Satellite Overpass Research Articles

Real-time gigahertz free-space quantum key distribution within an emulated satellite overpass.

Satellite quantum key distribution (SatQKD) intermediated by a trusted satellite in a low-Earth orbit to ground stations along the satellite's path allows remote users to connect securely. To establish a secure connection, a SatQKD session must be conducted to each user over a dynamically changing free-space link, all within just a few hundred seconds. Because of the short time and large losses under which the QKD protocol will be implemented, it has not yet been possible to form a complete key by transmitting all the relevant information required within a single overpass of the satellite. Here, we demonstrate a real-time QKD system that is capable of forming a 4.58-megabit secure key between two nodes within an emulated satellite overpass. We anticipate that our system will set the stage for practical implementations of intercontinental quantum secure communications that can operate over large networks of nodes and enable the secure transmission of data globally.

Evaluating Landsat-9 TIRS-2 calibrations and land surface temperature retrievals against ground measurements using multi-instrument spatial and temporal sampling along transects

High-resolution TIR data are essential for a wide range of studies, e.g., in environmental monitoring, urban effects, water stress, agricultural productivity, and land/water resources. Landsat series provides high-resolution TIR data, but data accuracy must be ensured to contribute to these fields. After the calibration problems of Landsat 8 (L8) TIRS due to a stray light effect, efforts were made in the design of Landsat 9 (L9) TIRS-2 to mitigate this effect. In any case, field calibration and validation activities are necessary to evaluate the accuracy of recently launched sensors. These activities require high-quality ground TIR radiance data that are representative at satellite spatial resolution and that quantify spatial–temporal fluctuations at the experimental sites concurrently to satellite overpasses. However, these requirements are not always accomplished in the literature.This paper provides vicarious calibrations of the L9 TIRS-2 data with accurate ground measurements acquired using multi-instrument spatial and temporal sampling in a rice paddy area from December 2021 to August 2023, in order to contribute to the calibration and validation of L9 TIRS-data.In March 1, 2023 L9 TIRS-2 scenes started to be reprocessed and calibration was updated. Both the original calibration and that after reprocessing were evaluated. Close results were obtained for band 10 (b10). When comparing satellite brightness temperatures and those determined from in situ LSTs, systematic differences of −0.3 K were shown, with root-mean-square differences (RMSDs) of 0.5 K. Results for band 11 showed a bias of −0.1 K for the original data and of 0.2 K after the reprocessing, with RMSDs of 0.6 K.The L2 LST product generated operationally from b10 data was also evaluated, together with alternative single-channel (SC) algorithms. A bias (RMSD) of 0.4 K (1.1 K) was obtained for the product. However, negative biases were shown both with the radiative transfer equation used on b10 radiances and the alternative SC algorithm.Finally, we validated six types of split-window (SW) algorithms applied to L9 TIRS-2 data before and after the reprocessing. A systematic difference of about −0.6 K between SW LST biases when using the reprocessed and original data was observed. In any case, for most of the algorithms with original and reprocessed data, LST biases and standard deviations were within the recommended threshold of 1 K, which proves the good performance of L9 TIRS-2 data to monitor LST at high spatial resolution.

TROPOMI NO2 Sentinel-5P data in the Community of Madrid: A detailed consistency analysis with in situ surface observations

Excess reactive nitrogen emissions from anthropogenic activities can negatively impact the natural environment. The sensor TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (Sentinel-5P) satellite is capable of providing data on tropospheric NO2 vertical column densities (VCDs). This work investigates NO2 data retrieved from TROPOMI to study the spatial and temporal patterns of NO2 pollution compared with surface-level concentration measurements in the Community of Madrid in 2022. A high linear correlation coefficient (r) was obtained by comparing daily Level-3 tropospheric TROPOMI NO2 data with ground-based data from air quality monitoring stations at satellite overpass time (r = 0.78). Since the overpasses of Sentinel-5P over the region consistently occur between 12:00 and 4:00 p.m. local time, a second correlation analysis was carried out by averaging all available data from both sources within that interval, resulting in an even higher coefficient (r = 0.81). Results showed a lower correlation at rural sites compared to urban and suburban sites. Examining the monthly and weekly variations of NO2 concentration from satellite data and in situ measurements, similar tendencies in NO2 concentration were observed from both sources. In agreement with previous studies, the cold months and weekdays exhibited higher NO2 concentrations. It was concluded that TROPOMI NO2 data and in situ measurements yield a strong correlation for the Community of Madrid target area, and TROPOMI data can be further investigated to propose pollution mitigation policies.

