MODIS Retrievals Research Articles

Intercomparison of aerosol optical depths from four reanalyses and their multi-reanalysis consensus

Abstract. The emergence of aerosol reanalyses in recent years has facilitated a comprehensive and systematic evaluation of aerosol optical depth (AOD) trends and attribution over multi-decadal timescales. Notable multi-year aerosol reanalyses currently available include NAAPS-RA from the US Naval Research Laboratory, the NASA MERRA-2, JRAero from the Japan Meteorological Agency (JMA), and CAMSRA from Copernicus/ECMWF. These aerosol reanalyses are based on differing underlying meteorology models, representations of aerosol processes, as well as data assimilation methods and treatment of AOD observations. This study presents the basic verification characteristics of these four reanalyses versus both AERONET and MODIS retrievals in monthly AOD properties and identifies the strength of each reanalysis and the regions where divergence and challenges are prominent. Regions with high pollution and often mixed fine-mode and coarse-mode aerosol environments, such as South Asia, East Asia, Southeast Asia, and the Maritime Continent, pose significant challenges, as indicated by higher monthly AOD root mean square error. Moreover, regions that are distant from major aerosol source areas, including the polar regions and remote oceans, exhibit large relative differences in speciated AODs and fine-mode versus coarse-mode AODs among the four reanalyses. To ensure consistency across the globe, a multi-reanalysis consensus (MRC, i.e., ensemble mean) approach was developed similarly to the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME). Like the ICAP-MME, while the MRC does not consistently rank first among the reanalyses for individual regions, it performs well by ranking first or second globally in AOD correlation and RMSE, making it a suitable candidate for climate studies that require robust and consistent assessments.

Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)

Abstract. The Goddard Chemistry Aerosol Radiation and Transport (GOCART) model, which controls the sources, sinks, and chemistry of aerosols within the Goddard Earth Observing System (GEOS), recently underwent a major refactoring and update, including a revision of the emissions datasets and the addition of brown carbon. A 4-year benchmark simulation utilizing the new version of the model code, termed GOCART Second Generation (GOCART-2G) and coupled to the Goddard Earth Observing System (GEOS) model, was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development. A comparison of simulated aerosol optical depth between GOCART-2G and MODIS retrievals indicates the model captures the overall spatial pattern and seasonal cycle of aerosol optical depth but overestimates aerosol extinction over dusty regions and underestimates aerosol extinction over Northern Hemisphere boreal forests, requiring further investigation and tuning of emissions. This MODIS-based analysis is corroborated by comparisons to MISR and selected AERONET stations; however, discrepancies between the Aqua and Terra satellites indicate there is a diurnal component to biases in aerosol optical depth over southern Asia and northern Africa. Despite the underestimate of aerosol optical depth in biomass burning regions in GEOS, there is an overestimate in the surface mass of organic carbon in the United States, especially during the summer months. Over Europe, GOCART-2G is unable to match the summertime peak in aerosol optical depth, opposing the observed late fall and early spring peaks in surface mass concentration. A comparison of the vertical profile of attenuated backscatter to observations from CALIPSO indicates the GEOS model is capable of capturing the vertical profile of aerosol; however, the mid-troposphere plumes of dust in the North Atlantic and smoke in the southeastern Atlantic are perhaps too low in altitude. The results presented highlight priorities for future development with GOCART-2G, including improvements for dust, biomass burning aerosols, and anthropogenic aerosols.

Forecasting wildfire hazard across northwestern south America

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r2 in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.

Simulating the impact of varying vegetation on West African monsoon surface fluxes using a regional convection-permitting model.

