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The Ozone Monitoring Suite-Nadir (OMS-N) instrument is the first hyperspectral remote sensor in the ultraviolet band of China's Fengyun series satellites. It can be used to detect several kinds of atmospheric constituents. This paper describes the prelaunch spectral calibration of the OMS-N onboard FengYun 3F. Several critical spectral parameters including the spectral resolution, spectral dispersion, and the instrument spectral response function were determined through laser-based measurements. A secondary peak of the instrument spectral response function from the short wavelength side of the ultraviolet band was found, and the possible influence on data applications was analyzed using a reference solar model and radiative transfer model. The results indicate that the spectral resolution and spectral accuracy of OMS-N meet the mission requirements. However, the asymmetries in the instrument spectral response function in the ultraviolet band were found near nadir rows, which are expressed as the "asymmetric central peak" and "secondary peak". The analysis results show that if the influences of the instrument spectral response function "asymmetric central peak" and "secondary peak" in the ultraviolet band are ignored, they will bring an error as large as 5% at the center of the absorption line. [ABSTRACT FROM AUTHOR]

The Gaofen-1 satellite is equipped with four wide-field-of-view (WFV) instruments, enabling an impressive spatial resolution of 16 m and a combined swath exceeding 800 km. These WFV images have shown their valuable applications across diverse fields. However, achieving accurate radiometric calibration is an essential prerequisite for establishing reliable connections between satellite signals and biophysical, as well as biochemical, parameters. However, observations with large viewing angles (>20°) pose new challenges due to the bidirectional reflectance distribution function (BRDF) effect having a pronounced impact on the accuracy of cross-radiation calibrations, especially for the off-nadir WFV1 and WFV4 cameras. To overcome this challenge, a novel approach was introduced utilizing the combined observations from the Gaofen-1 and Gaofen-6 satellites, with Landsat-8 OLI serving as a reference sensor. The key advantage of this synergistic observation strategy is the ability to obtain a greater number of image pairs that closely resemble Landsat-8 OLI reference images in terms of geometry and observation dates. This increased availability of matching images ensures a more representative dataset of the observation geometry, enabling the derived calibration coefficients to be applicable across various sun–target–sensor geometries. Then, the geometry angles and bidirectional reflectance information were put into a Particle Swarm Optimization (PSO) algorithm incorporating radiative transfer modeling. This PSO-based approach formulates cross-calibration as an optimization problem, eliminating the reliance on complex BRDF models and satellite-based BRDF products that can be affected by cloud contamination. Extensive validation experiments involving satellite data and in situ measurements demonstrated an average uncertainty of less than eight percent for the proposed cross-radiation calibration scheme. Comparisons of top-of-atmosphere (TOA) results calibrated using our proposed scheme, the previous traditional radiative transfer modeling using MODIS BRDF products for BRDF correction (RTM-BRDF) method, and official coefficients reveal the superior accuracy of our method. The proposed scheme achieves a 36.99% decrease in root mean square error (RMSE) and a 38.13% increase in mean absolute error (MAE) compared to official coefficients. Moreover, it achieves comparable accuracy to the RTM-BRDF method while eliminating the need for MODIS BRDF products, with a decrease in RMSE exceeding 14% for the off-nadir WFV1 and WFV4 cameras. The results substantiate the efficacy of the proposed scheme in enhancing cross-calibration accuracy by improving image match-up selection, efficiently removing BRDF effects, and expanding applicability to diverse observation geometries. [ABSTRACT FROM AUTHOR]

The on-orbit radiometric calibration is a fundamental task in quantitative remote sensing applications. A widely used calibration method is the cross-calibration based on Simultaneous Nadir Observation (SNO), which involves using high-precision reference instruments to calibrate lower-precision onboard instruments. However, despite efforts to match the observation time, spatial location, field geometry, and instrument spectra, errors can still be introduced during the matching processes and linear regression analysis. This paper focuses on the error generated by sample matching and the error fitting method generated by the sample fitting method. An error propagation analysis is performed to develop a generic model for assessing the uncertainty of the SNO cross-calibration method itself in meteorological satellite infrared channels. The model is validated using the payload parameters of the Hyperspectral Infrared Atmospheric Sounder (HIRAS) and the Medium Resolution Spectral Imager (MERSI) instruments aboard the FengYun-3D (FY-3D). Simulation experiments are performed considering typical bright temperatures, different background fields, and varying matching threshold conditions. The results demonstrate the effectiveness of the proposed model in capturing the error propagation chain in the SNO cross-calibration process. The model provides valuable insight into error analysis in the SNO cross-calibration method and can assist in determining the optimal sample matching threshold for achieving radiometric calibration accuracy. [ABSTRACT FROM AUTHOR]

