Ozone Monitoring Instrument aerosol products: a comparison study with ground-based airborne sun photometer measurements over Europe

Airborne sun photometer measurements are used to evaluate retrievals of extinction aerosol optical depth (AOD). These data are extracted from spatially coincident and temporally near-coincident measurements by the Ozone Monitoring Instrument (OMI) aboard the Aura satellite taken during 2005. OMI-measured top of atmosphere (TOA) reflectances are routinely inverted to yield aerosol products such as AOD using two different retrieval techniques: the Aura OMI Near-Ultraviolet Aerosol Data Product, OMAERUV, and the multi-wavelength Aura OMI Aerosol Data Product, OMAERO. In this work, we propose a study that specifically compares the instantaneous aerosol optical thicknesses retrieved from OMI at several locations containing sites and those of the Aerosol Robotic Network (AERONET). The result of the comparison shows that, just over Europe, OMI aerosol optical thicknesses are better retrieved in the multi-wavelength retrieval than in the near-ultraviolet. Correlations have been improved by applying a simple criterion to avoid scenes probably contaminated by thin clouds, and surface scattering. The ultraviolet irradiance positive bias in the OMI data is corrected using a procedure based on global climatological fields of aerosol absorption optical depth. The results generally show a bias significantly reduced by 5–20%, a lower variability and an unchanged, high correlation coefficient.     

Aerosol optical depth retrieval over snow using AATSR data

Aerosol observations over the Arctic are important because of the effects of aerosols on Arctic climate, such as their direct and indirect effects on the Earth's radiation balance and on snow albedo. Although information on aerosol properties is available from ground-based measurements, passive remote sensing using satellite measurements would offer the advantage of large spatial coverage with good temporal resolution, even though, due to light limitations, this is only available during the Arctic summer. However, aerosol optical depth (AOD) retrieval over the Arctic region is a great challenge due to the high reflectance of snow and ice and due to the high solar zenith angle. In this article, we describe a retrieval algorithm using Advanced Along-Track Scanning Radiometer (AATSR) data, a radiometer flying on the European Space Agency (ESA) Environmental Satellite (ENVISAT), which offers two views (near nadir and at 55° forward) at seven wavelengths in the visible thermal-infrared (VIS-TIR). The main idea of the Dual-View Multi-Spectral (DVMS) approach is to use the dual view to separate contributions to reflectance measured at the top of the atmosphere (TOA) due to atmospheric aerosol and the underlying surface. The algorithm uses an analytical snow bidirectional reflectance distribution function (BRDF) model for the estimation of the ratio of snow reflectances in the nadir and forward views, as well as an estimate of the atmospheric contribution to TOA reflectance obtained using the dark pixel method over the adjacent ocean surface, assuming that this value applies over nearby land surfaces in the absence of significant sources across the coastline. An iteration involving all four AATSR wavebands in the visible near-infrared (VIS-NIR) is used to retrieve the relevant information. The method is illustrated for AATSR overpasses over Greenland with clear sky in April 2009. Comparison of the retrieved AOD with AErosol Robotic Network (AERONET) data shows a correlation coefficient of 0.75. The AODs retrieved from AATSR using the DVMS approach and those obtained from AERONET data show similar temporal trends, but the AERONET results are more variable and the highest AOD values are mostly missed by the DVMS approach. Limitations of the DVMS method are discussed. The pure-snow BRDF model needs further correction in order to obtain a better estimation for mixtures of snow and ice.     

On determining appropriate aerosol optical depth values for atmospheric correction of satellite imagery for biophysical parameter retrieval: requirements and limitations under Australian conditions

Atmospheric correction of high spatial resolution (10–30 m pixel sizes) satellite imagery for use in large-area land-cover monitoring is difficult due to the lack of aerosol optical depth (AOD) estimates made coincident with image acquisition. We present a methodology to determine the upper and lower bounds of AOD estimates that allow the subsequent calculation of a biophysical variable of interest to a pre-determined precision. Knowledge of that range can be used to identify an appropriate method for estimating AOD. We applied the methodology to Landsat 5 Thematic Mapper data in Queensland (QLD) and New South Wales (NSW), Australia, and determined that AOD must be estimated within approximately 0.05 of actual AOD for retrieval of foliage projective cover (FPC) to a precision of 10%. That knowledge was then used to determine the relative merit of using a fixed constant, Aerosol Robotic Network (AERONET) climatology, or dense dark vegetation (DDV) method for estimating AOD in QLD and NSW. It was found that using a fixed AOD of 0.05 allows estimates of FPC within 10% of their true value when the true value of AOD is less than 0.1. Such AOD values account for approximately 90% of all inland observations and 65% of coastal observations as determined by analysis of data obtained from AERONET. Using an AERONET climatology to estimate AOD was found to increase the likelihood of accurate FPC retrieval in coastal locations to 83%, although it should be noted that AERONET data are very sparse. DDV has potential in eastern and central areas for retrieving AOD observations with greater precision than fixed values or climatologies. However, more work is needed to understand the temporal variation of vegetation reflectance before the DDV method can be used operationally.     

