Moderate Resolution Imaging Spectroradiometer Measurements Research Articles

Improving precipitable water vapor estimations of the Moderate Resolution Imaging Spectroradiometer (MODIS) using metaheuristic algorithms

Extracting Total Precipitable Water Vapor (TPW) and its variations from the Moderate Resolution Imaging Spectroradiometer (MODIS) data on a local scale is challenging. Meta-heuristic algorithms have a high ability to solve complex engineering problems of hydrology and climatic studies. Hence, the present study extracts TPW values using meta-heuristic algorithms for MODIS surface reflectance data. In this regard, radiosonde observations of six stations in the western part of Iran during 2019 and 2020 were used as the ground truth data for training algorithms and evaluating the results. The results were also validated with Global Positioning System (GPS) TPW data of Tehran-Mehrabad station. Moreover, MODIS surface reflectance (MOD021) and cloud mask (MOD35) products were prepared to distinguish cloudy days from non-cloudy, along with MODIS TPW (MOD05) for corresponding dates. Meta-heuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harmony Search (HS), Invasive Weed Optimization (IWO), Gray Wolf Optimization (GWO), and Grasshopper Optimization Algorithm (GOA), were applied to improve the performance of MOD05 algorithm. The results showed the proper performance of all the selected algorithms. Nevertheless, the GOA algorithm acquired the best result. The Root Mean Square Error (RMSE) and R values obtained from comparing GOA results with radiosonde data were 2.96 mm and 0.82. Those values for comparing GOA results with GPS data were 2.94 mm and 0.83, respectively. However, MODIS TPW, RMSE, and R showed 6.53 mm and 0.69, respectively, compared to radiosonde data and 6.55 mm and 0.68 compared to GPS data. Overall, the results proved the proper performance of the used meta-heuristic algorithms in extracting TPW from MODIS measurements on a local scale.

Discrimination of Biomass-Burning Smoke From Clouds Over the Ocean Using MODIS Measurements

Smokes from biomass burning can contribute substantial amounts of hazardous substances and carbon to the atmosphere. These substances can be transported seaward and deposited on the ocean surface. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to map the relative smoke concentration over the ocean between November 8 and 11, 2018 from the recent California fires, with the ultimate goal of developing a generally applicable approach to map smokes over oceans. Because both biomass-burning smokes and clouds can produce strong backscattering signals, two key differences are used to separate them: 1) water-vapor absorption in certain wavelengths only occurs in clouds and 2) cumulus and cirrus clouds occur at different altitudes, therefore, bearing different thermal signatures. Based on these observations, a decision-tree method is developed to separate smokes from clouds. First, MODIS top-of-atmosphere (TOA) reflectance at 936 nm is used to detect both clouds and smokes over oceans. Then, brightness temperature derived from the 9730-nm band is used to separate cirrus from others. Finally, a water absorption depth (WAD) index is used to distinguish cumulus clouds from smokes, whose relative concentration in each image pixel is estimated from the MODIS TOA reflectance at 859 nm. Such derived smoke distribution and concentration are validated using concurrent Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) data, which provide the fine mode aerosol optical thickness (AOT) of smokes. Test of the approach over the recent Australia fires shows promising results, suggesting that the approach might be implemented by operational agencies to monitor and quantify smokes from biomass burning on a routine basis.

Cloud-Free Land Surface Temperature Reconstructions Based on MODIS Measurements and Numerical Simulations for Characterizing Surface Urban Heat Islands

Land surface temperature (LST) data in the thermal infrared (TIR) band measured by the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument are critical for studying surface urban heat islands (SUHIs); however, these acquired TIR LST data are contaminated by clouds, so it is crucial to develop a method to generate cloud-free LST products. In this paper, employing Tianjin as the research area, we combined the Weather Research and Forecasting (WRF) model with a random forest (RF) and a spatial optimization algorithm to propose a cloud-free MODIS-like model (WRFFM). The model can reconstruct cloud-free MODIS-like LSTs and SUHIs are studied. The spatial patterns of the WRFFM LSTs and the MODIS LSTs are consistent; the correlation coefficients in July and December range from 0.8∼0.91 and 0.8∼0.93, respectively, and the RMSEs range from 0.5∼3.8 K and 0.4∼1.8 K, respectively, indicating that the modeled results are accurate. We use these WRFFM LSTs to study SUHIs and evaluate the deviations between the MODIS SUHIs and WRFFM SUHIs. When the proportion of clear-sky pixels is below 30%, the deviation is above 3 K, and when the proportion of clear-sky pixels is above 80%, the deviation is below 0.6 K. The results indicate that the developed model can be applied to improve the study of SUHIs and that the number of clear-sky pixels for a city is an important factor that affects the bias relative to the actual SUHI.