The measurement of sea surface temperature (SST) is of utmost importance in the realm of oceanography. The increasing utilization of satellite data in SST research has highlighted the crucial need to compare and evaluate various satellite data sources. Using iQuam2 in situ SST data, this study aims to assess the accuracy of SST datasets obtained from three polar-orbiting satellites (AVHRR, Modis-Aqua, and Modis-Terra) and one geostationary satellite (Himawari-8) in the Bohai-Yellow-East China Sea (BYECS) throughout 2019. The results showed a strong correlation between satellite and in situ data, with R correlation coefficients exceeding 0.99. However, the accuracy of the satellite datasets exhibited some variability, with Himawari-8 showing the highest deviation error and MODIS-Aqua showing the least. Subsequently, the Modis-Aqua data were used as a benchmark to evaluate the SST data of the other three satellites over the previous six years (July 2015–June 2021). The results indicate that, in addition to intricate temporal variations, the deviations of the three satellites from Modis-Aqua also show significant spatial disparities due to the effect of seawater temperature. Compared to Modis-Aqua, the deviation of Himawari-8 generally displayed a negative trend in BYECS and showed pronounced seasonal variation. The deviation of AVHRR showed a negative trend across all regions except for a substantial positive value in the coastal region, with the time variation exhibiting intricate features. The SST values obtained from MODIS-Terra exhibited only marginal disparities from MODIS-Aqua, with positive values during the day and negative values at night. All three satellites showed significantly abnormal bias values after December 2020, indicating that the MODIS-Aqua-derived SST reference dataset may contain outliers beyond this period. In conclusion, the accuracy of the four satellite datasets varies across different regions and time periods. However, they could be effectively utilized and integrated with relevant fusion algorithms to synthesize high-precision datasets in the future. [ABSTRACT FROM AUTHOR]

From December 2020 to February 2022, 66 lava fountains (LF) occurred at Etna volcano (Italy). Despite their short duration (an average of about two hours), they produced a strong impact on human life, environment, and air traffic. In this work, the measurements collected from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, on board Meteosat Second Generation (MSG) geostationary satellite, are processed every 15 min to characterize the volcanic clouds produced during the activities. In particular, a quantitative estimation of volcanic cloud top height (VCTH) and ash/ice/SO2 masses' time series are obtained. VCTHs are computed by integrating three different retrieval approaches based on coldest pixel detection, plume tracking, and HYSPLIT models, while particles and gas retrievals are realized simultaneously by exploiting the Volcanic Plume Retrieval (VPR) real-time procedure. The discrimination between ashy and icy pixels is carried out by applying the Brightness Temperature Difference (BTD) method with thresholds obtained by making specific Radiative Transfer Model simulations. Results indicate a VCTH variation during the entire period between 4 and 13 km, while the SO2, ash, and ice total masses reach maximum values of about 50, 100, and 300 Gg, respectively. The cumulative ash, ice, and SO2 emitted from all the 2020–2022 LFs in the atmosphere are about 750, 2300, and 670 Gg, respectively. All the retrievals indicate that the overall activity can be grouped into 3 main periods in which it passes from high (December 2020 to March 2021), low (March to June 2021), and medium/high (June 2021 to February 2022). The different products have been validated by using TROPOspheric Monitoring Instrument (TROPOMI) polar satellite sensor, Volcano Observatory Notices for Aviation (VONA) bulletins, and by processing the SEVIRI data considering a different and more accurate retrieval approach. The products' cross-comparison shows a generally good agreement, except for the SO2 total mass in case of high ash/ice content in the volcanic cloud. [ABSTRACT FROM AUTHOR]