Algorithm for retrieval of aerosol optical properties over the ocean from the Geostationary Ocean Color Imager

An aerosol retrieval algorithm for the first Geostationary Ocean Color Imager (GOCI) to be launched in March 2010 onboard the Communication, Ocean, and Meteorological Satellite (COMS) is presented. The algorithm retrieves aerosol optical depth (AOD), fine-mode fraction (FMF), and aerosol type in 500 m × 500 m resolution. All the products are retrieved over clear water which is defined by surface reflectance ratio between 640 nm and 860 nm (SRR) less or equal to 2.5, while only AOD is retrieved over turbid water (SRR > 2.5) due to high surface reflectance. To develop optimized algorithm for the target area of GOCI, optical properties of aerosol are analyzed from extensive observation of AERONET sunphotometers to generate lookup table. Surface reflectance of turbid water is determined from 30-day composite of Rayleigh- and gas corrected reflectance. By applying the present algorithm to MODIS top-of-the atmosphere reflectance, three different aerosol cases dominated by anthropogenic aerosol contains black carbon (BC), dust, and non-absorbing aerosol are analyzed to test the algorithm. The algorithm retrieves AOD, and size information together with aerosol type which are consistent with results inferred by RGB image in a qualitative way. The comparison of the retrieved AOD with those of MODIS collection 5 and AERONET sunphotometer observations shows reliable results. Especially, the application of turbid water algorithm significantly increases the accuracy in retrieving AOD at Anmyon station. The sensitivity study between MODIS and GOCI instruments in terms of relative sensitivity and scattering angle shows promising applicability of the present algorithm to future GOCI measurements.     

Aerosol optical thickness determination by exploiting the synergy of TERRA and AQUA MODIS

Aerosol retrieval over land remains a difficult task because the solar light reflected by the Earth-atmospheric system mainly comes from the ground surface. The dark dense vegetation (DDV) algorithm for MODIS data has shown excellent competence at retrieving the aerosol distribution and properties. However, this algorithm is restricted to lower surface reflectance, such as water bodies and dense vegetation. In this paper, we attempt to derive aerosol optical thickness (AOT) by exploiting the synergy of TERRA and AQUA MODIS data (SYNTAM), which can be used for various ground surfaces, including for high-reflective surface. Preliminary validation results by comparing with Aerosol Robotic Network (AERONET) data show good accuracy and promising potential.     

双通道算法与MODIS算法反演中国近海气溶胶光学特性的对比研究

利用双通道和IMAPP气溶胶反演算法处理TERRA/MODIS L1B数据得出中国近海气溶胶的光学厚度,与AERONET太阳光度计的反演结果作对比分析,验证了反演方法的可行性。同时,对各海域的反演结果及表征粒子谱宽度的Angstrom指数(α)的变化情况进行了分析,结果表明：在东海和日本以南等广阔海域,两种反演算法的结果同AERONET太阳光度计的观测结果基本一致,相关性较好;在渤海和黄海近海岸一带两者气溶胶光学厚度的反演值均偏高,其原因主要是由这些海域的二类水体的影响导致的。探讨分析了这些海域的水域特征及光学特性,为研究发展适合中国近海气溶胶特性的反演算法提供了依据。     

Aerosol mapping over land with imaging spectroscopy using spectral autocorrelation

A new method for aerosol retrieval over land is proposed that makes explicit use of the contiguous, high-resolution spectral coverage of imaging spectrometers. The method is labelled Aerosol Retrieval by Interrelated Abundances (ARIA) and is based on unmixing of the short-wave infrared sensor signal by region-specific endmembers, assuming low aerosol radiative influence in this spectral region. Derived endmember abundances are transferred to the visible part of the spectrum in order to approximate surface reflectance where aerosol influence is generally strongest. Spectral autocorrelation of surface spectra is a precondition for ARIA and demonstrated using a reference spectrum database. The re-mixed surface reflectance is used as input quantity for the inversion of aerosol optical depth τ_a at 0.55 µm wavelength on a pixel basis. Except for the choice of endmembers and the atmospheric vertical profile, no a priori assumptions on the image scene are required. The potential of the presented method for aerosol retrieval is demonstrated for an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scene, collected in California in 2000. Comparisons with existing aerosol retrieval methods showed encouraging results in terms of achieved spatial smoothness and degree of uncertainty of aerosol optical depth across the scene.     

