Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method

Theoretical analysis based on the atmospheric radiative transfer indicated a positive correlation between the aerosol optical thickness (AOT) and the surface-level particulate matter (PM) concentrations, and this correlation is improved significantly using vertical-and-RH correcting method. The correlative analysis of the ground-based measurement indicates that, (a) the correlation between AOT and the aerosol extinction coefficient at surface level (k_a,0) is improved as a result of the vertical correction, with the coefficient of determination R² increasing from 0.35 to 0.56; (b) the correlation between k_a,0 and PM concentrations can be significantly improved by the RH correction with the R² increasing from 0.43 to 0.77 for PM10, and from 0.35 to 0.66 for PM2.5. Based on the in-situ measurements in Beijing, two linear correlative models between the ground-based AOT and PMs (e.g. PM10 and PM2.5) concentrations were developed. These models are used to estimate the regional distribution of PM10 and PM2.5 using the satellite-retrieved AOT in Beijing area. Validation against the in-situ measurements in Beijing shows that both of the correlations of the satellite-estimated PM10 and PM2.5 with the measurements are R² = 0.47, and the biases are 26.33% and 6.49% respectively. When averaged in the urban area of Beijing, the R² between the estimated PM10 and the measurements increased to 0.66. These results suggest that by using the vertical-and-RH correcting method we can use the MODIS data to monitor the regional air pollution.     

Estimation of atmospheric particulate matter based on MODIS haze optimized transformation

This research is an attempt to simulate the relationship between haze optimized transformation (HOT) and aerosol optical thickness (AOT), and explore the influence of typical ground covers on this relationship using the 6S atmospheric radiative transfer model for the Chinese city of Nanjing. The HOT data were derived from moderate resolution imaging spectroradiometer (MODIS) satellite images recorded in the winter and spring seasons of December 2007–May 2009. They were analysed in conjunction with ground observed atmospheric particulate matter (PM) data so as to establish their quantitative relationship. Such a relationship may open a new avenue for remotely estimating atmospheric PM based on HOT. The results obtained indicate that HOT is related positively to AOT. This relationship is most accurately depicted by a second-order polynomial equation. Although built-up areas, waterbodies, and vegetation have differing HOT values, all of them bear a close and consistent correlation with AOT. HOT of built-up areas, waterbodies, and vegetative surfaces derived from MODIS images is also positively correlated with PM₁₀ (PM with diameter <10 μm), which was measured near the surface. The second-order polynomial equation has a coefficient of determination (R²) value of 0.375 (built-up), 0.344 (water), and 0.362 (vegetation) and a root mean squared error (RMSE) of 0.0258, 0.0264, and 0.0261, respectively. The closeness in R² value and RMSE for different ground covers suggests that correlation is marginally affected by the ground cover. It is thus concluded that HOT can be used as a reliable alternative for estimating PM₁₀ from MODIS data.     

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.     

Multi year satellite remote sensing of particulate matter air quality over Sydney,Australia

Particulate matter (PM) air‐quality information is usually derived from ground‐based instruments. These measurements, while valuable, are not well suited to provide air‐quality information over large spatial scales. In this study, using 4 years of satellite aerosol optical thickness (AOT) at 0.55 µm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA's Terra and Aqua satellites, we present a multi‐year air analysis of PM air quality over Sydney, Australia. We then compare the satellite data with PM_2.5 mass concentration measurements from six ground‐based stations in the area. Our results indicate significant diurnal variations and an overall increase in PM_2.5 during Southern Hemisphere spring and summer seasons due to bush fires. The air quality in Sydney, Australia is good throughout the year except during major bushfires when PM_2.5 mass loading can increase from normal (<20 µg m^?3) to unhealthy conditions (>70 µg m^?3). The satellite data also show corresponding AOT changes from less than 0.1 to greater than 1.0 during bushfire events. We conclude that satellite data are an excellent tool for studying PM air quality over large areas, especially when ground measurements are not available. While this is the first multi‐year combined satellite and ground‐based air quality analysis over Sydney, ancillary information from lidars, sun photometers, and size‐resolved chemistry measurements will further enhance our capability to monitor and forecast air quality in and around Sydney.     

