The integration of multi-source data to improve the classification of remotely sensed images

The number of remote sensing platforms and sensors rises almost every year, yet much work on the interpretation of land cover is still carried out using either single images or images from the same source taken at different dates. Two questions could be asked of this proliferation of images: can the information contained in different scenes be used to improve the classification accuracy and, what is the best way to combine the different imagery? Two of these multiple image sources are MODIS on the Terra platform and ETM+ on board Landsat7, which are suitably complementary. Daily MODIS images with 36 spectral bands in 250-1000 m spatial resolution and seven spectral bands of ETM+ with 30m and 16 days spatial and temporal resolution respectively are available. In the UK, cloud cover may mean that only a few ETM+ scenes may be available for any particular year and these may not be at the time of year of most interest. The MODIS data may provide information on land cover over the growing season, such as harvest dates, that is not present in the ETM+ data. Therefore, the primary objective of this work is to develop a methodology for the integration of medium spatial resolution Landsat ETM+ image, with multi-temporal, multi-spectral, low-resolution MODIS \Terra images, with the aim of improving the classification of agricultural land. Additionally other data may also be incorporated such as field boundaries from existing maps. When classifying agricultural land cover of the type seen in the UK, where crops are largely sown in homogenous fields with clear and often mapped boundaries, the classification is greatly improved using the mapped polygons and utilising the classification of the polygon as a whole as an apriori probability in classifying each individual pixel using a Bayesian approach. When dealing with multiple images from different platforms and dates it is highly unlikely that the pixels will be exactly co-registered and these pixels will contain a mixture of different real world land covers. Similarly the different atmospheric conditions prevailing during the different days will mean that the same emission from the ground will give rise to different sensor reception. Therefore, a method is presented with a model of the instantaneous field of view and atmospheric effects to enable different remote sensed data sources to be integrated.

Potential urban distribution of Phlebotomus mascittii Grassi and Phlebotomus neglectus Tonn. (Diptera: Psychodidae) in 2021–50 in Budapest, Hungary

Background & Objective: The most northern populations of two sand fly species (Phlebotomus mascittii and Phlebotomus neclectus) in the Carpathian Basin are known from Central Hungary. The most important limiting factor of the distribution of Phlebotomus species in the region is the annual minimum temperature which may be positively affected by the urban heat island and the climate change in the future. Method: Based on the latest case reports of the species, Climate Envelope Model was done for the period 1961-1990 and 2025-2050 to project the potential urban distribution of the species. The climatic data were obtained from RegCM regional climate model and MODIS satellite images. Results: The recent occurrence of the species in Central Hungary indicates that Phlebotomus species can overwinter in non-heated shelters in the built environment. Interpretation & Conclusion: Jointly heat island and future climate change seem to be able to provide suitable environment for the studied species in urban areas in a great extent.

Hurricane disturbance and recovery of energy balance, CO2 fluxes and canopy structure in a mangrove forest of the Florida Everglades

Eddy covariance (EC) estimates of carbon dioxide (CO2) fluxes and energy balance are examined to investigate the functional responses of a mature mangrove forest to a disturbance generated by Hurricane Wilma on October 24, 2005 in the Florida Everglades. At the EC site, high winds from the hurricane caused nearly 100% defoliation in the upper canopy and widespread tree mortality. Soil temperatures down to -50 cm increased, and air temperature lapse rates within the forest canopy switched from statically stable to statically unstable conditions following the disturbance. Unstable conditions allowed more efficient transport of water vapor and CO2 from the surface up to the upper canopy layer. Significant increases in latent heat fluxes (LE) and nighttime net ecosystem exchange (NEE) were also observed and sensible heat fluxes (H) as a proportion of net radiation decreased significantly in response to the disturbance. Many of these impacts persisted through much of the study period through 2009. However, local albedo and MODIS (Moderate Resolution Imaging Spectro-radiometer) data (the Enhanced Vegetation Index) indicated a substantial proportion of active leaf area recovered before the EC measurements began 1 year after the storm. Observed changes in the vertical distribution and the degree of clumping in newly emerged leaves may have affected the energy balance. Direct comparisons of daytime NEE values from before the storm and after our measurements resumed did not show substantial or consistent differences that could be attributed to the disturbance. Regression analyses on seasonal time scales were required to differentiate the storm's impact on monthly average daytime NEE from the changes caused by interannual variability in other environmental drivers. The effects of the storm were apparent on annual time scales, and CO2 uptake remained approximately 250 g C m-2 yr-1 lower in 2009 compared to the average annual values measured in 2004-2005. Dry season CO2 uptake was relatively more affected by the disturbance than wet season values. Complex leaf regeneration dynamics on damaged trees during ecosystem recovery are hypothesized to lead to the variable dry versus wet season impacts on daytime NEE. In contrast, nighttime CO2 release (i.e., nighttime respiration) was consistently and significantly greater, possibly as a result of the enhanced decomposition of litter and coarse woody debris generated by the storm, and this effect was most apparent in the wet seasons compared to the dry seasons. The largest pre- and post-storm differences in NEE coincided roughly with the delayed peak in cumulative mortality of stems in 2007-2008. Across the hurricane-impacted region, cumulative tree mortality rates were also closely correlated with declines in peat surface elevation. Mangrove forest-atmosphere interactions are interpreted with respect to the damage and recovery of stand dynamics and soil accretion processes following the hurricane.

