Distribution of inundation by the great tsunami of the 2011 Mw 9.0 earthquake off the Pacific coast of Tohoku (Japan), as revealed by ALOS imagery data

The 2011 M_w 9.0 earthquake off the Pacific coast of Tohoku (Japan) generated a great tsunami that inundated a wide area of the east coast of northeast Japan. In this study, we identify the distribution of tsunami inundation along a 340 km-long section of coast in northeast Honshu Island, based mainly on Advanced Land Observing Satellite (ALOS) imagery data and with cross-checking by aerial photographs and Google Earth images acquired before and after the 2011 earthquake. The analyses of remote-sensing imagery data, combined with fieldwork observations, show that (i) inundation by the tsunami extended over a wide area with a large inland limit of inundation, up to ～5.5 km from the coast in low-lying areas and up to ～15 km in a narrow bay area at the mouth of a large river; and (ii) high run-ups of up to ～35 m occurred in areas with a sawtooth coastline close to the epicentre of the 2011 Stimpson, I. 2011. Japan's Tohoku earthquake and tsunami. Geology Today, 27: 96–98. [Crossref] , [Google Scholar] main shock. The results demonstrate that remote-sensing techniques provide a powerful tool for rapidly identifying the distribution of tsunami inundation caused by the 2011 great earthquake, especially in remote and rugged coastal areas, and in areas threatened by nuclear radiation leaking from the nuclear power plant at Fukushima.     

Safety of ships’ evacuation from tsunami: survey unit of the Great East Japan earthquake

The Great East Japan Earthquake occurred at 14:46 h on Friday, 11 March 2011. It was the most powerful known earthquake to have hit Japan, and one of the five most powerful earthquakes overall in the world since modern record-keeping began in 1900. The earthquake triggered extremely destructive tsunami waves of up to 40.5 m in Miyako, Iwate. More than 20,000 people have been dead or missing. The International Research Center for Marine Policy is a think tank of Japan Coast Guard and belongs to the Japan Coast Guard Academy. Our research unit was organized to conduct survey and research on the Great East Japan Earthquake, in particular its effect on the ocean. This unit collects information and analyzes and synthesizes the collected information from a professional viewpoint. Then, the unit studies about damage by tsunami, damage of ships, urgent evacuate and search rescue to pick new actual explication and various lessons out.     

Assessment of tsunami and anthropogenic impacts on the forest of the North Andaman Islands,India

Forests are being depleted drastically at higher rates to cater to the needs of growing population. In this context, an attempt was made to identify the drivers of forest changes on the vegetation of the North Andaman islands by broadly categorising the changes as anthropogenic and natural disturbances (tsunami) using satellite images of 1976, 1999 and 2005. The images were classified using visual interpretation technique to generate land cover maps of the area under study. A detailed change analysis of the 1976, 1999 and 2005 images showed that a high proportion of the natural vegetation has been converted into agriculture, settlement, sand and water. The overall forest change from 1976 to 2005 is 11,670 ha with a deforestation rate of 389 ha yr^?1. The tsunami of 26 December, 2004 was found to be a major cause of deforestation of coastal forests in the North Andaman Islands, deforesting an area of 3292.5 ha. Simulation of forest cover in the next 25 and 50 years predicted a deforestation of 13,100 and 22,700 ha with a corresponding increase in non‐forest land cover to 19,600 and 29,600 ha respectively. It is predicted that after 50 years the forest area of 131,200 ha, estimated from the 1999 satellite data, may reduce to 108,500 ha, if proper conservation measures are not taken.     