Satellite-derived parameters for biological modelling in coastal waters: Illustration over the eastern continental shelf of the Bay of Biscay

In biological modelling of the coastal phytoplankton dynamics, the light attenuation coefficient is often expressed as a function of the concentrations of chlorophyll and mineral suspended particulate matter (SPM). In order to estimate the relationship between these parameters over the continental shelf of the northern Bay of Biscay, a set of in situ data has been gathered for the period 1998-2003 when SeaWiFS imagery is available. These data comprise surface measurements of the concentrations of total SPM, chlorophyll, and irradiance profiles from which is derived the attenuation coefficient of the photosynthetically available radiation, K_PAR. The performance of the IFREMER look-up table used to retrieve the chlorophyll concentration from the SeaWiFS radiance is evaluated on this new set of data. The quality of the estimated chlorophyll concentration is assessed from a comparison of the variograms of the in situ and satellite-derived chlorophyll concentrations. Once the chlorophyll concentration is determined, the non living SPM, which is defined as the SPM not related to the dead or alive endogenous phytoplankton, is estimated from the radiance at 555 nm by inverting a semi-analytic model. This method provides realistic estimations of concentrations of chlorophyll and SPM over the continental shelf all over the year. Finally, a relationship, based on non living SPM and chlorophyll, is proposed to estimate K_PAR on the continental shelf of the Bay of Biscay. The same formula is applied to non living SPM and chlorophyll concentrations, observed in situ or derived from SeaWiFS radiance.     

基于MODIS影像的PM10反演——以北京市为例

提出了一种从MODIS影像上反演可吸入颗粒物浓度(PM10)的方法。该方法的基础为从MODIS影像上反演得到的3个可见光波段气溶胶光学厚度(AOD)计算的ngstrm-α。ngstrm-α与颗粒物粒径有关,根据ngstrm-α能够得到颗粒物有效半径,进而估算颗粒物浓度。反演的气溶胶光学厚度由AERONET北京站与香河站验证。PM10反演结果由北京市环保局发布的AQI反插得到的PM10(AQI)进行验证。结果表明:从MODIS影像上反演的3个可见光波段AOD与AERONET基站AOD具有良好的相关性,相关系数为0.923,均方根误差为0.149。该方法反演的PM10与PM10(AQI)相关系数为0.794,均方根误差为48.34(μg/m3)。     

Technical note The SeaWiFS Automatic Data Processing System (SeaAPS)

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) was designed to measure ocean colour, the spectral variation of water-leaving radiance that can be related to the concentrations of phytoplankton pigments, coloured dissolved organic material and suspended particulate matter. The Dundee Satellite Receiving Station records and archives 1-km imagery covering the European shelf-seas, north-east Atlantic Ocean and Mediterranean Sea, which is subsequently processed in near-real time by Plymouth Marine Laboratory using SeaWiFS Automatic Processing System (SeaAPS). SeaWiFS imagery is combined with contemporary Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data to provide products, supplied via the World Wide Web, that are used within many areas of oceanographic research.     

Monitoring 2.5 μm particulate matter within urbanized regions using satellite-derived aerosol optical thickness,a study in Hong Kong

This study investigates the relationship between aerosol optical thickness (AOT) derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite and in situ particulate matter (PM_2.5) from Hong Kong air-quality monitoring stations. The relationship was analysed for three different AOT products, namely, MODIS collection 5 AOT data, MODIS collection 5 fine-mode fraction AOT data, both at 10 km resolution, and MODIS AOT data at 500 m resolution. In view of the predicted low accuracies obtainable for MODIS AOT products for the south China region, these AOT products were first validated against AOT measurements from an AErosol RObotic NETwork (AERONET) station near the centre of Hong Kong. Strong relationships of R ²?=?0.78 and R ²?=?0.77 for the 10 km and 500 m AOT data, respectively, were obtained, thus providing a robust AOT image database at both coarse and fine spatial resolution for comparison with PM_2.5 concentrations. When a whole year (2007) of AOT images was compared with PM_2.5 concentrations recorded at five ground stations, correlations of R ²?=?0.31, R ²?=?0.10 and R ²?=?0.67 were obtained for collection 5, fine-mode fraction of collection 5 (both at 10 km resolution) and 500 m AOT, respectively. Strong correlations between MODIS 500 m AOT and PM_2.5 concentration were also observed for individual stations (R ²?=?0.66, 0.74, 0.76, 0.56 and 0.62, for Central, Tung Chung, Tseun Wan, Yuen Long and Tap Mun stations, respectively). The study suggests that fine particle distributions at a high level of detail over whole cities may be obtained from satellite images. Since the model has potential for further refinement, monitoring of detailed PM_2.5 concentrations on a routine basis from satellite images will provide a highly useful tool for urban environmental authorities.     

