Combined remote-sensing,model, and in situ measurements of sea surface temperature as an aid to recreational navigation: crossing the Gulf Stream

Combined in situ, model, and satellite remote-sensing observations are used to determine the location of the Gulf Stream as an aid to safe navigation for small recreational vessels.

A field study was executed from Hamilton, Bermuda, to Virginia Beach, USA, over a period of 5 days, from 30 June 2010 to 4 July 2010 to test the feasibility of using remote-sensing products as an aid to cross the Gulf Stream from the point of view of a small, slow-moving (?6 knots, 3 m s^?1) sailboat. The in situ data collected were compared to NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) remote-sensing data, to the Global High Resolution Sea Surface Temperature (GHRSST) microwave and infrared blended data set, to the National Oceanic and Atmospheric Administration Real-Time Ocean Forecast System (NOAARTOFS) ocean model, and to selected NOAA buoy and ship measurements.

A spatio-temporal analysis was performed by comparing the in situ measurements with observations retrieved at the same time and location in each of the data sets. The least error (correlation coefficient r?=?0.94) was obtained using MODIS data, and the largest error (r?=?0.78) was obtained using the RTOFS model data. Overall, most observations agree with the general spatio-temporal trend of the in situ data, with 95% of the errors within ±1°C and 98% of the errors within ±2°C.

The study shows that MODIS data are particularly suited to identification of the location of the Gulf Stream, which can be used by small vessels to optimize the crossing route and to minimize the risks associated with the passage.     

Using remote sensing to estimate sea ice thickness in the Bohai Sea,China based on ice type

It is challenging to use traditional remote sensing techniques to accurately determine the extent and thickness of ice in the Bohai Sea, on account of the presence of sea impurities (i.e. mud, salt bubbles and sand) and shape irregularities. Accordingly, we performed a series of reflectance spectra experiments to empirically link remote measurements of surface reflectance with in situ sea ice thickness measurements in the Bohai Sea. Two years of Thematic Mapper (TM) band 2 and Moderate Resolution Imaging Spectroradiometer (MODIS) band 4 data were used to distinguish between the following sea ice types, using spectral reflectance thresholds of 6.4, 9.6, 10.3 and 12.1%: (a) clean nilas ice (a thin elastic crust of ice up to 10 cm thick that, under pressure, may deform by finger rafting; (b) nilas ice and pancake ice (roughly circular accumulations of frazil ice, usually less than about 3 m in diameter, with raised rims caused by collisions); (c) grey and grey–white ice; and (d) cumulative ice (<30 cm). By establishing a relationship between sea ice type and ice thickness, a novel, practical and low-cost remote sensing technique is introduced to estimate the extent and distribution of sea ice thickness over a large spatial scale. The results obtained by remote sensing are validated with in situ ice shape measurements. The MODIS and TM data are used to distinguish between three ice thickness grades (6–9, 10–20 and 20–30 cm).     

Remotely sensing the ecological influences of ditches in Zoige Peatland,eastern Tibetan Plateau

Zoige Peatland in the eastern Tibetan Plateau, the largest alpine peatland in China, was widely ditched in 1970s for pasture expansion. The ditching is believed to have caused peatland degradation, but there is still no widespread agreement on this due to the absence of essential regional and temporal information about ditch drainage. Therefore, this study used both remote-sensing observations and field surveys to examine the ecological influences of ditching for this alpine peatland. In the study, ditch distribution was interpreted with remote-sensing imagery and the ecological responses were investigated with temporal observation by Moderate Resolution Imaging Spectroradiometer (MODIS) and field surveys. The results showed that there were ~1200 km ditches interpreted, mainly in three spatial patterns depending on hydro-geomorphologies. The MODIS enhanced vegetation index (EVI) was more sensitive to peatland surface water depth (R² = 0.678, P < 0.001) than the normalized difference water index (NDWI) (R² = 0.583, P < 0.001), because the latter would become saturated at a certain surface water depth (~50 cm in Zoige). The temporal MODIS imagery reflected the ecological responses of ditched peatland to drainage in terms of vegetation density and water conditions. This study indicated that ditching depressed the surface water depth of the Zoige Peatland in summer, but not to the extent of completely transforming peatland into steppe due to the recharging of local beneficial hydro-geomorphologies. The MODIS indices investigated in the study could be used to monitor the annual regional status of vegetation cover and surface water for Zoige peatland.     