Perturbation to North Atlantic Ocean‐Climate Dynamics Tripled Whitings Mud Production in the Bahamas

AbstractProfound changes in Earth's biogeochemical cycles are recorded in the sedimentology and geochemistry of carbonate sediments, with much of the early geological history of our planet recorded in carbonate mud precipitated from seawater. Despite their relevance for understanding early Earth, the trigger for whitings—carbonate spontaneously precipitated from the water column—remains intensely debated. Here, we used a neural network to map whitings imaged by daily MODIS Aqua satellite overpasses between 2003 and 2018 on Great Bahama Bank (GBB). Our results show that the frequency of whitings after 2011 increased by 3.3× compared to those recorded between 2003 and 2010, with the frequency of winter whitings increasing more than those in the summer. We contend that the period of increased whitings frequency was spurred by concurrent perturbations to the North Atlantic Oscillation, Atlantic Multidecadal Oscillation, and the El Niño Southern Oscillation in 2010 that facilitated enhanced hydrodynamic mixing between the waters atop GBB and those that surround it. We conclude that whitings can exhibit a sensitive but non‐linear response to ocean‐atmosphere dynamics which is unlikely to be resolved in the geological record using conventional stratigraphic techniques. This pronounced fluctuation in mud production on short timescales contrasts with expectations of persistent tranquility in the depositional environments that produce lime mud. Our results suggest that long‐established links between ocean‐atmosphere dynamics and the lime mud budget through Earth history may require reevaluation.

Diurnal time representation of MODIS, VIIRS, MISR, and AHI over Asia and Oceania

Retrievals of satellite aerosol optical depth (AOD) products are used for studies of air pollution, energy balance, and climate change. However, few studies have evaluated the diurnal time representation (DTR) of satellite AOD products. In this study, we proposed a statistically valid threshold method to investigate the DTR of Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Multi-angle Imaging Spectroradiometer (MISR), and Advanced Himawari Imager (AHI) AOD products by comparing their retrievals to the daily AERONET AOD values and explored the ability of AHI and AERONET to capture daily aerosol variability at 54 sites in Asia and Oceania from July 2015 to December 2019. For polar-orbiting satellites, based on collocations with AERONET measurements, MISR-derived AOD showed high consistency with daily average AERONET AOD, with a correlation coefficient (R) of 0.94 and 80.85% matchups falling into the envelopes of expected error (EE, ± (0.05 + 0.15 × AODAERONET)). Compared with single MODIS products, a combination of satellite data from the Terra and Aqua MODIS dark target (DT) algorithm might obtain a dataset with better DTR (R = 0.94 and %Within EE = 72.10). The satellite-retrieved AOD did a good job to represent the daily mean AERONET AOD, because AERONET AOD averaged within a ± 30 min time window of different satellite overpasses closely mirrored the daily mean AERONET AOD. Furthermore, the seasonal average diurnal variations (SADV) of AERONET AOD were changing insignificant, except for the urban coverage area in the Southern Hemisphere and equatorial climate. For geostationary satellite, the DTR of AHI was relatively poorer in comparison to the other sensors (R = 0.84, %Within EE = 58.00), and AHI AOD was seriously underestimated in South Asia and overestimated in Oceania. Besides, the SADVs of AHI AOD presented several patterns summarized as wave type, unimodal type, stable fluctuation type, and other types, which meant that AHI AOD retrievals did not accurately capture the diurnal variability of aerosols. This study provides a reference for the aerosol community to select satellite AOD products with appropriate DTR.