New tools for the study of oceanic eddies: Satellite derived inherent optical properties

Satellite study of oceanic eddy formation, propagation, interactions, and fate was first conducted by sea surface temperature derived at infrared wavelengths. For visible wavelength ocean color reflectances, it is shown that recent radiative transfer model inversions provide additional characteristics of eddies: their constituent absorption and backscattering inherent optical properties. The chromophoric dissolved organic matter absorption coefficient has the highest contrast and is therefore the most visually evident inherent optical property (while the phytoplankton absorption coefficient and backscattering coefficients are respectively less discernible). For use as an analytical tool, comparisons suggests that the chromophoric dissolved organic matter absorption coefficient has a ∼10× higher contrast (i.e., ∼5% vs. 50%) in the Middle Atlantic Bight making eddy events detectable over longer time periods than with SST imagery. Example imagery illustrates the application of chromophoric dissolved organic matter and phytoplankton absorption coefficient inherent optical properties to the visual injection of dissolved and particulate organic carbon into the deep ocean by a Gulf Stream ring.     

Assessment of satellite ocean color products at a coastal site

A comprehensive set of bio-optical measurements collected at the Acqua Alta Oceanographic Tower site in the northern Adriatic Sea is used to assess satellite derived optical properties and concentrations of optically significant constituents. These include normalized water leaving radiance spectra L_WN, absorption spectra due to phytoplankton, non-pigmented particles and chromophoric dissolved organic matter, back-scattering spectra, concentrations of chlorophyll a, Chla, and total suspended matter, TSM, and diffuse attenuation coefficients, K_d, obtained with a diverse set of algorithms. A total of 81 and 21 match-ups are found for SeaWiFS and MODIS L_WN, respectively. For both sensor products, the match-ups show mean absolute percentage differences of approximately 30% at 412 nm, 20% at 443 nm, and 14% from 490 to 555 nm. Some dependence of these differences has been found with respect to the aerosol optical thickness and the single scattering albedo associated with the in-water constituents. However, the performance of the atmospheric correction scheme appears relatively robust with respect to angular and environmental conditions. The different Chla products generally show quite large uncertainties whereas a TSM product shows encouraging results. Three algorithms produce K_d (490) with a RMS uncertainty of 0.13 for log-transformed data. The comparison between in situ data and satellite derived absorption values yields varying levels of uncertainties for the three bio-optical algorithms considered here and for the different wavelengths. Preliminary improvements could be reached by reducing biases affecting the total absorption coefficient at various wavelengths. Another general result is that the bio-optical models are able to represent a larger part of the observed variability for phytoplankton absorption than for the absorption by dissolved and non-pigmented particulate matter. The assumed spectral shape of the latter absorption term appears logically as a strong determinant of the partition of the total absorption. The three sets of satellite derived back-scattering spectra compare favorably with in situ optical measurements, with mean RMS differences between 0.12 and 0.18. Importantly, the uncertainties obtained here for satellite match-ups of absorption coefficients are comparable to published estimates of the inherent uncertainties associated with the bio-optical algorithms.     

