Remote monitoring of aerosols with ocean colour sensors: then and now

Synoptic coverage of the temporal and spatial variability of aerosol distribution patterns can only be achieved with satellites. Results from the first ocean colour sensor, the Coastal Zone Color Scanner (CZCS), indicate an annual cycle of the major mineral aerosol plumes that is consistent with the published literature. Seasonality and interannual aerosol variability observed with the CZCS agrees well with that found by ground data measurements and other satellite platforms used to monitor aerosols. The successor to the CZCS—the Sea viewing Wide Field of view Sensor (SeaWiFS)—provides estimates of aerosol load and particle size, both on a global scale. Seasonal maps of both of these aerosol optical properties are in accord with well-known distribution patterns and also with independent satellite estimates. These results indicate that ocean colour sensors are capable of monitoring the variability of global aerosol loads and, more recently, with the retrieval of aerosol particle size, they can be used to characterize different aerosol events.     

Four years of ocean colour remote sensing with MOS-IRS

The imaging spectrometer MOS on IRS-P3 was launched in March 1996 as the first example of a new generation of ocean colour sensors. It consists of three different spectrometers in the visible/near-infrared spectral region with 18 channels. The IRS-P3 mission is focused on the remote sensing of case 2 water, particularly the derivation of different water constituents in coastal waters. Due to the more complex spectral behaviour of case 2 water, a new methodological approach was developed which works directly with satellite measured top-of-atmosphere radiance and accounts for the correlation of the different water constituents as well as for the spectral shape.

This paper gives an overview of the mission, the scientific goals and the development and improvement of the retrieval algorithms. The potential of the algorithm is demonstrated and examples of selected European coasts are shown. Derived maps of water constituents are presented.     

Remote sensing of the Indo-Pacific region: ocean colour,sea level,winds and sea surface temperatures

The 1997–1998 ENSO (El Niño-Southern Oscillation) was not only the largest event of the century but also the most comprehensively observed. Satellite data were employed for ocean colour, sea level, winds, sea surface temperature (SST), and outgoing longwave radiation (OLR) were used to describe the response of the surface marine ecosystem associated with the ENSO event. Some of the large-scale anomalies in ocean colour include elevated biological activity to the north of the Equator in the Pacific coincident with lower sea levels associated with the classic ENSO-horseshoe pattern ecosystem response to the anomalous upwelling in the eastern Indian Ocean caused by the 1997–1998 dipole event, and the dramatic eastward propagating feature in the Equatorial Pacific in response to the La Niña dynamics. Ocean general circulation model (OGCM) experiments show that capturing the high-frequency wind changes is crucial for simulating the La Niña and the coupled biological–physical model (OBGCM) runs clearly show that higher frequency winds are also important for capturing the mean upwelling and nutrient supply into the euphotic zone. Thus, the QuickSCAT winds are expected to play a major role in ecosystem modelling in the future. This study shows the utility of satellite data for understanding not only ocean circulation but also the coupled ecosystem variability. Morcover, it is also shown that spatio-temporal resolution of the satellite winds will directly affect the accuracy of oceanic and ecosystem simulations.     

Remote sensing of the colour and temperature of volcanic lakes

Crater lakes on active volcanoes act as heat and chemical traps, and are amenable to surveillance from space. By use of all seven spectral bands, the Landsat Thematic Mapper (TM) can simultaneously measure: (i) lake surface temperature, (ii) lake surface area, and (iii) lake colour which is related to water chemistry. Use of the total instrument in this way enhances its utility for volcano surveillance. This work examines TM data for crater lakes at the following volcanoes: Ruapehu (New Zealand), Taal (Philippines), Kawah Ijen and Kelut (Indonesia), Poas (Costa Rica), and Apoyeque and Jiloa (Nicaragua). Observatory data indicate that lake surface temperatures derived by TM band 6 are typically 1-4°C less than contemporaneously measured bulk temperatures, probably due to the skin effect, the difference between water bulk and surface temperature. For Ruapehu, TM band 6 hotspots coincide approximately with known upwelling sites above volcanic vents on the lakebed. Field observations at Kawah Ijen show that the skin effect (< 3°C) is strongly correlated with windspeed: wind gusts peaking at ≈ 5m s^?1 caused rapid decreases in surface temperature of ≈ 0.5-1.0°C. These fluctuations are small compared with the magnitude of volcanogenic changes in lake temperature and do not reduce the utility of infrared surveillance. TM-derived water surface spectral reflectances indicate high concentrations of suspended chemical sediment in the most active crater lakes: Ruapehu, Poás and Ijen. For Ruapehu, imaged on two dates, the later scene reveals an upwelling slick, bright in bands 5 and 7, possibly composed of hollow sulphur spherules emitted from a subaqueous vent. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) and Enhanced Thematic Mapper (ETM +) both due for launch in 1998, will offer improved capabilities for remote surveillance of crater lakes.     