Absolute radiometric calibration evaluation of the thermal infrared spectrometer onboard SDGSAT-1

ABSTRACTA thermal infrared spectrometer is an important sensor onboard the SDGSAT-1 dedicated to serving the 2030 Agenda for Sustainable Development of the United Nations. Field campaigns were conducted from January to December 2022 to evaluate its radiometric calibrations. A radiance-based calibration method was used to derive the spectral radiance of the lake surface around the satellite overpass using a boat-deployed TIR spectrometer or radiometer. The radiometric calibration conditions were quantified by examining the temperature bias between the satellite-based brightness temperature and the predicted at-sensor brightness temperature. Our results indicated that the average brightness temperature bias reached 0.772 K, −1.032 K, and −0.300 K, with RMSEs of 1.00, 1.10, and 0.43 K in bands 1, 2 and 3, respectively. Eighty percent of the field experiments achieved a high calibration accuracy with temperature biases of less than 1 K, and the calibration accuracy of band 3 was better than that of other bands. Atmospheric conditions and surface characterization play important roles in determining the calibration accuracy through surface observations and atmospheric transfer simulations. With the increasing need for SDGSAT-1 products, periodic field experiments are needed to monitor and improve the operational radiometric calibration of satellite instruments to create high-resolution and reasonable scientific datasets.

Regional Models for Sentinel-2/MSI Imagery of Chlorophyll a and TSS, Obtained for Oligotrophic Issyk-Kul Lake Using High-Resolution LIF LiDAR Data

The development of regional satellite bio-optical models for natural waters with high temporal and spatial variability, such as inland seas, reservoirs, and coastal ocean waters, requires the implementation of an intermediate measuring link in the chain, “water sampling—bio-optical models”, and this link must have certain intermediate characteristics. The most crucial of them are the high-precision measurements of the main water quality parameters, such as the concentration of chlorophyll a (Chl a), colored dissolved organic matter (CDOM), and total suspended sediments (TSS) in the upper water layer, together with a high operational rate and the ability to cover a large water area in a short time, which corresponds to a satellite overpass. A possible solution is to utilize laser-induced fluorescence (LIF) of water constituents measured by a marine LiDAR in situ with a high sampling rate from a high-speed vessel. This allows obtaining a large ground-truth dataset of the main water quality parameters simultaneously with the satellite overpass within the time interval determined by NASA protocols. This method was successfully applied to the oligotrophic Issyk-Kul Lake in Kyrgyzstan, where we obtained more than 4000 and 1000 matchups for the Chl a and TSS, respectively. New preliminary regional bio-optical models were developed on the basis of a one-day survey and tested for archive Sentinel-2A data for 2022. This approach can be applied for regular monitoring and further correction in accordance with seasonal variability. The obtained results, together with previously published similar studies for eutrophic coastal and productive inland waters, emphasize the applicability of the presented method for the development or adjustment of regional bio-optical models for water bodies of a wide trophic range.

Application of a two source energy balance model coupled with satellite based soil moisture and thermal infrared data

Satellite retrievals of thermal infrared data from the Earth’s surface when combined with the surface energy balance equation are widely used to monitor land surface evapotranspiration (ET). Unfortunately thermal infrared based models using this approach have a systematic positive bias with ET during soil water stress conditions. The Two-source Energy Balance model using information on Soil Moisture as another key constraint (TSEB-SM) improves model performance and utility. Here, high resolution satellite soil moisture data from SMAP (Soil Moisture Active Passive) were used to provide soil water boundary conditions for the TSEB-SM model. This SMAP driven TSEB-SM approach was applied in the Heihe river basin in China across a wide range of soil moisture conditions using MODIS thermal infrared land surface temperature (LST) data. The ET estimates from TSEB-SM were validated using flux tower measurements over the main land cover types in the study area and compared with the results from the original TSEB model. The comparison showed that the TSEB-SM approach reduced the RMSE error in latent heat flux estimates by 5 % to 20 % at satellite overpass time, and TSEB-SM outperformed the original TSEB model in daily ET estimates (RMSE values of 1.0 mm/d versus 1.5 mm/d, respectively) across all validation sites. The magnitude of the TSEB-SM improvements were larger early and late in the growing season and in arid areas, consistent with soil moisture improving estimates of ET particularly under times of higher water stress. The use of SMAP satellite soil moisture data with the TSEB-SM model increased the reliability of ET estimation and provides an improved capacity for continuos monitoring of drought effects on the global water cycle.