Spatial homogeneity from temporal stability: Exploiting the combined hyper-frequent revisit of Terra and Aqua to guide Earth System Science

The terrestrial component of the Earth system has witnessed considerable changes in the past decades due to anthropogenic action. Throughout this period, the NASA Terra mission has been constantly monitoring the surface with the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. When combined with the MODIS instrument on-board of the Aqua platform, we obtain a hyper-frequent revisit capability providing sub-daily observations globally at a moderate resolution of ~250 m, but with a strong multi-angular variation of the observation footprint. Here we propose to exploit this particular configuration provided by the combined Terra + Aqua constellation to infer spatial homogeneity from the temporal stability of the observation time series. We thus designed a Temporal Coherence Index (TCI) based on the concept of information entropy. We test it on both synthetic and real landscapes showing its capacity to isolate which time series correspond to homogeneous targets based only on the MODIS measurements themselves. The index is calculated annually for the entirety of emerged surfaces for the period 2003–2020 by leveraging on the scaling capacity of the cloud computing Google Earth Engine (GEE). We foresee multiple practical applications for this index as a novel information layer that should foster the integration of surface data with satellite records, a practice that is increasingly necessary in Earth System Science.

Evaluation of aqua MODIS thermal emissive bands stability through radiative transfer modeling

Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua has been in operation providing continuous global observations for science research and applications since 2002. The long-term stability of thermal emissive bands (TEBs) of Aqua MODIS was monitored through inter-comparisons with measurements by hyperspectral or multi-spectral infrared sensors or through vicarious monitoring over cold targets such as Dome-C and deep convective clouds. The radiative transfer modeling (RTM)-based simulation models are developed to perform the long-term monitoring of the stability of Aqua MODIS TEBs. Long-term European Centre for Medium-Range Weather Forecasts global atmospheric reanalysis data are used as inputs to the RTM simulation. By confining ocean in low to middle latitudes as the area of interest, the long-term stabilities of Aqua MODIS TEBs are monitored through observation minus background (O-B) brightness temperature (BT) bias between MODIS measurements and simulations from the RTM. In general, the RTM-based O-B analysis shows that the long-term stability of Aqua MODIS TEBs are maintained well. It is shown that Aqua MODIS surface bands are all radiometrically stable with yearly BT bias drifts <0.005 K / year for B20, B22, and B23 and ∼0.01 K / year for B31 and B32. The carbon dioxide (CO2) absorption channels of Aqua MODIS, e.g., B33 to B36, are stable with a BT bias drift <0.005 K / year. It is also found that after accounting for the long-term growth of greenhouse gas N2O, the O-B bias trends for B24 and B25 are quite stable with yearly BT bias drifts around 0.0002 and 0.0055 K / year, respectively. The stabilities of the moisture-sensitive channels B27 to B29 and the ozone channel B30 of Aqua MODIS are also evaluated and the remnant small variations in the O-B bias trending of these channels are further discussed. The RTM-based inter-comparison of MODIS TEB measurements with global atmospheric and climate re-analysis data provides validation of the long-term radiometric stability of Aqua MODIS TEBs. The analysis in this paper also demonstrates that the comparison of well-calibrated MODIS TEB measurements with RTM simulation helps quantify the long-term impacts of global greenhouse gas concentration growth on the BT decreases in the MODIS greenhouse gas-sensitive channels.

Remote sensing of droplet number concentration of aerosol-induced clouds during the 2019 fire event in Borneo, Indonesia

The droplet number concentration of liquid water clouds N is estimated based on the optical thickness τ and particle effective radius reff retrieved from the measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard of Terra satellite. Two measurements of MODIS taken from 15 June 2019 (case A) and 15 September 2019 (case B) are analyzed. They represent a condition before and during the 2019 fire event in Borneo, Indonesia. The result shows, that the domains of τ and reff are comparable for the two cases. While there is no sign of systematic bias in case of τ, the analysis of r eff tells differently. Clouds with smaller droplets, less than 12 µ m, are more prominent in case B, indicating the present of aerosol-induced clouds. In case A, the frequency distributions of clouds with larger droplets, more than 12 µ m, are systematically higher. The magnitudes of N in case A are mostly less than 100 cm-3, which exhibit a background condition. Extreme magnitudes of N, from 200 to 800 cm −3, are found in case B. Those excessive numbers are attributed to the region where the aerosol-induced clouds are observed.

