We tested and modified the quasi-analytical algorithm （QAA） using 57 groups of field data collected in the spring of 2003 in the Yellow Sea and East China Sea. The QAA performs well in deriving total absorption coef...We tested and modified the quasi-analytical algorithm （QAA） using 57 groups of field data collected in the spring of 2003 in the Yellow Sea and East China Sea. The QAA performs well in deriving total absorption coefficients of typical coastal waters. The average percentage difference （APD） is in a range of 13.9%-38.5% for the total absorption coefficient （13.9% at 440 nm）, and differences in particle backscattering coefficient bbp（2） are less than 50% （in the case of the updated QAA）. To obtain improved results, we modified the QAA by adjusting the empirical relationships. The modified algorithm is then applied to the field data to test its performance. The APDs were 44.7%-46.6% for bbp（λ） and 9.9%-32.8% （9.9% at 555 nm） for the total absorption coefficient. This indicates that the modified QAA derives better results. We also used the modified model to derive phytoplankton pigment absorption （aph） and detritus and CDOM absorption （aug） coefficients. The APDs for aph and a dg at 440 nm are 37.1% and 19.8%. In this paper, we discuss error sources using the measured dataset. More independent field data can improve this algorithm and derive better results.展开更多
Temporal and spatial patterns of inherent optical properties in the Bohai Sea are very complex. In this paper, we used 77 groups of field data of AOPs （apparent optical properties） and IOPs （inherent optical proper...Temporal and spatial patterns of inherent optical properties in the Bohai Sea are very complex. In this paper, we used 77 groups of field data of AOPs （apparent optical properties） and IOPs （inherent optical properties） collected in June, August, and September of 2005 in the Bohai Sea, to retrieve the spectral total absorption coefficient a（2） with the quasi-analytical algorithm （QAA）. For QAA implementation, different bands in the region 680-730 nm （in 5 nm intervals） were selected and compared, to determine the optimal band domain of the reference wavelength. On this basis, we proposed a new algorithm （QAA-Com）, a combination of QAA-685 and QAA-715, according to turbidity characterized by a（440）. The percentage difference of model retrievals in the visible domain was between 4.5%-45.1%, in average of 18.8% for a（2）. The QAA model was then applied to Medium Resolution Imaging Spectrometer （MERIS） radiometric products, which were temporally and spatially matched with in-situ optical measurements. Differences between MERIS retrievals and in-situ values were in the range 9.2%-27.8% for a（2） in the visible domain. Major errors in satellite retrieval are attributable to uncertainties of QAA model parameters and in-situ measurements, as well as imperfect atmospheric correction of MERIS data by the European Space Agency （ESA）. During a storm surge in April 2009, time series of MERIS images together with the QAA model were used to analyze spatial and temporal variability of the total absorption coefficient pattern in the Bohai Sea. It is necessary to collect more independent field data to improve this algorithm.展开更多
水体透明度(Secchi Disk depth,SDD)是水环境监测的重要参数,遥感技术对于监测水体透明度具有重要的应用前景。本文旨在分类和比较当前用于监测水体透明度的算法,并提出未来研究的方向,以推动水环境监测技术的进一步发展。文章对目前检...水体透明度(Secchi Disk depth,SDD)是水环境监测的重要参数,遥感技术对于监测水体透明度具有重要的应用前景。本文旨在分类和比较当前用于监测水体透明度的算法,并提出未来研究的方向,以推动水环境监测技术的进一步发展。文章对目前检索水体透明度的算法进行分类和比较。其中,经验算法、半分析算法和机器学习算法是目前研究的主要方向。通过分析算法特性和优缺点,提出未来研究的重点和方向。经验算法基于透明度与光谱数据、叶绿素a浓度等的相关性,半分析算法基于水下能见度理论,机器学习算法则基于更优的数据特征学习能力。不同算法具有各自的适用范围和限制。未来的研究应该着重于整合多源遥感数据,改进QAA(quasi-analytical-algorithm),深入分析光学参数与水体透明度的关系,将机器学习算法应用到水体透明度模型的建立中,以建立具有高精度、适用性广的反演模型。展开更多