Multifractals and resolution-independent remote sensing algorithms: The example of ocean colour

We argue that geophysical and geographical fields are generally characterised by wide range scaling implying systematic, strong (power law) resolution dependencies when they are remotely sensed. The corresponding geometric structures are fractal sets; the corresponding fields are multifractals. Mathematically, multifractals are measures that are singular with respect to the standard Lebesgue measures; therefore, they are outside the scope of many of the methods of classical geostatistics. Because the resolution of a measurement is generally (due to technical constraints) much larger than the inner scale of the variability/scaling, the observations will be fundamentally observer dependent; hence, standard remote sensing algorithms that do not explicitly take this dependence into account will depend on subjective resolution-dependent parameters. We argue that, on the contrary, the resolution dependence must be systematically removed so that scale-invariant algorithms independent of the observer can be produced. We illustrate these ideas in various ways with the help of eight-channel, 7 m resolution remote ocean colour data (from the MIES II sensor) over the St Lawrence estuary. First, we show that the data is indeed multiscaling over nearly four orders of magnitude in scale, and we quantify this using universal multifractal parameters. With the help of conditional multifractal statistics, we then show how to use multifractals in various practical ways such as for extrapolating from one resolution to another or from one location to another, or to correcting biases introduced when studying extreme, rare phenomena. We also show how the scaling interrelationship of surrogate and in situ data can be handled using vector multifractals and examine the resolution dependence of principle components in dual wavelength analyses. Finally, we indicate why the standard ocean colour algorithms have hidden resolution dependencies, and we show how they can (at least in principle) be removed.     

Diurnal variability of ocean optical properties during a coastal algal bloom: implications for ocean colour remote sensing

Understanding the diurnal variability of ocean optical properties is critical for better interpretation of satellite ocean colour data and characterizing biogeochemical processes. The daytime variability of ocean optical properties throughout an algal bloom event is analysed in this article based on in situ observations from dawn to dusk at a fixed coastal site in the South China Sea. Diurnal variability during the sunlit period of the ocean optical properties is found to be significant. During the 6 hours around noon, the temporal variability (defined by the coefficient of variation) of phytoplankton absorption, coloured dissolved organic matter and non-algal particle absorption, and particle backscattering at 443 nm can reach 21% ± 15%, 12% ± 9%, and 17% ± 9%, respectively. The diurnal variability during the bloom is much more pronounced than that of the non-bloom phase. With atmospheric radiative transfer modelling, it is further demonstrated that the geostationary satellite detection of within-day optical variability in algae-dominated waters depends on the reliability of the aerosol retrieval. The implications of the diurnal bio-optical variability for the retrieval, validation, and interpretation of satellite ocean colour products are also discussed.     

On the calibration and use of in situ ocean colour measurements for monitoring algal blooms

Simultaneous in situ measurements of chlorophyll concentration and water colour are reported at three diverse sites: a sea loch, the west Scottish shelf and the north Atlantic. A good (R² =0.97) log-log relation exists between the ratio, gamma, of the irradiance reflection coefficients at 490 and 570 nm and the chlorophyll concentration, C, over a range of chlorophyll concentrations from 0.01 to nearly 50 mg m ^-3     