Retrieving the Optical Properties of Aerosols Over Land With Directional Polarimetric Camera Observations and an Adaptive Algorithm

The Directional Polarimetric Camera (DPC) is a polarization sensor mounted on Chinese Gaofen-5 satellite. It has the capability of observing multispectral, multiangular, and polarized light, and can be used to monitor global aerosols and clouds. This work developed an adaptive algorithm to retrieve aerosol properties based on DPC measurements. It performs initial surface property estimations, aerosol parameter retrievals, surface parameter adjustments, and result assessments. In the algorithm, it allows global aerosol optical depths (AODs) and Angstrom exponents (AEs) to be retrieved. The AOD values show good consistency with that from AErosol RObotic NETwork (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS). The regression line between AODs from DPC and AERONET is y = 0.895x + 0.056, with a correlation coefficient of 0.894, and 49% of the DPC-retrieved AODs are within the expected error range of ±(15%AOD + 0.05). For the AEs, the regression line is y = 0.728x + 0.395, the correlation coefficient is 0.763, and 41.4% of the AEs are within the error range of (AE ± 0.4). We also investigated the possible influence of AOD errors retrieved from the DPC measurements over three typical areas. It was found that though the DPC-retrieved dust aerosols did not show so good consistency with the MODIS measurements as the smoke aerosols, the retrievals still matched well on the whole. It demonstrates the potential of DPC and the adaptive algorithm for aerosol remote sensing.

Comparison of summer Arctic sea ice surface temperatures from in situ and MODIS measurements

Ship-borne infrared radiometric measurements conducted during the Chinese National Arctic Research Expedition (CHINARE) in 2008, 2010, 2012, 2014, 2016 and 2017 were used for in situ validation studies of the Moderate Resolution Imaging Spectroradiometer (MODIS) sea ice surface temperature (IST) product. Observations of sea ice were made using a KT19.85 radiometer mounted on the Chinese icebreaker Xuelong between July and September over six years. The MODIS-derived ISTs from the satellites, Terra and Aqua, both show close correspondence with ISTs derived from radiometer spot measurements averaged over areas of 4 km×4 km, spanning the temperature range of 262–280 K with a ±1.7 K (Aqua) and ±1.6 K (Terra) variation. The consistency of the results over each year indicates that MODIS provides a suitable platform for remotely deriving surface temperature data when the sky is clear. Investigation into factors that cause the MODIS IST bias (defined as the difference between MODIS and KT19.85 ISTs) shows that large positive bias is caused by increased coverage of leads and melt ponds, while large negative bias mostly arises from undetected clouds. Thin vapor fog forming over Arctic sea ice may explain the cold bias when cloud cover is below 20%.

Water vapor variability in the Atacama Desert during the 20th century

This study focuses on integrated water vapor (IWV) which is the main source for precipitation, fog and dew formation in the Atacama Desert in northern Chile. In order to study its long-term variability, a consistent meteorological record is needed. Here, we utilize the European Centre for Medium-Range Weather Forecasts' reanalysis ERA-20C which provides IWV among other atmospheric variables over the course of the entire 20th century (1900–2010). In this two fold study, we first present a validation of ERA-20C IWV for the Atacama and the bordering southeast Pacific region. Comparisons to satellite observations, i.e. the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite data record and the Moderate Resolution Imaging Spectroradiometer measurements, for overlapping time periods prove the suitability of ERA-20C to study IWV variability. Assessment of the observation feedback in ERA-20C reveals a higher uncertainty for the beginning of the 20th century when fewer observations are assimilated. Nevertheless, departures of the assimilated observations do not show a systematic bias in space or time supporting suitability of ERA-20C for long-term investigations. In the second part of the study, we describe the IWV variability over the course of the 20th century. Deviations from the long-term mean greater than 30% are found on an inter-annual time scale over the continental Atacama. Furthermore, we investigate potential drivers of the IWV variability such as the Pacific Decadal Oscillation (PDO) and the El Niño Southern Oscillation (ENSO) phenomenon. The relationship between the local IWV and these large scale indices depends on region and season. For instance, during austral summer, La Niña conditions yield overall greater IWV variability in the Atacama allowing both drier and even more pronounced wetter extremes than El Niño conditions.