Unusual diurnal variation in surface ozone observed after the 26 December 2004 tsunami over the rural site of Bay of Bengal,India

Measurements of surface ozone (O₃) and nitrogen dioxide (NO₂) were studied from December 2004 to February 2005, covering the giant tsunami event on 26 December 2004 at Tranquebar (11° N, 79.9° E, 9 m) over the west coast of the Bay of Bengal, India. An unusual maximum O₃ concentration of 28 parts per billion by volume (ppbv) was observed in the morning and a minimum (16 ppbv) in the evening, indicating that pronounced chemical loss of O₃ occurred in the daytime after the tsunami over this coastal region. An increase in NO₂ concentration from 5.6 ppbv before the tsunami to 10.5 ppbv after the tsunami was observed in the daytime. The observed unusual diurnal changes in O₃ were not due to mass transport processes as the five-day back trajectories of air parcels transport before and after the tsunami remained unchanged. Similarly, meteorological and micrometeorological parameters were found to be normal before and after the tsunami. The unusual low O₃ level during the daytime was possibly due to prolonged excess emission of iodocarbons from the sea surface after the tsunami, which resulted in enhanced inorganic iodine (I _x ) concentration, leading to massive destruction of O₃. Similarly, unusually high O₃ levels during the night-time were possibly due to the intrusion of ozone-rich air after the tsunami from the free troposphere into the surface layer when the boundary layer height shrinks after midnight. The present work can be extended on a regional scale by incorporating modelling studies using recent remote sensing tools.     

MODIS and NOAA-AVHRR l and surface temperature data detect a thermal anomaly preceding the 11 March 2011 Tohoku earthquake

Geospatial data, coupled with ground-based observations where available, enable scientists to survey pre-earthquake signals in areas of strong tectonic activity. On 11 March 2011, at local time 14:46 JST (05:46 UTC), a mega-earthquake of moment magnitude (M _w) 9.0 and shallow focus (24 km), known as the Tohoku-Oki earthquake, occurred on the Japan Trench plate boundary off the eastern shore of northern Honshu, followed by a large tsunami on the Pacific coast of Japan. This article is an attempt to analyse the development of thermal anomalies in land surface temperature (LST) preceding the 11 March 2011 Tohoku earthquake. In order to correlate LST variations and the Tohoku earthquake, we analysed time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua satellite daytime/night-time and National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer (NOAA-AVHRR) data. A clear rise of LST (1–10°C), which is apparently related to pre-seismic activity, was observed 2 weeks before the major event in all analysed satellite data around the earthquake epicentre.     

Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security,determined using MODIS 250 m time-series data and spectral matching techniques,in the Krishna River basin (India)

The objective of this study was to investigate the changes in cropland areas as a result of water availability using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series data and spectral matching techniques (SMTs). The study was conducted in the Krishna River basin in India, a very large river basin with an area of 265 752 km² (26 575 200 ha), comparing a water-surplus year (2000–2001) and a water-deficit year (2002–2003). The MODIS 250 m time-series data and SMTs were found ideal for agricultural cropland change detection over large areas and provided fuzzy classification accuracies of 61–100% for various land‐use classes and 61–81% for the rain-fed and irrigated classes. The most mixing change occurred between rain-fed cropland areas and informally irrigated (e.g. groundwater and small reservoir) areas. Hence separation of these two classes was the most difficult. The MODIS 250 m-derived irrigated cropland areas for the districts were highly correlated with the Indian Bureau of Statistics data, with R ²-values between 0.82 and 0.86.

The change in the net area irrigated was modest, with an irrigated area of 8 669 881 ha during the water-surplus year, as compared with 7 718 900 ha during the water-deficit year. However, this is quite misleading as most of the major changes occurred in cropping intensity, such as changing from higher intensity to lower intensity (e.g. from double crop to single crop). The changes in cropping intensity of the agricultural cropland areas that took place in the water-deficit year (2002–2003) when compared with the water-surplus year (2000–2001) in the Krishna basin were: (a) 1 078 564 ha changed from double crop to single crop, (b) 1 461 177 ha changed from continuous crop to single crop, (c) 704 172 ha changed from irrigated single crop to fallow and (d) 1 314 522 ha changed from minor irrigation (e.g. tanks, small reservoirs) to rain-fed. These are highly significant changes that will have strong impact on food security. Such changes may be expected all over the world in a changing climate.     