Retrieval of aerosol type and optical thickness over the Mediterranean from SeaWiFS images using an automatic neural classification method

We present an automatic classification method based on topological neural network algorithms to retrieve aerosol optical properties from multi-spectral ocean-color satellite imagery. The first step of the method consisted in an unsupervised classification of a large set of clear-sky top of the atmosphere reflectance spectra measured by the sensor. We used the so-called Kohonen map which aggregates similar spectra into a reduced set of pertinent groups. The second step consisted in labeling these groups by clustering them with synthetic TOA reflectance spectra whose optical properties (i.e., aerosol type or optical thickness) are known. These synthetic spectra have been computed using a radiative transfer model. In the present study, we dealt with five aerosol types (maritime, coastal, tropospheric, oceanic and mineral) and several aerosol optical thickness values ranging from 0.05 to 0.8. These simulated spectra were then projected onto the Kohonen map to label each group of the map. The last step consisted in applying this method to the SeaWiFS imagery of the Mediterranean region for the years 1999 and 2000. The Kohonen map was “educated” from pixels randomly extracted during the year 1999 in this region. We accounted for the viewing geometry of the sensor by clustering the simulated spectra into ten groups of similar geometries, as defined by both scattering and sun zenith angles. The analysis of SeaWiFS images was performed pixel-by-pixel by selecting the suitable labeling (in terms of viewing geometry), then by identifying the closest spectrum in the Kohonen map, which finally gives the aerosol optical properties. This method led to accurate and coherent results, as shown by the comparison with in situ aerosol measurements provided by the AERONET station at Lampedusa and by the study of two aerosol events over the Mediterranean. One of the major advantages of this method is that it enables us to automatically identify the aerosol type and to retrieve the aerosol optical properties with a better accuracy than classical methods such as those used by SeaWifs. It gives accurate results for optical thickness values larger than 0.35 and is able to retrieve dust aerosols such as African dust aerosol (absorbing aerosol). These should ensure a more precise inversion of ocean-color imagery where the knowledge of atmospheric optical parameters is essential. Moreover the method is able to give probabilities for the estimate values of aerosol properties.     

The detection and mapping of algal blooms from space

Algal blooms change the colour of water through absorption by pigments and scattering by cells and associated detrital material. This paper gives a brief introduction, primarily using ocean colour imagery, to the use of satellite Earth observation measurements in detecting and mapping algal blooms. Two examples, using Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) imagery, show blooms within northern European waters.     

Simultaneous retrieval of aerosol and ocean properties by optimal estimation: SeaWiFS case studies for the Santa Barbara Channel

In this work, some sea viewing wide field‐of‐view sensor (SeaWiFS) images over the Santa Barbara Channel (SBC) are analysed. Pixel‐by‐pixel measurements of radiances at eight SeaWiFS channels and analytic Jacobians are simulated using a coupled atmosphere–ocean radiative transfer model. The inverse algorithm is based on optimal estimation with loosely constrained a priori data. The five‐element state vector has two aerosol (optical depth at 865 nm, bimodal fraction of particles) and three marine (chlorophyll concentration, detrital/dissolved‐matter absorption at 443 nm, and backscattering coefficient at 443 nm) parameters. The retrieval is stable and well posed; the results are smoother and show less spread than those derived from the standard SeaDAS v4.8 algorithm. For a 28 February 2003 SeaWiFS image, the average radiance residual is less than 1% for seven SeaWiFS channels, and less than 2% for the 765 nm channel. For a series of SBC SeaWiFS match‐up cases over a 4‐year period, estimated water‐leaving radiances agree well with field measurements.     

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.     

Detecting the red tide algal blooms from satellite ocean color observations in optically complex Northeast-Asia Coastal waters