The cloud thermodynamic phase is a crucial parameter to understand the Earth's radiation budget, the hydrological cycle, and atmospheric thermodynamic processes. Spaceborne active remote sensing such as the synergistic radar-lidar DARDAR product is considered the most reliable method to determine cloud phase; however, it lacks large-scale observations and high repetition rates. These can be provided by passive instruments such as SEVIRI aboard the geostationary Meteosat Second Generation (MSG) satellite, but passive remote sensing of the thermodynamic phase is challenging and confined to cloud top. Thus, it is necessary to understand to what extent passive sensors with the characteristics of SEVIRI are expected to provide a relevant contribution to cloud phase investigation. To reach this goal, we collect five years of DARDAR data to model the cloud top phase (CTP) for MSG/SEVIRI and create a SEVIRI-like CTP through an elaborate aggregation procedure. Thereby, we distinguish between ice (IC), mixed-phase (MP), supercooled (SC), and warm liquid (LQ). Overall, 65% of the resulting SEVIRI pixels are cloudy, consisting of 49% IC, 14% MP, 13% SC, and 24% LQ cloud tops. The spatial resolution has a significant effect on the occurrence of CTP, especially for MP cloud tops, which occur significantly more often at the lower SEVIRI resolution than at the higher DARDAR resolution (9%). We find that SC occurs most frequently at high southern latitudes, while MP is found mainly in both high southern and high northern latitudes. LQ dominates in the subsidence zones over the ocean, while IC occurrence dominates everywhere else. MP and SC show little seasonal variability apart from high latitudes, especially in the south. IC and LQ are affected by the shift of the Intertropical Convergence Zone. The peak of occurrence of SC is at −3 ∘ C, followed by that for MP at −13 ∘ C. Between 0 and −27 ∘ C, the occurrence of SC and MP dominates IC, while below −27 ∘ C, IC is the most frequent CTP. Finally, the occurrence of cloud top height (CTH) peaks lower over the ocean than over land, with MP, SC, and IC being undistinguishable in the tropics but with separated CTH peaks in the rest of the MSG disk. Finally, we test the ability of a state-of-the-art AI-based ice cloud detection algorithm for SEVIRI named CiPS (Cirrus Properties for SEVIRI) to detect cloud ice. We confirm previous evaluations with an ice detection probability of 77.1% and find a false alarm rate of 11.6%, of which 68% are due to misclassified cloud phases. CiPS is not sensitive to ice crystals in MP clouds and therefore not suitable for the detection of MP clouds but only for fully glaciated (i.e., IC) clouds. Our study demonstrates the need for the development of dedicated cloud phase distinction algorithms for all cloud phases (IC, LQ, MP, SC) from geostationary satellites. [ABSTRACT FROM AUTHOR]

The decades-long Clouds and Earth's Radiant Energy System (CERES) Project includes both cloud and radiation measurements from instruments on the Aqua, Terra, and Suomi National Polar-orbiting Partnership (SNPP) satellites. To build a reliable long-term climate data record, it is important to determine the accuracies of the parameters retrieved from the sensors on each satellite. Cloud amount, phase, and top height derived from radiances taken by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the SNPP are evaluated relative to the same quantities determined from measurements by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft. The accuracies of the VIIRS cloud fractions are found to be as good as or better than those for the CERES amounts determined from Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) data and for cloud fractions estimated by two other operational algorithms. Sensitivities of cloud fraction bias to CALIOP resolution, matching time window, and viewing zenith angle are examined. VIIRS cloud phase biases are slightly greater than their CERES MODIS counterparts. A majority of cloud phase errors are due to multilayer clouds during the daytime and supercooled liquid water clouds at night. CERES VIIRS cloud-top height biases are similar to those from CERES MODIS, except for ice clouds, which are smaller than those from CERES MODIS. CERES VIIRS cloud phase and top height uncertainties overall are very similar to or better than several operational algorithms, but fail to match the accuracies of experimental machine learning techniques. The greatest errors occur for multilayered clouds and clouds with phase misclassification. Cloud top heights can be improved by relaxing tropopause constraints, improving lapse-rate to model temperature profiles, and accounting for multilayer clouds. Other suggestions for improving the retrievals are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, an irradiance-based absolute radiometric calibration campaign at Baotou calibration site during June and July 2018 was described. This radiometric calibration campaign made use of six radiometric calibration tarps. The synchronous measurements of parameters such as surface reflectance, atmospheric parameters, and diffuse-to-global irradiance ratio were collected at the satellite overpass. The top-of-atmospheric radiance was predicted by radiative transfer model with these synchronous measurements. The linear relationship between DNs of satellite sensor and band-specific top-of-atmospheric spectral radiance was established, and a stable and reliable absolute calibration coefficient of ZY3-02 MUX was determined in this campaign. We compared the calibration results of the irradiance-based method with those of the reflectance-based method. The results suggested that the irradiance-based method is better than reflectance-based method. [ABSTRACT FROM AUTHOR]