Statistical characterization of the sea ice extent during different winter scenarios in the Gulf of Riga (Baltic Sea) using optical remote-sensing imagery

This study focuses on the statistical characterization of ice conditions (extent, sea ice occurrence probability (SIOP), and length of ice season) in the Gulf of Riga, Baltic Sea, using remote-sensing data. The optical remote-sensing data with 250 m resolution acquired by a Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002–2011 were used for statistical characterization of sea ice. A method based on bimodal histogram analysis of remote-sensing reflectance data was developed to discriminate ice from water. In general, ice extent information obtained from MODIS data agrees with the official ice chart data (synthetic aperture radar (SAR) and in situ measurements) and multi-sensor product containing data from microwave and infrared instruments (R² >0.83). However, in case of severe winters and extremely mild winters there are differences in the dates when maximum ice extent is registered. MODIS data can be used for detailed analysis of ice extent in specific basins of Baltic Sea. Depending on the year, the ice season length in the Gulf of Riga ranged from 68 to 146 days, and the maximum ice extent varied greatly from 329 to 15,350 km². SIOP and number of ice days increased significantly in areas where the depth is less than 15 m. Based on negative-degree days and ice cover characteristics (SIOP and ice season length), three winter scenarios were defined: severe (2003, 2006, 2010, and 2011), medium (2004 and 2005), and mild (2007, 2008, and 2009).     

Urban green space dynamics and socio-environmental inequity: multi-resolution and spatiotemporal data analysis of Kumasi,Ghana

Urban green spaces (UGS) are crucial for urban sustainability and resilience to environmental vulnerabilities but are often relegated in cities in the global south. This article analysed the spatio-temporal change, composition, extent, and distributional inequities associated with UGS in Kumasi, Ghana. Spatial techniques and Gini index were combined in the assessment. Kumasi UGS coverage is currently 33% but declined fourfold faster in recent years (2009–2014) than previously (1986–2002). The overall accuracy of the change maps: 1986–2014 and 2009–2014 were, respectively, 0.96 ± 0.02 and 0.97 ± 0.02. The Shannon entropy for built-up sprawl in 1986 and 2014 were 0.80 and 0.99, respectively. The UGS area per capita for 2009 (R² = 0.50, p = 0.049) and 2014 (R² = 0.53, p = 0.0398) were moderately correlated with socioeconomic conditions of sub-metropolises. The Gini coefficient for both vegetation and tree cover was 0.26. UGS cover is plummeting and somewhat unevenly distributed across Kumasi. Strategic planning for UGS can ensure ample availability, equity in access, and resilience to climate-related vulnerabilities.     

Changes in net primary production in the Tianmu Mountain Nature Reserve,China, from 1984 to 2014

Nature reserve establishment can lead to conflict with some stakeholders. Zoning management is useful to mitigate against the conflict between human development and nature reserves, and a nature reserve can be divided into three zones: the core zone, buffer zone, and experimental zone. So far, how to monitor and evaluate the effectiveness of zoning management in nature reserves is a problem faced by remote sensing scientists and ecologists. Net primary productivity (NPP) is a key indicator which can be used to monitor and evaluate the effectiveness of zoning management in nature reserves. However, to date there has been no research on the effectiveness of zoning management on NPP, and the estimation of NPP in the Tianmu Mountain Nature Reserve also has not been studied. Based on remote sensing data and in situ measurements, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate NPP in the Tianmu Mountain Nature Reserve during the period 1984–2014. We used the observed NPP to verify the simulated NPP, and the results show that the simulated NPP was consistent with the observed NPP (R² ≥ 0.85, p ≤ 0.0002, RMSE = 52.62 g C m^?2 year^?1, where R² represents coefficient of determination, p represents statistical significance, and RMSE represents root mean square error). This means that the CASA model is suitable for NPP estimation in the Tianmu Mountain Nature Reserve. The results also indicate that NPP showed an increasing trend during the period 1984–2014, and the increase over the whole period was 6.66%. The total of the annual averaged NPP was 3.07 × 10¹⁰ g C year^?1, while the annual averaged NPP per unit area was 708 g C m^?2 year^?1. The largest averaged annual NPP per unit appeared in the core zone (720 g C m^?2 year^?1), followed by the buffer zone (711 g C m^?2 year^?1), with the experimental zone having the smallest averaged annual NPP per unit (706 g C m^?2 year^?1). At the p < 0.1 level, there was no region where NPP had decreased significantly in the core zone and buffer zone, and the area of the regions where NPP had decreased significantly in the experimental zone was 8.04 ha. At the p < 0.05 level, there was no area where NPP had decreased significantly in the three zones of the Tianmu Mountain Nature Reserve. The results show that the zoning management on NPP was effective in the Tianmu Mountain Nature Reserve.     

