Moderate Resolution Imaging Spectroradiometer Retrievals Research Articles

Spatio-temporal correspondence of aerosol optical depth between CMIP6 simulations and MODIS retrievals over India.

In the present work, the aerosol optical depth (AOD) at 550nm of the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite was utilised to evaluate the AOD simulations of newly emerged general circulation models (GCMs) of coupled model intercomparison project-phase 6 (CMIP6) over the Indian landmass. Further, the AOD from the CMIP6 models has been compared with its previous generation models from CMIP5 to examine the extent of uncertainties in AOD with reference to the MODIS AOD datasets. The evolution of aerosols over India using the different shared socioeconomic pathways (SSPs) has also been studied till the year 2050. The results show that the CMIP5 and CMIP6 models underestimated the mean annual AOD of the Indian region as a whole. A multi-model mean (MMM) of thirteen GCMs from CMIP6 showed an underestimation of AOD by 40 to 60% over the Indo-Gangetic plains, while an overestimation of 60 to 80% in AOD was observed over the Peninsular and Central Indian regions in comparison with MODIS for the study period of 2001 to 2014. In future simulations, the pathway SSP370 has shown a significant increasing trend of AOD whereas SSP126 and SSP585 have shown significant decreasing trends of AOD by the year 2050. In the future, the changes in the AOD will mainly be contributed by the anthropogenic aerosols (AOA, BC, and Sulphates) emissions in all SSPs. The large bias of MMM with the MODIS requires further research in terms of analysing the accuracy of emission datasets that have been used to simulate the AODs by the CMIP6 models over the Indian region.

Accuracy assessment and climatology of MODIS aerosol optical properties over North Africa.

In this study, the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) product was compared with ground-based measurements at five sites of the Aerosol Robotic Network (AERONET) in North Africa. The MODIS AOD showed a good correlation coefficient of ~0.78, a very small mean bias error of 0.009, and a root mean square error of 0.126 with AERONET. The Dark Target/Deep Blue (DT/DB) algorithm showed better performance at low aerosol loading while underestimating AOD at higher aerosol loading, mainly for coarse-dominated aerosol types. This work also showed the benefits of using MODIS retrievals as a reliable data source for aerosols and providing a long-term aerosol type classification. The primary aerosol type is dust emitted from the Sahara Desert, and the dusty atmosphere becomes gradually mixed with pollution aerosols approaching the coastal region. The annual mean MODIS AOD at 550 nm and Ångström exponent at 412-650 nm (AE) ranged from 0.17 to 0.45 and from 0.13 to 1.25, respectively, in Algeria between 2001 and 2019. Lower AOD (< 0.22) and higher AE (> 1) were found in the northern region, while the highest AOD (0.35 to 0.45) and the lowest AE (< 0.25) were observed over the Tanezrouft desert in southern Algeria. The seasonal mean AOD was highest in summer, while the lowest was in winter due to very high easterly and northeasterly Harmattan surface wind over Zone of Chotts and the Tidikelt Depression, respectively. The negative AOD trends observed over Algeria could be partially connected to the decline (increase) in surface (850 hPa) winds over potential dust source areas in southern Algeria.

Severe Biomass-Burning Aerosol Pollution during the 2019 Amazon Wildfire and Its Direct Radiative-Forcing Impact: A Space Perspective from MODIS Retrievals

