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This paper presents the correlating synthetic aperture radar (CoSAR) technique, a novel radar imaging concept to observe statistical properties of fast decorrelating surfaces. A CoSAR system consists of two radars with a relative motion in the along-track (cross-range) dimension. The spatial autocorrelation function of the scattered signal can be estimated by combining quasi-simultaneously received radar echoes. By virtue of the Van Cittert–Zernike theorem, estimates of this autocorrelation function for different relative positions can be processed by generating images of several properties of the scene, including the normalized radar cross section, Doppler velocities, and surface topography. Aside from the geometric performance, a central aspect of this paper is a theoretical derivation of the radiometric performance of CoSAR. The radiometric quality is proportional to the number of independent samples available for the estimation of the spatial correlation, and to the ratio between the CoSAR azimuth resolution and the real-aperture resolution. A CoSAR mission concept is provided where two geosynchronous radar satellites fly at opposing sides of a quasi-circular trajectory. Such a mission could provide bidaily images of the ocean backscatter, mean Doppler, and surface topography at resolutions on the order of 500 m over wide areas. [ABSTRACT FROM PUBLISHER]

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]

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]

The advanced Himawari imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this paper, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (AOD) over land and land surface bidirectional reflectance. Instead of performing surface reflectance estimation before calculating AOD, the AOD and surface bidirectional reflectance were retrieved simultaneously using an optimal estimation method. The algorithm uses an atmospheric radiative transfer model coupled with a surface bidirectional reflectance factor (BRF) model. Based on the assumption that the surface bidirectional reflective properties are invariant during a short time period (i.e., a day), multiple temporal AHI observations were combined to calculate the AOD and surface BRF. The algorithm was tested over East Asia for year 2016, and the AOD retrieval results were validated against the aerosol robotic network (AERONET) sites observation and compared with the Moderate Resolution Imaging Spectroradiometer Collection 6.0 AOD product. The validation of the retrieved AOD with AERONET measurements using 14 713 colocation points in 2016 over East Asia shows a high correlation coefficient: $R = 0.88$ , root-mean-square error = 0.17, and approximately 69.9% AOD retrieval results within the expected error of $\pm 0.2\cdot {\mathrm {AOD}}_{\mathrm {AERONET}}\pm 0.05$. A brief comparison between our retrieval and AOD product provided by Japan Meteorological Agency is also presented. The comparison and validation demonstrates that the algorithm has the ability to estimate AOD with considerable accuracy over land. [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]

As a cost-effective remote sensing technique, global navigation satellite system reflectometry (GNSS-R) has recently drawn significant attention from both academia and industry. However, research on GNSS-R has mainly been focused on the global positioning system which consists of only medium earth orbit satellites, while the use of geostationary earth orbit (GEO) satellites, such as those in BeiDou navigation satellite system, has received little attention. This paper investigates the GEO-satellitebased GNSS-R with a focus on the application of soil moisture retrieval. Because GEO satellites remain static with the earth, the models of the reflected GNSS signals can be considerably simplified and their signals can be used to estimate soil moisture with a high update rate such as once per hour. Two new soil moisture estimation approaches using GEO signals are proposed, which are termed GEO interferometric reflectometry (GEO-IR) and GEO reflectometry (GEO-R). Two theoretical models (linear and second order) are developed for signal-to-noise ratio (SNR)-based GEO-IR as well as for phase-based GEO-IR. Meanwhile, two empirical models (linear and second order) are developed for signal amplitude-based GEO-R as well as for SNR ratiobased GEO-R. Experimental data sets collected from three different geographical regions were used to evaluate the proposed methods. The results demonstrate that the proposed GEO-IR and GEO-R are able to monitor soil moisture reliably under bare soil condition, augmenting GNSS-R through significantly reduced processing complexity and increased temporal coverage. [ABSTRACT FROM AUTHOR]

A technique using satellite-link signal attenuation measurements for estimating rainfall accumulation along the link path is evaluated. Power law relationships between attenuation rate A and rainfall rate R are used to estimate R and rainfall accumulation with a satellite link operating at Ku-band (12.3 GHz). Polarimetric radar measurements obtained from a National Weather Service Weather Surveillance Radar--1988 Doppler system near State College, Pennsylvania, are utilized to provide a comparison of rainfall accumulation estimates. A tipping-bucket rain gauge, colocated with the satellite receiver, is also used for comparison. A method based on bit error ratio measurements for the satellite link is used to identify periods of rain during which the rainfall rate is estimated from signal attenuation measurements. The effective rain height used in converting the attenuation rate along the link path into the rainfall rate is estimated from polarimetric radar observations. The Ku-band link is not very sensitive to light rain below 1.5 mm/h. Rainfall accumulation estimates obtained for 11 different days using satellite link attenuation show good comparisons with radar (within 19%) for accumulations greater than 6 mm and not so good (within 43%) for accumulations below 3 mm. The results presented in this paper show that using satellite-link attenuation measurements to estimate rainfall accumulations is a promising technique that requires further testing and refinement. [ABSTRACT FROM AUTHOR]