Variations of chlorophyll-a in the northeastern Indian Ocean after the 2004 South Asian tsunami

Analysis of satellite remote sensing data has revealed changes in distribution of chlorophyll-a (Chl-a) and sea surface temperature (SST) in the Indian Ocean during the South Asian tsunami in December 2004. Chl-a data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) images were examined for the period from 1998 to 2005. Around the epicentre of the Sumatra earthquake, the Chl-a concentration was found to increase prior to the main event on 26 December 2004 and then decrease during the tsunami event, while a high SST (～30–31°C) was observed in and around the epicentral region. Chl-a concentrations in the coastal waters of the Southeast Asian countries were remarkably low during and after the tsunami. Similar but relatively small variations in Chl-a and SST were observed during the second earthquake on 28 March 2005. Analysis of Chl-a, SST, wind and upwelling water has provided information for understanding the changes in Chl-a concentration during the tsunami. A very large offshore phytoplankton bloom (～300 km²) appeared to the southeast of Sri Lanka about 3 weeks after the tsunami; this might have been caused by a tropical storm that could be responsible for the enhancement of nutrients.     

Landsat 8 OLI satellite data for mapping of the Posidonia oceanica and benthic habitats of coastal ecosystems

The benthic seabeds and seagrass ecosystems, in particular the vulnerable Posidonia oceanica (PO), are increasingly threatened by climate change and other anthropogenic pressures. Along the 8000 km coastline of Italy, they are often poorly mapped and monitored to properly evaluate their health status. Thus to support these monitoring needs, the improved capabilities of the Landsat 8 Operational Land Imager (OLI) Earth Observation (EO) satellite system were tested for PO mapping by coupling its atmospherically corrected multispectral data with near-synchronous sea truth information. Two different approaches for the necessary atmospheric correction were exploited focusing on the Aerosol Optical Depth (AOD) and adjacency noise effects, which typically occur at land–sea interfaces. The general achievements demonstrated the effectiveness of High Resolution (HR) spectral responses captured by OLI sensor, for monitoring seagrass and sea beds in the optically complex Tyrrhenian shallow waters, with performance level dependent on the type of applied atmospheric pre-processing. The distribution of the PO leaf area index (LAI) on different substrates has been most effectively modelled using on purpose developed spectral indices. They were based on the coastal and blue-green OLI bands, atmospherically corrected using a recently introduced method for AOD retrieval, based on the Short Wave Infrared (SWIR) reflectance. The alternative correction method including a less effective AOD assessment but the removal of adjacency effects has proven its efficacy for improving the thematic discriminability of the seabed types characterized by different PO cover–substrate combinations.     