MERIS potential for ocean colour studies in the open ocean

The interest of space observations of ocean colour for determining variations in phytoplankton distribution and for deriving primary production (via models) has been largely demonstrated by the Coastal Zone Color Scanner (CZCS) which operated from 1978 to 1986. The capabilities of this pioneer sensor, however, were limited both in spectral resolution and radiometric accuracy. The next generation of ocean colour sensors will benefit from major improvements. The Medium Resolution Imaging Spectrometer (MERIS), planned by the European Space Agency (ESA) for the Envisat platform, has been designed to measure radiances in 15 visible and infrared channels. Three infrared channels will allow aerosol characterization, and therefore accurate atmospheric corrections, to be performed for each pixel. For the retrieval of marine parameters, nine channels between 410 and 705nm will be available (as opposed to only four with the CZCS). In coastal waters this should, in principle, allow a separate quantification of different substances (phytoplankton, mineral particles, yellow substance) to be performed. In open ocean waters optically dominated by phytoplankton and their associate detrital matter, the basic information (i.e. the concentration of phytoplanktonic pigments) will be retrieved with improved accuracy due to the increased radiometric performances of MERIS. The adoption of multi-wavelength algorithms could also lead to additional information concerning auxiliary pigments and taxonomic groups. Finally, MERIS will be one of the first sensors to allow measurements of Sun-induced chlorophyll a in vivo fluorescence, which could provide a complementary approach for the assessment of phytoplankton abundance. The development of these next-generation algorithms, however, requires a number of fundamental studies.     

Remote sensing of coastlines: detection,extraction and monitoring

This paper reviews the current status of the use of remote sensing for the detection, extraction and monitoring of coastlines. The review takes the US system as an example. However, the issues at hand can be applied to any other part of the world. Visual interpretation of airborne remote sensing data is still widely and popularly used for coastal delineation. However, a variety of remote sensing data and techniques are available to detect, extract and monitor the coastline. The developed techniques have reached a level of maturity such that they are applied in operational settings.     

Remote sensing and spectral analysis of plumes from ocean dumping in the new york bight apex

Experiments conducted in the Atlantic Coastal Zone indicate that plumes resulting from ocean dumping of acid wastes and sewage sludge have distinguishable spectral characteristics. Remotely sensed wide-area synoptic coverage provides information on these pollution features that is not readily available from other sources. Photographic and multispectral scanner data remotely sensed from aircraft were interpreted by two methods. First, qualitative analyses in which pollution features are located, mapped, and identified without concurrent sea truth and, second, quantitative analyses in which concurrently collected sea truth is used to calibrate the remotely sensed data and to determine quantitative distributions of one or more parameters in a plume. For the qualitative analyses an in-scene calibration technique was developed that “normalizes” environmental effects, thereby potentially providing a means of plume identification that is independent of the specific scene and the multispectral scanner used. Application of this technique to data from several experiments indicates that plumes resulting from acid wastes and sewage sludge have distinctive spectral characteristics over a range of environmental conditions and for two multispectral scanners flown at altitudes of 3.0 and 19.7 kilometers. In addition to qualitative analyses that used the in-scene calibration, quantitative analysis techniques were applied to sewage sludge dump plumes. A calibrated regression equation that related remotely sensed radiances to suspended solids concentrations was developed in order to map synoptic suspended solids distributions in plumes.     

Optical properties of inorganic suspended solids and their influence on ocean colour remote sensing in highly turbid coastal waters

The influence of the optical properties of inorganic suspended solids (ISS) on in-water algorithms was evaluated using an optical model in highly turbid coastal water, whose ISS concentration reached several hundred grams per cubic metre. The measurements were conducted in the upper Gulf of Thailand. The backscattering coefficient of the ISS was calculated using the Lorenz–Mie scattering theory. On the basis of the measurement, the ISS size distribution was parameterized as a function of ISS concentration, and both the spherical and non-spherical particle shape models were evaluated. For ISS concentrations of 10 g m^?3, an estimate of the chlorophyll-a (chl-a) concentration within a factor of 2 on a logarithmic scale is possible in a [chl-a] range of 4–30 mg m^?3. The differential coefficient of remote sensing reflectance was calculated to evaluate its respective sensitivities for chl-a and ISS concentrations. The use of radiometric data at 670 nm (700–900 nm) is valid for in-water algorithms used to estimate chl-a (ISS) concentration in highly turbid coastal waters.     