Patch imaging is an important capability of GaoFen4, which is the first Chinese high-resolution planar array satellite in geosynchronous orbit. When the satellite collects images in the patch-imaging mode, overlapping images can be successively obtained. As these images are captured at different times and from a very high orbit, the initial geometric accuracy of the overlapping area between images is inconsistent, which directly affects image mosaicking. In this paper, a novel bundle block adjustment method based on the rational function model without ground control points (GCPs) is proposed to eliminate the relative geometric error among the images and to generate refined rational polynomial coefficients (RPCs). In this method, the average elevation is used instead of the true elevation of the ground points to solve the problem of weak convergence of corresponding bundles, which would result in unstable calculation in the height direction. Virtual control points (VCPs) generated from the original RPCs are used to restrain the freedom of the entire block in the horizontal direction, thereby ensuring stable calculation without the use of GCPs. A successive RPC regeneration method based on VCPs is also presented. To verify the effectiveness of the proposed method, four experiments were performed using real data to assess the geometric accuracy of the method, and the satisfactory experimental results indicate that the presented method is both practical and effective. [ABSTRACT FROM AUTHOR]

The 3-D deformation retrieval is significant for the accurate evaluation of geologic disasters (e.g., earthquakes and landslides). Multiple-aperture interferometry (MAI) is an effective method to obtain 3-D deformation, combined with the cross-heading tracks synthetic aperture radar (SAR) data. However, because of the limitations of the low earth orbit SAR, a long satellite revisit time, small common areas of the cross-heading tracks data, and the unsatisfied along-track deformation measurement accuracy usually exist in the traditional MAI 3-D deformation retrieval. Geosynchronous SAR (GEO SAR) runs in the geosynchronous orbit, which has the advantages of a large observation area and a short revisit time. This paper focuses on 3-D deformation retrieval by GEO SAR MAI processing. Aiming at the high orbit and the squint looking of GEO SAR, the accurate expressions of the along-track deformation, 3-D deformation, and the errors in GEO SAR MAI processing are given. The distortions and their correction in the MAI interferogram brought by the geometrical difference between the forward- and backward-looking interferograms and the multicycles flat-earth and topographic phases are given. Moreover, an optimal subaperture selection method based on minimum position dilution of precision is proposed. Finally, the effectiveness of the proposed method is validated by simulations and the experiment of BeiDou-2 inclined geosynchronous orbit navigation satellite. The theoretical analysis and the experimental results suggest centimeter-level and even millimeter-level deformation measurement accuracy could be obtained in 3-D by GEO SAR MAI processing. [ABSTRACT FROM PUBLISHER]

An L-band geosynchronous synthetic aperture radar (GEO SAR) will be inevitably affected by ionosphere scintillation because of its low carrier frequency. Meanwhile, compared with the low Earth orbit (LEO) SAR, a higher orbit of GEO SAR makes it have a longer integration time and a longer operation time within the susceptible regions of ionospheric scintillation. Thus, its imaging is more sensitive to ionospheric scintillation, and the corresponding degradation will have a different pattern. However, few works are focused on the quantitative analysis of the ionospheric scintillation impacts on L-band SAR. Moreover, the parameters of ionospheric irregularities utilized in the analyses are hard to be determined. In this paper, we first deduced the azimuth point-spread function with the consideration of both the amplitude and phase scintillation. Then, based on the measurable statistical parameters of ionospheric scintillation, performance specifications, including azimuth resolution, azimuth peak-to-sidelobe ratio (PSLR), and azimuth integrated sidelobe ratio (ISLR) are obtained to fully evaluate the impacts. The analysis suggests that in GEO SAR imaging, the azimuth ISLR severely deteriorates, whereas degradations of the azimuth resolution and PSLR are negligible. Finally, the simulations and a real ionospheric scintillation monitoring experiment by employing Global Positioning System satellites receivers were conducted, verifying the conclusions that the serious degraded contrast and focus quality of the images are brought by the raised azimuth ISLR. [ABSTRACT FROM PUBLISHER]

Statistical approaches for quantitative precipitation nowcasts (QPNs) have emerged with recent advances in sensors in geostationary orbits, which provide more frequent observations at higher spatial resolutions. Advanced Meteorological Imager (AMI) onboard South Korea's second geostationary satellite (GEO-KOMPSAT-2A), scheduled for launch in early 2018, is an example of these sensors. This paper introduces operational prototype algorithms that attempt to produce QPN products for GEO-KOMPSAT-2A. The AMI QPN products include the potential accumulated rainfall and the probability of rainfall (PoR) for a 3-h lead time. The potential accumulated rainfall algorithm consists of two major procedures: 1) identification of rainfall features on the outputs from the GEO-KOMPSAT-2A rainfall rate algorithm; and 2) tracking of these rainfall features between two consecutive images. The potential accumulated rainfall algorithm extrapolates precipitation fields every 15 min. Rainfall rates at each time step are accumulated to yield the 3-hourly rainfall. In addition, the extrapolated precipitation fields at 15-min intervals are used as inputs for the PoR algorithm, which produces the probability of precipitation during the same 3-h period. The QPN products can be classified as extrapolated features associated with precipitation. The validation results show that the extrapolated features tend to meet the designated accuracy for the prototype development stage. We also confirm a tendency for decreasing accuracy of the QPN products with increasing forecast lead time. Mitigating the dependence on lead time may remain a challenge that can be incorporated into the next generation of QPN algorithms. [ABSTRACT FROM PUBLISHER]