Assessment of the offshore wind resource in Japan with the ASCAT microwave scatterometer

We analysed wind speed and direction off the coast of Japan using data from the satellite-borne Advanced Scatterometer (ASCAT) and the Weather Research and Forecasting model (WRF), validated these data using in situ wind measurements from 20 buoys, and evaluated the effect of the long time intervals from ASCAT observations on wind resource assessment. More than 25 km from the coast, and at heights of 10 m, the ASCAT wind speed has negative biases of up to 3.4% and root mean square errors of up to 18.5%; its wind direction has 11° to 27° of mean absolute error compared to buoy measurements at a height of 10 m. These accuracies are better than either the expected accuracies reported in the technical manual or those simulated with WRF with its spatial resolution of 10 km. We also evaluated long-term average ASCAT wind speeds in comparison to 4- and 5-year averages of in situ buoy wind speeds measured at three buoys, with resulting differences of –0.3%, –6.3%, and – 1.6%. Furthermore, wind roses show that appearance frequencies of the ASCAT wind direction for the long term are in a good agreement with those of the measurements at the three buoys. Our results show that the ASCAT-derived wind speed and direction are appropriate more than 25 km from the coast, and that the long time interval between ASCAT observations has an insignificant effect on wind resource assessment, if at least 4 or 5 years of averaged ASCAT data are used.     

Application of ALOS AVNIR-2 for the detection of seaweed and seagrass beds on the northeast of Brazil

Seaweed and seagrass beds play a multiplicity of functions within ecosystems, and have both ecological and economical value. However, anthropogenic activities, as well as climate change have contributed on the degradation. This study used data from Advanced Land Observing Satellite (ALOS) Advanced Visible and Near Infrared Radiometer Type 2 (AVNIR-2) orbital images to detect seaweeds and seagrasses. Qualitative and quantitative samples were used to validate the images, classified using the kappa coefficient (κ). The supervised classifications performed by the algorithms fuzzy logic – Fuzclass, maximum likelihood – Maxlike, minimum distance to means – Mindist, parallelepiped – Piped, showed an accuracy level of 0.93, 0.84, 0.83 (both excellent), and 0.62 (substantial), respectively. The results of the hard classifiers (Maxlike, Mindist, and Piped) submitted a new classification based on fuzzy logic (Fuzclass) demonstrated accuracy level of 0.74, 0.61 (both substantial), and 0.50 (moderate), respectively. Considered superior to the others, the Fuzclass classifier exhibited the best tendency in representing reef bottom–type distribution. Maxlike generated a map of seaweed and seagrass spatial distribution and abundance of the Maracajaú reef, identifying seven classes: (1) dense seaweeds; (2) sand; (3) dense seagrasses; (4) sparse seagrasses; (5) calcareous seaweeds; (6) sparse seaweeds; and (7) fine sand. The map of Maracajaú reef bottom type showed that it was possible to apply image processing and digital classification methodologies to distinguish submerged organisms, revealing information to help in planning and management of these ecosystems, enabling future monitoring.     

Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data

Leaf area index (LAI) is one of the most important plant parameters when observing agricultural crops and a decisive factor for yield estimates. Remote-sensing data provide spectral information on large areas and allow for a detailed quantitative assessment of LAI and other plant parameters. The present study compared support vector regression (SVR), random forest regression (RFR), and partial least-squares regression (PLSR) and their achieved model qualities for the assessment of LAI from wheat reflectance spectra. In this context, the validation technique used for verifying the accuracy of an empirical–statistical regression model was very important in order to allow the spatial transferability of models to unknown data. Thus, two different validation methods, leave-one-out cross-validation (cv) and independent validation (iv), were performed to determine model accuracy. The LAI and field reflectance spectra of 124 plots were collected from four fields during two stages of plant development in 2011 and 2012. In the case of cross-validation for the separate years, as well as the entire data set, SVR provided the best results (2011: R²_cv = 0.739, 2012: R²_cv = 0.85, 2011 and 2012: R²_cv = 0.944). Independent validation of the data set from both years led to completely different results. The accuracy of PLSR (R²_iv = 0.912) and RFR (R²_iv = 0.770) remained almost at the same level as that of cross-validation, while SVR showed a clear decline in model performance (R²_iv = 0.769). The results indicate that regression model robustness largely depends on the applied validation approach and the data range of the LAI used for model building.     