This study assessed the sensitivity of the West African climate to varying vegetation fractions. The assessment of a such relationship is critical in understanding the interactions between land surface and atmosphere. Two sets of convection-permitting simulations from the UK Met Office Unified Model at 12 km horizontal resolution covering the monsoon period May-September (MJJAS) were used, one with fixed vegetation fraction (MF-V) and the other with time-varying vegetation fraction (MV-V). Vegetation fractions are based on MODIS retrievals between May and September. We focused on three climatic zones over West Africa: Guinea Coast, Sudanian Sahel, and the Sahel while investigating heat fluxes, temperature, and evapotranspiration. Results reveal that latent heat fluxes are the most strongly affected by vegetation fraction over the Sahelian and Sudanian regions while sensible heat fluxes are more impacted over the Guinea Coast and Sudanian Sahel. Also, in MV-V simulation there is an increase in evapotranspiration mainly over the Sahel and some specific areas in Guinea Coast from June to September. Moreover, it is noticed that high near-surface temperature is associated with a weak vegetation fraction, especially during May and June. Finally, varying vegetation seems to improve the simulation of surface energy fluxes and in turn impact on climate parameters. This suggests that climate modelers should prioritize the use of varying vegetation options to improve the representation of the West African climate system.

Generating 250 m-resolution regional NO2 concentration products first from MODIS retrievals using extreme gradient boosting

Generating 250 m-resolution regional NO2 concentration products first from MODIS retrievals using extreme gradient boosting

Variation and comparison of cloud cover in MODIS and four reanalysis datasets of ERA-interim, ERA5, MERRA-2 and NCEP

Cloud plays an important role in climate change and radiative forcing. Atmospheric reanalysis datasets provide an economical and efficient way to explore the worldwide cloud variation. This study compares the monthly total cloud cover (TCC) and high cloud cover (HCC) of the four commonly used atmospheric reanalysis datasets, including European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), ECMWF's Fifth-generation Reanalysis (ERA5), Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and National Centers for Environmental Prediction (NCEP: only TCC), with MODIS retrievals during 2001–2018. The spatial and temporal distributions of TCC for the four reanalyses are generally consistent with MODIS results, while showing better consistency over land than over ocean. The largest TCC discrepancy between MODIS and the reanalysis appears over the polar regions. Among the four reanalysis datasets, ERA5 presents the highest correlation and the smallest mean bias compared with MODIS, while NCEP shows the lowest and the largest. Although all the three reanalyses (ERA-Interim, ERA5 and MERRA-2) generally give an overestimation of HCC compared with MODIS, the ERA-Interim shows the closest to MODIS. The largest difference of HCCs between MODIS and the reanalysis is located over the Tibet Plateau. To the annual change of global means, ERA-Interim and MERRA-2 appear a significant increasing trend of TCC over both land and ocean. ERA5 and MERRA-2 show a significant decreasing trend of HCC over ocean, and MODIS gives a reduce over land. Furthermore, During the ENSO events, a positive anomaly of TCC, which is positively correlated with the ENSO intensity over the eastern equatorial Pacific, is captured by all the four reanalyses, among which ERA5 and ERA-Interim appear more distinct.

Satellite products performance based on in-situ data to evaluate SST variability in a subtropical inner shelf in Brazil

This study compares in-situ, satellite (MODIS Terra and Aqua daytime and nighttime), and reanalysis (Operational Sea Surface Temperature and Sea Ice Analysis – OSTIA) sea surface temperature (SST) data of the inner shelf of Paraná – Brazil at daily scales. There are match-up pairs for all products covering the whole range of in-situ SST (19°C–30°C) and every month, consisting of an appropriate method to validate our satellite datasets in the study area. Although there is a good statistical agreement between MODIS retrievals and in-situ SST, cloud coverage over the study area results in a large number of gaps along with the time series. Thus, dominant features of the SST variability patterns were characterized based on 35 years (1985–2018) of OSTIA SST. There is a strong influence of the Brazil Coastal Current during late winter months contrasting with warmer waters from the Brazil Current during summer. Local high-frequency forcing dominates SST variability, mostly high-energy meteorological events, which act like pulses that intensify mixing processes, resulting in a stair-like variability pattern. At adequate temporal and spatial scales, the relatively cost-effective, spatially explicit, frequent, and continuous remote sensing data can provide the necessary information to support the reliance of our civilization on the ocean. As the SST regime in coastal waters differs from the open waters, the results presented here are unique. They can provide references to understanding the dominant scales of complex coastal dynamics, a key to predict impacts, draw scenarios and mitigate risks, supporting the sustainability of marine and coastal ecosystem services.