A consistency analysis of surface reflectance and leaf area index retrieval from overlapping clear-sky Landsat ETM+ imagery

In this study, the consistency of systematic retrievals of surface reflectance and leaf area index was assessed using overlap regions in adjacent Landsat Enhanced Thematic Mapper-Plus (ETM+) scenes. Adjacent scenes were acquired within 7-25 days apart to minimize variations in the land surface reflectance between acquisition dates. Each Landsat ETM+ scene was independently geo-referenced and atmospherically corrected using a variety of standard approaches. Leaf area index (LAI) models were then applied to the surface reflectance data and the difference in LAI between overlapping scenes was evaluated. The results from this analysis show that systematic LAI retrieval from Landsat ETM+ imagery using a baseline atmospheric correction approach that assumes a constant aerosol optical depth equal to 0.06 is consistent to within ±0.61 LAI units. The average absolute difference in LAI retrieval over all 10 image pairs was 26% for a mean LAI of 2.05 and the maximum absolute difference over any one pair was 61% for a mean LAI of 1.13. When no atmospheric correction was performed on the data, the consistency in LAI retrieval was improved by 1%. When a scene-based dense, dark vegetation atmospheric correction algorithm was used, the LAI retrieval differences increased to 28% for a mean LAI of 2.32. This implies that a scene-based atmospheric correction procedure may improve the absolute accuracy of LAI retrieval without having a major impact on retrieval consistency. Such consistency trials provide insight into the current limits concerning surface reflectance and LAI retrieval from fine spatial resolution remote sensing imagery with respect to the variability in clear-sky atmospheric conditions.     

Intercomparison of CALIOP,MODIS, and AERONET aerosol optical depth over China during the past decade

ABSTRACT

Aerosol optical depth (AOD) data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were intercompared and validated against ground-based measurements from Aerosol Robotic Network (AERONET) as well as space-based Moderate Resolution Imaging Spectroradiometer (MODIS) over China during June 2006 to December 2015. This article aims to evaluate CALIOP daytime AOD using MODIS and AERONET AODs. Comparing the AOD between CALIOP and AERONET in different regions over China using quality control flags to screen the AOD data, we find that CALIOP AOD is generally lower than AERONET AOD especially at optical depths over 0.4 likely due to differences in the cloud screening algorithms and general retrieval uncertainty. Comparison between CALIOP AOD and MODIS AOD results show that the overall spatio-temporal distribution of CALIOP AOD and MODIS AOD is basically consistent. As for the spatial distribution, both data sets show several high-value regions and low-value regions in China. CALIOP is systematically lower than MODIS over China, especially over high AOD value regions for all seasons. As for the temporal variation, both data sets show a significant seasonal variation: AOD is largest in spring, then less in summer, and smallest in winter and autumn. A long-term linear trend analysis based on the domain averaged monthly mean CALIOP and MODIS AOD shows agreement among CALIOP and MODIS for the trends over the 10-year period in four regions examined. The trends in AOD derived from CALIOP and MODIS indicate a decline in aerosol loading in China since 2006. It is found from frequency comparison that CALIOP and MODIS AOD generally exhibit a degree of correlation over China. Statistical frequency analysis shows that CALIOP AOD frequency distribution shows a higher peak than MODIS AOD when AOD < 0.4. For the most part, mean MODIS AOD is higher than mean CALIOP AOD. Evaluation of CALIOP AOD retrievals provides the prospect for application of CALIOP data. The intercomparison suggests that CALIOP has systematically underestimated daytime AOD retrievals, especially deteriorating with increasing AOD, and therefore, CALIOP daytime AOD retrievals should be treated with some degree of caution when the AOD is over 0.4.     