Modeling Light Use Efficiency in a Subtropical Mangrove Forest Equipped with CO2 Eddy Covariance °C

Despite the importance of mangrove ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these forests remain poorly understood. This limited understanding is partly a result of the challenges associated with in situ flux studies. Tower-based CO2 eddy covariance (EC) systems are installed in only a few mangrove forests worldwide, and the longest EC record from the Florida Everglades contains less than 9 years of observations. A primary goal of the present study was to develop a methodology to estimate canopy-scale photosynthetic light use efficiency in this forest. These tower-based observations represent a basis for associating CO2 fluxes with canopy light use properties, and thus provide the means for utilizing satellite-based reflectance data for larger scale investigations. We present a model for mangrove canopy light use efficiency utilizing the enhanced green vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) that is capable of predicting changes in mangrove forest CO2 fluxes caused by a hurricane disturbance and changes in regional environmental conditions, including temperature and salinity. Model parameters are solved for in a Bayesian framework. The model structure requires estimates of ecosystem respiration (RE), and we present the first ever tower-based estimates of mangrove forest RE derived from nighttime CO2 fluxes. Our investigation is also the first to show the effects of salinity on mangrove forest CO2 uptake, which declines 5% per each 10 parts per thousand (ppt) increase in salinity. Light use efficiency in this forest declines with increasing daily photosynthetic active radiation, which is an important departure from the assumption of constant light use efficiency typically applied in satellite-driven models. The model developed here provides a framework for estimating CO2 uptake by these forests from reflectance data and information about environmental conditions.

Influência das condições ambientais no verdor da vegetação da caatinga frente às mudanças climáticas

The Caatinga biome, a semi-arid climate ecosystem found in northeast Brazil, presents low rainfall regime and strong seasonality. It has the most alarming climate change projections within the country, with air temperature rising and rainfall reduction with stronger trends than the global average predictions. Climate change can present detrimental results in this biome, reducing vegetation cover and changing its distribution, as well as altering all ecosystem functioning and finally influencing species diversity. In this context, the purpose of this study is to model the environmental conditions (rainfall and temperature) that influence the Caatinga biome productivity and to predict the consequences of environmental conditions in the vegetation dynamics under future climate change scenarios. Enhanced Vegetation Index (EVI) was used to estimate vegetation greenness (presence and density) in the area. Considering the strong spatial and temporal autocorrelation as well as the heterogeneity of the data, various GLS models were developed and compared to obtain the best model that would reflect rainfall and temperature influence on vegetation greenness. Applying new climate change scenarios in the model, environmental determinants modification, rainfall and temperature, negatively influenced vegetation greenness in the Caatinga biome. This model was used to create potential vegetation maps for current and future of Caatinga cover considering 20% decrease in precipitation and 1 °C increase in temperature until 2040, 35% decrease in precipitation and 2.5 °C increase in temperature in the period 2041-2070 and 50% decrease in precipitation and 4.5 °C increase in temperature in the period 2071-2100. The results suggest that the ecosystem functioning will be affected on the future scenario of climate change with a decrease of 5.9% of the vegetation greenness until 2040, 14.2% until 2070 and 24.3% by the end of the century. The Caatinga vegetation in lower altitude areas (most of the biome) will be more affected by climatic changes.

A compilation of global bio-optical in situ data for ocean-colour satellite applications

A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately.

Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.