This paper develops a framework for geolocating ground-based moving targets with images taken from dual unmanned aerial vehicles (UAVs). Unlike the usual moving-target geolocation methods that rely heavily on accurate navigation state sensors or assumptions of the known target's altitude, the proposed framework does not have the same limitations and performs geolocation of moving targets utilizing dual UAVs equipped with the low-quality navigation state sensors. Considering the Gaussian measurement errors and yaw-angle measurement bias provided by low-quality sensors, we first propose an epipolar constraint-based corresponding-point-matching method, which enables the historical measurement data to be used to estimate the current position of the moving target; after that, we propose a target altitude estimation method based on multiview geometry, which utilizes multiple images, including historical images, to estimate the altitude of the moving target; finally, considering the negative influence of yaw-angle measurement bias on the processes of target altitude estimation and parameter regression, we take advantage of multiple iterations among the two processes to accurately estimate the moving target's two-dimensional position and the yaw-angle measurement biases of two UAVs. The effectiveness and practicability of the framework proposed in this paper are proved by simulation experiments and actual flight experiments. [ABSTRACT FROM AUTHOR]

Multi-epoch double-differenced pseudorange observation (MDPO) is a dual-satellite lunar navigation algorithm specially designed for a precursor mission, using a minimum number of lunar orbiting small satellites to realize a GNSS-like radio navigation system for the Moon. In this study, we evaluated the performance of the MDPO algorithm by using real pseudorange measurements obtained from a pair of GNSS ground stations, one of which represented a lander, and the other a rover on the Moon. It was natural that the resulting positioning accuracy varied largely by satellite geometry, but the estimated error distributions of the double-differenced pseudorange observations were consistent and agreed with the predicted value. The results showed that the MDPO algorithm worked properly with the real GNSS observables and was capable of providing the expected navigation performance for future lunar exploration missions. [ABSTRACT FROM AUTHOR]

For many years, the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments have been widely used to monitor the condition of surface vegetation. Since the polar-orbiting satellite provides limited daily samples on surface, a completed spatial coverage of land vegetation is often relied on over multiple days of observations. In this study, observations from the Japanese geostationary satellite imager Advanced Himawari Imagers (AHI) are used to derive the surface vegetation index. The AHI reflectance at visible and near-infrared bands are first corrected to the surface reflectance by using the 6S radiative transfer model. The AHI surface reflectance from various viewing angles and solar geometry is further normalized to form an angular-independent reflectance by using a BRDF model. Finally, the surface vegetation index is calculated and synthesized from the daytime AHI data. It is found that the high-frequency AHI observations can significantly reduce the impact of clouds on compositing land NDVI and require a shorter time for a complete coverage of surface conditions. Also, a single NDVI image from AHI exhibits spatial distribution similar to that from 16 days of MODIS data. [ABSTRACT FROM AUTHOR]

The North American Derecho of 29–30 June 2012 exhibits many classic progressive and serial derecho features. It remains one of the highest-impact derecho-producing convective systems (DCS) over CONUS since 2000. This research effort enhances the understanding of the science of operational forecasting of severe windstorms through examples of employing new satellite and ground-based microwave and vertical wind profile data. During the track of the derecho from the upper Midwestern U.S. through the Mid-Atlantic region on 29 June 2012, clear signatures associated with a severe MCS were apparent in polar-orbiting satellite imagery, especially from the EPS METOP-A Microwave Humidity Sounder (MHS), Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager Sounder (SSMIS), and NASA TERRA Moderate Resolution Imaging Spectroradiometer (MODIS). In addition, morning (descending node) and the evening (ascending node) METOP-A Infrared Atmospheric Sounding Interferometer (IASI) soundings are compared to soundings from surface-based Radiometrics Corporation MP-3000 series microwave radiometer profilers (MWRPs) along the track of the derecho system. The co-located IASI and MWRP soundings revealed a pre-convective environment that indicated a favorable volatile tropospheric profile for severe downburst wind generation. An important outcome of this study will be to formulate a functional relationship between satellite-derived parameters and signatures, and severe convective wind occurrence. Furthermore, a comprehensive approach to observational data analysis involves both surface- and satellite-based instrumentation. Because this approach utilizes operational products available to weather service forecasters, it can feasibly be used for monitoring and forecasting local-scale downburst occurrence within derecho systems, as well as larger-scale convective wind intensity associated with the entire DCS. [ABSTRACT FROM AUTHOR]