Evaluation of temporal variations in soil moisture based on the microwave polarization difference index using in situ data over agricultural areas in China

Soil moisture plays a critical role in the energy exchange and water redistribution of the land-atmosphere system. Knowledge of the temporal variations in soil moisture is vital in agricultural applications. Microwave indices are often used to characterize the temporal variations in soil moisture. In this study, we evaluate the temporal variations in soil moisture based on the microwave polarization difference index (MPDI) using ground-based measurements in China. In situ soil moisture at six test sites during the crop-growing season from 2009 to 2011 is obtained. The consistency of the temporal variations between the MPDI values and the in situ soil moisture is analysed in terms of (1) microwave frequencies, (2) satellite overpass times, and (3) measurement depths of soil moisture. The results show that the accuracies of the consistency vary from approximately 40% to 90%. Compared with the in situ soil moisture at 0–10 cm, the temporal variations in soil moisture are best characterized by the 6.9 GHz MPDI values from the ascending overpasses (MPDI_06A). Furthermore, the accuracies of the consistency between MPDI_06A and the in situ soil moisture at 0–10 cm are greater than those between MPDI_06A and the in situ soil moisture at 10–20 cm.     

Evaluation of the MODIS-Aqua Sea-Surface Temperature product in the inner and mid-shelves of southwest Buenos Aires Province,Argentina

Validation of sea-surface temperature (SST) provided by the MODIS-Aqua sensor (Moderate Resolution Imaging Spectroradiometer) for the inner and mid-shelves of the southwest of Buenos Aires Province (Argentina), is presented for the first time. In situ data obtained with a multi-parametric sonde YSI-6600 and a CTD SBE91 between 2002 and 2011 are used for comparison with the satellite SST product. The match-up exercise was established after comparing different spatial boxes, time difference windows, wind speeds, and also a coefficient of variation. The comparison exercise was made in the coastal zone and the rest of the inner and mid-shelves separately. In the coastal zone, applying a 3 × 2 pixel box and a time window of ±3 hours led to the most accurate results, with a coefficient of determination (R²) of 0.99, a bias of 0.62°C, and a root-mean-square-error (RMSE) of 0.79°C. In the inner-mid-shelves when applying a coefficient of variability <0.3, a time window of ±3 hours, and taking only values of wind speed > 6 m s^?1, R² is 0.97, bias is 0.46°C, and RMSE is 0.95°C. Wind speed plays a major role in the inner-mid-shelves as the SST product is affected by stratification and formation of a diurnal thermocline in the ‘skin and sub-skin layer’ when wind speed is below 6 m s^?1. The results for the two shelves are very similar. Finally, the spatial and temporal variability of the SST satellite product was analysed in the study area for the period August 2002–December 2010. The results show that inter-annual variability is not significant and that there is no positive or negative trend for the 9 years of the study. Seasonality is the main component of temporal variability, with variation in amplitude signal depending on bathymetry changes, physical forcing, stability of the water column, and presence of flood plains.     

Accuracy assessment and trend analysis of MODIS-derived data on snow-covered areas in the Sutlej basin,Western Himalayas

Snow is important for hydrological studies and is a variable very sensitive to climatic variations. In the present study, the variability of snow-covered areas (SCAs) obtained through Moderate Resolution Imaging Spectroradiometer (MODIS) snow data products was analysed using the Mann–Kendall test and Sen’s slope estimator in the Sutlej basin, Western Himalayas, India. However, due to the limitations of long time-series snow cover data, the study has been carried out for a time period from 2000 to 2009. Before trend analysis, the estimated SCA was validated using the ground-based snowfall data. A simple linear regression test was applied to analyse the relationship between the variation in SCA and snowfall. The relationship between the mean annual snowfall and SCA indicated a highly significant correlation (R² = 0.95). In order to have a better insight into the relationship, the regression test was also carried out for six elevation zones. The coefficient of determination (R²) varied from 0.78 at the 1500–2000 m asl zone to 0.96 at the 3000–3500 m asl zone. The trend analysis indicated reduction in SCA with significant negative behaviour for annual, winter, and post-monsoon seasons and for November and December months. The negative trend was observed for an elevation of <2500 m asl in the basin. Moreover, during the same period (2000–2009), the temperature (T_max and T_min) increased while there was a decrease in snowfall. The trend analysis of temperature from 1984 to 2009 revealed positive trends with significant trend in T_min as determined by using the Mann–Kendall statistical test. The reduction in SCA was, therefore, attributed to the increasing trends in temperature, particularly T_min, associated with reduction in snowfall. These SCA variations have significant implications for water resources managers in the area as some of these observed trends, if continue, may result in changes in hydrological/ecological balance of the Sutlej basin.     