Assessing trends in land use change in the Borana rangeland Ethiopia as one cause of greenhouse gas emissions and carbon sequestration variations

Landnutzungsänderungen sind eine wesentliche Ursache von Treibhausgasemissionen. Die Umwandlung von Ökosystemen mit permanenter natürlicher Vegetation hin zu Ackerbau mit zeitweise vegetationslosem Boden (z.B. nach der Bodenbearbeitung vor der Aussaat) führt häufig zu gesteigerten Treibhausgasemissionen und verminderter Kohlenstoffbindung. Weltweit dehnt sich Ackerbau sowohl in kleinbäuerlichen als auch in agro-industriellen Systemen aus, häufig in benachbarte semiaride bis subhumide Rangeland Ökosysteme. Die vorliegende Arbeit untersucht Trends der Landnutzungsänderung im Borana Rangeland Südäthiopiens. Bevölkerungswachstum, Landprivatisierung und damit einhergehende Einzäunung, veränderte Landnutzungspolitik und zunehmende Klimavariabilität führen zu raschen Veränderungen der traditionell auf Tierhaltung basierten, pastoralen Systeme. Mittels einer Literaturanalyse von Fallstudien in ostafrikanischen Rangelands wurde im Rahmen dieser Studie ein schematisches Modell der Zusammenhänge von Landnutzung, Treibhausgasemissionen und Kohlenstofffixierung entwickelt. Anhand von Satellitendaten und Daten aus Haushaltsbefragungen wurden Art und Umfang von Landnutzungsänderungen und Vegetationsveränderungen an fünf Untersuchungsstandorten (Darito/Yabelo Distrikt, Soda, Samaro, Haralo, Did Mega/alle Dire Distrikt) zwischen 1985 und 2011 analysiert. In Darito dehnte sich die Ackerbaufläche um 12% aus, überwiegend auf Kosten von Buschland. An den übrigen Standorten blieb die Ackerbaufläche relativ konstant, jedoch nahm Graslandvegetation um zwischen 16 und 28% zu, während Buschland um zwischen 23 und 31% abnahm. Lediglich am Standort Haralo nahm auch „bare land“, vegetationslose Flächen, um 13% zu. Faktoren, die zur Ausdehnung des Ackerbaus führen, wurden am Standort Darito detaillierter untersucht. GPS Daten und anbaugeschichtlichen Daten von 108 Feldern auf 54 Betrieben wurden in einem Geographischen Informationssystem (GIS) mit thematischen Boden-, Niederschlags-, und Hangneigungskarten sowie einem Digitales Höhenmodell überlagert. Multiple lineare Regression ermittelte Hangneigung und geographische Höhe als signifikante Erklärungsvariablen für die Ausdehnung von Ackerbau in niedrigere Lagen. Bodenart, Entfernung zum saisonalen Flusslauf und Niederschlag waren hingegen nicht signifikant. Das niedrige Bestimmtheitsmaß (R²=0,154) weist darauf hin, dass es weitere, hier nicht erfasste Erklärungsvariablen für die Richtung der räumlichen Ausweitung von Ackerland gibt. Streudiagramme zu Ackergröße und Anbaujahren in Relation zu geographischer Höhe zeigen seit dem Jahr 2000 eine Ausdehnung des Ackerbaus in Lagen unter 1620 müNN und eine Zunahme der Schlaggröße (>3ha). Die Analyse der phänologischen Entwicklung von Feldfrüchten im Jahresverlauf in Kombination mit Niederschlagsdaten und normalized difference vegetation index (NDVI) Zeitreihendaten dienten dazu, Zeitpunkte besonders hoher (Begrünung vor der Ernte) oder niedriger (nach der Bodenbearbeitung) Pflanzenbiomasse auf Ackerland zu identifizieren, um Ackerland und seine Ausdehnung von anderen Vegetationsformen fernerkundlich unterscheiden zu können. Anhand der NDVI Spektralprofile konnte Ackerland gut Wald, jedoch weniger gut von Gras- und Buschland unterschieden werden. Die geringe Auflösung (250m) der Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI Daten führte zu einem Mixed Pixel Effect, d.h. die Fläche eines Pixels beinhaltete häufig verschiedene Vegetationsformen in unterschiedlichen Anteilen, was deren Unterscheidung beeinträchtigte. Für die Entwicklung eines Echtzeit Monitoring Systems für die Ausdehnung des Ackerbaus wären höher auflösende NDVI Daten (z.B. Multispektralband, Hyperion EO-1 Sensor) notwendig, um kleinräumig eine bessere Differenzierung von Ackerland und natürlicher Rangeland-Vegetation zu erhalten. Die Entwicklung und der Einsatz solcher Methoden als Entscheidungshilfen für Land- und Ressourcennutzungsplanung könnte dazu beitragen, Produktions- und Entwicklungsziele der Borana Landnutzer mit nationalen Anstrengungen zur Eindämmung des Klimawandels durch Steigerung der Kohlenstofffixierung in Rangelands in Einklang zu bringen.

