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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 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]

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]

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]

From 2011 to 2015, 49 lava fountains occurred at Etna volcano. In this work, the measurements carried out from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, on board the Meteosat Second Generation (MSG) geostationary satellite, are processed to realize a proximal monitoring of the eruptive activity for each event. The SEVIRI measurements are managed to provide the time series of start and duration of eruption and fountains, Time Averaged Discharge Rate (TADR) and Volcanic Plume Top Height (VPTH). Due to its temperature responsivity, the eruptions start and duration, fountains start and duration and TADR are realized by exploiting the SEVIRI 3.9 μm channel, while the VPTH is carried out by applying a simplified procedure based on the SEVIRI 10.8 μm brightness temperature computation. For each event, the start, duration and TADR have been compared with ground-based observations. The VPTH time series is compared with the results obtained from a procedures-based on the volcanic cloud center of mass tracking in combination with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) back-trajectories. The results indicate that SEVIRI is generally able to detect the start of the lava emission few hours before the ground measurements. A good agreement is found for both the start and the duration of the fountains and the VPTH with mean differences of about 1 h, 50 min and 1 km respectively. [ABSTRACT FROM AUTHOR]