A Nowcasting Approach for Low-Earth-Orbiting Hyperspectral Infrared Soundings within the Convective Environment

Abstract Low-Earth-orbiting (LEO) hyperspectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of value to weather forecasters, the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment is limited. We have developed a novel nowcasting methodology to extend snapshots of LEO soundings forward in time up to 6 h to create a product available within National Weather Service systems for user assessment. Our methodology is based on parcel forward-trajectory calculations from the satellite-observing time to generate future soundings of temperature (T) and specific humidity (q) at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi National Polar-Orbiting Partnership (Suomi NPP) and NOAA-20 satellite platforms. The tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) are evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observations for 24 hand-selected cases over the contiguous United States. Areas with forecast increases in CAPE (reduced CIN) are shown to be associated with areas of precipitation. The increases in CAPE and decreases in CIN are largest for areas that have the heaviest precipitation and are statistically significant compared to areas without precipitation. These results imply that adiabatic parcel advection of LEO satellite sounding snapshots forward in time are capable of identifying convective initiation over an expanded temporal scale compared to soundings used only during the LEO satellite overpass time. Significance Statement Advection of low-Earth-orbiting (LEO) satellite observations of temperature and specific humidity forward in time exhibits skill in determining where and when convection eventually initiates. This approach provides a foundation for a new nowcasting methodology leveraging thermodynamic soundings derived from hyperspectral infrared (IR) sounders on LEO satellite platforms. This method may be useful for creating time-resolved soundings with the constellation of LEO satellites until hyperspectral infrared soundings are widely available from geostationary platforms.

Mapping river turbidity at a large basin-scale with a spatially transferable ensemble model using Landsat 8 multispectral imagery

ABSTRACT Mapping river water quality at a basin scale with satellite image data represents a tremendous technical challenge, since traditional empirical remote sensing models calibrated for specific reaches of a river cannot be applied to all the rivers and streams in the basin with an adequate prediction accuracy. In this study, we developed a multi-predictor ensemble model to map and assess water turbidity for all rivers and streams in the Tombigbee River Basin with Landsat 8 multispectral images. Coincident with Landsat 8 satellite overpass on 2 May 2019, we conducted boat-based water quality data collection in the confluence area of Tombigbee River and Black Warrior River near the city of Demopolis, Alabama. Based on the coincident satellite image and field water samples, we calibrated and evaluated the multi-predictor ensemble model, which consists four component empirical models. Our evaluations suggest that the multi-predictor ensemble model can improve turbidity estimation accuracy by 79% in comparison with the best traditional empirical model. More importantly, our multi-predictor ensemble model can be applied to other river and stream reaches across three major sub-basins with a high prediction accuracy. With the multi-predictor ensemble model, we derived a turbidity map for all rivers and streams with a width greater than 90 m in the basin, by processing three Landsat 8 image scenes. This basin-wide turbidity map indicates that the turbidity of rivers and streams exhibits longitudinal gradients and distinct spatial trends within the basin, which were related to the basin land cover/land use, hydrometeorological condition, and the spatial distribution of dams. Owing to its strong spatial transferability and high prediction accuracy, the ensemble model, in conjunction with synoptic multispectral satellite image data, is recommended as an effective and efficient tool for mapping and monitoring river and stream water quality at a large basin or regional scale.