Observation of absorbing aerosols above clouds over the south-east Atlantic Ocean from the geostationary satellite SEVIRI – Part 2: Comparison with MODIS and aircraft measurements from the CLARIFY-2017 field campaign

Abstract. To evaluate the SEVIRI retrieval for aerosols above clouds presented in Part 1 of the companion paper, the algorithm is applied over the south-east Atlantic Ocean during the CLARIFY-2017 field campaign period. The first step of our analysis compares the retrieved aerosol and cloud properties against equivalent products from the MODIS MOD06ACAERO retrieval (Meyer et al., 2015). While the correlation between the two satellite retrievals of the above-cloud aerosol optical thickness (AOT) is good (R = 0.78), the AOT retrieved by SEVIRI is 20.3 % smaller than that obtained from the MODIS retrieval. This difference in AOT is attributed mainly to the more absorbing aerosol model assumed for the SEVIRI retrieval compared to MODIS. The underlying cloud optical thickness (COT) derived from the two satellites is in good agreement (R = 0.90). The cloud droplet effective radius (CER) retrieved by SEVIRI is consistently smaller than MODIS by 2.2 µm, which is mainly caused by the use of different spectral bands of the satellite instruments. In the second part of our analysis, we compare the forecast water vapour profiles used for the SEVIRI atmospheric correction as well as the aforementioned aerosol and cloud products with in situ measurements made from the Facility for Airborne Atmospheric Measurements (FAAM) aircraft platform during the CLARIFY-2017 campaign. Around Ascension Island, the column water vapour used to correct the SEVIRI signal is overestimated by 3.1 mm in the forecast compared to that measured by dropsondes. However, the evidence suggests that the accuracy of the atmospheric correction improves closer to the African coast. Consistency is observed between the SEVIRI above-cloud AOT and in situ measurements (from cavity ring-down spectroscopy instruments) when the measured single-scattering albedo is close to that assumed in the retrieval algorithm. On the other hand, the satellite retrieval overestimates the AOT when the assumed aerosol model is not absorbing enough. Consistency is also found between the cloud properties retrieved by SEVIRI and the CER measured by a cloud droplet probe and the liquid water path derived from a microwave radiometer. Despite the instrumental limitations of the geostationary satellite, the consistency obtained between SEVIRI, MODIS and the aircraft measurements demonstrates the ability of the retrieval in providing additional information on the temporal evolution of the aerosol properties above clouds.

Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring

Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring

Applying the Dark Target aerosol algorithm with Advanced Himawari Imager observations during the KORUS-AQ field campaign

Abstract. For nearly 2 decades we have been quantitatively observing the Earth's aerosol system from space at one or two times of the day by applying the Dark Target family of algorithms to polar-orbiting satellite sensors, particularly MODIS and VIIRS. With the launch of the Advanced Himawari Imager (AHI) and the Advanced Baseline Imagers (ABIs) into geosynchronous orbits, we have the new ability to expand temporal coverage of the traditional aerosol optical depth (AOD) to resolve the diurnal signature of aerosol loading during daylight hours. The Korean–United States Air Quality (KORUS-AQ) campaign taking place in and around the Korean peninsula during May–June 2016 initiated a special processing of full-disk AHI observations that allowed us to make a preliminary adoption of Dark Target aerosol algorithms to the wavelengths and resolutions of AHI. Here, we describe the adaptation and show retrieval results from AHI for this 2-month period. The AHI-retrieved AOD is collocated in time and space with existing AErosol RObotic NETwork stations across Asia and with collocated Terra and Aqua MODIS retrievals. The new AHI AOD product matches AERONET, and the standard MODIS product does as well, and the agreement between AHI and MODIS retrieved AOD is excellent, as can be expected by maintaining consistency in algorithm architecture and most algorithm assumptions. Furthermore, we show that the new product approximates the AERONET-observed diurnal signature. Examining the diurnal patterns of the new AHI AOD product we find specific areas over land where the diurnal signal is spatially cohesive. For example, in Bangladesh the AOD increases by 0.50 from morning to evening, and in northeast China the AOD decreases by 0.25. However, over open ocean the observed diurnal cycle is driven by two artifacts, one associated with solar zenith angles greater than 70∘ that may be caused by a radiative transfer model that does not properly represent the spherical Earth and the other artifact associated with the fringes of the 40∘ glint angle mask. This opportunity during KORUS-AQ provides encouragement to move towards an operational Dark Target algorithm for AHI. Future work will need to re-examine masking including snow mask, re-evaluate assumed aerosol models for geosynchronous geometry, address the artifacts over the ocean, and investigate size parameter retrieval from the over-ocean algorithm.