Wildfires are one of the most destructive disasters on the planet. They also significantly impact the land surface. Satellite data have been widely used to detect the outbreak and monitor the expansion of fire incidents for damage assessment and disaster management. Polar-orbiting satellite data have been used for several decades but data from geostationary satellites, which can provide observations with a high temporal resolution, have received much less attention. This paper utilizes data from FengYun-2G, a Chinese geostationary satellite, to detect wildfires in two selected research regions in January 2016. The detection algorithm systemizes image-based analysis to filter out obvious nonfire pixels and temporal analysis to confirm the true detections. Fire detection is based on comparisons between predicted and observed values. The results show that the proposed method has some advantages compared with the use of polar-orbiting satellite data, including early detection and continuous observation. The validation work is conducted based on the collection 6.1 Global Monthly Fire Location Product generated from fire detections by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The average accuracy within the target time is 56%, while the omission error rate is over 78%. In detail, the algorithm has a lower omission error rate in Australia while it fails in detecting most of the fire pixels in India. The dominance of small fire incidents, as well as low spatial resolution greatly limit the detection ability. Many small fires were beyond the ability of Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR) data when no significant fire characteristics could be captured. Future development of the algorithm will focus on improving the results by enhancing the adaption to different regions, as well as, including multisource data sets. [ABSTRACT FROM AUTHOR]

New generation ocean color imagers on geostationary orbits are designed to provide a much higher temporal resolution along with enhanced spatial and spectral resolutions that will open up obvious opportunities for improving the sampling frequency and resolving diurnal variability of phytoplankton and other biogeochemical properties in dynamic coastal waters. Despite the capabilities of such new generation sensors to detect the diurnal cycles of various ocean phenomena, there is a lack of knowledge on their radiometric sensitivity and signal detectability for observing the ocean color at morning or evening hours. This paper aims to explore the capability of geostationary satellite ocean color sensor for detecting ocean biogeochemical properties [chlorophyll (CHL); total suspended matter (TSM); colored dissolved organic matter (CDOM)] under high solar zenith angles (SZAs). The analysis is based upon simulations from the vector radiative transfer model for the coupled ocean–atmosphere system (PCOART-SA), which considers the earth curvature effects. The unitless differential signal-to-noise ratio ($\Delta $ SNR) is used as a discriminant parameter to indicate the radiometric sensitivity to variation of different biogeochemical properties. The results showed that the SZAs have a significant impact on the signal detectability for CHL variation. For typical shelf water (CHL $= 1\,\,\mu \text{g}$ /L, TSM = 1 mg/L, CDOM = 0.15 m−1), with the typical observation zenith angle (OZA) = 30°, changes on the order of $\Delta $ CHL $= 0.024\,\,\mu \text{g}$ /L (2.4% to background CHL) were detectable when SZA = 30°; when SZA > 75°, the detectable minimal $\Delta $ CHL increased to $0.77~\mu \text{g}$ /L (77%), indicating the difficulty of detecting CHL under high SZA. For CDOM, the detectability of changes ($\Delta $ CDOM) was also found to be closely related to the SZAs, i.e., changes on the order of ten times depending on the SZA conditions. However, even under extremely high SZA conditions (SZA = 80°, OZA = 30°), $\Delta $ CDOM = 0.007 m−1 which is about 4.7% of the background CDOM was still detectable at 412 nm. On the other hand, under high SZA conditions (SZA = 80°, OZA = 30°), $\Delta $ TSM = 0.211 mg/L (2.1% to the background TSM) was also detectable. Overall, our results indicate that under high SZAs conditions, the geostationary satellite ocean color sensor may experience difficulty in detecting a slight change in CHL variation in productive waters, but it still can detect small changes in TSM and CDOM contents despite a reduced sensitivity at the steeper SZAs. [ABSTRACT FROM AUTHOR]

Boreal forests are characterized by a rather homogeneous stand structure that allows by means of a single allometric equation to estimate biomass from forest height with sufficient accuracy and, therefore, to use this equation for quantitative biomass classifications. In this paper, interferometric TanDEM-X DEM data are used to estimate forest height over boreal forest systems. The accuracy of the height inversion is evaluated for single- and dual-baseline scenarios, under summer and winter conditions. Then, an allometric equation is used to transfer forest height to biomass. For this, two forest sites, boreal (Krycklan) and hemi-boreal (Remningstorp) in north and southern Sweden, respectively, are investigated. A performance analysis is carried out to estimate the maximum number of biomass classes obtained, depending on the height estimation accuracy. For summer acquisitions, four biomass classes can be obtained, with a maximum biomass class of > 200 Mg/ha. For winter acquisitions or when a mixed summer—winter approach is applied, five biomass classes, up to 220 Mg/ha, can be obtained. This classification shows a good agreement with CORINE, an existing land cover classification, and can improve it by adding quantitative forest biomass classes with a high spatial resolution of 16 $\times$ 16 m. [ABSTRACT FROM AUTHOR]

Geometric positioning of a remote sensing image is one of the core technologies for the quantitative application of the geostationary satellite data. Affected by the change of the incident angle of the sunlight, the spatial thermal environment surrounding the remote sensing cameras (RSCs), especially the geostationary RSCs, fluctuates greatly and has a noticeable impact on the installation matrix based on the reference to the satellite body. Therefore, the spatial thermal environment will ultimately influence the camera’s geometric positioning model and the final positioning accuracy. This paper proposes a novel correction method based on stellar observations for correcting geometric positioning error caused by spatial thermal deformation (STD) of geostationary optical payloads. The proposed method overcomes the drawbacks associated with current stabilization methods that involve shutting down the camera to reduce STD effects. Experimental results show that the positioning error corrected by the proposed method can be within ±1.9 pixels ($2{\sigma }$) at a 95% confidence level and better than the ±18 pixels before correction. [ABSTRACT FROM AUTHOR]