Surface thermal analysis of North Brabant cities and neighbourhoods during heat waves

The urban heat island effect is often associated with large metropolises. However, in the Netherlands even small cities will be affected by the phenomenon in the future (Hove et al., 2011), due to the dispersed or mosaic urbanisation patterns in particularly the southern part of the country: the province of North Brabant. This study analyses the average night time land surface temperature (LST) of 21 North-Brabant urban areas through 22 satellite images retrieved by Modis 11A1 during the 2006 heat wave and uses Landsat 5 Thematic Mapper to map albedo and normalized difference temperature index (NDVI) values. Albedo, NDVI and imperviousness are found to play the most relevant role in the increase of nighttime LST. The surface cover cluster analysis of these three parameters reveals that the 12 “urban living environment” categories used in the region of North Brabant can actually be reduced to 7 categories, which simplifies the design guidelines to improve the surface thermal behaviour of the different neighbourhoods thus reducing the Urban Heat Island (UHI) effect in existing medium size cities and future developments adjacent to those cities.

Small-scale variability of the current in the Strait of Bonifacio

Current dynamics in the Strait of Bonifacio (south Corsica) were investigated at a small scale during the STELLAMARE1 multidisciplinary cruise in summer 2012, using in situ measurements and modeling data. The Strait of Bonifacio is a particularly sensitive marine area in which specific conservation measures have been taken to preserve the natural environment and wild species. Good knowledge of the hydrodynamics in this area is essential to optimize the Marine Protected Area's management rules. Therefore, we used a high-resolution model (400 m) based on the MARS3D code to investigate the main flux exchanges and to formulate certain hypotheses about the formation of possible eddy structures. The aim of the present paper is first to synthetize the results obtained by combining Acoustic Doppler Current Profiler data, hydrological parameters, Lagrangian drifter data, and satellite observations such as MODIS OC5 chlorophyll a data or Metop-A AVHRR Sea Surface Temperature (SST) data. These elements are then used to validate the presence of the mesoscale eddies simulated by the model and their recurrence outside the cruise period. To complete the analysis, the response of the 3D hydrodynamical model was evaluated under two opposing wind systems and certain biases were detected. Strong velocities up to 1 m s(-1) were recorded in the east part due to the Venturi effect; a complementary system of vortices governed by Coriolis effect and west wind was observed in the west part, and horizontal stratification in the central part has been identified under typical wind condition.

Fenologia kwitnienia i produkcja pyłku w zróżnicowanych warunkach mikroklimatycznych dużego miasta oraz ich wpływ na przebieg sezonu pyłkowego brzozy (Betula spp.) i bylicy (Artemisia spp.) w Poznaniu

Wydział Biologii: Instytut Biologii Środowiska, Pracownia Aeropalinologii

Modulation of sea surface temperature warming in the Bay of Biscay by Loire and Gironde Rivers

The influence of Loire and Gironde River discharges over the sea surface temperature (SST) in the eastern Bay of Biscay (0.6º–36.6ºW, 44.2º–47.8ºW) was analyzed by means of two complementary databases (MODIS and OISST1/4). The area influenced by river plume showed a different SST when compared with the adjacent oceanic area for the months when the plume attains its highest extension (December, January, and February). Ocean was observed to warm at a rate of approximately 0.3ºC dec−1 while temperature at the area influenced by the rivers cooled at a rate of −0.15ºC dec−1 over the period 1982–2014. The mere presence of a freshwater layer is able to modulate the warming observed at adjacent ocean locations since the coastal area is isolated from the rest of the Bay. This nearshore strip is the only part of the Bay where changes in SST depend on North Atlantic Oscillation (NAO) but not on North Atlantic SST represented by the Atlantic Multidecadal Oscillation (AMO). These different cooling-warming trends are even more patent over the last years (2002–2014) under atmospheric favorable conditions for plume enhancement. River runoff increased at a rate on the order of 120 m3s−1dec−1 over that period and southwesterly winds, which favor the confinement of the plume, showed a positive and significant trend both in duration and intensity. Thus, the coastal strip has been observed to cool at a rate of −0.5°C dec−1.