An extreme biomass burning event occurred in the Amazonian rainforest from July through September 2019 due to the extensive wildfires used to clear the land, which allowed for more significant forest burning than previously occurred. In this study, we reclustered the clear-sky ambient aerosols to adapt the black carbon (BC) aerosol retrieval algorithm to Amazonia. This not only isolated the volumetric fraction of BC (fbc) from moderate-resolution imaging spectroradiometer (MODIS) aerosol data, but also facilitated the use of aerosol mixing and scattering models to estimate the absorption properties of smoke plumes. The retrieved MODIS aerosol dataset provided a space perspective on characterizing the aerosol changes and trends of the 2019 pollution event. A very high aerosol optical depth (AOD) was found to affect the source areas continuously, with higher and thus stronger aerosol absorption. These pollutants also affected the atmosphere downwind due to the transport of air masses. In addition, properties of aerosols emitted from the 2019 Amazonian wildfire events visualized a significant year-to-year enhancement, with the averaged AOD at 550 nm increased by 150%. A 200% increase in the aerosol-absorption optical depth (AAOD) at 550 nm was recognized due to the low single-scattering albedo (SSA) caused by the explosive BC emissions during the pollution peak. Further simulations of aerosol radiative forcing (ARF) showed that the biomass-burning aerosols emitted during the extreme Amazonian wildfires event in 2019 forced a significant change in the radiative balance, which not only produced greater heating of the atmospheric column through strong absorption of BC, but also reduced the radiation reaching the top-of-atmosphere (TOA) and surface level. The negative radiative forcing at the TOA and surface level, as well as the positive radiative forcing in the atmosphere, were elevated by ~30% across the whole of South America compared to 2018. These radiative effects of the absorbing aerosol could have the ability to accelerate the deterioration cycle of drought and fire over the Amazonian rainforest.

Developing a Land Continuous Variable Estimator to Generate Daily Land Products From Landsat Data

Generating spatially and temporally consistent biophysical products at the global scale from Landsat data for monitoring and assessing surface change dynamics remains a challenge. This article presents an inversion framework called Land continuous Variable Estimator (LoVE)–Landsat for estimating a group of spatiotemporal continuous land surface variables with daily temporal resolution from Landsat 5, 7, and 8 top-of-atmosphere (TOA) data. LoVE–Landsat adopts a data assimilation approach originally developed for coarse-resolution satellite data, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Major improvements to the approach include constructing empirical dynamic equations based on MODIS retrievals and other ancillary information, developing an artificial neural networks (ANN)-based emulator of the coupled radiative transfer (RT) model of atmosphere and land surface (vegetation, soil, and snow) as the observation operator, and designing a hybrid four-dimensional variational (4DVar) and ensemble Kalman filter (EnKF) data assimilation algorithm. The approach starts with generating the initial cloud-free regularly distributed (every 16 days) time series of Landsat data. The 4DVar is then used to assimilate clear-sky snow-free Landsat TOA observations over one year into the empirical dynamic evolution models of the land surface variables (e.g., leaf area index—LAI). The EnKF is then used to further adjust the state vector at the actual Landsat acquisition times. After determining a core set of variables (e.g., LAI), other variables, such as broadband albedo, emissivity, and fraction of absorbed photosynthetically active radiation (FAPAR), are calculated by the coupled RT model. Several experimental cases are presented to demonstrate that the proposed LoVE–Landsat framework is effective to estimate daily land surface variables.

Evaluation of natural aerosols in CRESCENDO Earth system models (ESMs): mineral dust

Abstract. This paper presents an analysis of the mineral dust aerosol modelled by five Earth system models (ESMs) within the project entitled Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach (CRESCENDO). We quantify the global dust cycle described by each model in terms of global emissions, together with dry and wet deposition, reporting large differences in the ratio of dry over wet deposition across the models not directly correlated with the range of particle sizes emitted. The multi-model mean dust emissions with five ESMs is 2836 Tg yr−1 but with a large uncertainty due mainly to the difference in the maximum dust particle size emitted. The multi-model mean of the subset of four ESMs without particle diameters larger than 10 µ m is 1664 (σ=651) Tg yr−1. Total dust emissions in the simulations with identical nudged winds from reanalysis give us better consistency between models; i.e. the multi-model mean global emissions with three ESMs are 1613 (σ=278) Tg yr−1, but 1834 (σ=666) Tg yr−1 without nudged winds and the same models. Significant discrepancies in the globally averaged dust mass extinction efficiency explain why even models with relatively similar global dust load budgets can display strong differences in dust optical depth. The comparison against observations has been done in terms of dust optical depths based on MODIS (Moderate Resolution Imaging Spectroradiometer) satellite products, showing global consistency in terms of preferential dust sources and transport across the Atlantic. The global localisation of source regions is consistent with MODIS, but we found regional and seasonal differences between models and observations when we quantified the cross-correlation of time series over dust-emitting regions. To faithfully compare local emissions between models we introduce a re-gridded normalisation method that can also be compared with satellite products derived from dust event frequencies. Dust total deposition is compared with an instrumental network to assess global and regional differences. We find that models agree with observations within a factor of 10 for data stations distant from dust sources, but the approximations of dust particle size distribution at emission contributed to a misrepresentation of the actual range of deposition values when instruments are close to dust-emitting regions. The observed dust surface concentrations also are reproduced to within a factor of 10. The comparison of total aerosol optical depth with AERONET (AErosol RObotic NETwork) stations where dust is dominant shows large differences between models, although with an increase in the inter-model consistency when the simulations are conducted with nudged winds. The increase in the model ensemble consistency also means better agreement with observations, which we have ascertained for dust total deposition, surface concentrations and optical depths (against both AERONET and MODIS retrievals). We introduce a method to ascertain the contributions per mode consistent with the multi-modal direct radiative effects, which we apply to study the direct radiative effects of a multi-modal representation of the dust particle size distribution that includes the largest particles.

SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe

This paper presents the validation results of Aerosol Optical Depth (AOD) retrieved from the Spinning Enhanced Visible Infrared Radiometer (SEVIRI) data using the near-real-time algorithm further developed in the frame of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project. The SEVIRI AOD was compared against multiple data sources: six stations of the Aerosol Robotic Network (AERONET) in Romania and Poland, three stations of the Aerosol Research Network in Poland (Poland–AOD) and Moderate Resolution Imaging Spectroradiometer (MODIS) data overlapping Romania, Czech Republic and Poland. The correlation values between a four-month dataset (June–September 2014) from SEVIRI and the closest temporally available data for both ground-based and satellite products were identified. The comparison of the SEVIRI AOD with the AERONET AOD observations generally shows a good correlation (r = 0.48–0.83). The mean bias is 0.10–0.14 and the root mean square error RMSE is between 0.11 and 0.15 for all six stations cases. For the comparison with Poland–AOD correlation values are 0.55 to 0.71. The mean bias is 0.04–0.13 and RMSE is between 0.10 and 0.14. As for the intercomparison to MODIS AOD, correlations values were generally lower (r = 0.33–0.39). Biases of −0.06 to 0.24 and RMSE of 0.04 to 0.28 were in good agreement with the ground–stations retrievals. The validation of SEVIRI AOD with AERONET results in the best correlations followed by the Poland–AOD network and MODIS retrievals. The average uncertainty estimates are evaluated resulting in most of the AOD values falling above the expected error range. A revised uncertainty estimate is proposed by including the observed bias form the AERONET validation efforts.

Performance of water vapour retrieval from MODIS and ECMWF and their validation with ground based GPS measurements over Varanasi

Water vapour is highly variable over tropical region and sensitive to weather condition, monsoon onset, green house effect, and pollution level in Ganga River. In the present study, variability in water vapour derived from Global Positioning System (GPS) over Varanasi (25°20′N, 82°59′E) during the period 2007–2010 has been studied. The GPS-derived water vapour (WV) has been compared with those retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) and ECMWF. The GPS-WV data concurrent to MODIS and ECMWF timing has been correlated to perform further analysis. To study the accuracy of water vapour retrieved from the MODIS and ECMWF, root mean square error (RMSE), absolute error (AE), correlation and standard deviation in it are computed with respect to GPS-derived water vapour. Analysis shows an annual correlation R2 = 86%, RMSE = 9.5 mm and AE (MODIS–GPS) = 7.0 mm in MODIS retrieval and annual correlation R2 = 86%, RMSE = 6.1 mm and AE (ECMWF–GPS) = 2.4 mm in ECMWF reanalysis retrieval. Correlation of ECMWF and MODIS datasets with the GPS datasets are found to vary significantly with seasons. The correlation is high during monsoon season and low during spring season. Water vapour is found to be an indicator for the onset of monsoon.