Microwave radiometers measure weak thermal emission from the Earth, which is broadband in nature. Radio frequency interference (RFI) originates from active transmitters and is typically narrow band, directional, and continuous or intermittent. The Global Precipitation Measurement (GPM) Microwave Imager (GMI) has seen RFI caused by ocean reflections from direct broadcast and communication satellites in the shared 18.7-GHz allocated band. This paper focuses on the use of a complex signal kurtosis algorithm to detect direct broadcast satellite (DBS) signals at 18.7 GHz. An experiment was conducted in August 2017 at the Harvest oil platform, located about 10 km off the coast of central California. Data were collected for direct and ocean reflected DBS transmissions in the K- and Ku-bands from a commercial geostationary satellite. Results are presented for the complex kurtosis performance for a five-channel quadrature phase-shift keying (QPSK) signal versus the seven-channel case. As the spectrum becomes more occupied, detector performance decreases. Filtering of RFI in the fully occupied spectrum is very difficult, and detection using the complex kurtosis detector is only possible for very large interference-to-noise ratio (INR) values at −5 dB and higher. This corresponds to over 100 K in a real system such as GMI; therefore, other detection approaches might be more appropriate. [ABSTRACT FROM AUTHOR]

The Japan Meteorological Agency (JMA) successfully launched the Himawari-8 (H-8) new-generation geostationary meteorological satellite with the Advanced Himawari Imager (AHI) sensor on October 7, 2014. The H-8/AHI level-2 (L2) operational cloud property products were released by the Japan Aerospace Exploration Agency during September 2016. The Voronoi light scattering model, which is a fractal ice particle habit, was utilized to develop the retrieval algorithm called “Comprehensive Analysis Program for Cloud Optical Measurement” (CAPCOM-INV)-ice for the AHI ice cloud product. In this paper, we describe the CAPCOM-INV-ice algorithm for ice cloud products from AHI data. To investigate its retrieval performance, retrieval results were compared with 2000 samples of the ice cloud optical thickness and effective particle radius values. Furthermore, AHI ice cloud products are evaluated by comparing them with the MODIS collection-6 (C6) products. As an experiment, cloud property retrievals from AHI measurements, with an observation interval time of 2.5 min and ground-based rainfall observation radar data (the latter of which is supplied by the JMA, with a 1-km grid mesh), are used to investigate the generation processes of deep convective (DC) cloud in the vicinity of the Kyushu island, Japan. It revealed that AHI measurements have the capability of monitoring the growth processes, including variation of the cloud properties and the precipitation in the DC cloud. [ABSTRACT FROM AUTHOR]

A technique to estimate tropical cyclone (TC) current intensity based on geostationary satellite infrared window (IRW) and water vapor (WV) imagery is explored in this paper. First, to combine the advantages of the IRW imagery and the WV minus IRW (WV-IRW) imagery, a WV-IRW-to-IRW ratio (WIRa)-based indicator is proposed. This indicator not only can display the inner-core convection's symmetrization level and vigor but also is able to screen out thin cirrus, stratospheric WV anomaly, and overshooting tops from average deep convection. It is highly correlated with the best track minimum sea-level pressure and thus used to estimate the western North Pacific TC current intensity. Detailed analyses have demonstrated that the WIRa-based indicator can further improve the estimation of TC current intensity alongside the existing algorithms. The WIRa-based indicator is designed based on the hypothesis that “overshooting top is more useful for forecasting than initial estimation,” and the satisfying results of the WIRa-based method perhaps provide indirect evidence to support this hypothesis in turn. [ABSTRACT FROM PUBLISHER]

The radiometric calibration of satellite data is critical in many environmental studies and applications that are based on remote sensing data. The Geostationary Ocean Color Imager (GOCI) has suffered from what is called an interslot radiometric discrepancy (ISRD), which creates clear inconsistency between the adjacent slots in GOCI Level 1B (L1B) radiometric products, the largest source of which is currently believed to be the stray light generated in the sensor instrument. Difficulties in removing the stray-light-driven anomalies are that the intensity and the spatial extent vary with time and location, depending on the reflectance of nearby bright targets, such as cloud and land. This paper proposes an image-based correction method that removes the stray-light-driven radiometric inflation without involving an independent reference so that the method can be used for GOCI operational data processing. First, the radiometric inflation pattern is characterized by independent sources, such as Moderate Resolution Imaging Spectrometer (MODIS) data, and the inflation pattern is modeled by the minimum noise fraction transform of the input data. The modeled inflation patterns in individual slots are then adjusted across the slots in such a way that the overall ISRD in all slot boundaries is minimized. The analysis shows that the stray-light-driven radiometric anomalies can be up to 20% of the normal signals in Bands 6 (680 nm) and 8 (865 nm) of the uncorrected L1B images, and the proposed correction method reduces it to less than 2% in most of the cases, recovering the spatial continuity of natural variability across the slots. [ABSTRACT FROM PUBLISHER]

Geosynchronous synthetic aperture radar (GEO SAR) has been studied for several decades but has not yet been implemented. This paper provides an overview of mission design, describing significant constraints (atmosphere, orbit, temporal stability of the surface and atmosphere, measurement physics, and radar performance) and then uses these to propose an approach to initial system design. The methodology encompasses all GEO SAR mission concepts proposed to date. Important classifications of missions are: 1) those that require atmospheric phase compensation to achieve their design spatial resolution; and 2) those that achieve full spatial resolution without phase compensation. Means of estimating the atmospheric phase screen are noted, including a novel measurement of the mean rate of change of the atmospheric phase delay, which GEO SAR enables. Candidate mission concepts are described. It seems likely that GEO SAR will be feasible in a wide range of situations, although extreme weather and unstable surfaces (e.g., water, tall vegetation) prevent 100% coverage. GEO SAR offers an exciting imaging capability that powerfully complements existing systems. [ABSTRACT FROM PUBLISHER]