Satellite imagery and data are playing an increasingly important role in scientific studies of the Earth and its climate. The scientific community has been demanding ever-increasing capabilities and accuracy from the data provided by these satellites. One key instrument on board a series of satellite platforms is the Visible Infrared Imaging Radiometer Suite (VIIRS), which provides high-quality data of the Earth from low Earth orbit covering the visible to long-wave infrared parts of the spectrum. The fourth build in the series, set to be launched on the Joint Polar-orbiting Satellite System 3 (JPSS-3) platform, has recently completed its main ground calibration program and is set to be integrated into the satellite bus in the near future. This calibration program covered a comprehensive series of performance metrics designed to demonstrate the quality of the science data and ensure the instrument can maintain its calibration successfully once on-orbit. The subject of this work covers the radiometric calibration metrics including dynamic range, signal-to-noise ratio/noise equivalent differential temperature (SNR/NEdT), polarization sensitivity, scattered light response, relative spectral response, response versus scan angle, and uniformity, as well as uncertainties; all key metrics met or exceeded their design requirements with some minor exceptions. Comparisons to previous builds will also be provided. [ABSTRACT FROM AUTHOR]

A significant difficulty in urban studies is obtaining urban areas. Nighttime light (NTL) data provide efficient approaches to map urban areas. Previous methods have utilized visual particularities of cities with ancillary data to obtain the optimal thresholds. How cities behave differently from rural areas should be considered. A Zipf's law-based method is proposed to bootstrap the optimal threshold based on the statistical properties of a Zipf's law model on continuous thresholds at the country scale. In our method, the Zipf's law model is utilized to quantify fractal, self-organized, and agglomeration behaviors of cities. The three-phase cluster dynamics are discovered and the abrupt transition between Phase 1 and Phase 2 denotes the rural-urban demarcation point. The urban areas are derived by the proposed method from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data in 2013 in China. An accuracy assessment is conducted to compare it with the GlobeLand30-2010 data and the overall accuracy has directly confirmed the effectiveness of the method. The validation using point of interest (POI) data verifies that the urban areas show strong responses to social interactions and production with R2 values of 0.91 and 0.92, respectively, implying that the city areas extracted by our method can be a proxy for human activities. Comparisons with existing methods validate the effectiveness and high degree of automation of the proposed method in mapping urban areas at the country level. According to our analyses, the Zipf's law-based method shows great potential to provide a universal criterion to map urban areas from the perspective of the behaviors of urban systems without ancillary data, and a valuable tool for spatial and temporal urban studies. [ABSTRACT FROM AUTHOR]

Volcanic activity is observed worldwide with a variety of ground and space-based remote sensing instruments, each with advantages and drawbacks. No single system can give a comprehensive description of eruptive activity, and so, a multi-sensor approach is required. This work integrates infrared and microwave volcanic ash retrievals obtained from the geostationary Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI), the polar-orbiting Aqua-MODIS and ground-based weather radar. The expected outcomes are improvements in satellite volcanic ash cloud retrieval (altitude, mass, aerosol optical depth and effective radius), the generation of new satellite products (ash concentration and particle number density in the thermal infrared) and better characterization of volcanic eruptions (plume altitude, total ash mass erupted and particle number density from thermal infrared to microwave). This approach is the core of the multi-platform volcanic ash cloud estimation procedure being developed within the European FP7-APhoRISM project. The Mt. Etna (Sicily, Italy) volcano lava fountaining event of 23 November 2013 was considered as a test case. The results of the integration show the presence of two volcanic cloud layers at different altitudes. The improvement of the volcanic ash cloud altitude leads to a mean difference between the SEVIRI ash mass estimations, before and after the integration, of about the 30%. Moreover, the percentage of the airborne "fine" ash retrieved from the satellite is estimated to be about 1%-2% of the total ash emitted during the eruption. Finally, all of the estimated parameters (volcanic ash cloud altitude, thickness and total mass) were also validated with ground-based visible camera measurements, HYSPLIT forward trajectories, Infrared Atmospheric Sounding Interferometer (IASI) satellite data and tephra deposits. [ABSTRACT FROM AUTHOR]