Field study and numerical simulation of developing red tide in the northern Strait of Hormuz

The catastrophic event of red tide has happened in the Strait of Hormuz, the Persian Gulf and Gulf of Oman from late summer 2008 to spring 2009. With its devastating effects, the phenomenon shocked all the countries located in the margin of the Persian Gulf and the Gulf of Oman and caused considerable losses to fishery industries, tourism, and tourist and trade economy of the region. In the maritime cruise carried out by the Persian Gulf and Gulf of Oman Ecological Research Institute, field data, including temperature, salinity, chlorophyll-a, dissolved oxygen and algal density were obtained for this research. Satellite information was received from MODIS and MERIS and SeaWiFS sensors. Temperature and surface chlorophyll images were obtained and compared with the field data and data of PROBE model. The results obtained from the present research indicated that with the occurrence of harmful algal blooms (HAB), the Chlorophyll-a and the dissolved oxygen contents increased in the surface water. Maximum algal density was seen in the northern coasts of the Strait of Hormuz. Less concentration of algal density was detected in deep and surface offshore water. Our results show that the occurred algal bloom was the result of seawater temperature drop, water circulation and the adverse environmental pollutions caused by industrial and urban sewages entering the coastal waters in this region of the Persian Gulf ,This red tide phenomenon was started in the Strait of Hormuz and eventually covered about 140,000 km2 of the Persian Gulf and total area of Strait of Hormuz and it survived for 10 months which is a record amongst the occurred algal blooms across the world. Temperature and chlorophyll satellite images were proportionate to the measured values obtained by the field method. This indicates that satellite measurements have acceptable precisions and they can be used in sea monitoring and modeling.

The impact of tides and waves on near-surface suspended sediment concentrations in the English Channel

Numerous ecological problems of continental shelf ecosystems require a refined knowledge of the evolution of suspended sediment concentrations (SSC). The present investigation focuses on the spatial and temporal variabilities of near-surface SSC in coastal waters of the English Channel (western Europe) by exploiting numerical predictions from the Regional Ocean Modeling System ROMS. Extending previous investigations of ROMS performances in the Channel, this analysis refines, with increased spatial and temporal resolutions, the characterization of near-surface SSC patterns revealing areas where concentrations are highly correlated with evolutions of tides and waves. Significant tidal modulations of near-surface concentrations are thus found in the eastern English Channel and the French Dover Strait while a pronounced influence of waves is exhibited in the Channel Islands Gulf. Coastal waters present furthermore strong SSC temporal variations, particularly noticeable during storm events of autumn and winter, with maximum near-surface concentrations exceeding 40 mg l−1 and increase by a factor from 10 to 18 in comparison with time-averaged concentrations. This temporal variability strongly depends on the granulometric distribution of suspended sediments characterized by local bi-modal contributions of silts and sands off coastal irregularities of the Isle of Wight, the Cotentin Peninsula and the southern Dover Strait.

Multi-sensor Cloud and Aerosol Retrieval Simulator and Its Applications

Executing a cloud or aerosol physical properties retrieval algorithm from controlled synthetic data is an important step in retrieval algorithm development. Synthetic data can help answer questions about the sensitivity and performance of the algorithm or aid in determining how an existing retrieval algorithm may perform with a planned sensor. Synthetic data can also help in solving issues that may have surfaced in the retrieval results. Synthetic data become very important when other validation methods, such as field campaigns,are of limited scope. These tend to be of relatively short duration and often are costly. Ground stations have limited spatial coverage whilesynthetic data can cover large spatial and temporal scales and a wide variety of conditions at a low cost. In this work I develop an advanced cloud and aerosol retrieval simulator for the MODIS instrument, also known as Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS). In a close collaboration with the modeling community I have seamlessly combined the GEOS-5 global climate model with the DISORT radiative transfer code, widely used by the remote sensing community, with the observations from the MODIS instrument to create the simulator. With the MCARS simulator it was then possible to solve the long standing issue with the MODIS aerosol optical depth retrievals that had a low bias for smoke aerosols. MODIS aerosol retrieval did not account for effects of humidity on smoke aerosols. The MCARS simulator also revealed an issue that has not been recognized previously, namely,the value of fine mode fraction could create a linear dependence between retrieved aerosol optical depth and land surface reflectance. MCARS provided the ability to examine aerosol retrievals against “ground truth” for hundreds of thousands of simultaneous samples for an area covered by only three AERONET ground stations. Findings from MCARS are already being used to improve the performance of operational MODIS aerosol properties retrieval algorithms. The modeling community will use the MCARS data to create new parameterizations for aerosol properties as a function of properties of the atmospheric column and gain the ability to correct any assimilated retrieval data that may display similar dependencies in comparisons with ground measurements.