Hourly measurements during 9:00 to 16:00 local time are a unique capability of the Korean Geostationary Ocean Color Imager (GOCI) in monitoring fast-evolving ocean features. In this paper, we derive ocean surface currents in the Bohai Sea from GOCI data using the maximum cross correlation (MCC) feature tracking method and compare the results with altimetry-inversed tidal currents produced from the Oregon State University Tidal Inversion Software (OTIS) after the latter is validated using in situ measurements. The performance of the GOCI-based MCC method is assessed and the discrepancies between the GOCI and OTIS currents are evaluated through a series of sensitivity studies, including studies of different ocean color products, MCC and filter parameters, and time interval between image pairs, in order to find the best setups for optimal MCC performance. Our results show that a box size of 10–15 km and a search range of 4–8 km for every 1 h of time difference, combined with subsequent filters that use the cutoff correlation coefficient of 0.5 and 3 or 4 neighbors within 15 cm/s difference, gives optimal results. Under these setups, the bias errors of GOCI-derived current maps are about 8 cm/s for current vectors and 3° for current directions, respectively. The noise errors are about 20 cm/s in current velocity and 20° in current direction with correlation coefficients of ~0.75 and ~0.9 for the current velocity and current direction, respectively. These findings are useful in developing an hourly surface current product from GOCI measurements for the Bohai Sea. [ABSTRACT FROM PUBLISHER]

The National Aeronautics and Space Administration's (NASA) Soil Moisture Active and Passive (SMAP) mission, which was launched on January 31, 2015, is providing global measurements of soil moisture and freeze/thaw state. The SMAP radiometer operates within the protected Earth Exploration Satellite Service passive frequency allocation of 1400–1427 MHz. However, unauthorized in-band transmitters and out-of-band emissions from transmitters operating at frequencies adjacent to this allocated spectrum are known to cause interference to microwave radiometry in this band. Because measurement corruption by these terrestrial transmissions, which is referred to as radio-frequency interference (RFI), threatens mission success, the SMAP radiometer includes special flight hardware to enable the detection and filtering of RFI. Results from the first year of SMAP data show the presence of RFI with frequent occurrence over Asia and Europe. During the calibration/validation stage of the mission, the RFI detection and mitigation algorithms were modified to provide enhanced performance. Analysis of the L1B_TB products indicates good algorithmic performance with respect to RFI detection and removal. However, some regions of the globe (e.g., Japan) continue to experience complete data loss. This paper summarizes updates to the SMAP RFI processing algorithms based on prelaunch tests and on-orbit measurements, as well as RFI information obtained in SMAP's first year on orbit. [ABSTRACT FROM AUTHOR]

In this paper, the performance of a nearly zero inclination and low eccentricity geosynchronous synthetic aperture radar (GEOSAR) mission for midlatitude (30°-60°) Earth observation is analyzed. The slow motion of such satellites with respect to the Earth's surface makes it necessary to consider long coherent combination of pulses during hours to reach the desired along-track resolution. A system based on moderate transmitted powers and antenna sizes is considered. The necessary sensitivity in such GEOSAR system is obtained from the accumulated energy of the raw data using a pulse repetition frequency above the Doppler bandwidth and a long integration time. Several issues as a result of the long acquisition, such as target and atmospheric phase screen decorrelation, speckle noise impact on the received signal, and satellite station-keeping requirements, are analyzed. The feasibility of such systems to be placed on a broadcasting communication satellite makes nearly zero inclination GEOSAR a low-cost alternative of current SAR missions. [ABSTRACT FROM PUBLISHER]

A new algorithm has been developed to retrieve total column ozone (TOZ) from the infrared measurements of a geostationary satellite imager such as the Advanced Himawari Imager (AHI) aboard Himawari-8 and the advanced meteorological imager flying with the second generation geostationary satellite of South Korea. Based on a nonlinear optimal estimation method, this operational algorithm iteratively retrieves TOZ from AHI thermal measurements under clear-sky conditions as well as over low clouds at 10-min intervals with 6-km spatial resolutions. The first guess for ozone is prepared by combining a monthly climatology with the daily updated TOZ from polar orbiting satellites, while the first guess for the temperature and humidity profiles are from a numerical weather prediction model. AHI contains only one ozone absorption channel ($9.6~\mu \text{m}$), providing information contents (degrees of freedom for signal) of about 0.98 for the ozone retrieval. However, the study shows that the retrieved TOZ successfully captures the temporal and latitudinal variations of ozone flow particularly at mid- and high-latitudes. A validation using the Ozone Monitoring Instrument, Infrared Atmospheric Sounding Interferometer (IASI), and ozonesondes shows that the AHI TOZ are in a good agreement with the three references with mean difference of 2.5%, −0.5%, and 2.6%, respectively. This impressive performance is attributed to the improvements in both first-guess information and the accuracy of the retrieved temperature and humidity profiles. In particular, the good agreement shown in the comparison with the night-time IASI TOZ suggests potential benefits of using the retrieved TOZ for a continuous monitoring of global ozone evolution with high spatial resolution. [ABSTRACT FROM AUTHOR]