Cloud property data sets derived from passive sensors onboard the polar orbiting satellites (such as the NOAA's Advanced Very High Resolution Radiometer) have global coverage and now span a climatological time period. Synoptic surface observations (SYNOP) are often used to characterize the accuracy of satellite-based cloud cover. Infrequent overpasses of polar orbiting satellites combined with the 3- or 6-h SYNOP frequency lead to collocation time differences of up to 3 h. The associated collocation error degrades the cloud cover performance statistics such as the Hanssen-Kuiper's discriminant (HK) by up to 45%. Limiting the time difference to 10 min, on the other hand, introduces a sampling error due to a lower number of corresponding satellite and SYNOP observations. This error depends on both the length of the validated time series and the SYNOP frequency. The trade-off between collocation and sampling error call for an optimum collocation time difference. It however depends on cloud cover characteristics and SYNOP frequency, and cannot be generalized. Instead, a method is presented to reconstruct the unbiased (true) HK from HK affected by the collocation differences, which significantly (t-test p < 0.01) improves the validation results. [ABSTRACT FROM AUTHOR]

The launch of VIIRS on-board the Suomi-National Polar-orbiting Partnership (S-NPP) on 28 October 2011, marked the beginning of the next chapter on nighttime lights observation started by the Defense Meteorological Satellite Program's (DMSP) OLS sensor more than two decades ago. The VIIRS observes the nighttime lights on Earth through its day-night band (DNB), a panchromatic channel covering the wavelengths from 500 nm to 900 nm. Compared to its predecessors, the VIIRS DNB has a much improved spatial/temporal resolution, radiometric sensitivity and, more importantly, continuous calibration using on-board calibrators (OBCs). In this paper, we describe the current state of the NASA calibration and characterization methodology used in supporting mission data quality assurance and producing consistent mission-wide sensor data records (SDRs) through NASA's Land Product Evaluation and Analysis Tool Element (Land PEATE). The NASA calibration method utilizes the OBCs to determine gains, offset drift and sign-to-noise ratio (SNR) over the entire mission. In gain determination, the time-dependent relative spectral response (RSR) is used to correct the optical throughput change over time. A deep space view acquired during an S-NPP pitch maneuver is used to compute the airglow free dark offset for DNB's high gain stage. The DNB stray light is estimated each month from new-moon dark Earth surface observations to remove the excessive stray light over the day-night terminators. As the VIIRS DNB on-orbit calibration is the first of its kind, the evolution of the calibration methodology is evident when the S-NPP VIIRS's official calibrations are compared with our latest mission-wide reprocessing. In the future, the DNB calibration methodology is likely to continue evolving, and the mission-wide reprocessing is a key to providing consistently calibrated DNB SDRs for the user community. In the meantime, the NASA Land PEATE provides an alternative source to obtain mission-wide DNB SDR products that are calibrated based on the latest NASA DNB calibration methodology. [ABSTRACT FROM AUTHOR]

Time series of remotely sensed data are an important source of information for understanding land cover dynamics. In particular, the fraction of absorbed photosynthetic active radiation (fAPAR) is a key variable in the assessment of vegetation primary production over time. However, the fAPAR series derived from polar orbit satellites are not continuous and consistent in space and time. Filtering methods are thus required to fill in gaps and produce high-quality time series. This study proposes an adapted (iteratively reweighted) local regression filter (LOESS) and performs a benchmarking intercomparison with four popular and generally applicable smoothing methods: Double Logistic (DLOG), smoothing spline (SSP), Interpolation for Data Reconstruction (IDR) and adaptive Savitzky-Golay (ASG). This paper evaluates the main advantages and drawbacks of the considered techniques. The results have shown that ASG and the adapted LOESS perform better in recovering fAPAR time series over multiple controlled noisy scenarios. Both methods can robustly reconstruct the fAPAR trajectories, reducing the noise up to 80% in the worst simulation scenario, which might be attributed to the quality control (QC) MODIS information incorporated into these filtering algorithms, their flexibility and adaptation to the upper envelope. The adapted LOESS is particularly resistant to outliers. This method clearly outperforms the other considered methods to deal with the high presence of gaps and noise in satellite data records. The low RMSE and biases obtained with the LOESS method (¶rMBE¶ < 8%; rRMSE < 20%) reveals an optimal reconstruction even in most extreme situations with long seasonal gaps. An example of application of the LOESS method to fill in invalid values in real MODIS images presenting persistent cloud and snow coverage is also shown. The LOESS approach is recommended in most remote sensing applications, such as gap-filling, cloud-replacement, and observing temporal dynamics in situ where rapid seasonal changes are produced. [ABSTRACT FROM AUTHOR]