Satellite imagery and household survey for tracking chickpea adoption in Andhra Pradesh,India

The objective of this study was to map the temporal changes in chickpea cropped area over the last decade in Andhra Pradesh using remote-sensing imagery. Moderate Resolution Imaging Spectroradiometer (MODIS) data composited for every 16 days were used to map the spatial distribution of seasonal crop extent in Andhra Pradesh. MODIS derived 16 day normalized difference vegetation index (NDVI) and maximum value composite (MVC) with seasonal ground survey information for the years 2005–2006 and 2012–2013 were used. A subset of ground survey information was also used to assess the pixel-based accuracies of the MODIS-derived major cropland extent. Chickpea-growing areas were identified and mapped based on their characteristic growing periods during the post-rainy season. Significant growth in the chickpea-growing areas was observed in the four districts of Andhra Pradesh between 2001 and 2012. The area cropped to chickpea almost tripled from 0.22 million ha during 2000–2001 to 0.6 million ha by 2012–2013. Furthermore, survey data were also used to assess the accuracy of the MODIS estimates of chickpea-growing areas. When compared with ground survey, the 10 land-use and land-cover classes derived from the MODIS temporal imagery resulted in overall accuracies of 86% of actual. The accuracy of areas identified as cropped to chickpea was 94%. To complement this remote-sensing study, a state-level representative primary household survey was conducted to elicit information on the socio-economic characteristics of chickpea-growing farmers, the extent of adoption of improved cultivars, costs and returns from chickpea cultivation, competitiveness of chickpea with other post-rainy crops, etc. during 2012–13. The findings revealed that nearly 98% of the chickpea cropped area is now under improved cultivars, with an average increase in yield of 37% over yields achieved with unimproved varieties. The average annual per capita incomes have increased to US$ 1.89 day^?1 with this silent chickpea revolution across the rain-fed areas of Andhra Pradesh.     

Analysis of natural background and dredging-induced changes in TSM concentration from MERIS images near commercial harbours in the Estonian coastal sea

Medium Resolution Imaging Spectrometer (MERIS) products with 300 m resolution from 2006 to 2011 were used to evaluate the local background of total suspended matter (TSM) in the vicinity of commercial harbours located along the Estonian coastline in the Baltic Sea. The difference between background TSM maps (mainly influenced by spring bloom, cyanobacterial bloom, resuspension, and river inflow) and dredging period mean maps was used for the estimation of dredging-induced turbidity at the time of dredging operations. Validation of Case II Regional (C2 R) and Free University of Berlin (FUB) MERIS processors with point measurements showed that both processors represent the changes in TSM concentration adequately. C2 R processors showed better statistics (R² = 0.61, root mean square error = 0.82 mg l^–1, SD = 0.77 mg l^–1, mean bias = –0.28 mg l^–1) compared to the FUB processor. Analysis of monthly mean TSM maps revealed that the variability of TSM concentration, showing the resilience level of the local ecosystem, is very different along the Estonian coastline – varying between 0.75 and 2.60 mg l^–1 near the Port of Tallinn, located in the Gulf of Finland, and between 10.04 and 24.23 mg l^–1 near the Port of Pärnu, located in the Gulf of Riga. The viability of the method for dredging impact detection was tested by evaluating the dredging-induced turbidity on monthly mean TSM maps for the dredging period in autumn 2008 in Pakri Bay, which is an environmentally sensitive area. A threshold TSM concentration value of >2.26 mg l^–1 difference from background TSM was defined as a criterion for dredging impact detection for Pakri Bay. The area of dredging-induced turbidity was between 0.56 and 1.25 km² and did not reach the environmentally sensitive NATURA 2000 region adjoining Paldiski South Harbour.     

Absorption characteristics of ocean water in the Arabian Sea during winter bloom from in situ measurements and Oceansat 2 OCM and MODIS data