Impact of biomass burning on regional aerosol optical properties: A case study over northern India

The present study examines the spatial, seasonal and inter annual variation of biomass burning and its impact on regional aerosol optical properties over Northern India using multi-satellite aerosol observations: Active fire points, AOD (550 nm) and AE (550−860 nm) from MODIS retrievals during January 2003–December 2017 and AAOD (388 nm), SSA (388 nm) and AI from OMI UV retrievals during January 2005–December 2017. Results from MODIS active fire count statistics indicate an increase in the number of fire occurrences (average 1477 fires per year) over India in a period of 15 years (2003-2017). The dominant fire seasons are (i) Pre-monsoon (March to May) accounting to more than 45% and (ii) Post-Monsoon having 24% of total annual fires counts. However, the crop residue burning hotspot region located in Punjab and Haryana, constitutes 26% of the total fires in India. At an average, 15456 (77.08%) fire counts were reported during the paddy season, whereas 3296 (16.44%) fire counts during wheat season respectively. The crop residue burning over the northwest IGP (Punjab) significantly affect the aerosol optical properties locally as well in the downwind regions during post-monsoon season i.e., crop residue fires increased by 4% (170 fires per year) with corresponding AOD, AAOD & AI increased by 8%, 9% & 11% respectively. The satellite observation shows large gradient of aerosol parameters from north-west to south-east along the Himalayan foot-hills which indicates the regional transport of smoke aerosols over the region. This is also supported by ground based AOD observations at four locations (Patiala, Delhi, Dehradun and Kanpur) and Black Carbon measurements at two locations (Patiala and Dehradun). The climatological averaged values of ground based AOD550 for Patiala, Delhi, Dehradun and Kanpur are 0.52 ± 0.26, 0.75 ± 0.40, 0.45 ± 0.24 and 0.57 ± 0.29 respectively whereas BC concentrations are 8.43 ± 3.14 μg m−3 & 3.36 ± 1.26 μg m−3 for Patiala & Dehradun respectively. Comparison of MODIS derived AOD agrees well with ground based AODs (overall R = 0.86 and RMSE = 0.14). In addition, CALIPSO shows the maximum amount of biomass burning smoke aerosols present within the atmospheric boundary layer and some cases it extending up to 2–3 km altitudes. The smoke aerosol transport pathways originated from crop residue burning were analyzed using Hysplit forward trajectories. The results reveal that majority of smoke aerosols are transported to eastern IGP, central India and adjacent oceanic regions during post-monsoon season.

Soil Moisture Retrievals in Aeolian Sand Mining Areas Using Temporal, Single-Polarization, High-Resolution SAR

Abstract Changes in the soil moisture are a key factor in the deterioration of the ecological environment caused by mining geological disasters. In this study, which presents a case study of the arid and semi-arid aeolian sand mining area along the Inner Mongolia-Shaanxi Province boundary, a method for retrieving the soil moisture based on RADARSAT-2 SAR scattering data and Terra MODIS surface reflectance data was proposed. The retrieval of RADARSAT-2 mainly used the Alpha approximation model based on the change detection technique, a model proposed by Balenzano et al., which can effectively decouple the impact of surface vegetation and roughness on radar backscattering coefficient, when the volume scattering is not dominant. Using 12 periods of RADARSAT-2 HH polarization data in conjunction with the Alpha approximation model, a matrix equation was constructed, which contains 11 equations and 12 unknowns. To solve this underdetermined system, a bounded linear least-squares optimization was adopted. Once the unknowns were determined, the relative dielectric constant could be analytically derived and then the soil moisture could be estimated by using the dielectric mixing model and compared with the MODIS retrieval results based on the spatial feature method. Finally, the DInSAR results of RADARSAT-2 were used to investigate the effects of high-intensity underground mining activities on the surface soil moisture. The study found that the RADARSAT-2 soil moisture estimates demonstrated good consistency with the MODIS retrieval results. Among four comparison groups, the maximum correlation coefficient was 0.599, and the highest proportion of sampling points for which the absolute error was less than 3 % was 55.6 %. The absolute error of all of the sampling points did not exceed 10 %, which demonstrates the reliability of the RADARSAT-2 retrieval results. A comparison among the 72 soil moisture values from six mining subsidence areas and corresponding non-subsidence areas in the study area in 2012 showed that 38 soil moisture values from the non-subsidence areas were higher than those from the subsidence areas. These values accounted for 53 % of the total, indicating that high-intensity mining activities have a certain negative impact on the surface soil moisture, although this impact is slightly insignificant.