Based on the results of mathematical modeling, we consider the possibility to estimate the plasma frequency F2 layer maximum of the polar ionosphere (critical frequency, foF2) using frequency-sweeping radiation from a highly elliptical spacecraft orbit in the Arctic zone. Our modeling concerning the energy problem of radio sensing consisted of analyzing wave field parameters, received field strength, and SNR on two radio paths with the distances 1900 and 2500 km along the earth's surface, with the satellite height varying from 10,000 to 30,000 km. Radio path orientations were selected to be close to the classical limit cases of radio wave propagation in the anisotropic ionospheric plasma: quasi-longitudinal approximation and, to a large extent, the quasi-transversal one for the quiet midday and midnight conditions. As a result of these simulations and following specific spacecraft conditions, working with an optimal probing signal was proposed for the appropriate emission power for the onboard transmitter. In the inverse problem of radio sounding of an ionized media, common mathematical inaccuracy in foF2 calculated from the transionogram, frequency dependence of the probing signals magneto-ionic group delay, was estimated. Considering and founding a possible realization of the method, physical prerequisites are discussed based on the experimental data of radio waves passing the 16,000 km long radio path for Moscow–Antarctica (UAS Vernadsky). [ABSTRACT FROM AUTHOR]

The Geostationary Lightning Mapper (GLM) on the Geostationary Operational Environmental Satellite 16 (GOES-16) detects total lightning continuously, with a high spatial resolution and detection efficiency. Coincident data from the GLM and the Advanced Baseline Imager (ABI) are used to explore the correlation between the cloud top properties and flash activity across the continental United States (CONUS) sector from May to September 2020. A large number of collocated infrared (IR) brightness temperature (TBB), cloud top height (CTH) and lightning data provides robust statistics. Overall, the likelihood of lightning occurrence and high flash density is higher if the TBB is colder than 225 K. The higher CTH is observed to be correlated with a larger flash rate, a smaller flash size, stronger updraft, and larger optical energy. Furthermore, the cloud top updraft velocity (w) is estimated based on the decreasing rate of TBB, but it is smaller than the updraft velocity of the convective core. As a result, the relationship between CTH and lightning flash rate is investigated independently of w over the continental, oceanic and coastal regimes in the tropics and mid-latitudes. When the CTH is higher than 12 km, the flash rates of oceanic lightning are 38% smaller than those of both coastal and continental lightning. In addition, it should be noted that more studies are necessary to examine why the oceanic lightning with low clouds (CTH < 8 km) has higher flash rates than lightning over land and coast. Finally, the exponents of derived power relationship between CTH and lightning flash rate are smaller than four, which is underestimated due to the GLM detection efficiency and the difference between IR CTH and 20 dBZ CTH. The results from combining the ABI and GLM products suggest that merging multiple satellite datasets could benefit both lightning activity and parameterization studies, although the parallax corrections should be considered. [ABSTRACT FROM AUTHOR]

The High-resolution Infrared Radiation Sounder (HIRS) has been flown on 17 polar-orbiting satellites between the late 1970s and the present day. HIRS applications require accurate characterisation of uncertainties and inter-channel error correlations, which has so far been lacking. Here, we calculate error correlation matrices by accumulating count deviations for sequential sets of calibration measurements, and then correlating deviations between channels (for a fixed view) or views (for a fixed channel). The inter-channel error covariance is usually assumed to be diagonal, but we show that large error correlations, both positive and negative, exist between channels and between views close in time. We show that correlated error exists for all HIRS and that the degree of correlation varies markedly on both short and long timescales. Error correlations in excess of 0.5 are not unusual. Correlations between calibration observations taken sequentially in time arise from periodic error affecting both calibration and Earth counts. A Fourier spectral analysis shows that, for some HIRS instruments, this instrumental effect dominates at some or all spatial frequencies. These findings are significant for application of HIRS data in various applications, and related information will be made available as part of an upcoming Fundamental Climate Data Record covering all HIRS channels and satellites. [ABSTRACT FROM AUTHOR]