Improved algorithm for MODIS satellite retrievals of aerosol optical thickness over land in dusty atmosphere: Implications for air quality monitoring in China

A new algorithm, using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite reflectance and aerosol single scattering properties simulated from a chemistry transport model (GEOS-Chem), is developed to retrieve aerosol optical thickness (AOT) over land in China during the spring dust season. The algorithm first uses a “dynamic lower envelope” approach to sample the MODIS dark-pixel reflectance data in low AOT conditions, to derive the local surface visible (0.65 μm)/near infrared (NIR, 2.1 μm) reflectance ratio. Joint retrievals of AOT at 0.65 μm and surface reflectance at 2.1 μm are then performed, based on the time, location, and spectral-dependent single scattering properties of the dusty atmosphere as simulated by the GEOS-Chem. A linearized vector radiative transfer model (VLIDORT) that simultaneously computes the top-of-atmosphere reflectance and its Jacobian with respect to AOT, is used in the forward component of the inversion of MODIS reflectance to AOT. Comparison of retrieved AOT results in April and May of 2008 with AERONET observations shows a strong correlation (R = 0.83), with small bias (0.01), and small RMSE (0.17); the figures are a substantial improvement over corresponding values obtained with the MODIS Collection 5 AOT algorithm for the same study region and time period. The small bias is partially due to the consideration of dust effect at 2.1 μm channel, without which the bias is − 0.05. The surface PM₁₀ (particulate matter with diameter less than 10 μm) concentrations derived using this improved AOT retrieval show better agreement with ground observations than those derived from GEOS-Chem simulations alone, or those inferred from the MODIS Collection 5 AOT. This study underscores the value of using satellite reflectance to improve the air quality modeling and monitoring.     

Validation of satellite and model aerosol optical depth and precipitable water vapour observations with AERONET data over Pune,India

ABSTRACT

The present work concerns with a detailed study of the validation of the Moderate Resolution Imaging Spectroradiometer (MODIS) and model products, and investigates the spatial and temporal variations in the correlation coefficient of the validation results obtained from the analysis of Aerosol Robotic Network (AERONET) sun–sky radiometer data archived at Pune during 2005–2015. Combining the confidence intervals and prediction levels, the ground-based AERONET aerosol optical depth (AOD) at 550 nm and precipitable water vapour (PWV) have been used to validate the MODIS, model AOD (550 nm), and PWV (cm) observations. The correlation coefficients (r) of AOD for the linear regression fits are 0.73, 0.75, and 0.79, and of PWV are 0.88, 0.89, and 0.97 for Terra, Aqua, and model simulations, respectively. Month-to-month/seasonal variation of AOD (550 nm) and PWV observations of satellite and model observations are also compared with AERONET observations. Additionally, various statistical metrics, including the root mean square error, mean absolute error, and root mean bias values were calculated using AERONET, satellite, and model simulations data. Furthermore, a frequency distribution of AOD (550 nm) and PWV observations are studied from AERONET, satellite, and model data. The study emphasizes that the globally distributed AERONET observations help to improve the satellite retrievals and model predictions to enrich our knowledge of aerosols and their impact on climate, the hydrological cycle, and air quality.     

基于深蓝算法的Sentinel-2数据气溶胶光学厚度反演

中高分辨率气溶胶信息对于高精度地表反射率反演以及城市空气环境质量监测具有重大意义,但在城市及稀疏植被等高亮地表区域,气溶胶光学厚度(AOD)的高精度反演一直是定量遥感领域的难点之一。以北京城市区和包头沙漠区为例,利用MODIS地表反射率产品构建先验知识约束条件,基于深蓝算法实现了13景Sentinel-2高亮地表的AOD反演。为验证算法精度,将反演结果与全球气溶胶自动观测网(AERONET)站点实测值、Sentinel-2官方插件Sen2Cor处理结果、Landsat-8反演值作对比。结果表明:①采用深蓝算法反演的AOD值与AERONET实测值具有显著的相关性(R^2>0.9,RMSE=0.056);②无论是沙漠高亮区还是植被较少的城市高亮区,Sen2Cor插件反演的AOD值整景均为固定值,无空间分布,不符合实际情况;③Sentinel-2深蓝算法反演结果与准同步过境的Landsat-8反演的AOD产品在空间分布上具有高度一致性,较好地反映了人类活动特征。相比于目前官方产品,深蓝算法适合Sentinel-2数据高亮区域的气溶胶反演,在绝对精度和空间分布趋势方面均具有明显优势。     

Influence of land surface effects on MODIS aerosol retrieval using the BAER method over Korea

