An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing

Satellite remote sensing of ocean colour is the only method currently available for synoptically measuring wide-area properties of ocean ecosystems, such as phytoplankton chlorophyll biomass. Recently, a variety of bio-optical and ecological methods have been established that use satellite data to identify and differentiate between either phytoplankton functional types (PFTs) or phytoplankton size classes (PSCs). In this study, several of these techniques were evaluated against in situ observations to determine their ability to detect dominant phytoplankton size classes (micro-, nano- and picoplankton). The techniques are applied to a 10-year ocean-colour data series from the SeaWiFS satellite sensor and compared with in situ data (6504 samples) from a variety of locations in the global ocean. Results show that spectral-response, ecological and abundance-based approaches can all perform with similar accuracy. Detection of microplankton and picoplankton were generally better than detection of nanoplankton. Abundance-based approaches were shown to provide better spatial retrieval of PSCs. Individual model performance varied according to PSC, input satellite data sources and in situ validation data types. Uncertainty in the comparison procedure and data sources was considered. Improved availability of in situ observations would aid ongoing research in this field.     

Remote sensing of phytoplankton functional types

The principal goal in early missions of satellite-borne visible spectral radiometry (ocean colour) was to create synoptic fields of phytoplankton biomass indexed as concentration of chlorophyll-a. In the context of climate change, a major application of the results has been in the modelling of primary production and the ocean carbon cycle. It is now recognised that a partition of the marine autotrophic pool into a suite of phytoplankton functional types, each type having a characteristic role in the biogeochemical cycle of the ocean, would increase our understanding of the role of phytoplankton in the global carbon cycle. At the same time, new methods have been emerging that use visible spectral radiometry to map some of the phytoplankton functional types. Here, we assess the state of the art, and suggest paths for future work.     

Remote sensing of phytoplankton pigments: A comparison of empirical and theoretical approaches

Algorithms that have been used on a routine basis for remote sensing of the phytoplankton pigment, chlorophyll- a, from ocean colour data from satellite sensors such as the CZCS (Coastal Zone Color Scanner), SeaWiFS (Sea Viewing Wide Field-of-View Sensor) and OCTS (Ocean Colour and Temperature Scanner) are all of an empirical nature. However, there exist theoretical models that allow ocean colour to be expressed as a function of the inherent optical properties of seawater, such as the absorption coefficient and the backscattering coefficient. These properties can in turn be expressed as functions of chlorophyll- a, at least for the so-called Case 1 waters in which phytoplankton may be considered to be the single, independent variable responsible for most of the variations in the marine optical properties. Here, we use such a theoretical approach to model variations in ocean colour as a function of chlorophyll- a concentration, and compare the results with some empirical models in routine use. The parameters of phytoplankton absorption necessary for the implementation of the ocean colour model are derived from our database of over 700 observations of phytoplankton absorption spectra and concurrent measurements of phytoplankton pigments by HPLC (High Performance Liquid Chromatography) techniques. Since there are reports in the literature that significant differences exist in the performance of the algorithms in polar regions compared with lower latitudes, the model is first implemented using observations made at latitudes less than 50. It is then applied to the Labrador Sea, a high-latitude environment. Our results show that there are indeed differences in the performance of the algorithm at high latitudes, and that these differences may be attributed to changes in the optical characteristics of phytoplankton that accompany changes in the taxonomic composition of their assemblages. The sensitivities of the model to assumptions made regarding absorption by coloured dissolved organic matter (or yellow substances) and backscattering by particles are examined. The importance of Raman scattering on ocean colour and its influence on the algorithms are also investigated.     

