Comparative analysis of upwelling influence between the western and northern coast of the Iberian Peninsula

Upwelling conditions have been simultaneously analyzed along the western and northern coast of the Iberian Peninsula in terms of wind forcing and water temperature response. The wind forcing analysis showed that the season under more upwelling favorable conditions corresponds to spring-summer (April-September) along the western coast and only to summer (June-August) along the northern one. Taking into account the upwelling period common to both coasts (June-August), it was observed that the occurrence of upwelling events simultaneously along both coasts is the most probable situation (∼46%) followed by upwelling unfavorable conditions also along both coasts (∼26%). The analysis of sea surface temperature data also showed the existence of an upwelling season in spring-summer along both coasts, although upwelling events are more frequent and intense along the western coast than along the northern one. Chlorophyll concentrations showed a high seasonal variability at the western coast with the highest concentrations values in spring-summer months while at the northern coast the maximum values were observed in spring and autumn.     

Adjusting chlorophyll-a estimates through temporal weighting based on the seasonal development of phytobiomass

North-temperate lakes have been shown to progress through a general cycle of chlorophyll-a phenology. Because estimates of seasonal means are often based on only a few samples collected against this variable background, these estimates can be biased or uncertain. Our goal was to reduce the seasonal uncertainty and thereby produce more accurate estimates of chlorophyll concentration by defining a correction for phenological development. Time-series data from 149 lake-years were used to develop equations from which chlorophyll values could be corrected to the seasonal mean in relation to their particular date of measurement. However, we found the seasonal correction to be ineffectual in reducing uncertainty about nutrient-response regressions. After reviewing a number of hypotheses, we conclude that the correction derived from the average response for many lakes will be inadequate to adjust for the seasonal pattern occurring within any particular lake. This occurs because the temporal weighting correction, generated through repeated averaging, underestimates the seasonal variability which exists among individual lakes. An effective correction, if it is developed will have to be based on patterns within single lakes or possibly different lakes within a single region.     

Vegetation chlorophyll estimates in the Amazon from multi-angle MODIS observations and canopy reflectance model

As a preparatory study for future hyperspectral missions that can measure canopy chemistry, we introduce a novel approach to investigate whether multi-angle Moderate Resolution Imaging Spectroradiometer (MODIS) data can be used to generate a preliminary database with long-term estimates of chlorophyll. MODIS monthly chlorophyll estimates between 2000 and 2015, derived from a fully coupled canopy reflectance model (ProSAIL), were inspected for consistency with eddy covariance fluxes, tower-based hyperspectral images and chlorophyll measurements. MODIS chlorophyll estimates from the inverse model showed strong seasonal variations across two flux-tower sites in central and eastern Amazon. Marked increases in chlorophyll concentrations were observed during the early dry season. Remotely sensed chlorophyll concentrations were correlated to field measurements (r² = 0.73 and r² = 0.98) but the data deviated from the 1:1 line with root mean square errors (RMSE) ranging from 0.355 μg cm⁻² (Tapajós tower) to 0.470 μg cm⁻² (Manaus tower). The chlorophyll estimates were consistent with flux tower measurements of photosynthetically active radiation (PAR) and net ecosystem productivity (NEP). We also applied ProSAIL to mono-angle hyperspectral observations from a camera installed on a tower to scale modeled chlorophyll pigments to MODIS observations (r² = 0.73). Chlorophyll pigment concentrations (Chl_A+B) were correlated to changes in the amount of young and mature leaf area per month (0.59 ≤  r² ≤  0.64). Increases in MODIS observed Chl_A+B were preceded by increased PAR during the dry season (0.61 ≤ r² ≤  0.62) and followed by changes in net carbon uptake. We conclude that, at these two sites, changes in LAI, coupled with changes in leaf chlorophyll, are comparable with seasonality of plant productivity. Our results allowed the preliminary development of a 15-year time series of chlorophyll estimates over the Amazon to support canopy chemistry studies using future hyperspectral sensors.     

Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels

Leaf and canopy nitrogen (N) status relates strongly to leaf and canopy chlorophyll (Chl) content. Remote sensing is a tool that has the potential to assess N content at leaf, plant, field, regional and global scales. In this study, remote sensing techniques were applied to estimate N and Chl contents of irrigated maize (Zea mays L.) fertilized at five N rates. Leaf N and Chl contents were determined using the red-edge chlorophyll index with R² of 0.74 and 0.94, respectively. Results showed that at the canopy level, Chl and N contents can be accurately retrieved using green and red-edge Chl indices using near infrared (780–800 nm) and either green (540–560 nm) or red-edge (730–750 nm) spectral bands. Spectral bands that were found optimal for Chl and N estimations coincide well with the red-edge band of the MSI sensor onboard the near future Sentinel-2 satellite. The coefficient of determination for the relationships between the red-edge chlorophyll index, simulated in Sentinel-2 bands, and Chl and N content was 0.90 and 0.87, respectively.     

Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation

The red edge position (REP) in the vegetation spectral reflectance is a surrogate measure of vegetation chlorophyll content, and hence can be used to monitor the health and function of vegetation. The Multi-Spectral Instrument (MSI) aboard the future ESA Sentinel-2 (S-2) satellite will provide the opportunity for estimation of the REP at much higher spatial resolution (20 m) than has been previously possible with spaceborne sensors such as Medium Resolution Imaging Spectrometer (MERIS) aboard ENVISAT. This study aims to evaluate the potential of S-2 MSI sensor for estimation of canopy chlorophyll content, leaf area index (LAI) and leaf chlorophyll concentration (LCC) using data from multiple field campaigns. Included in the assessed field campaigns are results from SEN3Exp in Barrax, Spain composed of 35 elementary sampling units (ESUs) of LCC and LAI which have been assessed for correlation with simulated MSI data using a CASI airborne imaging spectrometer. Analysis also presents results from SicilyS2EVAL, a campaign consisting of 25 ESUs in Sicily, Italy supported by a simultaneous Specim Aisa-Eagle data acquisition. In addition, these results were compared to outputs from the PROSAIL model for similar values of biophysical variables in the ESUs. The paper in turn assessed the scope of S-2 for retrieval of biophysical variables using these combined datasets through investigating the performance of the relevant Vegetation Indices (VIs) as well as presenting the novel Inverted Red-Edge Chlorophyll Index (IRECI) and Sentinel-2 Red-Edge Position (S2REP). Results indicated significant relationships between both canopy chlorophyll content and LAI for simulated MSI data using IRECI or the Normalised Difference Vegetation Index (NDVI) while S2REP and the MERIS Terrestrial Chlorophyll Index (MTCI) were found to have the strongest correlation for retrieval of LCC.     

Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval

ESA’s upcoming Sentinel-2 (S2) Multispectral Instrument (MSI) foresees to provide continuity to land monitoring services by relying on optical payload with visible, near infrared and shortwave infrared sensors with high spectral, spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods, which ideally should provide uncertainty intervals for the predictions. Statistical learning regression algorithms are powerful candidats for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. In this paper, we focus on a new emerging technique in the field of Bayesian nonparametric modeling. We exploit Gaussian process regression (GPR) for retrieval, which is an accurate method that also provides uncertainty intervals along with the mean estimates. This distinct feature is not shared by other machine learning approaches. In view of implementing the regressor into operational monitoring applications, here the portability of locally trained GPR models was evaluated. Experimental data came from the ESA-led field campaign SPARC (Barrax, Spain). For various simulated S2 configurations (S2-10m, S2-20m and S2-60m) two important biophysical parameters were estimated: leaf chlorophyll content (LCC) and leaf area index (LAI). Local evaluation of an extended training dataset with more variation over bare soil sites led to improved LCC and LAI mapping with reduced uncertainties. GPR reached the 10% precision required by end users, with for LCC a NRMSE of 3.5–9.2% (r²: 0.95–0.99) and for LAI a NRMSE of 6.5–7.3% (r²: 0.95–0.96). The developed GPR models were subsequently applied to simulated Sentinel images over various sites. The associated uncertainty maps proved to be a good indicator for evaluating the robustness of the retrieval performance. The generally low uncertainty intervals over vegetated surfaces suggest that the locally trained GPR models are portable to other sites and conditions.     

4种微量金属元素对绿色巴夫藻生长、叶绿素a及大小的影响

把 4种微量金属离子以不同的浓度添加到绿色巴夫藻的培养液中 ,结果表明 :Hg2 +和Pb2 +浓度分别在 5μg/L和 2 0 0 μg/L时 ,对绿色巴夫藻的生长有较好的促进作用 ,同时也使叶绿素a的含量提高 ;Hg2 +≥ 2 0 μg/L时 ,绿色巴夫藻的生长受到抑制 ,叶绿素a的含量降低。Pb2 +所有的试验浓度都使绿色巴夫藻生长加快 ,叶绿素a的含量提高 ;Co2 +在 1μg/L时对绿色巴夫藻的生长有最好的促进效果 ,但当Co2 +≥ 5μg/L时 ,开始对生长出现抑制作用 ,而Co2 +所有的试验浓度都使叶绿素a的含量降低 ;Mn2 +浓度在 10 0 μg/L时 ,对促进绿色巴夫藻生长的效果最佳 ,4 0 0 μg/L的浓度开始对生长出现抑制作用 ,但所有Mn2 +的试验浓度对叶绿素a含量的变化影响不显著。 4种微量金属元素都使绿色巴夫藻略微变小。     