In the northern Arabian Sea, blooms usually occur during the northeast monsoon (November–January) and inter-monsoon (February–April) periods. After death, these phytoplankton blooms produce massive subsurface zones of low dissolved oxygen levels that have a major impact on the ocean water ecosystem. Many studies have been done to identify the bloom in this region, but those on the optical properties of bloom water are scarce. The present study emphasizes the optical properties (inherent) of the bloom water in the study region using in situ and satellite data. The total absorption coefficient of ocean water was measured from in situ radiance data collected in the northern Arabian Sea from the Sagar Sampada cruise (SS-286) during March 2011. The same data were also derived from the top-of-atmosphere radiance and remote sensing reflectance of the Oceansat 2 Ocean Colour Monitor (OCM-2) and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, respectively. A comparison between measured (in situ) and retrieved total absorption coefficients from OCM-2 was made. The measured and retrieved absorption coefficients are in good agreement. Root mean square errors between measured and retrieved absorption coefficients are 0.018 m^?1, 0.026 m^?1, and 0.034 m^?1 for 490 nm, 510 nm, and 555 nm, respectively. An inter-comparison of total absorption properties retrieved from OCM-2 and MODIS data in the region of one degree radius around the stations was also made. A fairly good match was observed on 10, 14, and 16 March 2011 (coefficient of determination, R² = 0.75, 0.87, and 0.62, respectively) for the blue band (490 nm) and (R² = 0.77, 0.79, and 0.71, respectively) the green band (555 nm). The study demonstrates the potential of using remote-sensing optical data for identifying bloom waters.     

Satellite monitoring of phytoplankton in the East Mediterranean Sea after the 2006 Lebanon oil spill

Multi-source remote-sensing data were used to study the aftermath of the 2006 Lebanon oil spill: Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data and advanced synthetic aperture radar (SAR) images were used to delineate the extent of the oil spill; QuikScat data were used to derive the wind field; and MODIS Ocean Colour data were used to demonstrate the variability of chlorophyll-a concentration (Chl-a) and sea surface temperature (SST). No significant changes were found in marine phytoplankton in the first few months after the oil spill. However, a big phytoplankton bloom was observed after 10 months, when Chl-a increased from 0.1 to 1.0 mg m^–3. After considering the time-series variability of Chl-a and SST and the spatial variability of Chl-a and bathymetry, we concluded that this phytoplankton bloom was probably related to the Lebanon oil spill.     

Evaluation of the NIR-SWIR atmospheric correction algorithm for MODIS-Aqua over the Eastern China Seas

The performance of the near-infrared (NIR) and short-wave infrared (SWIR) combined atmospheric correction algorithm (NIR-SWIR) for Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua over the Eastern China Seas (ECS) was evaluated. The in situ data set for evaluation in this study was collected during 2005 and 2012 through eight cruises in the ECS, wherein 49 in situ observation points were successfully matched with MODIS-Aqua data. The remote-sensing reflectance derived from MODIS-Aqua data using the NIR-SWIR algorithm and the NIR algorithm were compared to the in situ measurements in the matched-up data set, which included ocean bands (412, 443, 488, 531, 547, 667, and 678 nm) and land bands (469, 555, and 645 nm). The results show that the performance of the NIR-SWIR algorithm has been improved in turbid waters, and the effect at the short-wave bands (blue and green bands) is more significant than that at the long-wave bands (red bands). In addition, MODIS-Aqua data at the land bands (469, 555, and 645 nm) show a similar performance to those of nearby ocean bands. However, the lower estimation accuracy is still a remarkable question at bands 412, 645, 667, 678 nm. The results from both the NIR-SWIR and NIR algorithms were applied to the images of MODIS-Aqua in the ECS and they indicate that the extent to which the quality of the derived remote-sensing reflectance using the NIR-SWIR algorithm can be improved shows major differences for different seasons. The minimum area is in summer, and the maximum area in winter. The NIR-SWIR algorithm should be used for the whole of the Bohai Sea in winter.     

The satellite-measured sea surface temperature change in the Gulf of Finland

Climate change in Baltic region and in the Gulf of Finland is an accomplished fact in human brains and in science. The purpose of this research is to retrieve quantitative level of changes for sea surface temperature (SST) of the Gulf of Finland. Two space systems National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) provided satellite data about temperature of the sea surface. SST data covers period 1981–2014 and includes 444 monthly data scenes with spatial resolution about 10 km. Data quality analysis displays high reliability of NOAA/AVHRR and Aqua/MODIS satellite information. The Gulf of Finland’s average annual SST has changed from 6.8°C in 1982 up to 8.2°C in 2014. Its mean speed of warming is about 0.04°C year^–1. The growth of the temperature was irregular, in the middle of 80th year, the temperature dropped down to 5.0°C, and then sharply increased up to 7.3°C in 1989. SST growth in the Gulf of Finland coincides with air temperature and sea temperature growth. The climate change in the Gulf of Finland has special significance due to the fragility of the northern ecosystems and high anthropogenic load.     