Over the last few decades, the coastal regions throughout the world have experienced incidences of algal blooms, which are harmful or otherwise toxic because of their potential threat to humans as well as marine organisms, owing to accelerated eutrophication from human activities and certain oceanic processes. Previous studies have found that correct identification of these blooms remains a great challenge with the standard bio-optical algorithms applied to satellite ocean color data in optically complex coastal waters containing high concentrations of the interfered dissolved organic and particulate inorganic materials. Here a new method called the red tide index (RI) is presented which is capable of identifying potential areas of harmful algal blooms (HABs) from SeaWiFS ocean color measurements representing the typical Case-2 water environments off the Korean and Chinese coasts. The RI method employs the water-leaving radiances (L_w), collected from in-situ radiometric measurements of three SeaWiFS bands centered at 443 nm, 510 nm and 555 nm, to achieve derivation of indices that are then related to absorbing characteristics of harmful algae (i.e., L_w at 443 nm) from which a best fit with a cubic polynomial function with correlation coefficient of R² = 0.91 is obtained providing indices of higher ranges for HABs and lower and slightly reduced ranges for turbid and non-bloom waters. Similar indices derived from the use of remote sensing reflectance (R_rs), normalized water-leaving radiance (nL_w) and combination of both are found rather inadequate to characterize the variability of the encountered bloom. In order to quantify the HABs in terms of chlorophyll (Chl), an empirical relationship is established between the RI and in-situ Chl in surface waters from about 0.4-71 mg m^− 3, which yields a Red tide index Chlorophyll Algorithm (RCA) based on an exponential function with correlation coefficient R² = 0.92. The established methods were extensively tested and compared with the performances of standard Ocean Chlorophyll 4 (OC4) algorithm and Local Chlorophyll Algorithm (LCA) using SeaWiFS images collected from typical red tide waters of Korean South Sea (KSS), East China Sea (ECS), Yellow Sea (YS) and Bohai Sea (BS) during 1999-2002. The standard spectral ratio algorithms, the OC4 and LCA, yielded large errors in Chl retrievals for coastal areas, besides providing false information about the encountered HABs in KSS, ECS, YS and BS waters. On the contrary, the RI coupled with the standard spectral ratios yielded comprehensive information about various ranges of algal blooms, while RCA Chl showing a good agreement with in-situ data led to enhanced understanding of the spatial and temporal variability of the recent HAB occurrences in high scattering and absorbing waters off the Korean and Chinese coasts.     

An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea

An extensive bio-optical data set from field measurements was used to evaluate the performance of standard Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color (in-water) algorithms in the Baltic Sea, which represents an example of optically complex Case 2 waters with high concentration of colored dissolved organic matter (CDOM). The data set includes coincident measurements of radiometric quantities, chlorophyll a concentration (Chl a), and absorption coefficient of CDOM, which were taken on 25 cruises between 1993 and 2001. The data cover a wide range of variability with Chl a in surface waters from about 0.3 to 100 mg m⁻³. All the MODIS pigment algorithms examined as well as the SeaWiFS OC4v4 algorithm showed a systematic and large overestimation in chlorophyll retrievals. The mean systematic and random errors based on our entire data set exceeded 150% or even 200% in some cases, making these standard algorithms inadequate for pigment determinations in the Baltic. Although new parameterization of the standard pigment algorithms based on our field measurements in the Baltic resulted in a significant reduction of errors, the overall performance of such regionally tuned algorithms remained unsatisfactory. For example, the mean normalized bias (MNB) for the regionally tuned MODIS chlor_a_2 algorithm was reduced to 26% (from over 200% for the standard algorithm), but the root mean square (RMS) error was still large (>100%). The MODIS K_490 algorithm for estimating the diffuse attenuation coefficient of downwelling irradiance showed the best performance among all the algorithms examined. With the new coefficients based on our field data, the regional version of this algorithm showed an acceptable level of errors, MNB=4% and RMS=30%. In addition to the apparent problems of the standard in-water bio-optical algorithms, we found that the atmospheric correction currently in use for MODIS and SeaWiFS imagery usually fails to retrieve upwelling radiances emerging from the Baltic Sea. The match-up comparisons of the coincident in situ and satellite determinations of normalized water-leaving radiances showed generally poor agreement, especially in the blue spectral region. It appears that new approaches for ocean color algorithms are required in the Baltic Sea.     

Multispectral remote-sensing algorithms for particulate organic carbon (POC): The Gulf of Mexico

To greatly increase the spatial and temporal resolution for studying carbon dynamics in the marine environment, we have developed remote-sensing algorithms for particulate organic carbon (POC) by matching in situ POC measurements in the Gulf of Mexico with matching SeaWiFS remote-sensing reflectance. Data on total particulate matter (PM) as well as POC collected during nine cruises in spring, summer and early winter from 1997-2000 as part of the Northeastern Gulf of Mexico (NEGOM) study were used to test algorithms across a range of environments from low %POC coastal waters to high %POC open-ocean waters. Finding that the remote-sensing reflectance clearly exhibited a peak shift from blue-to-green wavelengths with increasing POC concentration, we developed a Maximum Normalized Difference Carbon Index (MNDCI) algorithm which uses the maximum band ratio of all available blue-to-green wavelengths, and provides a very robust estimate over a wide range of POC and PM concentrations (R² = 0.99, N = 58). The algorithm can be extrapolated throughout the region of shipboard sampling for more detailed coverage and analysis.     