Effect of dust loadings, meteorological conditions, and local emissions on aerosol mixing and loading variability over highly urbanized semiarid countries: United Arab Emirates case study

Long-term natural and anthropogenic aerosol characteristics over the United Arab Emirates (UAE) are investigated in this study. Aerosol characteristics were analyzed using data from the Moderate Resolution Imaging Spectroradiometer (MODIS), local meteorological stations, and the Aerosol Robotic Network (AERONET) ground-based station during 2006–2015. AOD values significantly increase during the spring and summer seasons and are associated with dust loading during major dust events. The mean annual variation of aerosol loading indicates about a 4.32 and a 11.36% change in the AOD and Ångström Exponent (AE), respectively, during the study period. Monthly trends show larger AOD and AE variability, 5.4 and 37.8% respectively, with higher aerosol concentrations during the summer. In general, from November to March an upward trend in aerosol characteristics is observed from 2011 to 2015 compared to 2006–2010. In contrast, a neutral to weak decrease in AOD measurements are observed during April–September. Overall, this trend variability is sustained by natural aerosol emissions throughout the year. Meanwhile, a significant increase in AE was measured from May to August of 2011–2015 compared to 2006–2010. This is an indication of an increment of anthropogenic emissions from 2011 to 2015. A correlation between several meteorological parameters (temperature, relative humidity, wind speed, and wind direction) and MODIS measurements was performed to investigate the effect of local meteorological conditions on AOD from 2006 to 2015 over the UAE. Some statistical correlation was observed between AOD, wind speed, and air temperature. A weak correlation was observed between temperature, relative humidity, and AOD, while a strong correlation was observed between wind speed and AOD. Dry soil formed due to a lack of rainfall, along with high summer temperatures and high-speed winds which stimulate dust aerosol loading and lifetime in the atmosphere. Aerosol characteristics, including dominant aerosols, were identified over the UAE using the Mezaria AERONET station. Air mass flow towards the UAE from the North West (NW) and West (W) directions moves over highly polluted regions and desert areas. Coarse-mode aerosol particles dominate over the UAE during March–June associated with the dust season, while a high loading of fine-mode particles is mostly observed during September–January.

High Efficiency of Solar Cell Model Based on Two Types of Nanoparticles

Climatology of aerosols, their trends and classification based on the long-term Moderate Resolution Imaging Spectroradiometer (MODIS) measurements (from February 2000 to July 2015) of aerosol optical depths at 550 nm (τ550) and Angstrom exponent (α470-660) using the wavelengths of 470 and 660nm in Nairobi, Skukuza and Ilorin AERONET stations were analyzed in this work. The level-2 collection-6 Deep Blue (L2 C006 DB) of the parameters listed above from the aqua- (MYD04) and terra- (MOD04) MODIS of the study area were statistically analyzed using SPSS. To be able to understand the temporal variation in the characteristics of aerosols in the three stations and during each season separately, MODIS measurements of τ, retrieved for the study area, were compared with AERONET τ. Overall, aqua-MODIS τ corroborate the AERONET measurements well in Nairobi and Ilorin stations with underestimation of 29.80 % and overestimation of 2.90 % respectively, whereas Skukuza station has terra-MODIS τ as the best representation of the AERONET measurements with underestimation of 1.90 %. ....

Analysis of temporal and spatial variations of total precipitable water vapor in western Iran using radiosonde and MODIS measurements

Investigating time series variations of total precipitable water (TPW) vapor is of great importance in meteorology and hydrology. The objective of this study is to use two nonparametric tests including Mann–Kendall and Sen’s slope estimator to investigate the annual and seasonal trend of TPW changes using two remote sensing datasets and a ground dataset in western Iran. The daily near-infrared product of TPW from Moderate Resolution Imaging Spectroradiometer (MODIS), MOD05, and a daily TPW extracted from a developed regional TPW MODIS algorithm were considered as remote sensing data. The remote sensing estimations were utilized in six climatic zones, including hyperarid, arid, semiarid, semihumid, humid, and hyperhumid. For the ground data, the daily TPW was prepared from five radiosonde stations. At a significance level of 0.05, in the annual trend, MOD05 results showed a significant increase in the hyperarid, semiarid, semihumid, and hyperhumid climates, and regional MODIS TPW algorithm results showed a significant increase in all six climates. In the seasonal trend of spring in the Ahvaz radiosonde station, as well as in the seasonal trend of summer of MOD05 results in the hyperarid, arid, semihumid, and humid climates, there was a significant increase. Regional MODIS TPW algorithm results demonstrated a significant increase trend in all six climates in the spring and summer. Generally, the results proved the TPW incremental trend in the study area.