Prototyping of LAI and FPAR Retrievals From GOES-16 Advanced Baseline Imager Data Using Global Optimizing Algorithm

The latest Geostationary (GEO) Operational Environmental Satellite-16 (GOES-16) equipped with Advanced Baseline Imager (ABI) has comparable spectral and spatial resolution as low earth orbiting (LEO) sensors [i.e., the Moderate Resolution Imaging Spectroradiometer (MODIS)], but with up-to-the-minute image acquisition capability. This enables greater opportunities to generate two essential climate variables-Leaf area index (LAI) and the fraction of photosynthetically active radiation (FPAR) absorbed by vegetation with more cloud-free observations and at much higher frequency. The improved GEO LAI/FPAR products will increase the capacity for monitoring highly dynamic ecosystems in a timely manner. However, the radiative transfer (RT)-based MODIS operational algorithm cannot be directly applied to GOES-16 ABI data due to different sensor characteristics. Fortunately, it has been shown theoretically and practically, that the RT-based algorithm can be transplanted to any other optical sensors by optimizing the sensor-specific parameters-the single scattering albedo (SSA) and relative stabilized precision (RSP). We built the RT-based ABI-specific lookup tables (LUTs) using a global optimizing algorithm (SCE-UA) that can quickly find the optimal solution. SCE-UA optimizes the SSAs and RSPs in the LUTs by minimizing the difference between ABI and MODIS retrievals and maximizing the main algorithm execution rate. Our efforts indicate that these strategies of parametric optimization is able to decrease the discrepancy between the ABI and MODIS LAI/FPAR products. Comprehensive evaluations were conducted to evaluate ABI retrievals. These indirect inter-comparisons suggest a spatiotemporal consistency between ABI and the benchmark MODIS products, while direct validation with field measurements increases confidence in their accuracy. The proposed approach is applicable to any other optical sensors for LAI/FPAR estimation, especially, GEO sensors (i.e., Himawari-8, Geo-KOMPSAT-2A, FengYun-4 etc.).

CERES MODIS Cloud Product Retrievals for Edition 4—Part I: Algorithm Changes

The Edition 2 (Ed2) cloud property retrieval algorithm system was upgraded and applied to the MODerate-resolution Imaging Spectroradiometer (MODIS) data for the Clouds and the Earth's Radiant Energy System (CERES) Edition 4 (Ed4) products. New calibrations for solar channels and the use of the 1.24-μm channel for cloud optical depth (COD) over snow improve the daytime consistency between Terra and Aqua MODIS retrievals. Use of additional spectral channels and revised logic enhanced the cloud-top phase retrieval accuracy. A new ice crystal reflectance model and a CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -channel algorithm retrieved higher ice clouds, while a new regional lapse rate technique produced more accurate water cloud heights than in Ed2. Ice cloud base heights are more accurate due to a new cloud thickness parameterization. Overall, CODs increased, especially over the polar (PO) regions. The mean particle sizes increased slightly for water clouds, but more so for ice clouds in the PO areas. New experimental parameters introduced in Ed4 are limited in utility, but will be revised for the next CERES edition. As part of the Ed4 retrieval evaluation, the average properties are compared with those from other algorithms and the differences between individual reference data and matched Ed4 retrievals are explored. Part II of this article provides a comprehensive, objective evaluation of selected parameters. More accurate interpretation of the CERES radiation measurements has resulted from the use of the Ed4 cloud properties.

Near Real-Time Monitoring of the Christmas 2018 Etna Eruption Using SEVIRI and Products Validation