Performance evaluation of MODIS and VIIRS satellite AOD products over the Indian subcontinent

In the present study, the first systematic performance evaluation of aerosol optical depth (AOD) products retrieved using two satellite sensors i.e., Visible Infrared Imaging Radiometer Suite (VIIRS) and Aqua-Moderate-Resolution Imaging Spectroradiometer (MODIS) is carried out over India. We have used ground-based AOD from AERONET at 550 nm wavelength for inter-comparison with MODIS Aqua version C6.1 (C061) Deep Blue (DB) aerosol product and VIIRS/SNPP collection version 1.1 (V1.1) DB aerosol product over the time span of 7-year (2014–2020) observation periods. For validation, the average value of satellite pixels falling within the box of 50 Km x 50 Km keeping the AERONET station at the center is retrieved. The average daily data from the AERONET sun photometer (2014–2019) were obtained within ±15 min of satellite overpass time. Statistical parameters like correlation coefficient (R), RMSE, MAE, and RMB were calculated. The uncertainty of satellite AOD is evaluated using an envelope of Expected Error (EE = ±0.05 + 0.15 AOD for land). Statistical analysis shows that the MODIS AOD product outperforms VIIRS-retrieved AOD. The AOD retrieved from both sensors yields a high correlation (0.86—Jaipur, 0.79—Kanpur, 0.84—Gandhi College, and 0.74—Pune for MODIS and 0.75—Jaipur, 0.77—Kanpur, 0.49—Gandhi College, and 0.86—Pune for VIIRS) and low MAE (0.12—Jaipur, 0.20—Kanpur, 0.15—Gandhi College, and 0.09—Pune for MODIS and 0.13—Jaipur, 0.13—Kanpur, 0.26—Gandhi College, and 0.10—Pune for VIIRS). Other statistical measures such as RMSE, RMB, and P also suggest similar performance. More than 66% of the total data fall within the range of EE for both the satellite products at each station. Spatial comparison exhibits the same AOD pattern seasonally as well as annually having a minimum bias from −0.3 to +0.3 between MODIS and VIIRS. Slight underestimation and overestimation are observed in all the stations by MODIS, whereas VIIRS continuously underestimates AOD with increase in optical depth, suggesting improvements in the aerosol model and surface reflection in retrieval. Overall, the comparison of ground AERONET AOD reveals better accuracy of MODIS AOD with that of VIIRS satellite datasets over India.

Monitoring volcano deformation at La Soufrière, St Vincent during the 2020–21 eruption with insights into its magma plumbing system architecture

Abstract Measurements of surface deformation provide valuable insight into sub-volcanic processes operating before, during and after eruptions. Here, we investigate the drivers behind the 2020–21 effusive–explosive episode at La Soufrière volcano in St Vincent using Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR) data between 2018 and 2021, and geodetic modelling. We observe inflation up to six months before the start to the effusive phase, which continued as the dome extruded. Once the eruption transitioned to the explosive phase, the volcano rapidly deflated, the bulk happening within the first three days of explosions. Our analytical modelling distinguishes three pressure source depth ranges contributing to this eruptive episode: 16–20, c. 6 and <1 km. Deformation data are therefore in line with a vertically extensive magmatic system being tapped pre- and syn-eruption with interaction between deep and shallow reservoirs by ascending magma batches. The combined use of GPS and InSAR proved to be instrumental for constraining the deformation field active during this eruptive episode. The direction of future geodetic monitoring at La Soufrière should therefore utilize both techniques with a view towards maximizing coverage while making up for shortfalls in station upkeep and variations in satellite overpass regularity.

A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands

Wetlands are a critical component of the landscape for climate mitigation, adaptation, biodiversity, and human health and prosperity. Keeping an eye on wetland vegetation is crucial due to it playing a major role in the planet’s carbon cycle and ecosystem management. By measuring the chlorophyll fluorescence (ChF) emitted by plants, we can get a precise understanding of the current state and photosynthetic activity. In this study, we applied the Extreme Gradient Boost (XGBoost) algorithm to map ChF in the Biebrza Valley, which has a unique ecosystem in Europe for peatlands, as well as highly diversified flora and fauna. Our results revealed the advantages of using a set of classifiers derived from EO Sentinel-2 (S-2) satellite image mosaics to accurately map the spatio-temporal distribution of ChF in a terrestrial landscape. The validation proved that the XGBoost algorithm is quite accurate in estimating ChF with a good determination of 0.71 and least bias of 0.012. The precision of chlorophyll fluorescence measurements is reliant upon determining the optimal S-2 satellite overpass time, which is influenced by the developmental stage of the plants at various points during the growing season. Finally, the model performance results indicated that biophysical factors are characterized by greenness- and leaf-pigment-related spectral indices. However, utilizing vegetation indices based on extended periods of remote sensing data that better capture land phenology features can improve the accuracy of mapping chlorophyll fluorescence.