As satellite receiving signals are affected by complex radiative transfer processes in the atmosphere and on land surfaces, aerosol retrieval over land from space requires the ability to determine surface reflectance from the remote measurements. To use the Bremen Aerosol Retrieval (BAER) method for aerosol optical thickness (AOT) retrieval over land at a spatial scale of 1×1 km² from Moderate Resolution Imaging Spectroradiometer (MODIS) data, a linear mixing model with a vegetation index was used to calculate surface reflectances. As the vegetation index is affected by the aerosol present in the atmosphere, an empirical linear relationship between short wavelength infrared (SWIR) channel reflectance and visible reflectance was estimated to calculate a modified aerosol free vegetation index (AFRI) value. Based on a modified AFRI obtained from MODIS SWIR channel reflectance, an improved linear mixing model was applied for aerosol retrieval. A comparison of results between calculated and apparent surface reflectance was satisfactory, with a linear fit slope above 0.94, correlation coefficients above 0.84, and standard deviation below 0.008 for the study area. These results can therefore be used for improved aerosol retrieval over land by the BAER method with MODIS Level 1 data.     

Retrieval of aerosol spectral optical thickness from AVIRIS data

The ER-2, with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) onboard, overflew Rapid City, South Dakota, and the United States Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Site, Oklahoma, on 9 August and 15 August 1993, respectively. High contrast natural and artificial surfaces present in the imagery were used as a basis for retrieving aerosol spectral optical depth (SOD) over these two sites. Coincident measurements of spectral optical depth from a surface-based sunphotometer also were obtained and used as a validation of the AVIRIS derived retrievals. The accuracy of the retrievals is discussed as a function of measurement uncertainty and surface contrast. The results indicate that, given sufficiently small sensor errors and spectrally uniform surfaces with a reflectance difference of at least 0.5, aerosol spectral optical depth over clear continental atmospheres can be retrieved from high spatial resolution space-based imagery to an accuracy of approximately 0.1. Although only two cases are reported here and additional tests are required, these preliminary results suggest that background aerosol spectral optical depth (i.e., τ_aerosol, <01) cannot be retrieved with adequate accuracy from space; however, the aerosol spectral optical depth of more polluted atmospheres (i.e., τ_aerosol < 0.2) can be retrieved with adequate accuracy.     

Prior knowledge-supported aerosol optical depth retrieval over land surfaces at 500 m spatial resolution with MODIS data

Aerosol optical depth (AOD) values at a spatial resolution of 500 m were retrieved over terrain areas by applying a time series of Moderate resolution Imaging Spectroradiometer (MODIS) 500 m resolution data in the Heihe region (36–42° N, 97–104° E) of Gansu Province, China; in the Pearl River Delta (18–30° N, 108–122° E), China; and in Beijing (39–41° N, 115–118° E), China. A novel prior knowledge scheme was used in the algorithm that performs cloud screening, simultaneous AOD and surface reflectance retrieval from the MODIS 500 m Level 1B data. This prior knowledge scheme produced a new Ångström exponent α, utilizing a Terra pass time α and an Aqua pass time α to better satisfy the invariant α assumption. The retrieved AOD data were compared with AOD data observed with the ground-based, automatic Sun-tracking photometer CE318 at corresponding bands in the Heihe region and with Aerosol Robotic Network (AERONET) data in the Pearl River Delta and in Beijing. Validation experiments demonstrated the potential of applying the algorithm to MODIS 500 m AOD retrieval on land; validation showed the uncertainty of Δτ = ±0.1±0.2τ over various types of underlying land surface, including cities, where τ is the aerosol optical depth. The root mean square errors (RMSEs) were around 0.1 for inland regions and up to 0.24 for cities by the sea, such as Hong Kong and Zhongshan, China.     

Retrieval of AOD from Sentinel-2 Data based on Deep Blue Algorithm

Medium-to-high resolution aerosol information is of great significance for surface reflectance inversion and urban ambient air quality monitoring. However, the high-precision aerosol optical thickness (AOD) retrieval in bright areas, such as cities and sparse vegetation areas, has long plagued the quantitative remote sensing applications. Taking Beijing urban area and Baotou desert area as examples, using MODIS surface reflectance products to construct prior knowledge constraints, the AOD inversion of 13 scenes Sentinel-2 images in bright areas was realized based on the deep blue algorithm. To verify the accuracy of the algorithm, the result were compared with the Sentinel-2 official algorithm processing result, the Landsat-8 official aerosol products and the ground-measured AOD data from the Global Aerosol Automated Observing Network (AERONET). The results indicate that the retrieved AOD values from deep blue algorithm is significantly correlated with the measured value of AERONET(R² > 0.90, RMSE = 0.056 0), and the AOD spatial distributions are also well consistent with those from Landsat-8, which reflects the characteristics of human activities. But, whether in desert bright area or urban bright area with less vegetation, the AOD values retrieved by Sen2Cor plug-in are fixed, no spatial distribution and do not conform to the actual situation. In general, compared with the current official products, the deep blue algorithm is suitable for aerosol retrieval in high-brightness areas of Sentinel-2 data，and has obvious advantages in terms of estimation accuracy and spatial distribution trend.     