On the morning of 24 December 2018, an eruptive event occurred at Etna, which was followed the next day by a strong sequence of shallow earthquakes. The eruptive episode lasted until 30 December, ranging from moderate strombolian to lava fountain activity coupled with vigorous ash/gas emissions and a lava flow effusion toward the eastern volcano flank of Valle del Bove. In this work, the data collected from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instruments on board the Meteosat Second Generation (MSG) geostationary satellite are used to characterize the Etna activity by estimating the proximal and distal eruption parameters in near real time. The inversion of data indicates the onset of eruption on 24 December at 11:15 UTC, a maximum Time Average Discharge Rate (TADR) of 8.3 m3/s, a cumulative lava volume emitted of 0.5 Mm3, and a Volcanic Plume Top Height (VPTH) that reached a maximum altitude of 8 km above sea level (asl). The volcanic cloud ash and SO2 result totally collocated, with an ash amount generally lower than SO2 except on 24 December during the climax phase. A total amount of about 100 and 35 kt of SO2 and ash respectively was emitted during the entire eruptive period, while the SO2 fluxes reached peaks of more than 600 kg/s, with a mean value of about 185 kg/s. The SEVIRI VPTH, ash/SO2 masses, and flux time series have been compared with the results obtained from the ground-based visible (VIS) cameras and FLux Automatic MEasurements (FLAME) networks, and the satellite images collected by the MODerate resolution Imaging Spectroradiometer (MODIS) instruments on board the Terra and Aqua- polar satellites. The analysis indicates good agreement between SEVIRI, VIS camera, and MODIS retrievals with VPTH, ash, and SO2 estimations all within measurement errors. The SEVIRI and FLAME SO2 flux retrievals show significant discrepancies due to the presence of volcanic ash and a gap of data on the FLAME network. The results obtained in this study show the ability of geostationary satellite systems to characterize eruptive events from the source to the atmosphere in near real time during the day and night, thus offering a powerful tool to mitigate volcanic risk on both local population and airspace and to give insight on volcanic processes.

Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar

Abstract. To better understand the accuracy of cloud top heights (CTHs) derived from passive satellite data, ground-based Ka-band radar measurements from 2016 and 2017 in Beijing are compared with CTH data inferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Himawari Imager (AHI). Relative to the radar CTHs, the MODIS CTHs are found to be underestimated by−1.10 ± 2.53 km on average and 49 % of CTH differences are within 1.0 km. The AHI CTHs are underestimated by −1.10 ± 2.27 km and 42 % are within 1.0 km. Both the MODIS and AHI CTH retrieval accuracy depends strongly on the cloud depth (CD). Large differences are mainly due to the retrieval of thin clouds of CD &lt;1 km, especially when the cloud base height is higher than 4 km. For clouds with CD &gt;1 km, the mean CTH difference decreases to -0.48±1.70 km for MODIS and to -0.76±1.63 km for AHI. It is found that MODIS CTHs with higher values (i.e. &gt;6 km) show smaller discrepancy with radar CTH than those MODIS CTHs with lower values (i.e. &lt;4 km). Statistical analysis illustrate that the CTH difference between the two satellite instruments is lower than the difference between the satellite instrument and the ground-based Ka-band radar. The monthly accuracy of both CTH retrieval algorithms is investigated and it is found that summer has the smallest retrieval difference.

Performance of MODIS Collection 6.1 Level 3 aerosol products in spatial-temporal variations over land

This study evaluates the long-term Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Level 3 atmospheric aerosol products over land. For this purpose, three monthly aerosol optical depth (AOD) datasets, including Dark Target (DT), Deep Blue (DB) and combined DT and DB (DTB) during 2003–2017, are collected. Aerosol Robotic Network (AERONET) Version 2 Level 2.0 (cloud-screened and quality-assured) monthly measurements at 431 sites around the world are selected for comparison. This study attempts to provide a better understanding of the different MODIS products for their applicability at multiple spatial scales and their suitability for representing the long-term trend of aerosol characteristics. Experiments are performed with direct comparisons between MODIS retrievals and AERONET measurements at global, local to site scales. Meanwhile, the spatial and temporal variations are also compared and discussed. Our results illustrate that C6.1 MODIS AOD retrievals are well correlated with AERONET AOD measurements globally, while the DTB product performs best at most regions, yet the DB product is superior at the site scale. In general, Terra AOD products always overestimate while Aqua AOD products are more accurate in describing the annual mean AOD loadings over land. For long-term aerosol trends, there are small differences between Terra and Aqua aerosol products. Among the three aerosol datasets, neither one can consistently outperform the others in both spatial and temporal aerosol variations over land. In general, DTB products can more accurately capture the correct aerosol changes and are strongly recommended for selection in related aerosol studies at the global scale.