Remote sensing for detection and monitoring of vegetation affected by oil spills

This study is aimed at demonstrating the application of vegetation spectral techniques for detection and monitoring of the impact of oil spills on vegetation. Vegetation spectral reflectance from Landsat 8 data were used in the calculation of five vegetation indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), adjusted resistant vegetation index 2 (ARVI2), green-infrared index (G-NIR) and green-shortwave infrared (G-SWIR) from the spill sites (SS) and non-spill sites (NSS) in 2013 (pre-oil spill), 2014 (oil spill date) and 2015 (post-oil spill) for statistical comparison. The result shows that NDVI, SAVI, ARVI2, G-NIR and G-SWIR indicated a certain level of significant difference between vegetation condition at the SS and the NSS in December 2013. In December 2014 vegetation conditions indicated higher level of significant difference between the vegetation at the SS and NSS as follows where NDVI, SAVI and ARVI2 with p-value 0.005, G-NIR – p-value 0.01 and G-SWIR p-value 0.05. Similarly, in January 2015 a very significant difference with p-value <0.005. Three indices NDVI, ARVI2 and G-NIR indicated highly significant difference in vegetation conditions with p-value <0.005 between December 2013 and December 2014 at the same sites. Post-spill analysis shows that NDVI and ARVI2 indicated low level of significance difference p-value <0.05 suggesting subtle change in vegetation conditions between December 2014 and January 2015. This technique may help with the real time detection, response and monitoring of oil spills from pipelines for mitigation of pollution at the affected sites in mangrove forests.     

Remote sensing in ecological botany

At the XIIth International Botanical Congress, on July 4, 1975, a new direction in scientific methodology was evaluated for the first time within the framework of an International Union of Biological Sciences—remote sensing of vegetation and the environment. Remote sensing is a method of studying the composition, structure, dynamics, and productivity of ecosystems and the state of the biosphere by means of reflectance and emittance characteristics of the earth's surface measureable from aircraft and spacecraft, and the interpretation of such remotely sensed imagery. Remote cartography is conducted with aerial and space images with a scale of from 1: 1000 to 1:30 000 000. Phytomass can be measured by comparing the dependence of the phytocenometric characteristics with the magnitude of the remotely obtained signal. Phenology and dynamics are revealed by means of optical comparison of successive images. Structural ecological investigations can be based on spatial and factoral integration of ecosystems on single, remotely-sensed images. Remote sensors record spatial and temporal variability of the reflective and emissive characteristics of vegetative ground cover. Anthropogeneous effects are recognized by indication of vegetation clearing, fires, ploughing, overgrazing, water and air pollution, and water and wind erosion.     

Cloud motion in the GOCI/COMS ocean colour data

The Geostationary Ocean Colour Imager (GOCI) instrument, on Korea’s Communications, Oceans, and Meteorological Satellite (COMS), can produce a spectral artefact arising from the motion of clouds – the cloud is spatially shifted and the amount of shift varies by spectral band. The length of time it takes to acquire all eight GOCI bands for a given slot (portion of a scene) is sufficient to require that cloud motion be taken into account to fully mask or correct the effects of clouds in all bands. Inter-band correlations can be used to measure the amount of cloud shift, which can then be used to adjust the cloud mask so that the union of all shifted masks can act as a mask for all bands. This approach reduces the amount of masking required versus a simple expansion of the mask in all directions away from clouds. Cloud motion can also affect regions with unidentified clouds – thin or fractional clouds that evade the cloud identification process – yielding degraded quality in retrieved ocean colour parameters. Areas with moving and unidentified clouds require more elaborate masking algorithms to remove these degraded retrievals. Correction for the effects of moving fractional clouds may also be possible. The cloud shift information can be used to determine cloud motion and thus wind at the cloud levels on sub-minute timescales. The beneficial and negative effects of moving clouds should be considered for any ocean colour instrument design and associated data processing plans.     