A vicarious calibration with reference to characterized surface tarps was conducted to determine the first radiometric characteristics of KOMPSAT-3. The 6S radiative transfer model was also used by inputting various initial parameters, such as the spectral response function of KOMPSAT-3, and atmospheric and geometric conditions. Moderate-Resolution Imaging Spectroradiometer atmospheric products, such as aerosol optical depth, precipitable water, and total ozone, were used as input parameters to interpret solar radiation reflection, scattering, and absorption effects. In the first field campaign, the radiometric coefficients from each of the spectral bands were estimated by calculating the predicted radiance at sensor level and the digital number (DN) of KOMPSAT-3 based on a linear least squares fit over a range of target reflectance levels. The second field campaign measurements were also used to upgrade the KOMPSAT-3 DNs to radiance coefficients. The root-mean-square error differences between simulated radiance and measured radiance during the second field campaign for “sensor-to-itself” calibration were 2.072 W/m2 sr (blue), 6.80 W/m2sr (green), 7.512 W/m2sr (NIR), and 5.712 W/m2sr (red), respectively. This highlights that radiometric calibration with tarps is a reliable method. Furthermore, the gain ratio between the first and the second one was < 5%, indicating reasonable radiometric calibration results. Additionally, cross-validation of KOMPSAT-3 with radiometrically well-calibrated Landsat-8 was performed over bright desert. Although the difference between the vicarious calibration with surface tarps and cross-validation with Landsat-8 was significant, reasonable results were obtained under close geometrical conditions, despite inherent vicarious calibration error. [ABSTRACT FROM AUTHOR]

High-frequency aerosol observation from a new-generation geostationary meteorological satellite is capable to capture and monitor the spatiotemporal dynamic variation of aerosols, which is of vital significance to environmental research and climate studies. Due to the diversity and complexity of land cover, it is a challenge to retrieve aerosol properties with high accuracy over land, especially over heterogeneous land surfaces. In this study, a geometry-discrete minimum reflectance aerosol retrieval algorithm (GeoMRA) has been proposed to retrieve 10-min high temporal resolution aerosol optical depth (AOD) datasets for geostationary Himawari-8 Advanced Himawari Imager (AHI) sensor, aiming at providing universal bidirectional reflectance distribution function (BRDF) descriptions for different land surfaces with different heterogeneous extent. The AOD retrievals from GeoMRA demonstrate good consistency against the ground-based AERONET measurements in East Asia from 2015 to 2020, with a correlation coefficient (${R}$) of 0.883 and approximately 65.6% of matchups falling within the expected error envelope of ±(0.05% +15%). Intercomparison between the GeoMRA retrieved AOD and other operational AOD products shows that the GeoMRA AOD retrievals, which generally possess similar spatial distribution and accuracy as Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products, have better performances than the Japan Aerospace Exploration Agency (JAXA) AOD products by providing more accurate AOD retrievals with higher spatial coverage. Moreover, the AOD bias analyses further demonstrate the robustness of GeoMRA algorithm, and an extreme haze event shows that the continuous GeoMRA AOD images illustrate smoother temporal variations than JAXA AOD products, demonstrating its efficacy and reliability in capturing the process of haze transport and monitoring the continuous spatiotemporal variation of aerosol. The above results suggest the considerable accuracy of GeoMRA algorithm for scientific application requirement and demonstrate the robustness of the proposed BRDF scheme in describing heterogeneous surfaces with diverse reflectance distribution. [ABSTRACT FROM AUTHOR]

Investigations of the effects of clouds on Earth’s radiation budget demand accurate representations of cloud top parameters, which can be efficiently obtained by large-scale satellite remote sensing approaches. However, the insufficient utilization of multiband information is one of the major sources of uncertainty in cloud top products derived from geostationary satellites. In this study, we developed a new algorithm to estimate Cloud, Atmospheric Radiation and renewal Energy application (CARE) version 1.0 cloud top properties [cloud top height (CTH), cloud top pressure (CTP), and cloud top temperature (CTT)]. The algorithm is constructed from ten thermal spectral measurements in Himawari-8 observations by using the random forest (RF) method to comprehensively consider the contribution of each band to the cloud top parameters. We chose the highly accurate Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products in 2018 as the true values. The sensitivity analysis demonstrated that the products can be fully reproduced by using multiple Himawari-8 channels with the addition of the digital elevation model (DEM) data. The validation results of the 2019 CALIOP data confirm that the new algorithm shows an effective performance, with correlation coefficients ($R$) of 0.89, 0.89, and 0.90 for CTH, CTP, and CTT, respectively. Moreover, a significant improvement in the ice cloud estimation is achieved, in which the CTT $R$ value increased from 0.46 to 0.70, as well as an improvement in the sea area, where the CTT $R$ value increased from 0.71 to 0.84 compared with the Himawari-8 products of the Japan Aerospace Exploration Agency (JAXA) P-tree system. The further analyses performed here capture the diurnal cycle of cloud top parameters well in different temporal scales over the Asia–Pacific region. [ABSTRACT FROM AUTHOR]