Classification of dust days by satellite remotely sensed aerosol products

Considerable progress in satellite remote sensing (SRS) of dust particles has been seen in the last decade. From an environmental health perspective, such an event detection, after linking it to ground particulate matter (PM) concentrations, can proxy acute exposure to respirable particles of certain properties (i.e. size, composition, and toxicity). Being affected considerably by atmospheric dust, previous studies in the Eastern Mediterranean, and in Israel in particular, have focused on mechanistic and synoptic prediction, classification, and characterization of dust events. In particular, a scheme for identifying dust days (DD) in Israel based on ground PM₁₀ (particulate matter of size smaller than 10 μm) measurements has been suggested, which has been validated by compositional analysis. This scheme requires information regarding ground PM₁₀ levels, which is naturally limited in places with sparse ground-monitoring coverage. In such cases, SRS may be an efficient and cost-effective alternative to ground measurements. This work demonstrates a new model for identifying DD and non-DD (NDD) over Israel based on an integration of aerosol products from different satellite platforms (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)).

Analysis of ground-monitoring data from 2007 to 2008 in southern Israel revealed 67 DD, with more than 88% occurring during winter and spring. A Classification and Regression Tree (CART) model that was applied to a database containing ground monitoring (the dependent variable) and SRS aerosol product (the independent variables) records revealed an optimal set of binary variables for the identification of DD. These variables are combinations of the following primary variables: the calendar month, ground-level relative humidity (RH), the aerosol optical depth (AOD) from MODIS, and the aerosol absorbing index (AAI) from OMI. A logistic regression that uses these variables, coded as binary variables, demonstrated 93.2% correct classifications of DD and NDD. Evaluation of the combined CART–logistic regression scheme in an adjacent geographical region (Gush Dan) demonstrated good results. Using SRS aerosol products for DD and NDD, identification may enable us to distinguish between health, ecological, and environmental effects that result from exposure to these distinct particle populations.     

SeaWiFS data interpretation in a coastal area in the Bay of Biscay

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) instrument, launched onboard the Orbimage 2 satellite, is composed of an optical scanner with eight channels that are used to interpret the ocean colour, and has been operational since September 1997. SeaWiFS data were received by the Dundee Satellite Receiving Station and processed by the Plymouth Marine Laboratory with a slight time-lag. In situ measurements of reflectance, salinity, seston and chlorophyll a were analysed during the Biomet surveys to gain a better knowledge of the dynamics of the Gironde turbid plume during this period. The results showed logarithmic relationships between the SeaWiFS normalized water-leaving radiances (n L w ) at 490 and 555 nm (n L w (490) and n L w (555)), and the suspended particle matter concentrations. The relationship between the n L w (555) radiances and these concentrations is used to map the coastal terrigenous turbidities. But the chlorophyll a concentrations calculated from SeaWiFS are overestimated in the turbid waters. The n L w (490)/n L w (555) ratio decreases with increasing turbidity and with increasing chlorophyll a concentration. To distinguish the chlorophyll a in turbid waters, the n L w (490) radiances are calculated from n L w (555) considering only the effect of terrigenous turbidity. Then, the n L w (490) SeaWiFS image is compared to the calculated n L w (490), to reveal the 'negative' areas caused by the chlorophyll a and yellow substance absorption.     

Validation of SeaWiFS-derived chlorophyll for the Rio de la Plata Estuary and adjacent waters

The South Atlantic Ocean near the Río de la Plata Estuary is a highly dynamic region that encompasses four different water bodies: the Río de la Plata, the continental shelf and the Brazil and Malvinas currents. Bio-optical measurements obtained during 6–11 November 1999 provided the initial field data for validating Sea viewing Wide Field of view Sensor (SeaWiFS)-derived chlorophyll imagery for this region. The turbid waters of the Río de la Plata Estuary showed the highest variability and complexity in water optical properties, while the offshore waters of the Brazil Current had the lowest. In most cases, the estimates of chlorophyll using the SeaWiFS algorithm were higher than in situ measured values.