Scaling of coastal phytoplankton features by optical remote sensors: comparison with a regional ecosystem model

Different scales of hydrological and biological patterns of the Bay of Biscay are assessed using space‐borne and airborne optical remote sensing data, field measurements and a 3‐dimensional biophysical model. If field measurements provide accurate values on the vertical dimension, ocean colour data offer frequent observations of surface biological patterns at various scales of major importance for the validation of ecosystem modelling. Although the hydro‐biological model of the continental margin reproduces the main seasonal variability of surface biomass, the optical remote sensing data have helped to identify low grid resolution, input inaccuracies and neglect of swell‐induced erosion mechanism as model limitations in shallow waters. Airborne remote sensing is used to show that satellite data and field measurements are unsuitable for comparison in the extreme case of phytoplankton blooms in patches of a few hundred metres. Vertically, the satellite observation is consistent with near surface in situ measurements as the sub‐surface chlorophyll maximum usually encountered in summer is not detected by optical remote sensing. A mean error (δC/C) of 50.5% of the chlorophyll‐a estimate in turbid waters using the SeaWiFS‐OC5 algorithm allows the quantitative use of ocean colour data by the coastal oceanographic community.     

Remote-sensing reflectance and true colour produced by a coupled hydrodynamic,optical, sediment,biogeochemical model of the Great Barrier Reef,Australia: Comparison with satellite data

Aquatic biogeochemical models are vital tools in understanding and predicting human impacts on water clarity. In this paper, we develop a spectrally-resolved optical model that produces remote-sensing reflectance as a function of depth-resolved biogeochemical model properties such as phytoplankton biomass, suspended sediment concentrations and benthic reflectance. We compare simulated remote-sensing reflectance from a 4 km resolution coupled hydrodynamic, optical, sediment and biogeochemical model configured for the Great Barrier Reef with observed remote-sensing reflectance from the MODIS sensor at the 8 ocean colour bands. The optical model is sufficiently accurate to capture the remote-sensing reflectance that would arise from a specific biogeochemical state. Thus the mismatch between simulated and observed remote-sensing reflectance provides an excellent metric for model assessment of the coupled biogeochemical model. Finally, we combine simulated remote-sensing reflectance in a red/green/blue colour model to produce simulated true colour images during the passage of Tropical Cyclone Yasi in February 2011.     

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 phytoplankton pigment distribution in the United States northeast coast

Phytoplankton pigments constitute many more compounds than chlorophyll a that can be applied to study phytoplankton diversity, populations, and primary production. In this study, field measurements were applied to develop ocean color satellite algorithms of phytoplankton pigments from in-water radiometry measurements. The match-up comparisons showed that the satellite-derived pigments from our algorithms agree reasonably well (e.g. 30-55% of uncertainty for SeaWiFS and 37-50% for MODIS-Aqua) to field data, with better agreement (e.g. 30-38% of uncertainty for SeaWiFS and 39-44% for MODIS-Aqua) for pigments abundant in diatoms. The seasonal and spatial variations of satellite-derived phytoplankton biomarker pigments, such as fucoxanthin, which is abundant in diatoms, peridinin, which is found only in peridinin-containing dinoflagellates, and zeaxanthin, which is primarily from cyanobacteria in coastal waters, revealed that higher densities of diatoms are more likely to occur on the inner shelf and during winter-spring and obscure other abundant phytoplankton groups. However, relatively higher densities of other phytoplankton, such as dinoflagellates and cyanobacteria, are likely to occur on the mid- to outer-continental shelf and during summer. Seasonal variation of riverine discharge may play an important role in stimulating algal blooms, in particular diatoms, while higher abundances of cyanobacteria coincide with warmer water temperatures and lower nutrient concentrations.     

基于转移矩阵的生态变化可视分析系统

生态系统变化对我们的生产生活和健康等各个方面具有重要影响. 生态覆被数据中蕴含了生态系统变化的重要特征. 为了利用生态覆被数据研究生态系统的空间划分和时序变化, 本文基于生态数据转移矩阵, 建立了区域生态覆被变化数据模型. 然后设计了基于降维算法的生态变化可视分析系统ECOVIS, 其中改进的桑基图用以实现对生态覆被变化数据的可视化, 可交互散点图被设计用来进行交互聚类分析, 基于地图的热图用来显示选中聚类数据在空间上的分布. 本文利用该系统应用到我国生态覆被数据中, 实现对局部地区森林和城镇变化的时序可视分析, 和对整体生态空间的聚类划分分析. 分析结果证明该方法对生态系统数据具有较好的空间聚类和时序对比功能, 可以提高生态覆被数据的分析效率.     