Bayesian Merging of MISR and MODIS Aerosol Optical Depth Products Using Error Distributions From AERONET

Aerosol Optical depth (AOD) routinely retrieved by spaceborne sensors (e.g., MODIS and MISR) is widely used in studies focused on atmospheric aerosol characterization, their variability/trends, and environmental/climate impacts. Despite significant advancement in the understanding of aerosol loading patterns, there exist global/regional differences among AOD products from various satellite sensors; owing to differences in retrieval algorithms, spatial/temporal resolution and sampling, and cloud-screening schemes. A merged AOD dataset combining multiple satellite retrievals is therefore desirable, which utilizes the strengths of individual products, and at the same time reduces biases relative to ground-based measurements (e.g., from aerosol robotic network (AERONET)). In view of this, a Level-2 ‘‘merged’’ AOD dataset based on MODIS and MISR retrievals is developed in this study using Bayesian principles, which takes into account the error distribution of AOD from AERONET data. The merged AOD dataset is demonstrated over the Indo-Gangetic Plains, in southern Asia, and is intercompared with existing satellite and AERONET data. The RMSE of the merged AOD data (0.08–0.13) is lower than the MISR and MODIS retrievals. Additionally, the merged AOD data have higher correlation with AERONET data ( r within 0.89–0.93), compared to MISR (0.82–0.89) and MODIS (0.76–0.77) data. In terms of the expected error (EE), the accuracy of merged data is found to be higher, with larger percent of merged AOD within the EE envelope (74.7%–88.7%) compared to MISR (62.7%–84.9%) and MODIS (67.9%–69.8%) data. The merging methodology and resulting dataset are especially relevant in the scenario of fusing multisensor retrievals for producing long-term satellite-based climate data records.

Validating MODIS above-cloud aerosol optical depth retrieved from “color ratio” algorithm using direct measurements made by NASA's airborne AATS and 4STAR sensors

Abstract. We present the validation analysis of above-cloud aerosol optical depth (ACAOD) retrieved from the “color ratio” method applied to MODIS cloudy-sky reflectance measurements using the limited direct measurements made by NASA's airborne Ames Airborne Tracking Sunphotometer (AATS) and Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) sensors. A thorough search of the airborne database collection revealed a total of five significant events in which an airborne sun photometer, coincident with the MODIS overpass, observed partially absorbing aerosols emitted from agricultural biomass burning, dust, and wildfires over a low-level cloud deck during SAFARI-2000, ACE-ASIA 2001, and SEAC4RS 2013 campaigns, respectively. The co-located satellite-airborne matchups revealed a good agreement (root-mean-square difference < 0.1), with most matchups falling within the estimated uncertainties associated the MODIS retrievals (about −10 to +50 %). The co-retrieved cloud optical depth was comparable to that of the MODIS operational cloud product for ACE-ASIA and SEAC4RS, however, higher by 30–50 % for the SAFARI-2000 case study. The reason for this discrepancy could be attributed to the distinct aerosol optical properties encountered during respective campaigns. A brief discussion on the sources of uncertainty in the satellite-based ACAOD retrieval and co-location procedure is presented. Field experiments dedicated to making direct measurements of aerosols above cloud are needed for the extensive validation of satellite-based retrievals.