Variation in Lake Baikal's phytoplankton distribution and fluvial input assessed by SeaWiFS satellite data

The current behaviour of selected climate proxies in Lake Baikal was assessed by remote sensing analyses of ‘Sea viewing Wide Field of view Sensor’ (SeaWiFS) satellite data. Suitable proxies include optically visible water constituents such as phytoplankton, suspended terrigenous matter and yellow substance. These limnological parameters reflect the present-day climate bioproductivity and the river discharge in the catchment area.A biological and geochemical ground truth data set for Lake Baikal was established with the help of members of the paleoclimate project ‘high-resolution CONTINENTal paleoclimate record in Lake Baikal’ (CONTINENT). For processing the SeaWiFS satellite data, the atmospheric correction was adapted to the case of Lake Baikal. Chlorophyll as a proxy for phytoplankton was quantified using global NASA ocean colour algorithms OC2 and OC4. In cases of no optical interferences by terrigenous input, the calculated chlorophyll concentrations in clear pelagic waters were within ±30% accuracy with the CONTINENT cruise data during the summers of 2001 and 2002. Within this range of accuracy, the SeaWiFS time series will be able to show the seasonal variations of chlorophyll of specified bio-optical provinces of Lake Baikal and of CONTINENT sites. In this study, the suspended matter as a proxy for the terrigenous input was calculated according to an empirical algorithm using ground truth data in the time frame of flooding events in summer 2001. The approach chosen correlates the suspended matter concentration with the remotely sensed parameter of ‘attenuation coefficient’ to account for the organic-rich terrigenous input that originates from the swampy watersheds.Seasonal and spatial information that is provided by the analyses of the SeaWiFS satellite data will assist paleoclimate researchers to interpret the autochthonous and allochthonous influences at the CONTINENT coring sites.     

珠江口黄茅海冬季海洋生态环境特征

根据2007年12月在珠江口西部黄茅海的调查采样,对该海域的叶绿素a、初级生产力(C)、浮游动、植物进行了初步的研究.分析了浮游动、植物的种类组成、群落结构、数量和生物景等.结果表明,叶绿素a浓度和初级生产力(C)均值分别为2.75 mg/m3和61.16 mg/m2·d.浮游植物共有43种,以硅藻为主,绝对优势种为颗粒直链藻,平均细胞丰度为20.1×104 cell/m3,以近海广布种为主要类群,多样性指数和均匀度均值分别为2.59和0.73.浮游动物共有57种,暖水种沿岸类群种类占大多数,以丹氏纺缍水蚤为绝对优势种,平均丰度和牛物量分别1 850 ing/m3和679.74 mg/m3,多样性指数和均匀度均值分别为2.92和0.63.     

北太平洋中尺度涡海表温度和叶绿素浓度特征分析

本文利用AVISO卫星高度计资料识别并追踪了北太平洋2007年—2012年的中尺度涡,并利用OSTIA的海表温度SST(Sea Surface Temperature)资料与MODIS的叶绿素a浓度(Chl-a)资料,研究了北太平洋2007年—2012年中尺度涡SST和Chl-a浓度的时空分布特征,并分析北太平洋典型中尺度涡SST与Chl-a浓度的变化特征,主要结论如下:本文共识别出992个中尺度涡,其中442个气旋涡,550个反气旋涡。中尺度涡SST时空分布特征为:气旋涡温度强度(ICE)月变化特征比反气旋涡温度强度(IAE)更强。ICE年际变化显著,IAE则不明显。温度强度较强的气旋涡和反气旋涡集中分布在黑潮延伸区。中尺度涡Chl-a浓度时空分布特征如下:气旋涡和反气旋涡Chl-a浓度月变化特征明显,且二者的变化趋势一致;年际变化则均不明显。Chl-a浓度值高的中尺度涡主要分布在高纬海域。中尺度涡SST与海洋动力参数(振幅、涡度和涡动能(EKE))的相互关系为:反气旋涡SST与振幅的相关性亦正亦负,且在空间上均匀分布。气旋涡SST与振幅的负相关系数主要分布在黑潮延伸区。正相关性强的反气旋涡多于气旋涡。反气旋涡SST与涡度的相关性亦正亦负,气旋涡SST与涡度呈负相关。反气旋涡SST与EKE的相关性亦正亦负;气旋涡的相关性为正。中尺度涡Chl-a浓度与海洋动力参数的相互关系为:反气旋涡Chl-a浓度与振幅的相关性为正,且在空间上均匀分布;气旋涡在黑潮延伸区与阿拉斯加湾呈正相关。反气旋涡和气旋涡Chl-a浓度与涡度均呈正相关。反气旋涡Chl-a浓度与EKE呈正相关;气旋涡Chl-a浓度与EKE相关性亦正亦负。