology is introduced for the fusion of measured data for detecting and predicting weather-driven natural hazards. The proposed research introduces a robust theoretical and algorithmic framework for the fusion of heterogeneous data in near real time. We establish a flexible information-based fusion framework with a target optimality criterion of choice, which for illustration, is specialized to a maximum entropy principle and a least effort principle for semisupervised learning with noisy labels. We develop a methodology to account for multimodality data and a solution for addressing inherent sensor limitations. In our case study of interest, namely, that of flood density estimation, we further show that by fusing remote sensing and social media data, we can develop well founded and actionable flood maps. This capability is valuable in situations where environmental hazards, such as hurricanes or severe weather, affect very large areas. Relative to the state of the art working with such data, our proposed information-theoretic solution is principled and systematic, while offering a joint exploitation of any set of heterogeneous sensor modalities with minimally assuming priors. This flexibility is coupled with the ability to quantitatively and clearly state the fusion principles with very reasonable computational costs. The proposed method is tested and substantiated with the multimodality data of a 2013 Boulder Colorado flood event. [ABSTRACT FROM AUTHOR]