As a vital parameter describing the Earth surface energy budget, surface all-wave net radiation ($R_{n}$) drives many physical and biological processes. Remote estimation of $R_{n}$ using satellite data is an effective approach to monitor the spatial and temporal dynamics of $R_{n}$. Accurate daily $R_{n}$ estimation typically depends on the spatio-temporal resolutions of satellite data. There are currently few high-spatial-resolution daily $R_{n}$ products from polar-orbiting satellite data, and they exhibit limited accuracy due to sparse diurnal observations. In addition, traditional estimation approaches typically require cloud mask and clear-sky albedo as inputs and ignore the length ratio of daytime (LRD), which may lead to large errors. To overcome these challenges and obtain $R_{n}$ data with improved spatial resolution and accuracy, an operational approach was proposed in this study to derive daily 1-km $R_{n}$ , which takes the advantages from a radiative transfer model, a machine learning algorithm, and multispectral and dense diurnal temporal information of geostationary satellite observations. An improved all-sky hybrid model (AHM) coupling radiative transfer simulations with a random forest (RF) model was first developed to estimate the shortwave net radiation ($R_{ns}$). Then, another RF model was developed to estimate the daily $R_{n}$ from $R_{ns}$ , incorporating the LRD, which is called extended hybrid model (EHM). Data from the Advanced Baseline Imager (ABI) onboard the new-generation Geostationary Operational Environmental Satellite (GOES)-16 with a 5-min temporal resolution and a 1-km spatial resolution were used to test the proposed method. Compared to traditional lookup table (LUT) algorithms, the results show that AHM not only makes the process of $R_{ns}$ estimation simple and efficient but also has high accuracy in estimating instantaneous all-sky $R_{ns}$. Benefiting from high spatio-temporal resolutions, our daily $R_{ns}$ estimates using GOSE-16 data exhibited superior performance compared to using the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) and 1° Clouds and the Earth’s Radiant Energy System (CERES) product. Using accurate daily $R_{ns}$ estimates and LRD as inputs, the EHM model shows reasonably good results for estimating $R_{n}$ ($R^{2}$ , RMSE, and bias of 0.91, 20.95 W/m2, and −0.05 W/m2, respectively). Maps of 1-km $R_{ns}$ and $R_{n}$ exhibit similar spatial patterns to those from the 1° CERES product, but with substantially more spatial details. Overall, the proposed $R_{n}$ retrieval scheme can accurately estimate all-sky 1-km $R_{ns}$ and $R_{n}$ at mid- to low-latitudes and can be easily adapted and applied to other GOES- 16-like satellites, such as Himawari-8, Meteosat Third Generation (MTG), and Fenyun-4. This study demonstrates the advantages of estimating $R_{n}$ using geostationary satellites with improved accuracy and resolutions. [ABSTRACT FROM AUTHOR]

Sea surface nitrate (SSN) is an important parameter to characterize physical and biogeochemical processes, particularly to quantify oceanic new primary production, yet its remote estimation from satellite has been difficult due to the complex relationships between environmental variables and SSN. In the central and southern sections of the California Current System (CSCCS), this challenge is attempted through modeling, validation, and extensive tests in different oceanic scenarios. Specifically, using extensive SSN datasets collected by many cruises spanning 40 years (1978–2018) and Moderate Resolution Imaging Spectroradiometer (MODIS) estimated sea surface temperature (SST) and chlorophyll-a (Chl), a stacking random forest (SRF) model of SSN has been developed and validated with a spatial resolution of ~4 km. The model showed an overall performance of root mean square difference (RMSD) $=0.83\,\,\mu $ mol/kg, with coefficient of determination ($R^{2}) =0.87$ , mean bias $= -0.11\,\,\mu $ mol/kg, and mean ratio = 1.15 for SSN ranging between 0.05 and $19.90~\mu $ mol/kg ($N =1034$). Furthermore, tests of the model with its original parameterization for the upwelling period, oceanic period, and winter period all showed satisfactory performance with an overall RMSD of 1.95 $\mu $ mol/kg. The sensitivity of the SRF model to uncertainties of MODIS SST and Chl was examined, with induced uncertainties of $\le 2.22~\mu $ mol/kg. The extensive evaluation and sensitivity tests indicated the robustness of the SRF model in estimating SSN in the study area of the CSCCS, and it could serve as a robust approach for other regions once sufficient in situ SSN data are available for model calibration. [ABSTRACT FROM AUTHOR]

Hurricane top structures are not well captured by airborne or dropsonde observations. Total column ozone (TCO) observations provided by the Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper (NM) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite are employed in an investigation of hurricane top structures. We show that the OMPS NM TCO data can capture the top structures of Hurricane Maria (2017) over the Atlantic Ocean. An observed local maximum of TCO in the eye region reveals a strong upper tropospheric downward motion that lowers the tropopause above the hurricane eye. A rainband-like distribution of low TCO content reflects strong convection areas where the tropopause is raised and well correlates spatially with the high cloud top regions derived from the S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS). A sixth-order even polynomial fitting is used to reveal the TCO radial structures by introducing two characteristic parameters. One is a radial distance parameter ($R_{\mathrm {TCO}}$) representing the spatial range, and the other is an intensity parameter defined as the TCO difference from the hurricane center to 600-km radial distance from the hurricane center ($\Delta {\mathrm {TCO}}_{600\,{\mathrm {km}}} $). Based on an analysis of ten hurricanes over the Northern Atlantic Ocean in 2017, we show that before a hurricane reaches its maximum strength, there is always a decrease of $R_{\mathrm {TCO}}$ and an increase of $\Delta {\mathrm {TCO}}_{600\,{\mathrm {km}}} $. It is anticipated that more accurate initial hurricanes could be produced if the TCO structures were combined with other surface, near surface, and tropospheric information in vortex initialization. [ABSTRACT FROM AUTHOR]

Video satellite can generate video image sequences with rich dynamic information, thus providing a new way for monitoring moving objects. However, to maintain high temporal resolution, video satellite images usually sacrifice their spatial resolution. Therefore, super-resolution (SR) plays a vital role in improving the quality of video satellite images. In this article, we propose a multiframe video SR neural network (MVSRnet) for video satellite image SR reconstruction. The proposed MVSRnet consists of three main subnetworks: an optical flow estimation subnetwork (OFEnet), an upscaling subnetwork (Upnet) and an attention-based residual learning subnetwork (ARLnet). The OFEnet aims to estimate low-resolution (LR) optical flow of multiple image frames. Upnet is then constructed to enhance the resolution of both input frames and the estimated LR optical flows. Motion compensation is subsequently performed according to the high-resolution (HR) optical flows. Finally, the compensated HR cube is fed to the ARLnet to generate SR results. Different from existing video satellite image SR methods, the proposed MVSRnet is a multiframe-based method with an attention mechanism, which can merge the motion information among adjacent frames and highlight the importance of extracted features. Experiments conducted on Jilin-1 and OVS-1 video satellite images demonstrate that the proposed MVSRnet significantly outperforms some state-of-the-art SR methods. [ABSTRACT FROM AUTHOR]

