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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]

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]

This paper reports progress in the development of an image formation algorithm suitable for stripmap space-surface bistatic synthetic aperture radar. A description of the proposed algorithm, which is a modification of the standard range—Doppler algorithm, is provided for the case when the transmitter and the receiver have parallel flight paths and unequal velocities. Both simulation and initial experimental results are presented to verify our analysis. [ABSTRACT FROM AUTHOR]

The combination of multiple detectors and a double-sided mirror in the Moderate Resolution Imaging Spectroradiometer (MODIS) introduces striping in the imagery despite the use of onboard calibration systems. This results in a sharp unidirectional pattern across scan lines and leads to significant radiometric uncertainties in level-1 radiances, which further propagate into derived level-2 geophysical products such as the sea surface temperature. Quantifying and minimizing such uncertainties are the key steps in the improvement of remotely sensed data from MODIS. This paper describes a deterministic technique for the estimation of biases due to detector-to-detector miscalibration, mirror banding, and random striping. Using a unidirectional variational model previously introduced for destriping purposes, the scan line noise is first extracted from the observed scenes and then used to evaluate the thermal bias of detectors. The algorithm is applied to Terra and Aqua MODIS thermal emissive bands. Its qualitative performance is illustrated on several representative scenes, and quantitative results of detector biases are reported and discussed. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm, Searchlight, capable of tracking precipitation advection through the complex spatiotemporal sampling pattern of multiplatform low-Earth-orbiting (LEO) passive and active microwave satellite data sets. The algorithm is based on the concept of “shining” a searchlight forward in time along proposed advection velocity vectors from a given LEO overpass. The searchlight “beam” links precipitation patterns in the current satellite overpass to corresponding patterns in multiple future overpasses. A global optimization determines an advection model by maximizing mutual information between linked precipitation measurements. The algorithm has been validated against surface radar over a region covering the Central and Eastern U.S. [ABSTRACT FROM PUBLISHER]

This paper describes a technique that uses the information gathered from the geostationary instrumentation [Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI)] to investigate the pixels detected as "uncertain" by the operational Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-mask algorithm. This technique analyzes the uncertain MODIS areas by using a time series of MSG-SEVIRI images taken at infrared (IR) and visible (VIS) wavelengths. In order to classify the uncertain pixels related to the granules acquired during the daytime and completely included in the high-resolution visible (HRV) image, the spectral and textural features derived from a time series of HRV images are used as inputs in a K-nearest neighbor (K-NN) classifier. For the areas not included in the HRV image and for those acquired during nighttime, the input parameters are determined from a time series of IR/VIS and IR images, respectively. The K-NN classifier detected 52.0%, 48.7%, and 37.0% of the MOD35/MYD35 uncertain pixels analyzed over land and 54.5%, 45.4%, and 49.7% of those analyzed over sea as cloud free, when using HRV, IR, and IR/VIS features as inputs, respectively. Percentages of 39.8%, 31.8%, and 37.3% of the pixels analyzed over land and 40.7%, 47.4%, and 38.0% of those analyzed over sea were classified as cloudy when using HRV, IR, and IR/VIS features as inputs, respectively. The remaining uncertain pixels were classified as low confidence cloudy or cloud free by the K-NN classifier. A set of comparisons was made with cloud-profiling radar/cloud-aerosol lidar with orthogonal polarization 2B-Geometrical Profiling-Lidar product results. [ABSTRACT FROM AUTHOR]

The Geostationary Operational Environmental Satellite (GOES) program is developing the Advanced Baseline Imager (ABI), a new generation sensor to be carried onboard the GEOS-R satellite (launch expected in 2014). Compared to the current GOES Imager, ABI will have significant advantages for retrieving land surface temperature (LST) as well as providing qualitative and quantitative data for a wide range of applications. The infrared bands of the ABI sensor are designed to achieve a spatial resolution of 2 km at nadir and a noise equivalent temperature of 0.1 K. These improve the imager specifications and compare well with those of polar-orbiting sensors (e.g., Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer). In this paper, we discuss the development of a split window LST algorithm for the ABI sensor. First, we simulated ABI sensor data using the MODTRAN radiative transfer model and NOAA88 atmospheric profiles. To model land conditions, we developed emissivity data for 78 virtual surface types using the surface emissivity library from Snyder et al. Using the simulation results, we performed regression analyses with the candidate LST algorithms. Algorithm coefficients were stratified for dry and moist atmospheres as well as for daytime and nighttime conditions. We estimated the accuracy and sensitivity of each algorithm for different sun-view geometries, emissivity errors, and atmospheric assessments. Finally, we evaluated the most promising algorithm using real data from the GOES-8 Imager and SURFace RADiation Network. The results indicate that the optimized LST algorithm meets the required accuracy (2.3 K) of the GOES-R mission. [ABSTRACT FROM AUTHOR]