Investigating extracted total precipitable water vapor from Weather Research and Forecasting (WRF) model and MODIS measurements

Total Precipitable Water (TPW) vapor value in the earth's atmosphere and its variations are of great importance in atmospheric and climatic studies. Among the most important methods introduced for estimation of TPW are satellite products such as Moderate Resolution Imaging Spectroradiometer (MODIS) and numerical methods such as Weather Research and Forecasting (WRF). The performance of TPW estimation methods are different in various locations and require evaluations. Then, the goal of this study is investigating the performance of estimated TPW from MODIS product (MOD05) and WRF model. In this regard, two infrared (IR) and near infrared (NIR) algorithms of MOD05 were evaluated. Moreover, three nested resolutions of 27, 9, and 3 km of the WRF in 0000 and 1200 UTC were investigated. Mehrabad radiosonde station in the south of Tehran, Iran was considered as the study area. Evaluation of the two selected models was performed using radiosonde measurements of 240 days in 2013 and 2014. Among the nested domains for WRF model, the 3 km model at 0000 UTC provided the best results with determination coefficient (R2) of 0.81 and Root Mean Square Difference (RMSD) of 2.98 mm. Among the MODIS products, MODIS IR showed a better performance with R2 of 0.72 and RMSD of 3.46 mm. In general, the estimated TPW from MODIS-IR and WRF3 showed that WRF has a better performance. Then, the results proved that WRF model can be used in various meteorological conditions for estimation TPW with an acceptable accuracy.

A Global Analysis of Land Surface Temperature Diurnal Cycle Using MODIS Observations

AbstractDiurnal variations of land surface temperature (LST) play a vital role in a wide range of applications such as climate change assessment, land–atmosphere interactions, and heat-related health issues in urban regions. This study uses 15 years (2003–17) of daily observations of LST Collection 6 from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board the Aqua and the Terra satellites. A spline interpolation method is used to estimate half-hourly global LST from the MODIS measurements. A preliminary assessment of interpolated LST with hourly ground-based observations over selected stations of North America shows bias and an error of less than 1 K. Results suggest that the present interpolation method is capable of capturing the diurnal variations of LST reasonably well for different land-cover types. The diurnal cycle of LST and time of occurrence of maximum temperature are computed from the spatially and temporally consistent interpolated diurnal LST data at a global scale. Regions with higher variability in the timing of maximum LST hours and diurnal amplitude are identified in this study. The global desert regions show generally small variability of the monthly mean diurnal LST range, whereas larger areas of the global land exhibit rather higher variability in the diurnal LST range during the study period. Moreover, the changes in diurnal temperature range for the study period are examined for distinct land-cover types. Analysis of the 15-yr time series of the diurnal LST record shows an overall decrease of 0.5 K in amplitude over the Northern Hemisphere. However, the diurnal LST range shows variant changes in the Southern Hemisphere.

Monitoring Extreme Agricultural Drought over the Horn of Africa (HOA) Using Remote Sensing Measurements

The Horn of Africa ((HOA), including Djibouti, Eritrea, Ethiopia, and Somalia) has been slammed by extreme drought within the past years, and has become one of the most food-insecure regions in the world. Millions of people in the HOA are undernourished and are at risk of famine. Meanwhile, global climate change continues to cause more extreme weather and climate events, such as drought and heat waves, which have significant impacts on crop production and food security. This study aimed to investigate extreme drought in the Horn of Africa region, using satellite remote sensing data products from the Moderate Resolution Imaging Spectroradiometer (MODIS), a key instrument onboard the National Aeronautics and Space Administration (NASA) satellites Terra and Aqua, as well as Tropical Rainfall Measuring Mission (TRMM) precipitation data products. Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI) data from 2000 to 2017 were derived from the MODIS measurements and analyzed for assessments of the temporal trend of vegetation health and the impacts of extreme drought events. The results demonstrated the severity of vegetation stress and extreme drought during the past decades. From 1998 to 2017, monthly precipitation over major crop growth seasons decreased significantly. From 2001 to 2017, the mean VHI anomaly of HOA cropland decreased significantly, at a trend of −0.2364 ± 0.1446/year, and the mean TCI anomaly decreased at a trend of −0.2315 ± 0.2009/year. This indicated a deterioration of cropland due to drought conditions in the HOA. During most of the crop growth seasons in 2015 and 2016, the VHI values were below the 10-year (2001–2010) average: This was caused by extreme drought during the 2015–2016 El Niño event, one of the strongest El Niño events in recorded history. In addition, monthly VHI anomalies demonstrated a high correlation with monthly rainfall anomalies in July and August (the growth season of major crops in the HOA), and the trough points of the monthly rainfall and VHI anomaly time series of July and August were consistent with the timing of drought events and El Niño events.