An Assessment of the Impacts of Cloud Vertical Heterogeneity on Global Ice Cloud Data Records From Passive Satellite Retrievals

AbstractSpaceborne passive instruments are widely used to infer long‐term ice cloud properties due to their large temporal and spatial coverage. Although observations from active instruments demonstrate ice particle variability in the vertical dimension, a pragmatic assumption made in passive cloud retrieval algorithms is that the observed scene consists of a plane‐parallel cloud. In this study, a theoretical exploration on how ice cloud vertical heterogeneity (ICVH) influences passive retrievals (i.e., cloud optical thickness, cloud effective radius, and ice water path, IWP) is implemented at the pixel scale. Specifically, with an established ice cloud profile database inferred from 1‐year Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation/CloudSat, we quantitatively estimate ICVH‐induced biases on monthly averaged cloud macrophysical and radiative properties. Results show an average underestimation (−35%) of Moderate Resolution Imaging Spectroradiometer (MODIS) monthly IWP due to the ICVH for global ice clouds over ocean. The ICVH impacts are enhanced at large IWPs (e.g., &gt; 500 g/m2) and solar zenith angles, resulting in a profound underestimation of MODIS IWP (up to −50%) in deep convective regions and middle to high‐latitude regions in the winter hemisphere. The global‐averaged ice cloudy‐sky reflected solar radiation and outgoing longwave radiation derived from MODIS retrievals are slightly overestimated, suggesting that the ICVH has little impact on cloud radiative properties. Relatively large reflected solar radiation (0.3 W/m2) and outgoing longwave radiation (0.1 W/m2) flux differences occur at high and low IWPs, respectively. The largest total flux difference (~2 W/m2), mainly contributed by shortwave part, is associated with deep convection where the typical IWP is greater than 2,000 g/m2.

Comparative Accuracy Assessment of Combined MODIS and NAAPS Aerosol Optical Depth with AERONET Data over North Africa

Aerosol is one of the important geophysical parameters that determine the earth’s radiation budget, energy balance and hydrological cycle. The “Deep Blue” Moderate Resolution Imaging Spectro-radiometer (MODIS) Aerosol Optical Depth (AOD) retrieval algorithm was designed to complement existing “Dark Target” Ocean and Land algorithms to be able to retrieve AOD over bright land surface. Using level 2 AOD data from five Aerosol Robotic Network (AERONET) stations over the study location of North Africa (0°S - 40°N, 30°W - 60°E), comparative accuracy assessments are made for combined MODIS AOD aboard Terra and Aqua satellites and US Navy Aerosol Analysis and Prediction System (NAAPS) forecast AOD data. The aerosol transport and vertical mixing over the region are investigated at different altitudes up to 3000 m above ground level using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT). The MODIS validation result shows highest correlation in the Sub-Sahel (0.811) followed by Sahel (0.726) and then Sahara region (0.662). Furthermore, the combined retrieval algorithm of Terra and Aqua MODIS shows statistically significant discrepancies from AERONET AOD values in term of mean, t-test value, index of agreement and fractional error. The comparison of NAAPS predicted soil dust to AERONET AOD fared best in December to February (DJF) season for the Sahel region and June to August (JJA) season for the Sahara when the dust emission and transport are at the peak. However, median ratios of NAAPS to AERONET AOD indicated bias in some island sites in the Atlantic Ocean which may be due to the presence of sea salt over the site. The analysis carried out in this study reveals that both MODIS retrieval algorithm and NAAPS model could be improved by incorporating some local aerosol sources from the study area.

Cloud Adiabaticity and Its Relationship to Marine Stratocumulus Characteristics Over the Northeast Pacific Ocean

AbstractCloud adiabaticity (α) is defined as the ratio of the actual liquid water path (LWPmeasured) in a cloud to its corresponding adiabatic value (LWPad). Processes such as drizzle and entrainment can lead to subadiabatic LWPmeasured. This study examines α and its relationship to microphysical properties for 86 cloud events over the Northeast Pacific Ocean based on data collected during four separate summertime airborne campaigns. For the study region, α was found to be 0.766 ± 0.134. For most cases, clouds with a low value of α were found to have lower droplet number concentration (Nd), higher droplet effective radius (re), higher relative dispersion (d), and higher rain rate (R). The subcloud aerosol concentration (Na) was often less for the low‐α cases. The relationship between α and the vertical profiles and cloud‐top characteristics for both the cloud droplet‐only spectrum and full spectrum (cloud and rain droplets) is also examined. Inclusion of rain droplets produced a larger change in d for the low‐α clouds as compared to the high‐α clouds. On average, R increased at cloud top for high‐α clouds but decreased at cloud top for low‐α clouds. Accounting for α when estimating Nd from Moderate Resolution Imaging Spectroradiometer retrievals results in better agreement with in situ Nd values. Results of this work motivate the need for additional focus on the factors governing α, such as cloud type, and implications of its value, especially for remote‐sensing retrievals.