THE SPATIAL DISTRIBUTION OF SELECTED OPTICAL ACTIVE COMPONENTS IN THE GULF OF IZMIT USING BIVARIATE/MULTIVARIATE REGRESSION ANALYSIS

Abstract. Compared to traditional field-based (in-situ sampling) measurements, satellite-based remote sensing is an accurate, timely and cost-effective approach to monitor the dynamics of water bodies using images at different spatial and temporal scales. With satellite-based remote sensing techniques, qualitative measurements obtained directly from satellite images are integrated with in-situ measurements, enabling the establishment of spectral statistical relationships between satellite data and water quality physical indicators such as suspended solids, turbidity and chlorophyll-a. In this study, the spatial distribution of three water quality parameters (Chlorophyll-a (Chl-a), Secchi disk and Conductivity (EC)) which are optical active components (OAC) in the Gulf of Izmit were evaluated using in-situ water quality measurements, together with both field-spectroradiometer measurements and Sentinel-2 satellite imagery. In-situ water quality and field-spectroradiometer measurements were collected at the same date with the satellite overpass. Bivariate and multivariate regression models were established to analyse the correlation of in-situ water quality measurements with two different measurement datasets (i.e. satellite and spectroradiometer), and then the results were evaluated with two accuracy metrics Root Mean Square Error (RMSE) and Mean Absolute Error (MAE); and compared visually with the spatial distribution maps of the three water quality parameters generated by the ordinary Kriging interpolation method.

Remote Sensing and Aerodynamic Temperature-Based Energy Balance Models to Estimate Crop Evapotranspiration Rates

Different methods exist to measure or estimate actual crop evapotranspiration (ETa). However, some methods require a large number of data input or strict field conditions. Remote sensing based ETa algorithms based on extreme thermal pixels (hot and cold) have limitations when required extreme pixels are not present in the acquired thermal infra-red imagery. In addition, satellite overpass frequency and spatial pixel resolution may be a limitation for some agricultural fields and micro-climates. Surface energy balance methods that use surface radiometric temperatures often fail to perform well under drought, limited irrigation, salt affected soils, or under sparse vegetation conditions. One option is to measure or estimate the crop/surface sensible heat flux through the aerodynamic temperature approach, then calculate the available energy and solve the energy balance for latent heat flux. Thus, this study presents different published algorithms that characterize the crop or field surface aerodynamic temperature and then applies them to different conditions for evaluation. Determining spatial ETa continuously has the potential to improve the irrigation water management decision making. The aerodynamic temperature approach was initially developed with good results as a function of surface radiometric temperature, air temperature, crop leaf area index, and wind speed or surface aerodynamic resistance. However, the inclusion of the crop fractional percent cover and of a new resistance term (turbulent-mixing row resistance) greatly improved the estimation of the sensible heat and latent heat fluxes, when evaluated with heat flux data derived from eddy covariance energy balance towers. Results also indicate that the aerodynamic method has transferability potential to different regions, crops, and irrigation methods than the conditions encountered in the method development.

