A new merged dataset of global ocean chlorophyll a concentration with higher spatial and temporal coverage

Understanding the ocean＇s role in the global carbon cycle and its response to environmental change requires a high spatio-temporal resolution of observation.Merging ocean color data from multiple sources is an effective way to alleviate the limitation of individual ocean color sensors（e.g.,swath width and gaps,cloudy or rainy weather,and sun glint） and to improve the temporal and spatial coverage.Since the missions of Sea-Viewing Wide Field-of-View Sensor（Sea Wi FS） and Medium-spectral Resolution Imaging Spectrometer（MERIS） ended on December 11,2010 and May 9,2012,respectively,the number of available ocean color sensors has declined,reducing the benefits of the merged ocean color data with respect to the spatial and temporal coverage.In present work,Medium Resolution Spectral Imager（MERSI）/FY-3 of China is added in merged processing and a new dataset of global ocean chlorophyll a（Chl a） concentration（2000–2015） is generated from the remote sensing reflectance（Rrs（λ）） observations of MERIS,Moderate-resolution imaging spectra-radiometer（MODIS）-AQUA,Visible infrared Imaging Radiometer（VIIRS） and MERSI.These data resources are first merged into unified remote sensing reflectance data,and then Chl a concentration data are inversed using the combined Chl a algorithm of color index-based algorithm（CIA） and OC3.The merged data products show major improvements in spatial and temporal coverage from the addition of MERSI.The average daily coverage of merged products is approximately 24% of the global ocean and increases by approximately 9% when MERSI data are added in the merging process.Sampling frequency（temporal coverage） is greatly improved by combining MERSI data,with the median sampling frequency increasing from 15.6%（57 d/a） to 29.9%（109 d/a）.The merged Chl a products herein were validated by in situ measurements and comparing them with the merged products using the same approach except for omitting MERSI and Glob Colour and MEa SUREs merged data.Correlation and relative error between the new merged Chl a products and in situ observation are stable relative to the results of the merged products without the addition of MERSI.Time series of the Chl a concentration anomalies are similar to the merged products without adding MERSI and single sensors.The new merged products agree within approximately 10% of the merged Chl a product from Glob Colour and MEa SUREs.

海洋水色遥感元数据及其系统设计

在参考国内外元数据标准的基础上,提出了一个海洋水色遥感元数据框架,可以用来对海洋水色遥感数据进行描述、组织、存储和管理;在此基础上,利用XML Schema对此元数据框架进行描述,从而可以用于规范海洋水色遥感元数据。其次,从元数据的访问接口、存储系统及其安全体系结构方面设计元数据系统,实现对海洋水色遥感元数据的有效存储和管理。最后,对海洋水色遥感元数据系统进行功能和性能评价。

MODIS水色遥感数据的获取与产品处理综述

EOS-AM/PM上的中分辨率成像光谱仪MODIS上的水色波段与宽视场水色扫描仪SeaWi-FS相比有更高的信噪比和更窄的波段宽度。以EOS-AM为例,首先对MODIS仪器的特性做一概述,然后以水色遥感为主,描述了MODIS0级数据到1级、2级及更高级产品的处理流程与算法。着重介绍MODIS0级数据到1B数据处理的IMAPP程序,简要介绍NASA发布的MODIS标准产品,并将具有广泛应用性的MODIS1B数据产品格式及内容做了详细说明,同时给出了应用于海洋水色方面的二级产品的详细说明。

基于水上法的四种典型估算遥感反射率方法的应用效果评估

本文简要介绍了M99、R06、G01、L10四种典型水上法估算遥感反射率方法的原理,利用现场观测数据,对四种方法的应用效果进行了评估分析。利用高光谱SAS实测的76个站位数据,对比分析了四种方法结果的差异性以及不同太阳天顶角和云况条件的差异分布,结果表明,四种方法估算的遥感反射率总体差异性不大,有80%站位的差异在10%以内,L10方法的结果更接近四种方法的平均值,R06方法的结果与L10大体相当,当太阳天顶角过大时,四种方法估算遥感反射率的差异较大,在阴天条件下,差异性有小幅度增大。利用高光谱SAS和剖面仪MicroPro同步观测的73个站位数据,分析了四种水上法与水中法计算遥感反射率的差异性,以及不同水体和太阳天顶角条件的差异分布,结果显示,四种水上法与水中法计算的遥感反射率结果差异分布趋势大体一致,约75%站位的差异在15%以内,在较好的比对环境下(去掉近岸站位和太阳天顶角较大的站位),四种方法与水中法差异明显减小,其中,R06和L10方法在处理粗糙海面的天空和太阳耀斑修正方面略优。

走航水体高光谱观测系统SAS的数据处理方法开发与检验

针对商业化的走航水体高光谱观测系统SAS(Surface Acquisition Systems),提出了在走航观测的复杂条件下,针对关键角度的计算、水气界面反射系数的估算和异常数据剔除等问题,开发出实用的数据处理方法,并利用现场数据进行了检验.结果表明,SAS走航观测获得的离水辐亮度与站点测量结果具有很好的一致性,且与同步走航水体浊度的变化趋势一致,具有较好的可靠性.