Ocean colour estimates of particulate organic carbon reservoirs in the global ocean – revisited

We have examined 16 years (1998–2013) of particulate organic carbon (POC) concentrations derived from remotely sensed ocean colour. POC concentrations vary spatially from more than 300 mg m^?3 in the northern North Atlantic in summer to about 20 mg m^?3 in the oligotrophic South Pacific (16-year global average = 67.7 mg m^?3). The seasonal variability is weak at lower latitudes and stronger at higher latitudes. The annual mean surface POC concentrations show statistically significant regional trends (p < 0.05, 95% confidence level), and are decreasing in the North Atlantic and North Pacific and increasing in the South Pacific and Southern Oceans. The global trend is not significant. The 16-year global average water column POC biomass integrated over the euphotic depth, the mixed layer depth, or based on a combination of these two depths is estimated to be about 3.97, 3.92, and 5.03 g m^?2, respectively. Water column integrated biomass shows different spatial and seasonal patterns than the surface POC concentrations, and is increasing in many ocean regions. Globally averaged POC biomass is also increasing. At the same time ocean colour data indicate a decrease in the global oceanic productivity (PP). This means that there is a negative trend in the ratio of PP to POC biomass almost everywhere in the ocean. Such a decrease could indicate that the biological pump in the ocean is weakening, but longer time series of the ocean colour data are needed to confirm this observation.     

基于CBERS-2卫星数据的艾比湖浮游植物生物量的反演研究

以艾比湖湖区为研究对象,利用CBERS\|2卫星影像的光谱信息分析了遥感影像提取的各因子与浮游植物实测生物量之间的相关关系,建立了相关性显著的遥感因子与浮游植物生物量的线性和非线性回归模型。通过对比分析和残差分析得到最优模型,对艾比湖进行了浮游植物生物量的遥感反演,分析湖区浮游植物生物量的分布特征并估算湖体浮游植物生物总量。艾比湖浮游植物生物量的最优估测模型是二元线性回归模型:Y=3.819-0.027(G-B)-0.04(G-R),其拟合度为0.832,平均残差系数为6.9%,艾比湖湖体浮游植物的总生物量为9.95×10⁵ kg。利用遥感方法研究艾比湖浮游植物的生物量对于艾比湖水域生物估产及其生物量消长规律,以及艾比湖生态系统具有重要意义。研究分析艾比湖生物量的空间分布特征,为艾比湖水体大范围\,快速\,长期的动态监测和获取浮游生物信息和水质参数提供了有力依据。     

Sea surface colour in the field of biological oceanography

Abstract

Variations of marine surface optical properties (generally grouped under the term ‘sea surface colour’) are due to dissolved and suspended materials, with different absorption and scattering characteristics, present in sea water. Remote assessments of sea surface colour, therefore, can be used to determine the presence and abundance of water constituents such as biological pigments, suspended sediments or other products of organic matter degradation (the so-called yellow substance). In open sea waters, the pigments due to biological activities, and particularly phytoplankton chlorophyll-like pigments, are the main contributors to surface colour. Hence, observations in the visible spectrum can provide synoptic and repetitive information on parameters linked to biological production and patchiness, or bio-geo-chemical cycles in general. Since water constituents act as tracers of various marine processes, bio-optical patterns on the sea surface can also provide indications about the relationships existing between forcing mechanisms and biological response in the marine environment. These capabilities render optical remote sensing an invaluable tool in the field of biological oceanography, although atmospheric processes and signal ambiguities in the water column may pose severe limitations on this technique. The feasibility and potential of passive remote sensing in the visible spectrum have been demonstrated primarily by the Coastal Zone Color Scanner (CZCS) experiment. Important results of this experiment have been reported in the study of coastal phenomena, sediment transport, frisheries, upwelling, climatic events, and factors controlling the distribution, growth and fate of phytoplankton. On these latter topics, indications of a strong coupling between dynamical and bio-optical conditions of the marine environment are emerging from the analysis of CZCS image series, for open ocean, near-coastal and enclosed basin conditions. Examples of such studies, covering regions of both the North Atlantic and North Pacific Oceans and of the Mediterranean Sea, provide clues on the promises of large-scale sea surface colour assessments in the field of biological oceanography.     