Variable effects of spatial resolution on modeling of nitrogen oxides

Abstract. The lifetime and concentration of nitrogen oxides (NOx) are susceptible to nonlinear production and loss and to the resolution of a chemical transport model (CTM). This is due to the strong spatial gradients of NOx and the dependence of its own chemical loss on such gradients. In this study, we use the GEOS-Chem CTM in its high-performance implementation (GCHP) to investigate NOx simulations over the eastern United States across a wide range of spatial model resolutions (six different horizontal grids from 13 to 181 km). Following increasing grid size, afternoon surface NOx mixing ratios over July 2015 generally decrease over the Great Lakes region (GL) and increase over the southern states of the US region (SS), yielding regional differences (181 km vs. 13 km) of −16 % (in the GL) to 7 % (in the SS); meanwhile, hydrogen oxide radicals (HOx) increase over both regions, consistent with their different chemical regimes (i.e., NOx-saturated in the GL and NOx-limited in the SS). Nighttime titration of ozone by surface nitric oxide (NO) was found to be more efficient at coarser resolutions, leading to longer NOx lifetimes and higher surface mixing ratios of nitrogen dioxide (NO2) over the GL in January 2015. The tropospheric NO2 column density at typical afternoon satellite overpass time has spatially more coherent negative biases (e.g., −8 % over the GL) at coarser resolutions in July, which reversed the positive biases of surface NOx over the SS. The reduced NOx aloft (>1 km altitude) at coarser resolutions was attributable to the enhanced HOx that intrudes into the upper troposphere. Application of coarse-resolution simulations for interpreting satellite NO2 columns will generally underestimate surface NO2 over the GL and overestimate surface NO2 over the SS in summer, but it will uniformly overestimate NOx emissions over both regions. This study significantly broadens understanding of factors contributing to NOx resolution effects and the role of fine-resolution data in accurately simulating and interpreting NOx and its relevance to air quality.

Evaluation of the Potential of Sentinel-5P TROPOMI and AIS Marine Traffic Data for the Monitoring of Anthropogenic Activity and Maritime Transport NOx-Emissions in Canary Islands Waters

This work studies air quality by analysing NOx emissions in the inland waters of the Canary Islands, with particular emphasis on determining how maritime transport activity contributes to the emission of NO2 in the environment of the two main islands, Tenerife and Gran Canaria. We explored the capabilities of tropospheric NO2 density derived from the TROPOMI sensor onboard ESA’s Sentinel 5P Satellite to be used as an air quality monitoring tool at the regional scale of the Canary Islands. The studied mesoscale emission scenarios allowed us to identify the main sources, associated with urban areas, heavy roads, power plants, ports, and to a lesser extent, shipping routes. Mean values for the metropolitan area of Santa Cruz de Tenerife were 1.38 × 1015 molec cm−2. Similarly, in port areas, mean values of 1.22 × 1015 molec cm−2 were found. These levels can confidently be associated with anthropogenic activities. These were clearly distinguishable from background (noise) values of 7.08 × 1014 molec cm−2 obtained in maritime areas away from the influence of the islands. To investigate the maritime contribution to the NO2 emissions, ship tracks were obtained from an Automated Identification System (AIS) receiving station that covered the channel between the Tenerife and Gran Canaria islands. Multitemporal, and hence accumulative, NO2 scenarios were compared with the ship traffic density within a given temporal window before satellite overpass. We found good spatial agreement between NO2 signal and frequent ship routes between the major islands at several time scales, particularly in weekly averaged scenarios. Enhancements up to 2.0 × 1015 molecules cm−2 relative to surrounding waters were identified in the middle of the main shipping routes between the main islands. Thus, multitemporal NO2 scenarios derived from TROPOMI can lead to an estimate of the ship traffic contribution to NOx emissions in complex environments, such as this one, influenced by land emissions.

On-Orbit Absolute Radiometric Calibration and Uncertainty Analysis of OCM2 Sensor onboard Oceansat-2 Satellite

ABSTRACT This study describes On-Orbit absolute radiometric calibration for the Ocean Colour Monitor2 (OCM2) onboard Oceansat-2 satellite through a vicarious calibration experiment performed at the Great Rann of Kutch calibration site in Gujarat, India, in February 2022. To achieve accurate and consistent calibration for the OCM2 channels, a reflectance-based calibration method was used which relies on synchronous in-situ measurements of surface reflectance and atmospheric parameters at the time of satellite overpass. In this exercise, the 6 SV (Second Simulation of a Satellite Signal in the Solar Spectrum Vector) radiative transfer (RT) model was used to simulate the Top Of Atmosphere (TOA) spectral radiance for the OCM2 channels. The on-orbit radiometric performance/changes were derived by comparing the 6 SV simulated TOA radiance with those of the OCM2 Level 1B (L1B) data product. The results indicate that the average gain for band 1 to band 8 was found in the range from 0.88 to 1.23 and the relative error from 1.58% to 18.79% for the OCM2 sensor. However, the Root-Mean-Square-Error (RMSE) between the OCM2 measured and the 6 SV simulated TOA radiance data range from 0.17 (µW cm−2 sr−1 nm−1) at 443 nm to 2.04 (µW cm−2 sr−1 nm−1) at 620 nm. Furthermore, we analysed in detail the various uncertainties in this approach emanating from surface reflectance, atmospheric conditions (ozone, water vapour and aerosol optical depth), aerosol-type assumption in the RT model, BRDF, and inherent accuracy of the 6 SV RT model. The overall uncertainty was within 6% estimated using the reflectance-based calibration method.