Uncertainties in MODIS‐Based Cloud Liquid Water Path Retrievals at High Latitudes Due to Mixed‐Phase Clouds and Cloud Top Height Inhomogeneity

AbstractCombined A‐train remote sensing measurements from Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR‐E), CloudSat, and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used to study MODIS liquid water path (LWP) uncertainties at high latitudes. The focus is on quantifying uncertainties due to mixed‐phase clouds and solar zenith angle‐dependent bias, both of which disproportionately affect the MODIS data set in the polar regions. Multisensor LWP retrievals in stratiform mixed‐phase clouds show that treating mixed‐phase clouds as liquid clouds result in LWP bias that is related to the ice water path (IWP) on average and reaches close to 15% at IWP of 150 g/m2 and can reach 40% or higher when IWP is greater than 400 g/m2. Moreover, A‐train measurements and radiative transfer modeling are used to further understand the well‐known yet unresolved solar zenith angle‐dependent high bias in MODIS LWP. It is shown that the cloud top height variation is one of the main factors that contribute to this bias due to three‐dimensional radiative interactions with cloud top inhomogeneity. Excluding only 0.5% of data points that show significant three‐dimensional errors reduces the bias by 25 g/m2 at solar zenith angle of 80° and improves agreement with the AMSR‐E LWP trends. Three‐dimensional radiative transfer simulations confirm that cloud top inhomogeneity is primarily responsible for the solar zenith angle‐dependent LWP bias as observed by the MODIS measurements. This study provides a framework to guide future improvements of MODIS LWP data set, which is a key data source to constrain climate models.

Remote sensing estimation of surface oil volume during the 2010 Deepwater Horizon oil blowout in the Gulf of Mexico: scaling up AVIRIS observations with MODIS measurements

The Deepwater Horizon (DWH) oil blowout in the Gulf of Mexico (GoM) led to the largest offshore oil spill in U.S. history. The accident resulted in oil slicks that covered between 10,000 and upward of 40,000 km2 of the Gulf between April and July 2010. Quantifying the actual spatial extent of oil over such synoptic scales on an operational basis and, in particular, estimating the oil volume (or slick thickness) of large oil slicks on the ocean surface has proven to be a challenge to researchers and responders alike. This challenge must be addressed to assess and understand impacts on marine and coastal resources and to prepare a response to future spills. We estimated surface oil volume and probability of occurrence of different oil thicknesses during the DWH blowout in the GoM by combining synoptic measurements (2330-km swath) from the satellite-borne NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and near-concurrent, much narrower swath (∼5 km) hyperspectral observations from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). A histogram-matching approach was used to transfer AVIRIS-derived oil volume to MODIS pixel-scale dimensions, after masking clouds under both sun glint and nonglint conditions. Probability functions were used to apply the transformation to 19 MODIS images collected during the DWH event. This generated three types of MODIS oil maps: maps of surface oil volume, maps of relative oil thickness with four different classes (i.e., 0 μm, 8 μm), and maps of probability distributions of different thicknesses. The results were compared with satellite-based synthetic aperture radar measurements and evaluated with concurrent aerial photographs. Although the methods may not be ideal and the results may contain large uncertainties, the current attempt suggests that coarse-resolution optical remote sensing observations can provide estimates of relative oil thickness/volume for large oil slicks captured by satellites.