Remote sensing monitoring of the suspended particle size in Hongze Lake based on GF-1 data

In remote sensing analyses of water colour, suspended particle size is an important optical parameter that also plays an important role in inland and coastal biogeochemical processes. Knowledge of the suspended particle size and its changes in month and area can be used to assess the contributions by suspended particulate matter to backscatter coefficient, particle sinking, and carbon sequestration under lake water. In this study, in situ samples collected in the summer and winter from Hongze Lake (HZL), 2016, were used to develop an empirical model to estimate the median diameter (D_V⁵⁰) of suspended particle sizes. The spatial distributions of D_V⁵⁰ were derived using 37 WFV (Wide Field Viewer) images of GF-1 (GaoFen-1), China, and the fluctuational diversification and the potential influencing factors were discussed. Several crucial findings can be drawn: (1) the empirical band ratio algorithm R_rs,green: R_rs,red was suitable for D_V⁵⁰ estimation with a coefficient of determination (R²) of approximately 0.7 for the modelling data. In addition, the validation data showed that the MAPE (mean absolute percentage error) is below 34%, the RMSE (root mean square error) is less than 4.2 μm, and the Mean ratio is close to 1; (2) the average median particle size shows an increasing trend from the northeast of the lake (NE) to Chengzi Lake (CZL) and the wetland (WL) in HZL from 2015 to 2016; (3) the D_V⁵⁰ of HZL is higher in summer than in the other seasons during the study period; (4) the fluctuation in hydrological factors, especially the monthly water discharge and flow, might be the driving force behind the seasonal variations in D_V⁵⁰ of HZL; and (5) channel transportation reduced D_V⁵⁰, and the reduced amplitude might be more than 22%.     

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.     

Remote sensing of foliar chemistry

Remotely sensed data are being used to estimate foliar chemical content as a result of our need for the information and our increasing ability to understand and measure foliar spectra. This paper reviews how stepwise multiple regression and deconvolution have been used to extract chemical information from foliar spectra, and concludes that both methods are useful, but neither is ideal. It is recommended that the focus of research be modeling in the long term and experimentation in the short term. Long-term research should increase our understanding of the interaction between radiation and foliar chemistry so that the focus of research can move from leaf model to canopy model to field experiment. Short-term research should aim to design experiments in which remotely sensed data are used to generate unambiguous and accurate estimates of foliar chemical content.     

Remote sensing of climate change,long-term monitoring of air pollution and stone material corrosion in Estonia

The Fifth National Communication Under the Framework Convention on Climate Change covered the policies and activities in Estonia of the period 2006–2009. Climate change and air pollution cause significant damage, especially to various materials. Worldwide, 387 objects belong to the United Nations Educational, Scientific and Cultural Organization (UNESCO) World heritage list, including the Old Town of Tallinn. The International Cooperative Programme on Effects on Materials, including Historic and Cultural Monuments (ICP Materials) is an ongoing research programme in the framework of which air pollution and the effect of climate on the corrosion of various natural and synthetic materials, including historical and cultural monuments, is studied. So far, the studies have shown that even though air pollution has continuously declined in Estonia, no significant reduction in corrosion has been noticed on the displayed limestone sample plates at the Lahemaa air monitoring station. On the contrary, in 2005–2006, the corrosion loss of limestone was greater compared with that in 1987–2003. It could be explained partly by changing environmental conditions but another possible explanation is that the Portland limestone as a stone material has a different quality.     

Remote sensing of rice crop areas

Rice means life for millions of people and it is planted in many regions of the world. It primarily grows in the major river deltas of Asia and Southeast Asia, such as the Mekong Delta, known as the Rice Bowl of Vietnam, the second-largest rice-producing nation on Earth. However, Latin America, the USA, and Australia have extensive rice-growing regions. In addition, rice is the most rapidly growing source of food in Africa. Rice is therefore of significant importance to food security in an increasing number of low-income food-deficit countries. This review article gives a complementary overview of how remote sensing can support the assessment of paddy rice cultivation worldwide. This article presents and discusses methods for rice mapping and monitoring, differentiating between the results achievable using different sensors of various spectral characteristics and spatial resolution. The remote sensing of rice-growing areas can not only contribute to the precise mapping of rice areas and the assessment of the dynamics in rice-growing regions, but can also contribute to harvest prediction modelling, the analyses of plant diseases, the assessment of rice-based greenhouse gas (methane) emission due to vegetation submersion, the investigation of erosion-control-adapted agricultural systems, and the assessment of ecosystem services in rice-growing areas.