利用MODIS资料反演杭州市500米分辨率气溶胶光学厚度

反演城市/区域范围内高空间分辨率的气溶胶光学厚度时,如果气溶胶类型选取的不合理造成的反演误差会很大,甚至超过地表反射率确定误差导致的反演误差。针对这一问题,本文提出了一种结合MODIS L1B资料和AERONET(Aerosol Robotic Network)的气溶胶光学厚度产品,基于6S大气辐射传输模型的计算,确定杭州市在2008年12月16日的气溶胶类型的方法。利用得到的气溶胶类型,结合改进的暗像元法,反演了杭州市500m空间分辨率的气溶胶光学厚度。将气溶胶光学厚度反演结果与采用标准气溶胶类型时的反演结果进行比较,结果表明,本文确定的气溶胶类型更符合杭州市当天的情况,应用到气溶胶光学厚度反演中,精度也最好,相对误差的绝对值在20%以内。     

Remote sensing of aerosols optical thickness over various sites using SeaWiFS or VEGETATION and ground measurements

A key problem in aerosol retrieval is to distinguish between surface and atmospheric contributions to the variability in the satellite signal. A major contribution in the surface-related variability is caused by the non-Lambertian nature of the Earth surface reflectance and the fact that the illumination/observation geometry varies considerably between successive observations of the same area (with a polar orbiting sensor). In principle, if the surface boundary condition can be specified with sufficient accuracy by means of a bidirectional reflectance distribution function (BRDF), the two contributions can be unfolded and aerosol information retrieved. This approach has been tested using combined datasets made of satellite measured “top of atmosphere” (TOA) radiance and corresponding ground estimation of the aerosol optical thickness. Studying a time series of data, taking into account geometrical conditions and assuming the ground BRDF to be constant during the time period, a coupled surface/atmosphere model was used to investigate the retrieval of aerosol optical thickness (AOT) over several sites. By fitting a subset of satellite observations associated with ground photometer data, a best fit of BRDF model parameters could be determined. This surface characterization is then used to reduce the model unknowns to AOT only and thereby to permit its retrieval from the satellite data alone, by means of a simple inversion process. The study was conducted on three European AERONET sites and using satellite data from both the VEGETATION and Sea viewing Wide Field of view (SeaWiFS) sensors. In all cases, the AOT retrieved from satellite was in good agreement with the measurements.     

Implementation of GOES‐based land surface temperature diurnal cycle to AVHRR

To enable frequent estimates of land surface temperature (LST) from satellite measurements, and to characterize the land surface temperature diurnal (LSTD) cycle, two new LST retrieval algorithms are applied to observations from the Geostationary Operational Environmental Satellite (GOES). Evaluation against the atmospheric radiation measurement (ARM) observations indicates that LST can be determined from the real‐time GOES‐8 observations within r.m.s. accuracy of about 2 K. In order to combine the advantages of geostationary and polar orbiting instruments, the LSTD estimated from GOES can be incorporated into LST retrievals from polar orbiting imager National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) using a newly proposed fitting algorithm, with r.m.s. errors close to those obtained directly from GOES‐8.     

Improved estimation of aerosol optical depth from MODIS imagery over land surfaces

Estimation of aerosol loadings is of great importance to the studies on global climate changes. The current Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol estimation algorithm over land is based on the “dark-object” approach, which works only over densely vegetated (“dark”) surfaces. In this study, we develop a new aerosol estimation algorithm that uses the temporal signatures from a sequence of MODIS imagery over land surfaces, particularly “bright” surfaces. The estimated aerosol optical depth is validated by Aerosol Robotic Network (AERONET) measurements. Case studies indicate that this algorithm can retrieve aerosol optical depths reasonably well from the winter MODIS imagery at seven sites: four sites in the greater Washington, DC area, USA; Beijing City, China; Banizoumbou, Niger, Africa; and Bratts Lake, Canada. The MODIS aerosol estimation algorithm over land (MOD04), however, does not perform well over these non-vegetated surfaces. This new algorithm has the potential to be used for other satellite images that have similar temporal resolutions.