Using an Atlantis model of the southern Benguela to explore the response of ecosystem indicators for fisheries management

Atlantis is a whole-of-system modelling framework developed for Management Strategy Evaluation. This paper describes an Atlantis model that was built to simulate the southern Benguela ecosystem and its major associated fisheries to assist fisheries management in the region. We divided the region into spatial zones based on hydrodynamics, current fishing management, and important ecosystem processes. We divided the biological components of the system into functional groups based on trophic interaction, life history traits and fisheries management objectives. We evaluated the model against historical data and known ecosystem interactions (such as competition and predation), and found that it simulates important ecological processes well at multiple trophic levels. We tested the model under fishing pressure scenarios and evaluated the performance of common ecosystem-level indicators. The response of the modelled system (as shown by indicators) was in line with expected behaviour of the indicators, reinforcing our confidence in the usefulness of the model.     

Evaluating the performance of remote sensed rain-use efficiency as an indicator of ecosystem functioning in semi-arid ecosystems

Monitoring ecological functioning is a significant step towards detecting changes in ecosystem attributes that could be linked to desertification processes in drylands. The remote sensing proxies of ecological functioning, attract substantial attention due to its advantage on large spatial and increasingly long temporal scales. Remote sensed Vegetation Indices (VIs) have been proposed as the approach to plant productivity to be indicators of ecosystem functioning in local drylands. However, VIs are easily affected by rainfall, a limiting source in arid and semi-arid areas. Therefore, they may not be suitable indicators of ecosystem functioning when applied at large scales, with different rainfall regimes. To overcome the influence of precipitation, the performance of the remote sensing Rain-Use Efficiency (RUE, defined as aboveground net primary production divided by rainfall) was evaluated in 78 global drylands (of which 74 are located in semi-arid areas), as an indicator of multiple ecosystem functions, quantified by ecological multifunctionality index (EMI, integrated by carbon, nitrogen and phosphorus cycles). The correlation analysis showed that during the growing season, the linear relationships of summed EVI (Enhanced Vegetation Index) and RUE with EMI are both significant positive. However, RUE explained more variation (about 44%) in EMI than summed EVI (about 32%) did. The results obtained by partial correlation analysis by controlling the rainfall showed that correlation coefficient between summed EVI and EMI, increased about 20%, while correlation coefficient between RUE and EMI increased very slightly (about 3%). Similar results were also found by using the Normalized Difference Vegetation Index (NDVI). These facts indicated that both remote sensed VIs and RUE could be indicators of ecological multifunctioning. However, RUE was better due to its robustness to rainfall. Also, we must take care that the core assumptions related to the RUE should be fulfilled before using it as an indicator. The relationships between RUE and nutrient cycles showed that in comparison to phosphorus cycle, the carbon and nitrogen cycles had an apparent higher weight in determining the relationship between RUE and EMI. Our findings support the use of remote sensed RUE to monitor ecosystem functioning which could be linked with alternative dryland states and early detection of desertification in drylands.     