基金Supported by the National Natural Science Foundation of China (Nos.40706060,60802089)the National High Technology Research and Development Program of China (863 Program) (No.2007AA092102)the Dragon Project (No.5292)
文摘We tested and modified the quasi-analytical algorithm （QAA） using 57 groups of field data collected in the spring of 2003 in the Yellow Sea and East China Sea. The QAA performs well in deriving total absorption coefficients of typical coastal waters. The average percentage difference （APD） is in a range of 13.9%-38.5% for the total absorption coefficient （13.9% at 440 nm）, and differences in particle backscattering coefficient bbp（2） are less than 50% （in the case of the updated QAA）. To obtain improved results, we modified the QAA by adjusting the empirical relationships. The modified algorithm is then applied to the field data to test its performance. The APDs were 44.7%-46.6% for bbp（λ） and 9.9%-32.8% （9.9% at 555 nm） for the total absorption coefficient. This indicates that the modified QAA derives better results. We also used the modified model to derive phytoplankton pigment absorption （aph） and detritus and CDOM absorption （aug） coefficients. The APDs for aph and a dg at 440 nm are 37.1% and 19.8%. In this paper, we discuss error sources using the measured dataset. More independent field data can improve this algorithm and derive better results.
基金Supported by the National Natural Science Foundation of China(Nos. 60802089,40801176,40706060)the National High Technology Research and Development Program of China(863 Program)(No. 2007AA092102)
文摘Temporal and spatial patterns of inherent optical properties in the Bohai Sea are very complex. In this paper, we used 77 groups of field data of AOPs （apparent optical properties） and IOPs （inherent optical properties） collected in June, August, and September of 2005 in the Bohai Sea, to retrieve the spectral total absorption coefficient a（2） with the quasi-analytical algorithm （QAA）. For QAA implementation, different bands in the region 680-730 nm （in 5 nm intervals） were selected and compared, to determine the optimal band domain of the reference wavelength. On this basis, we proposed a new algorithm （QAA-Com）, a combination of QAA-685 and QAA-715, according to turbidity characterized by a（440）. The percentage difference of model retrievals in the visible domain was between 4.5%-45.1%, in average of 18.8% for a（2）. The QAA model was then applied to Medium Resolution Imaging Spectrometer （MERIS） radiometric products, which were temporally and spatially matched with in-situ optical measurements. Differences between MERIS retrievals and in-situ values were in the range 9.2%-27.8% for a（2） in the visible domain. Major errors in satellite retrieval are attributable to uncertainties of QAA model parameters and in-situ measurements, as well as imperfect atmospheric correction of MERIS data by the European Space Agency （ESA）. During a storm surge in April 2009, time series of MERIS images together with the QAA model were used to analyze spatial and temporal variability of the total absorption coefficient pattern in the Bohai Sea. It is necessary to collect more independent field data to improve this algorithm.
文摘水体透明度(Secchi Disk depth,SDD)是水环境监测的重要参数,遥感技术对于监测水体透明度具有重要的应用前景。本文旨在分类和比较当前用于监测水体透明度的算法,并提出未来研究的方向,以推动水环境监测技术的进一步发展。文章对目前检索水体透明度的算法进行分类和比较。其中,经验算法、半分析算法和机器学习算法是目前研究的主要方向。通过分析算法特性和优缺点,提出未来研究的重点和方向。经验算法基于透明度与光谱数据、叶绿素a浓度等的相关性,半分析算法基于水下能见度理论,机器学习算法则基于更优的数据特征学习能力。不同算法具有各自的适用范围和限制。未来的研究应该着重于整合多源遥感数据,改进QAA(quasi-analytical-algorithm),深入分析光学参数与水体透明度的关系,将机器学习算法应用到水体透明度模型的建立中,以建立具有高精度、适用性广的反演模型。