Detection of algal blooms over optically complex waters of the Arabian Gulf and Sea of Oman using MODIS fluorescence data

The Arabian Gulf and the Sea of Oman are two of the most complex and turbid ecosystems in the world where algal blooms frequently occur. The conventional blue/green band ratio shows low performance to detect these algal batches in this region due to the effect of the non-algal parameters, shallow water depth, and atmospheric aerosols. Thus, an attempt to use MODIS (Moderate Resolution Imaging Spectroradiometer) fluorescence for the detection of algal blooms in this region have been undertaken using in situ measurements (Chlorophyll a: Chl-a, coloured dissolved organic matters: CDOM, Secchi disk depth: SDD, and radiometric) collected in 2006, 2013, and 2014, and MODIS satellite images. MODIS fluorescence line height (FLH in W m^?2 µm^?1 sr^?1) data showed low correlation (coefficient of determination: R² ~0.46) with near-concurrent in situ Chl-a (mg m^?3). This disparity is caused by the effect of the suspended sediments (SDD), CDOM (<2 mg m^?3 or >2 mg m^?3), and bottom reflectance (water depth: WD) parameters, where an increase of 1% in their magnitudes can cause a respective change of 13.4%, ?0.8% or 6%, and 1.4% in the FLH. In this work, the positions of the FLH bands have been relocated to include 645 nm to reduce the effect of these parameters on Chl-a, which has improved the performance to R² of 0.76. This modified FLH (MFLH) model was found to perform well in the Arabian Gulf where the estimated bias, root-mean-square error (RMSE), and coefficient of determination are, respectively, 0.03, 1.06, and 0.76. High values of MFLH are indicating the areas of the algal blooms, while no overestimation was observed in the mixed pixel coastal areas. This result is explained by less sensitivity of this model to the non-algal particles, shallow water, and aerosols.     

Northern Gulf of Mexico estuarine coloured dissolved organic matter derived from MODIS data

Coloured dissolved organic matter (CDOM) is relevant for water quality management and may become an important measure to complement future water quality assessment programmes. An approach to derive CDOM using the Moderate Resolution Imaging Spectroradiometer (MODIS) was developed that may be more accessible to water quality managers by selecting an off-the-shelf software and algorithm with standard atmospheric correction. This study focused on demonstrating the transferability of a remote-sensing reflectance (R_rs) band ratio algorithm, R_rs(667)/R_rs(488), previously developed to derive CDOM absorption (a_g(λ)) at multiple MODIS wavebands in open ocean and coastal waters to the United States northern Gulf of Mexico estuaries. In situ calibration regressions at 412, 443, 469, and 488 nm had coefficients of determination (R²) of 0.76, 0.71, 0.69, and 0.57, respectively. Waveband calibrations at 531, 547, and 555 nm were below R² = 0.50, and therefore were not considered further. MODIS R_rs, from the standard atmospheric correction, followed nearly identical spectral shapes to the in situ HyperSAS R_rs, but were on average 0.002 ± 0.0004 sr^?1 less. A satellite to in situ validation match-up window of ≤1 hour was selected with an R² = 0.82 and root mean square error (RMSE = 1.79) at 412 nm. An in situ water quality mooring demonstrated that the overall response and range of MODIS a_g(412) were similar, with relative mean error from –32% to 42%. The advantage to managers was synoptic coverage across multiple estuaries and the ability to provide estimates of derived water quality parameters between the water quality assessment programme sample collection periods, which could offer more holistic assessment.     

Development of a MODIS data based algorithm for retrieving nearshore sea surface salinity along the northern Gulf of Mexico coast