This paper introduces a hierarchical mesh-based tracking algorithm designed to retrieve two-dimensional cloud-top advection to near-pixel resolutions from geostationary satellite imagery. The algorithm associates each cloudy pixel in the current image with pixel locations in the previous and subsequent images corresponding to the equivalent location within the same cloud. Such a product resembles a high-resolution cloud-derived wind field but explicitly incorporates the effects of cloud growth, deformation, and dispersal. Within the algorithm, cloud advection is represented by the topological transformation of a rectangular mesh. An iterative procedure progressively refines the spatial resolution of an initially coarse mesh until image pixel resolution is reached. The algorithm has been validated using Geostationary Operational Environmental Satellites (GOES) infrared imagery. [ABSTRACT FROM AUTHOR]

Validation of the land surface temperature (LST) algorithm and product is a challenging task for future Geostationary Operational Environmental Satellite R-Series (GOES-R) applications. Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) full-disk data have been used as the key proxy data for the GOES-R LST algorithm and product development. A split window algorithm developed to generate GOES-R LST was applied to MSG SEVIRI data with the algorithm coefficients adjusted to the specific SEVIRI bands. The retrieved LST values were evaluated with in situ LST obtained from four validation stations with different surface features over various time periods. The results presented here clearly highlight the importance of accurate and seasonally representative site characterizations for the LST validation process. Furthermore, the study gives valuable insights into the limitations of the current version of the LST retrieval algorithm and on how to further refine it for the next generation of satellite sensors. [ABSTRACT FROM PUBLISHER]

A space-variant chirp scaling algorithm based on the range cell migration (RCM) equalization and azimuth subband synthesis has been studied to process simulated geosynchronous synthetic aperture radar (GEO-SAR) data. The acceptable order of terms in polynomials for the slant range models in the RCM correction and phase error compensation, division of subband, and suppression of grating lobes of the subbands was investigated. Qualitatively and quantitatively, the method was able to focus simulated GEO-SAR signals well. Finally, the constraint on the spatial extent of azimuth and range dimensions using the algorithm was assessed. [ABSTRACT FROM PUBLISHER]

A new cloud detection technique has been developed and applied to GOES-12 Imager data. The bispectral composite threshold (BCT) technique uses only the 11- and 3.9-μm channels, and composite imagery generated from these channels, in a four-step cloud detection procedure to produce a binary cloud mask at single-pixel resolution. An innovative aspect of this algorithm is the use of 20-day composites of the 11- and the 11-3.9-μm channel difference imagery to represent spatially and temporally varying clear-sky thresholds for the bispectral cloud tests. The BCT cloud detection technique has been validated against a "truth" data set generated by the manual determination of the sky conditions from available satellite imagery for four seasons during 2003-2004. The day-and-night algorithm has been shown to determine the correct sky conditions 87.6% of the time (on average) over the eastern two-thirds of the U.S. and surroundings oceans. The incorrectly determined conditions arose from missing clouds 8.9% of the time or from overdetermining clouds 3.5% of the time. Nearly 82% of the misses came in the presence of low clouds. Only small variations in algorithm performance occurred between day-night, land-ocean, and between seasons. The algorithm performed best in the warmer seasons (90.9% correct during the summer versus 81.8% correct in the winter season) and during the day, when the solar illumination provides enhanced surface atmospheric cloud contrast in the infrared channels, and least well during the winter season. The algorithm was found to slightly underdetermine clouds at night and during times of low sun angle and tends to be cloud conservative during the day, particularly in the summertime. [ABSTRACT FROM AUTHOR]