Synergy of AERONET and MODIS AOD products in the estimation of PM2.5 concentrations in Beijing

Satellite aerosol optical depth (AOD) is widely used to estimate particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) mass concentrations. Polar orbiting satellite retrieval 1–2 times each day is frequently affected by cloud, snow cover or misclassification of heavy pollution. Novel methods are therefore required to improve AOD sampling. Sunphotometer provides much more AODs than satellite at a fixed point. Furthermore, much of the aerosol pollution is regional. Both factors indicate that sunphotometer has great potential for PM2.5 concentration estimation. The spatial representativeness of the Aerosol Robotic Network (AERONET) AOD at Beijing site is investigated by linear regression analysis of 13-year daily paired AODs at each grid from Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Beijing AERONET. The result suggests a good correlation for the whole Beijing Administrative region, with regional mean correlation coefficient exceeding 0.73. Pixel AODs are then estimated from AERONET AOD using linear equations, which are verified to have the same accuracy as that of MODIS AOD. Either AOD from MODIS retrieval or estimation from AERONET AOD in the absence of MODIS pixel AOD is finally used to predict PM2.5 concentration. Daily AOD sampling in average is enhanced by 59% in winter when MODIS AODs are very limited. More importantly, synergy of AERONET and MODIS AOD is able to improve the estimation of regional mean PM2.5 concentrations, which indicates this method would play a significant role in monitoring regional aerosol pollution.

Linking Spaceborne and Ground Observations of Autumn Foliage Senescence in Southern Québec, Canada

Autumn senescence progresses over several weeks during which leaves change their colors. The onset of leaf coloring and its progression have environmental and economic consequences, however, very few efforts have been devoted to monitoring regional foliage color change in autumn using remote sensing imagery. This study aimed to monitor the progression of autumn phenology using satellite remote sensing across a region in Southern Québec, Canada, where phenological observations are frequently performed in autumn across a large number of sites, and to evaluate the satellite retrievals against these in-situ observations. We used a temporally-normalized time-series of Normalized Difference Vegetation Index (NDVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to monitor the different phases of autumn foliage during 2011–2015, and compared the results with ground observations from 38 locations. Since the NDVI time-series is separately normalized per pixel, the outcome is a time-series of foliage coloration status that is independent of the land cover. The results show a significant correlation between the timing of peak autumn coloration to elevation and latitude, but not to longitude, and suggest that temperature is likely a main driver of variation in autumn foliage progression. The interannual coloration phase differences for MODIS retrievals are larger than for ground observations, but most ground site observations correlate significantly with MODIS retrievals. The mean absolute error for the timing of all foliage phases is smaller than the frequency of both ground observation reports and the frequency of the MODIS NDVI time-series, and therefore considered acceptable. Despite this, the observations at four of the ground sites did not correspond well with the MODIS retrievals, and therefore we conclude that further methodological refinements to improve the quality of the time series are required for MODIS spatial monitoring of autumn phenology over Québec to be operationally employed in a reliable manner.

Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia

The insufficient number of ground-based stations for measuring Particulate Matter <10μm (PM10) in the developing countries hinders PM10 monitoring at a regional scale. The present study aims to develop empirical models for PM10 estimation from space over Malaysia using aerosol optical depth (AOD550) and meteorological (surface temperature, relative humidity and atmospheric stability) data (retrieved or estimated) from Moderate Resolution Imaging Spectroradiometer (MODIS) during the period 2007–2011. The MODIS retrievals are found to be satisfactorily correlated with ground-based measurements at Malaysia. Multiple linear regressions (MLR) and Artificial Neural Network (ANN) techniques are utilized to develop the empirical models for PM10 estimation. The model development and training are performed via comparison with measured PM10 at 29 stations over Malaysia and reveal that the ANN provides slightly higher accuracy with R2=0.71 and RMSE=11.61μgm−3 compared to the MLR method (R2=0.66 and RMSE=12.39μgm−3). Stepwise regression analysis performed on the MLR method reveals that the MODIS AOD550 is the most important parameter for PM10 estimations (R2=0.59 and RMSE=13.61μgm−3); however, the inclusion of the meteorological parameters in the MLR increases the accuracy of the retrievals (R2=0.66, RMSE=12.39μgm−3). The estimated PM10 concentrations are finally validated against surface measurements at 16 stations resulting in similar performance from the ANN model (R2=0.58, RMSE=10.16μgm−3) and MLR technique (R2=0.56, RMSE=10.58μgm−3). The significant accuracy that has been attained in PM10 estimations from space allows us to assess the pollution levels in Malaysia and map the PM10 distribution at large spatial and temporal scales.

Exploiting TERRA-AQUA MODIS Relationship in the Reflective Solar Bands for Aerosol Retrieval

Satellite remote sensing has been providing aerosol data with ever-increasing accuracy, representative of the MODerate-resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) and Deep Blue (DB) aerosol retrievals. These retrievals are generally performed over spectrally dark objects and therefore may struggle over bright surfaces. This study proposed an analytical TERRA-AQUA MODIS relationship in the reflective solar bands for aerosol retrieval. For the relationship development, the bidirectional reflectance distribution function (BRDF) effects were adjusted using reflectance ratios in the MODIS 2.13 μm band and the path radiance was approximated as an analytical function of aerosol optical thickness (AOT) and scattering phase function. Comparisons with MODIS observation data, MODIS AOT data, and sun photometer measurements demonstrate the validity of the proposed relationship for aerosol retrieval. The synergetic TERRA-AQUA MODIS retrievals are highly correlated with the ground measured AOT at TERRA MODIS overpass time (R2 = 0.617; RMSE = 0.043) and AQUA overpass time (R2 = 0.737; RMSE = 0.036). Compared to our retrievals, both the MODIS DT and DB retrievals are subject to severe underestimation. Sensitivity analyses reveal that the proposed method may perform better over non-vegetated than vegetated surfaces, which can offer a complement to MODIS operational algorithms. In an analytical form, the proposed method also has advantages in computational efficiency, and therefore can be employed for fine-scale (relative to operational 10 km MODIS product) MODIS aerosol retrieval. Overall, this study provides insight into aerosol retrievals and other applications regarding TERRA-AQUA MODIS data.

Validation of MODIS liquid water path for oceanic nonraining warm clouds: Implications on the vertical profile of cloud water content

AbstractLiquid water path (LWP) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) is validated using the Advanced Microwave Scanning Radiometer–EOS (AMSR‐E) retrievals for global oceanic nonraining warm clouds, with focus on the vertically homogeneous (VH) model and adiabatically stratified (AS) model of liquid water content (LWC) profile used in MODIS retrieval. With respect to AMSR‐E LWP that acts as ground truth under a series of constraints, the global average of MODIS‐LWPVH and MODIS‐LWPAS has a positive (11.8%) and negative (−6.8%) bias, respectively. Most of the oceanic warm clouds tend to have adiabatic origin and correspondingly form AS‐like profiles, which could be well retained if drizzle is absent. Besides, the presence of drizzle, cloud top entrainment seems to be another cause that modifies the original LWC profiles to become VH‐like, which is notable for the very low clouds that have rather small thickness. These factors jointly determine the appearance of LWP profiles and in turn their spatial pattern across global oceans, with AS‐like profiles dominant in the areas where nonraining warm clouds occur very frequently in the form of stratocumulus. The modified MODIS LWP shows significant improvement compared with either MODIS‐LWPVH or MODIS‐LWPAS. This is achieved by using the two physically explicit models flexibly, in which the elementary MODIS retrievals of cloud top temperature, cloud optical thickness, and droplet effective radius play a determinant role. A combined use of VH and AS model in the MODIS retrieval is demonstrated to be effective for improving the LWP estimation for oceanic nonraining warm clouds.