Remote sensing algorithms for retrieval of Sea Surface Salinity (SSS) were generally developed for deep ocean waters while practical applications of SSS are commonly involved in coastal (particularly nearshore) waters. To fill the gap, this paper presents a new SSS algorithm, called Nearshore SSS Algorithm, developed using the Artificial Neural Networks toolbox in the MATLAB Program and 7 years of cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua data as well as in situ data collected from United States Geological Survey (USGS) stations along the Northern Gulf of Mexico coast. The independent input variables involved in the new algorithm include the water-leaving reflectance at 412 nm, 443 nm, 488 nm, 555 nm, 678 nm, and 645 nm of MODIS Aqua sensor. Results of sensitivity analysis indicate that SSS in nearshore waters is most sensitive to reflectance at 412 nm and 488 nm, followed by the reflectance at 555 nm and 645 nm. While reflectance at 678 nm is also important, reflectance at 443 nm is the least important to SSS in nearshore waters, providing new insights into SSS in coastal waters. MODIS data collected from January 2012 to September 2014 were used for validation of the nearshore SSS algorithm. The linear correlation coefficient of this algorithm for Louisiana coast was 0.7256 and 0.6985 for the development and validation, respectively. A unique feature of this paper is that while the new algorithm is particularly useful to the retrieval of SSS in nearshore waters, it is also expanded to further offshore waters, providing reliable SSS data for various applications of coastal environment and resources management particularly along the northern Gulf of Mexico coast.     

Recent trends of ice phenology for eight large lakes using MODIS products in Northeast China

Optical remote sensing images with high temporal resolution can be used to monitor lake ice phenology, a periodic freezing and thawing cycle of ice resulting from seasonal and inter-annual climate variations. In the research reported here, we used MODIS satellite data to establish the time series of lake ice extent and extracted lake ice phenology dates and durations for eight large typical lakes in Northeast China for the hydrological years from 2003 to 2016. The MODIS-derived results were validated against ice records at hydrological stations. The mean absolute error for a freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS) and break-up end (BUE) was 3.1, 4.8, 6.6 and 6.6 days, respectively. Our findings indicated that the investigated lakes were tending to freeze later and melt earlier and were frozen for a shortened period over time. FUS was experiencing a delay of 0.65 days per year and BUE was advancing by 0.19 days per year, implying a decrease of frozen duration (FD) of 0.84 days per year taking all eight lakes into consideration. The lake ice duration increased with latitude, and the lakes with a relatively smaller area had a higher yearly rate of change and were more variable compared with the larger ones. The relationship between lake ice phenology and other influencing factors was evaluated using correlation coefficients and partial least squares regression. The results showed that the freeze-up process was more dependent on the lake morphometry, while the break-up process was more dependent on climate changes, particularly on air temperature, which had the highest correlation coefficient (r = ?0.69, p < 0.01).     

Reconciled freshwater flux into the Godthåbsfjord system from satellite and airborne remote sensing

As the rapid reduction in ice volume of the Greenland ice sheet (GrIS) continues, increased melt water flux from the GrIS enters the deep Greenlandic fjords. This increased freshwater flux may change the salinity and eventually the ecology of the fjords. Here, we present a case study in which we, from various remote-sensing data sets, estimate the freshwater flux from the GrIS into a specific fjord system, the Godthåbsfjord, in southwest Greenland. The area of the GrIS draining into Godthåbsfjord covers approximately 36,700 km². The large areal extent and the multiple outlets from the GrIS hamper in situ observations. Here, we evaluate available data from remote sensing and find a drainage basin in rapid change. An analysis of data from the Gravity Recovery and Climate Experiment (GRACE) satellites shows a mean seasonal freshwater flux into Godthåbsfjord of 18.2 ± 1.2 Gt, in addition to an imbalance in the mass balance of the drainage basin from 2003 to 2013 of 14.4 ± 0.2 Gt year^?1. Altimetry data from air and spaceborne missions also suggest rapid changes in the outlet glacier dynamics. We find that only applying data from the Ice, Cloud, and land Elevation Satellite (ICESat) mission the mass change of the Godthåbsfjord drainage basin is significantly underestimated. When including additional laser-altimetry surveys, to account for changes in the outlet glaciers elevation, not captured by ICESat, the altimetry data were able to reconcile the basin mass balance with the gravimetric estimate and provide a higher spatial resolution of the mass changes.     

Classification of MODIS images combining surface temperature and texture features using the Support Vector Machine method for estimation of the extent of sea ice in the frozen Bohai Bay,China

Image classification of frozen areas and adjacent sea ice is important for monitoring the evolution of ocean freezing. This paper proposes a novel approach to the Moderate Resolution Imaging Spectroradiometer (MODIS) image classification and estimation of the extent of sea ice in frozen areas during recent global surface warming hiatus. We derived the texture feature (TF) and surface temperature (ST) from the MODIS image for classification and sea ice detection. We extracted MODIS TF by a grey-level co-occurrence matrix (GLCM), and retrieved the MODIS ST using a split-window method, and finally classified the image using a Support Vector Machine (SVM) convoluting the ST and TF methods. Results were compared and validated with those of conventional spectral-based supervised classification approaches. Results show that the overall accuracy and kappa coefficient (κ) using the proposed method was much higher in comparison with those of the spectral-based maximum likelihood and SVM methods. The SVM fusion ST and TF method was effective and useful for MODIS 500 m image classification and sea ice mapping in frozen area. Combining ST and TF can improve sea ice extent estimation accuracy in the frozen Bohai Bay.     