Remote sensing of selected water-quality indicators with the hyperspectral imager for the coastal ocean (HICO) sensor

The Hyperspectral Imager for the Coastal Ocean (HICO) offers the coastal environmental monitoring community an unprecedented opportunity to observe changes in coastal and estuarine water quality across a range of spatial scales not feasible with traditional field-based monitoring or existing ocean colour satellites. HICO, an Office of Naval Research-sponsored programme, is the first space-based maritime hyperspectral imaging instrument designed specifically for the coastal ocean. HICO has been operating since September 2009 from the Japanese Experiment Module – Exposed Facility on the International Space Station (ISS). The high pixel resolution (approximately 95 m at nadir) and hyperspectral imaging capability offer a unique opportunity for characterizing a wide range of water colour constituents that could be used to assess environmental condition. In this study, we transform atmospherically corrected ISS/HICO hyperspectral imagery and derive environmental response variables routinely used for evaluating the environmental condition of coastal ecosystem resources. Using atmospherically corrected HICO imagery and a comprehensive field validation programme, three regionally specific algorithms were developed to estimate basic water-quality properties traditionally measured by monitoring agencies. Results indicated that a three-band chlorophyll a algorithm performed best (R² = 0.62) when compared with in situ measurement data collected 2–4 hours of HICO acquisitions. Coloured dissolved organic matter (CDOM) (R² = 0.93) and turbidity (R² = 0.67) were also highly correlated. The distributions of these water-quality indicators were mapped for four estuaries along the northwest coast of Florida from April 2010 to May 2012. However, before the HICO sensor can be transitioned from proof-of-concept to operational status and its data applied to benefit decisions made by coastal managers, problems with vicarious calibration of the sensor need to be resolved and standardized protocols are required for atmospheric correction. Ideally, the sensor should be placed on a polar orbiting platform for greater spatial and temporal coverage as well as for image synchronization with field validation efforts.     

SAR-derived wind fields at the coastal region in the East/Japan Sea and relation to coastal upwelling

The relationship between the modification of synthetic aperture radar (SAR) wind field and coastal upwelling was investigated using high-resolution wind fields from Advanced Land Observing Satellite (ALOS) Phased Array type L-band synthetic aperture radar (PALSAR) imagery and sea-surface temperature (SST) from National Oceanic and Atmospheric Administration/Advanced Very-High-Resolution Radiometer (NOAA/AVHRR) data. The retrieved SAR wind speeds seem to agree well with in situ buoy measurements with only a relatively small error of 0.7 m s^?1. The SAR wind fields retrieved from the east coast of Korea in August 2007 revealed a spatial distinction between near and offshore regions. Low wind speeds of less than 3 m s^?1 were associated with cold water regions with dominant coastal upwelling. Time series of in situ measurements of both wind speed and water temperature indicated that the upwelling was induced by the wind field. The low wind field from SAR was mainly induced by changes in atmospheric stability due to air–sea temperature differences. In addition, wind speed magnitude showed a positive correlation with the difference between SST and air temperature (R² = 0.63). The dependence of viscosity of water on radar backscattering at the present upwelling region was negligible since SAR data showed a relatively large backscattering attenuation to an SST ratio of 1.2 dB °C^?1. This study also addressed the important role of coastal upwelling on biological bloom under oligotrophic environments during summer.