A Simplified Method for Retrieving Aerosol Optical Thickness Using Visibility Data Between 1980 and 2014, A Case Study in China

Analysis of long-term variations of aerosol optical thickness (AOT) is essential to understand the climate change and earth radiation budget. A simplified method was developed in this study to derive the AOT using more than thirty-year surface visibility and aerosol vertical distribution from LiDAR climatology of vertical aerosol structure for space-based LiDAR simulation studies data. Moderate resolution imaging spectroradiometer (MODIS) TERRA and AQUA daily measurements were used to evaluate the performance of the inferred AOT. Results show that the correlations of monthly AOT between MODIS measurements and inferred data are higher than 0.59 and the annual AOT values have stronger correlations ( r > 0.82) than monthly data. The inferred AOT can capture the general spatial distribution similar to satellite images with long-term scale. The correlation of seasonal variation between two datasets is also higher than 0.77. The root mean squared error of inferred AOT relative to MODIS AOT is about 0.22 and 0.11 for monthly and yearly data, respectively. Analysis using singular value decomposition shows that the inferred AOT is consistent with MODIS measurements during 2002–2014 spatially and temporally. The average annual AOT in over 200 stations in China between 1980 and 2014 shows generally upward trends. Results from this paper can be used for further analysis of the aerosol effects on climate change in a regional scale.

Comparison of PMCAMx aerosol optical depth predictions over Europe with AERONET and MODIS measurements

Abstract. The ability of chemical transport model (CTM) PMCAMx to reproduce aerosol optical depth (AOD) measurements by the Aerosol Robotic Network (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) over Europe during the photochemically active period of May 2008 (EUCAARI campaign) is evaluated. Periods with high dust or sea-salt levels are excluded, so the analysis focuses on the ability of the model to simulate the mostly secondary aerosol and its interactions with water. PMCAMx reproduces the monthly mean MODIS and AERONET AOD values over the Iberian Peninsula, the British Isles, central Europe, and Russia with a fractional bias of less than 15 % and a fractional error of less than 30 %. However, the model overestimates the AOD over northern Europe, most probably due to an overestimation of organic aerosol and sulfates. At the other end, PMCAMx underestimates the monthly mean MODIS AOD over the Balkans, the Mediterranean, and the South Atlantic. These errors appear to be related to an underestimation of sulfates. Sensitivity tests indicate that the evaluation results of the monthly mean AODs are quite sensitive to the relative humidity (RH) fields used by PMCAMx, but are not sensitive to the simulated size distribution and the black carbon mixing state. The screening of the satellite retrievals for periods with high dust (or coarse particles in general) concentrations as well as the combination of the MODIS and AERONET datasets lead to more robust conclusions about the ability of the model to simulate the secondary aerosol components that dominate the AOD during this period.

How big is an OMI pixel?

Abstract. The Ozone Monitoring Instrument (OMI) is a push-broom imaging spectrometer, observing solar radiation backscattered by the Earth's atmosphere and surface. The incoming radiation is detected using a static imaging CCD (charge-coupled device) detector array with no moving parts, as opposed to most of the previous satellite spectrometers, which used a moving mirror to scan the Earth in the across-track direction. The field of view (FoV) of detector pixels is the solid angle from which radiation is observed, averaged over the integration time of a measurement. The OMI FoV is not quadrangular, which is common for scanning instruments, but rather super-Gaussian shaped and overlapping with the FoV of neighbouring pixels. This has consequences for pixel-area-dependent applications, like cloud fraction products, and visualisation.The shapes and sizes of OMI FoVs were determined pre-flight by theoretical and experimental tests but never verified after launch. In this paper the OMI FoV is characterised using collocated MODerate resolution Imaging Spectroradiometer (MODIS) reflectance measurements. MODIS measurements have a much higher spatial resolution than OMI measurements and spectrally overlap at 469 nm. The OMI FoV was verified by finding the highest correlation between MODIS and OMI reflectances in cloud-free scenes, assuming a 2-D super-Gaussian function with varying size and shape to represent the OMI FoV. Our results show that the OMPIXCOR product 75FoV corner coordinates are accurate as the full width at half maximum (FWHM) of a super-Gaussian FoV model when this function is assumed. The softness of the function edges, modelled by the super-Gaussian exponents, is different in both directions and is view angle dependent.The optimal overlap function between OMI and MODIS reflectances is scene dependent and highly dependent on time differences between overpasses, especially with clouds in the scene. For partially clouded scenes, the optimal overlap function was represented by super-Gaussian exponents around 1 or smaller, which indicates that this function is unsuitable to represent the overlap sensitivity function in these cases. This was especially true for scenes before 2008, when the time differences between Aqua and Aura overpasses was about 15 min, instead of 8 min after 2008. During the time between overpasses, clouds change the scene reflectance, reducing the correlation and influencing the shape of the optimal overlap function.