Remote sensing of the El Hierro submarine volcanic eruption plume

Submarine volcanic eruptions took place at the island El Hierro (Canary Islands) between October 2011 and March 2012. The event produced plumes of discoloured waters due to the discharge of volcanic matter, magmatic gases, and hydrothermal fluids. The expelled materials, which behaved like oceanic tracers, were detected from the site of the volcano to the open sea by remote-sensing techniques using different level-2 (L2) products of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. In order to assess the effect of the anomalous turbidity, three atmospheric correction schemes were evaluated: SeaWiFS Data Analysis System (SeaDAS) standard, near-infrared–shortwave-infrared (NIR-SWIR), and Management Unit of the North Sea Mathematical Models (MUMM). The comparison between them verified that the SeaDAS standard atmospheric correction was the most suitable. The downwelling diffuse attenuation coefficient at 490 nm (K_d(490)) was used as a measure of plume intensity in comparison with other submarine volcanic eruptions. The MODIS-derived K_d(490) values verified that the intensity of the El Hierro plume was moderate. Only in some specific situations did the values barely exceed 0.4 m^–1. The remote-sensing reflectance (R_rs) was used for the characterization of the affected waters. The R_rs spectra also allowed a comparison with other volcanic and sulphide events reported in previous studies. Similarities were found, both with submarine volcanic eruptions in the southwestern Pacific and with sulphide events at the Namibian coast, in composition and properties of optically active water constituents. A classification schema based on K_d(490) values and R_rs ratios was developed and used in connection with MODIS red–green–blue (RGB) composites as well as surface current velocities from altimeter missions to investigate the spatio-temporal development of the volcanic plume. The spreading and transport of volcanic material observed at the ocean surface was caused by the predominant surface currents coupled with different mesoscale eddies. Discoloured waters were identified more than 200 km away from the eruption site. Field data from oceanographic surveys verified the high concentration of sulphur compounds in affected waters and confirmed the overestimation by the MODIS default algorithm of chlorophyll-a concentrations in the volcanic plume.     

Harbour dredging and fish mortality in an aquaculture zone: assessment of changes in suspended particulate matter using multi-sensor remote-sensing data

The semi-closed Rushan Bay is one of the largest aquaculture bases in North China, with its only navigation channel connecting the Rushan harbour and the Yellow Sea. Recent economic growth with increased import and export demands have stimulated a dredging operation, starting on 26 March 2010, which was paused on 17 June 2010 after numerous reports of mortality of cultured shellfish, such as clam (Ruditapes philippinarum), among other precious marine animals. A lawsuit was filed to settle the dispute between the dredging company and an aquaculture company, yet there was endless debate on whether the mortality was caused by the dredging operations. Here, using multi-sensor remote-sensing data collected by Landsat/ETM+, HJ-1A&1 B/CCD, and Aqua & Terra/MODIS, we addressed the two critical questions of (1) whether there was a significant increase in the suspended particulate matter (SPM) in the aquaculture area during and after the dredging operations; and (2) if the answer is yes, whether such an increase was a direct result of the dredging. After careful selection of the satellite data and algorithms, the results from all three sensors suggested positive answers to both questions. In the 2 km² aquaculture zone where significant mortality of cultured clam was reported, SPM derived from all three sensors during the dredging period was found to be at least 20 mg l^–1 higher than that during the same period in previous years, far exceeding the 10 mg l^–1 threshold value that has been used to gauge water quality degradation.     