On-Orbit Vicarious Radiometric Calibration and Validation of ZY1-02E Thermal Infrared Sensor

The ZY1-02E satellite carrying a thermal infrared sensor was successfully launched from the Taiyuan Satellite Launch Center on 26 December 2021. The quantitative characteristics of this thermal infrared camera, for use in supporting applications, were acquired as part of an absolute radiometric calibration campaign performed at the Ulansuhai Nur and Baotou calibration site (Inner Mongolia, July 2022). In this paper, we propose a novel on-orbit absolute radiometric calibration technique, based on multiple ground observations, that considers the radiometric characteristics of the ZY1-02E thermal infrared sensor. A variety of natural surface objects were selected as references, including bodies of water, bare soil, a desert in Kubuqi, and sand and vegetation at the Baotou calibration site. During satellite overpass, the 102F Fourier transform thermal infrared spectrometer and the SI-111 infrared temperature sensor were used to measure temperature and ground-leaving radiance for these surface profiles. Atmospheric water vapor, aerosol optical depth, and ozone concentration were simultaneously obtained from the CIMEL CE318 Sun photometer and the MICROTOP II ozonometer. Atmospheric profile information was acquired from radiosonde instruments carried by sounding balloons. Synchronous measurements of atmospheric parameters and ECMWF ERA5 reanalysis data were then combined and input to an atmospheric radiative transfer model (MODTRAN6.0) used to calculate apparent radiance. Calibration coefficients were determined from the measured apparent radiance and satellite-observed digital number (DN), for use in calculating the on-orbit observed radiance of typical surface objects. These values were then compared with the apparent radiance of each object, using radiative transfer calculations to evaluate the accuracy of on-orbit absolute radiometric calibration. The results show that the accuracy of this absolute radiometric calibration is better than 0.6 K. This approach allows the thermal infrared channel to be unrestricted by the limitations of spectrum matching between a satellite and field measurements, with strong applicability to various types of calibration sites.

Fire Influence on Regional to Global Environments and Air Quality (FIREX‐AQ)

AbstractThe NOAA/NASA Fire Influence on Regional to Global Environments and Air Quality (FIREX‐AQ) experiment was a multi‐agency, inter‐disciplinary research effort to: (a) obtain detailed measurements of trace gas and aerosol emissions from wildfires and prescribed fires using aircraft, satellites and ground‐based instruments, (b) make extensive suborbital remote sensing measurements of fire dynamics, (c) assess local, regional, and global modeling of fires, and (d) strengthen connections to observables on the ground such as fuels and fuel consumption and satellite products such as burned area and fire radiative power. From Boise, ID western wildfires were studied with the NASA DC‐8 and two NOAA Twin Otter aircraft. The high‐altitude NASA ER‐2 was deployed from Palmdale, CA to observe some of these fires in conjunction with satellite overpasses and the other aircraft. Further research was conducted on three mobile laboratories and ground sites, and 17 different modeling forecast and analyses products for fire, fuels and air quality and climate implications. From Salina, KS the DC‐8 investigated 87 smaller fires in the Southeast with remote and in‐situ data collection. Sampling by all platforms was designed to measure emissions of trace gases and aerosols with multiple transects to capture the chemical transformation of these emissions and perform remote sensing observations of fire and smoke plumes under day and night conditions. The emissions were linked to fuels consumed and fire radiative power using orbital and suborbital remote sensing observations collected during overflights of the fires and smoke plumes and ground sampling of fuels.