This article presents a novel technique for automatically locating tropical cyclone (TC) centers based on top cloud motions in consecutive geostationary satellite images. The high imaging rate and spatial resolution images of the Gaofen-4 geostationary satellite enable us to derive pixel-wise top cloud motion data of TCs, and from the data, TC spiral centers can be accurately determined based on an entirely different principle from those based on static image features. First, a physical motion field decomposition is proposed to eliminate scene shift and TC migration in the motion data without requiring any auxiliary geolocation data. This decomposition does not generate the artifacts that appear in the results of the previously published motion field decomposition. Then, an algorithm of a motion direction-based index embedded in a pyramid searching structure is fully designed to automatically and effectively locate the TC centers. The test shows that the TC concentric motions are more clearly revealed after the proposed motion field decomposition and the located centers are in good agreement with the cloud pattern centers in a visual sense and also with the best track data sets of four meteorological agencies. [ABSTRACT FROM AUTHOR]

The Advanced Himawari Imager (AHI) onboard Japanese geostationary satellite Himawari-8 provides two more visible, three more near-infrared, and six more infrared channels than the only one visible and four infrared channels available from the previous geostationary imager instruments. By taking advantage of AHI’s newly added channels 1, 3, and 4 with wavelengths centered at 0.46, 0.64, and 0.86~\mu \textm , respectively, a fast cloud detection algorithm is developed. Since the spectral differences of the reflectance between any two of AHI’s channels 1, 3, and 4 over clouds are smaller than those over land and ocean, a visible-based cloud index (VCI) for daytime cloud detection can thus be defined by the root mean square of the three differences between any two of these three channels. An AHI pixel is identified as cloudy if the VCI is smaller than a threshold, which has different values over ocean and land. Cloud detection is further adjusted by a bias correction using AHI channels 7 and 13. The average accuracy of the proposed simple cloud detection is comparable with those obtained from a more complicated cloud mask algorithm involving not only more channels but also model simulations. It is also found that the bias correction is needed mostly over cirrus clouds and Gobi. [ABSTRACT FROM PUBLISHER]

Geostationary satellite data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) in conjunction with A-Train data are used to develop an algorithm for detecting biomass-burning smoke aerosols above closed-cell stratocumulus (Sc) clouds. The detection relies on spectral signatures, textural characteristics, and time-dependent spectral variation of SEVIRI data. A-Train data including the Ozone Monitoring Instrument (OMI) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used as reference data for the SEVIRI algorithm development. The 15-min repeat cycle of SEVIRI provides the capability for identifying smoke above closed-cell Sc with an OMI aerosol index value exceeding 0.5 and a cloud optical thickness greater than 6 at 0.81 \mu\mboxm. The user accuracy of this algorithm is ∼49% when using only spectral signature and textural tests. When incorporating the “temporal consistency” tests into the algorithm, the user accuracy increases to ∼65%. The producer accuracy is over ∼77%, implying that the SEVIRI algorithm generally identifies smoke above clouds when CALIOP also identifies the same feature at the collocated pixel. However, CALIOP has the tendency to underestimate the presence of thin smoke aerosols above liquid clouds during daytime. This algorithm can be used to detect and study the daytime variation of smoke above liquid clouds. [ABSTRACT FROM AUTHOR]

The Geostationary Interferometric Microwave Sounder (GIMS) is a new concept of atmospheric microwave sounder for China's future geostationary Earth orbit meteorological satellite (FY-4). It is a microwave interferometric radiometer (MIR) using aperture synthesis and working in rotating time-sharing mode with a circular antenna array. A GIMS proof-of-concept demonstrator operating in the temperature sounding bands of 50-56 GHz has been successfully developed. The instrument uses a circular array with 28 elements (including one in the center of the array). It is capable of imaging a scene with an angular resolution of about 0.08° and a radiometric resolution of less than 1 K with 5-m integration time within a 5° field of view (FOV). Some theoretical aspects of the imaging characteristics of GIMS are discussed, such as the alias-free FOV, angular resolution, radiometric resolution, and imaging algorithm. Some early tests and preliminary imaging experiments of the GIMS demonstrator are also presented. [ABSTRACT FROM PUBLISHER]

To study the effects of radio-frequency interference (RFI) on remote sensing data, we examined five years of WindSat ocean observations from regions affected by reflected X-band emissions from geostationary communication satellites. We compared measured brightness temperatures to modeled brightness temperatures obtained using mitigated retrievals as input to the WindSat parameterized radiative transfer model, demonstrating a considerable contribution to the radiometric measurements from interference. Comparisons of potentially contaminated retrievals of sea surface temperature (SST), wind speed, and wind direction with surface reanalyses confirmed that the presence of RFI can bias retrievals while also increasing retrieval uncertainty. Mitigation removed most of the biases. The quality of mitigated SST and wind speed retrievals was comparable to uncontaminated retrievals; however, the performance of first-rank wind directions was noticeably degraded. Recommendations are given on the use of contaminated and mitigated data, from radiance assimilation to climate studies. [ABSTRACT FROM PUBLISHER]