Assessment of SeaWiFS,MODIS, and MERIS ocean colour products in the South China Sea

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS) remote-sensing radiometric and chlorophyll-a (chl-a) concentration products for the South China Sea (SCS) from October 2003 to May 2010 were assessed using in situ data. A strict spatiotemporal match-up method was used to minimize the temporal variability effects of atmosphere and seawater around the measurement site. A comparison of the remote-sensing reflectance (R_rs(λ)) of the three sensors with in situ values from the open waters of the SCS showed that the mean absolute percentage difference varied from 13% to 55% in the 412–560 nm spectral range. Generally, the MERIS radiometric products exhibited higher typical uncertainties and bias than the SeaWiFS and MODIS products. The R_rs(443) to R_rs(555/551/560) band ratios of the satellite data were in good agreement with in situ observations for these sensors. The SeaWiFS, MODIS, and MERIS chl-a products overestimated in situ values by 74%, 42%, and 120%, respectively. MODIS retrieval accuracy was better than those of the other sensors, with MERIS performing the worst. When the match-up criteria were relaxed, the assessment results degraded systematically. Therefore, strict spatiotemporal match-up is recommended to minimize the possible influences of small-scale variation in geophysical properties around the measurement site. Coastal and open-sea areas in the SCS should be assessed separately because their biooptical properties are different and the results suggest different atmospheric correction problems.     

Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS

Accurate production of regional burned area maps are necessary to reduce uncertainty in emission estimates from African savannah fires. Numerous methods have been developed that map burned and unburned surfaces. These methods are typically applied to coarse spatial resolution (1 km) data to produce regional estimates of the area burned, while higher spatial resolution (<30 m) data are used to assess their accuracy with little regard to the accuracy of the higher spatial resolution reference data. In this study we aimed to investigate whether Landsat Enhanced Thematic Mapper (ETM+)‐derived reference imagery can be more accurately produced using such spectrally informed methods. The efficacy of several spectral index methods to discriminate between burned and unburned surfaces over a series of spatial scales (ground, IKONOS, Landsat ETM+ and data from the MOderate Resolution Imaging Spectrometer, MODIS) were evaluated. The optimal Landsat ETM+ reference image of burned area was achieved using a charcoal fraction map derived by linear spectral unmixing (k = 1.00, a = 99.5%), where pixels were defined as burnt if the charcoal fraction per pixel exceeded 50%. Comparison of coincident Landsat ETM+ and IKONOS burned area maps of a neighbouring region in Mongu (Zambia) indicated that the charcoal fraction map method overestimated the area burned by 1.6%. This method was, however, unstable, with the optimal fixed threshold occurring at >65% at the MODIS scale, presumably because of the decrease in signal‐to‐noise ratio as compared to the Landsat scale. At the MODIS scale the Mid‐Infrared Bispectral Index (MIRBI) using a fixed threshold of >1.75 was determined to be the optimal regional burned area mapping index (slope = 0.99, r ² = 0.95, SE = 61.40, y = Landsat burned area, x = MODIS burned area). Application of MIRBI to the entire MODIS temporal series measured the burned area as 10 267 km² during the 2001 fire season. The char fraction map and the MIRBI methodologies, which both produced reasonable burned area maps within southern African savannah environments, should also be evaluated in woodland and forested environments.     

Observations of ice surface temperature and thickness in the Baltic Sea

This paper presents the results of an airborne thermal infrared (TIR) experiment. The data were obtained during 6–9 February 1992 period in the Bay of Bothnia, the northern section of the Baltic Sea, in connection with an ERS‐1 field campaign. Two Aegema model 880 TIR cameras were used with nominal wavelengths at 5 and 10 µm, attached to the front of the helicopter with a Tyler mount. The camera's thermal resolution is 0.2 K; after corrections for atmosphere effects, the surface temperature accuracy is 0.5 K. The spatial resolution of individual images is 70 cm at the 300‐m flight altitude. The measurement programme was successful, producing high‐quality TIR data over ice for two different days, even under difficult weather conditions. Ice temperatures ranged from open water temperatures to 261 K for fast ice conditions on the day on the first flight. The standard deviation of the surface temperature, generally, increased with ice thickness with a value of ～0.4 K for maximum thickness, and autocorrelation length scales not exceeding a value of 5 m. Generally, all the higher values of standard deviation (>0.7) of surface temperatures were for scenes with mixed ice/open water. The results show that TIR has a substantial ability to classify ice type and thickness when the air temperature is less than 269 K, from open water at the freezing point to thin nilas and thicknesses up to 20–45 cm in the fast ice zone. In addition, a quasi‐steady sea‐ice model is used to provide a physical interpretation of the sea‐ice surface temperatures. The use of the model requires information on the atmospheric surface layer and snow thickness data, together with calibration points. The model worked well when the air temperature was around 260 K or less.