Resolving ice cloud optical thickness biases between CALIOP and MODIS using infrared retrievals

Abstract. Despite its importance as one of the key radiative properties that determines the impact of upper tropospheric clouds on the radiation balance, ice cloud optical thickness (IOT) has proven to be one of the more challenging properties to retrieve from space-based remote sensing measurements. In particular, optically thin upper tropospheric ice clouds (cirrus) have been especially challenging due to their tenuous nature, extensive spatial scales, and complex particle shapes and light-scattering characteristics. The lack of independent validation motivates the investigation presented in this paper, wherein systematic biases between MODIS Collection 5 (C5) and CALIOP Version 3 (V3) unconstrained retrievals of tenuous IOT (< 3) are examined using a month of collocated A-Train observations. An initial comparison revealed a factor of 2 bias between the MODIS and CALIOP IOT retrievals. This bias is investigated using an infrared (IR) radiative closure approach that compares both products with MODIS IR cirrus retrievals developed for this assessment. The analysis finds that both the MODIS C5 and the unconstrained CALIOP V3 retrievals are biased (high and low, respectively) relative to the IR IOT retrievals. Based on this finding, the MODIS and CALIOP algorithms are investigated with the goal of explaining and minimizing the biases relative to the IR. For MODIS we find that the assumed ice single-scattering properties used for the C5 retrievals are not consistent with the mean IR COT distribution. The C5 ice scattering database results in the asymmetry parameter (g) varying as a function of effective radius with mean values that are too large. The MODIS retrievals have been brought into agreement with the IR by adopting a new ice scattering model for Collection 6 (C6) consisting of a modified gamma distribution comprised of a single habit (severely roughened aggregated columns); the C6 ice cloud optical property models have a constant g ≈ 0.75 in the mid-visible spectrum, 5–15 % smaller than C5. For CALIOP, the assumed lidar ratio for unconstrained retrievals is fixed at 25 sr for the V3 data products. This value is found to be inconsistent with the constrained (predominantly nighttime) CALIOP retrievals. An experimental data set was produced using a modified lidar ratio of 32 sr for the unconstrained retrievals (an increase of 28 %), selected to provide consistency with the constrained V3 results. These modifications greatly improve the agreement with the IR and provide consistency between the MODIS and CALIOP products. Based on these results the recently released MODIS C6 optical products use the single-habit distribution given above, while the upcoming CALIOP V4 unconstrained algorithm will use higher lidar ratios for unconstrained retrievals.

Potential of shipborne GPS atmospheric delay data for prediction of Mediterranean intense weather events

AbstractHigh spatial and temporal variability of mesoscale moisture fields is still a challenge for quantitative precipitation forecast within numerical weather prediction (NWP) models especially over ocean regions where observations are lacking. This study presents the comparison between integrated water vapor over the Mediterranean Sea determined from shipborne GPS, the NWP ALADIN/Météo‐France model and MODIS retrieval during a 4‐month campaign (autumn 2008).While moisture prediction of the NWP remains accurate during most of the observation period, there are significant periods with offsets between GPS data and model predictions. We analyze such events and discuss the associated meteorological situation. Copyright © 2012 Royal Meteorological Society

Remote Sensing of Land Surface Parameters in the Middle Reaches of Yarlung Zangbo River and Its Two Tributaries from AVHRR and MODIS Data

The middle reaches of Yarlung Zangbo River (YR) and its two tributaries is a region in central Tibet Autonomous Region where the industrial and agricultural economy is most concentrative and developed. Remote sensing of land surface parameters has significant meaning for crop growth monitoring, crop yield assessment and disaster monitoring. It also has great theoretical importance for understanding energy and cycle in local area and even for surrounding regions. In this paper, four scenes data in 2003 under nearly clear weather conditions are selected as the spring, summer, autumn and winter cases. Different algorithms for AVHRR and MODIS data will be applied to retrieve land surface temperature (LST), albedo, Normalized Difference Vegetation Index (NDVI), vegetation coverage, emissivity, and vapour content, respectively. The derived LST, albedo are validated with field measurements and MODIS products. The estimations are in good agreement with in-situ measurements, with MODIS retrievals being slightly better than those of AVHRR. This means that the adopted remote sensing retrieval algorithms are not only applicable but also simple for the study area. As called the water vapour inflow corridor, the valleys of YR and its two tributaries are proved to have higher LST and larger atmospheric vapour content. Spatiotemporal variations of land surface parameters in the middle reaches of YR and its two tributaries can also be clearly identified.