Optimum pixel size for hyperspectral studies of ecosystem function in southern California chaparral and grassland

Hyperspectral remotely sensed data are useful for studying ecosystem processes and patterns. However, spatial characterization of such remotely sensed images is needed to optimize sampling procedures and address scaling issues. We have investigated spatial scaling in ground-based and airborne hyperspectral data for canopy- to watershed-level ecosystem studies of southern California chaparral and grassland vegetation. Three optical reflectance indices, namely, Normalized Difference Vegetation Index (NDVI), Water Band Index (WBI) and Photochemical Reflectance Index (PRI) were used as indicators of biomass, plant water content and photosynthetic activity, respectively. Two geostatistical procedures, the semivariogram and local variance, were used for the spatial scaling analysis of these indices. The results indicate that a pixel size of 6 m or less would be optimal for studying functional properties of southern California grassland and chaparral ecosystems using hyperspectral remote sensing. These results provide a guide for selecting the spatial resolution of future airborne and satellite-based hyperspectral sensors.     

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 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.     

A Bayesian network approach to model farmers' crop choice using socio-psychological measurements of expected benefits of ecosystem services

Models of ecosystem management typically measure the benefits of ecosystem services in terms of ecological or biophysical variables, which are influenced by management decisions and biophysical/ecological conditions. This study uses farmers' expected benefits of ecosystem services as input variables to model their decision between planting rice, annual crops or perennial crops. Based on the theory of planned behavior, a Bayesian network is constructed to model crop choice depending on attitudes toward the ecosystem services of biomass production, reduction of soil erosion, and water quality improvement. The relative importance of these decision-making criteria is quantified using the Analytical Hierarchy Process. Results indicate that Bayesian networks can use socio-psychological measurements to model decision-making. Especially as an extension to biophysical or economic models, they can serve as a powerful tool for grasping the more abstract socio-psychological dimensions of benefits of ecosystem services, and how they translate into the decisions of ecosystem managers.     

Development and application of a marine ecosystem dynamic model

A nutrient-phytoplankton-zooplankton-detritus (NPZD) type of marine ecosystem model was developed in this study, and was further coupled to a three-dimensional primitive-equation ocean circulation model with a river discharge model and a solar radiation model to reproduce the dynamics of the low nutrition level in the Bohai Sea (BS). The simulation results were validated by observations and it was shown that the seasonal variation in the phytoplankton biomass could be characterized by the double-peak structure, corresponding to the spring and summer blooms, respectively. It was also found that both nitrogen and phosphate declined to the lowest level after the onset of the summer bloom, since the large amounts of nutrients were exhausted by phytoplankton for photosynthesis, and the concentrations of nutrients could resume in winter after a series of the biogeochemical-physical processes. By calculating the nitrogen/phosphorus (N/P) ratio, it is easy to see that the phytoplankton dynamics is nitrogen-limited as a whole in BS, though the phosphorus limitation may occur in the Yellow River (YR) Estuary where the input of riverine nitrogen is much more than that of phosphate.     

Evaluation of SeaWiFS,MODIS Terra and MODIS Aqua coverage for studies of phytoplankton diurnal variability

Contemporaneous satellite ocean colour data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and MODIS Aqua instruments are consistently calibrated and uniformly processed using equivalent code and algorithms. These sensors cover the globe at different times of a local day. Combining the three instruments provides an unprecedented opportunity to monitor global and local oceanic phytoplankton fluctuations continuously throughout the day. While a daily maximum of 3 hours of coverage is expected from the three sensors, longer observation periods are achieved by combining hourly coverage from a number of consecutive days. The southern hemisphere is then observed continually between 9 am and 3 pm local time. The northern hemisphere is monitored for shorter periods, largely between 10 am and 2 pm local time. This study proposes methodologies to investigate diurnal variability in phytoplankton optical properties from satellite instruments. It also makes an initial attempt to extract diurnal phytoplankton trends from the data and highlights the requirements for accurate instrument calibration and algorithm consistency in ocean colour.     

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.