The Comparison of Aerosol Optical Parameter between MODIS and Two-Channel Retrieval Algorithm over East China Sea

Based on the level 1 data and the relevant ancillary data of the Moderate-resolution Imaging Spectro-radiometer(TERRA/MODIS),the aerosol optical depth over East China Sea was retrieved by International MODIS/AIRS Processing Package(IMAPP) and Two-Channel Aerosol Retrieval Algorithm,the inverse methods were validated with Aerosol Robtic Network(AERONET) solar direct radiance measurements.The distribution characteristics of aerosol optical depth and ngstrm exponent(which symbolizes the width of particle spectrum) over East China Sea during Oct-Nov 2002 were studied.The results indicate that the aerosol optical depth obtained by both methods over East Sea and the open sea area south of Japan are basically in consistent with the result from AERONET.However,due to the influence of Case 2 water,the aerosol optical depth is a bit high along the coastal area of Bohai Sea and Yellow Sea.The characteristics of East China Sea are analyzed,which provide the basic data for retrieval method over East China Sea.

An evaluation of satellite aerosol products against sunphotometer measurements

Because atmospheric aerosols scatter sunlight back to space, reflectance measurements from spaceborne radiometers can be used to estimate the aerosol load and its optical properties. Several aerosol products have been generated in a systematic way, and are available for further studies. In this paper, we evaluate the accuracy of such aerosol products derived from the measurements of POLDER, MODIS, MERIS, SEVIRI and CALIOP, through a statistical comparison with Aerosol Optical Depth (AOD) measurements from the AERONET sunphotometer network. Although this method is commonly used, this study is, to our knowledge, among the most extensive of its type since it compares the performance of the products from 5 different sensors using up to five years of data for each of them at global scale. The choice of these satellite aerosol datasets was based on their availability at the ICARE Data and Service Centre ( www.icare.univ-lille1.fr). We distinguish between retrievals over land and ocean and between estimates of total and fine mode AOD. Over the oceans, POLDER and MODIS retrievals are of similar quality, with RMS difference lower than 0.1 and a correlation with AERONET of around 0.9. The POLDER estimates suffer from a small positive bias for clean atmospheres, which weakens its statistics. The other aerosol products are of lesser quality, although the SEVIRI products may be of interest for some applications that require a high temporal resolution. The MERIS product shows a very high bias. Over land, only the MODIS product offers a reliable estimate of the total AOD. On the other hand, the polarization-based retrieval using POLDER data allows a better fine mode estimate than that from MODIS. These results suggest the need for a product combining POLDER and MODIS products over land. The paper also analyses how the statistics change with the spatial and temporal thresholds that are used. Spatio-temporal averaging improves the statistics only slightly, which indicates that random errors are not dominant in the error budget. The paper includes various statistical indicators at global scale, and detailed results at individual ground stations can be obtained on request from the authors.

Evaluation of Moderate-Resolution Imaging Spectroradiometer (MODIS) Deep Blue Aerosol Products Using Ground-Based Measurements over Beijing

Deep Blue (DB) is a new MODIS retrieval algorithm to retrieve aerosol properties over bright surfaces such as arid, semiarid and urban areas. It's expected to fill in data gaps over bright surface left by the standard algorithm based on Dark Target (DT) retrieval algorithm. DB has been employed in recent Collection 5.1 AOD product. This study presents a comparison between the DT approach and the more recent DB algorithm using international Aerosol Robotic Network (AERONET) data as reference, analyzes the improvement in DB AOD product over urban surface and discusses the influence of aerosol model variation on MODIS AOD retrieval. Results show that DB products perform better than DT products under clear condition, and a systematic AOD overestimation in DT products doesn't occur in DB products. DT and DB AOD products both appear strong seasonal pattern, with better performance in autumn and winter and worse performance in spring and summer, and the underestimated Aerosol Single Scattering Albedo (SSA) during spring and summer over Beijing in MODIS retrieval probably is the major factor inducing the AOD overestimation in these seasons. In comparison with DB AOD, DT AOD is more likely overestimated, unsuitable surface reflectance over Beijing may be the key factor, and it may play a more important role in MODIS DT AOD retrieval than unsuitable SSA.