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1.
黄海、东海二类水体漫衰减系数与透明度反演模式研究   总被引:25,自引:0,他引:25  
黄海、东海是典型的二类水体区域,总悬浮物含量高,水体光学特性复杂.利用2003年春秋季黄海、东海水色联合试验中获取的高质量现场实测数据,建立了由遥感反射比反演水体在490nm波段的漫衰减系数和海水透明度的统计反演模式.这两种模式皆采用490,555,670nm三个波段的组合,漫衰减系数的反演值和实测值的相关系数为0.96,平均相对误差为17.2%;透明度的反演值与实测值的相关系数为0.95,平均相对误差为16.8%.对两种反演模式对遥感反射比输入误差的敏感性进行了分析,结果表明反演模式对±5%的遥感反射比输入误差导致490nm波段的漫衰减系数反演误差最大为27.3%,透明度最大误差为22.7%,并利用2003年春秋季同一海区的实测数据对模型进行了检验,漫衰减系数的平均相对误差为25.0%,透明度的为16.5%.给出了412,443,510,520,555,565nm各波段的漫衰减系数同波段490nm的漫衰减系数之间的关系,结果表明,在400~600nm波段中的每一个波段的漫衰减系数与490nm波段的漫衰减系数的相关性较高,相关系数都超过了0.98.这样利用建立的各波段漫衰减系数关系模型可以从一个已知波段的漫衰减系数反演出其他任何波段的漫衰减系数,这就在水色反演和应用中大大减少了未知因子的个数.  相似文献   

2.
黄、东海二类水体水色要素的统计反演模式   总被引:9,自引:0,他引:9  
根据高质量的现场实测数据,借鉴Tassan模式,得到了中国黄、东海近岸二类水体水色要素统计反演模式,填补了中国近岸二类水体水色遥感三要素反演模式的空白.现场测量数据的反演与实测的平均相对误差分别为 Chl-a 36 %~45 %,总悬浮物浓度20 %~30 %,黄色物质约为20 %.对该三要素统计模式对遥感反射率误差的敏感性进行了分析.模式比较表明,中国黄、东海长江口近岸水体的模型,特别是高浑浊度水体区域,与国际其他水体的模型参数有较大的差异.  相似文献   

3.
利用现场实测的表观光学量和固有光学量数据,得到了我国黄海、东海近岸二类水体多个波段的总吸收系数的统计反演模式。此反演模式采用412/555、490/555两个波段遥感反射比比值的二项式,得到波长412、440、488、510、532、555nm处的总吸收系数,其反演值和实测值的平均相对误差不大于25.8%,相关系数R2达到了0.75到0.85。水体总吸收系数几种统计模型的误差敏感性分析表明,反演模式对±5%的遥感反射比输入误差导致结果增加误差最大为24.0%,因此反演模式是可用的。同时给出了412、488、510、532、555nm各波段的总吸收系数同波段440nm的总吸收系数之间的关系。结果表明,在400—600nm波段范围内,每一个波段的总吸收系数与440nm波段的总吸收系数的相关性均较高,相关系数R2都超过了0.99。通过对拟合直线的斜率与波长进行回归,得到斜率和波长的关系,其相关系数R2为0.99,这样利用本文中建立的各波段总吸收系数关系模型,可以从一个已知波段的总吸收系数反演出任何另外一个波段的总吸收系数,这就在水色反演与应用中大大减少了未知因子的个数。  相似文献   

4.
构建了一种适用于河北海域二类水体的叶绿素a浓度遥感反演业务化模型。将MODIS 1B数据第一波段反射率与河北海域叶绿素a浓度实测数据进行相关分析,通过回归拟合建立遥感反演模型,并选择不同时间、不同区域的实测数据对模型精度与稳定性进行了检验。结果表明:模型相关系数为0.73,平均相对误差31.4%~35.9%之间,模型适用于河北海域叶绿素a浓度业务化遥感监测,这对于监测河北海域赤潮和富营养化状况具有重要的现实意义。  相似文献   

5.
中国东部海域浮游植物类群遥感反演研究   总被引:1,自引:0,他引:1       下载免费PDF全文
浮游植物类群遥感反演能够为全面认识浮游植物在海洋生态系统中的作用提供重要的数据资料。但由于复杂的水体光学特性,近海浮游植物类群遥感反演存在着巨大挑战。本研究以复杂光学二类水体—中国东部海域为研究区,通过使用3种建模方法,即波段组合法、基于奇异值分解的多元线性回归法、基于奇异值分解的XGBoost回归法,利用遥感反射率数据反演浮游植物类群。经原位实测数据集验证,基于奇异值分解的XGBoost回归法构建的8类浮游植物叶绿素a浓度反演模型的精度最高,其中硅藻、甲藻的叶绿素a浓度反演模型在验证集上的决定系数均大于0.7。相比之下,3种建模方法估算得到的绿藻、蓝藻和金藻的叶绿素a浓度精度较低(验证结果的决定系数小于0.45)。同时,研究评估了OLCI影像的3种大气校正方法(C2RCC、POLYMER、MUMM)在中国东部海域的适用性。结果显示,相对于其他两种大气校正算法,C2RCC在各波段有较好的表现(均方根误差小于0.004 8 sr?1)。将3种浮游植物类群反演模型应用到大气校正后的OLCI影像,验证结果显示,利用基于奇异值分解的多元线性回归法建立的硅藻叶绿素a浓度模型有较好的反演精度(决定系数为0.56)。  相似文献   

6.
基于半分析算法的赤潮水体固有光学性质反演   总被引:2,自引:1,他引:1  
在珠江口海域海洋光学浮标实验期间获取了赤潮生消过程的水体光学数据和相应的生化数据.利用该实验数据开展了准分析算法(quasi-analytical algorithm,QAA)反演赤潮水体固有光学参数的精度检验和模型修正工作.(1)利用遥感反射率和QAA反演主要水色波段(412,443,490,510,560,620和...  相似文献   

7.
滕越  邹斌  叶小敏 《海洋学报》2022,44(5):25-34
叶绿素a作为最重要的水质参数之一,是评价水体富营养化和初级生产力状况的主要因素。我国海洋一号C(HY-1C)卫星海岸带成像仪(CZI)具有高时空分辨率的观测优势。本文基于东海和南海现场实测数据建立了HY-1C卫星CZI叶绿素a浓度反演模型并在实测水域进行反演,与MODIS叶绿素a浓度反演产品进行了对比验证,应用CZI叶绿素a浓度模型在珠江口、长江口、渤海湾水域进行了叶绿素a浓度反演示例试验。结果表明,叶绿素a浓度模型估算浓度与实测浓度相关系数为0.774 3,平均相对误差为24.58%,利用实测叶绿素a浓度对模型进行精度验证,相关系数达到0.993 9,平均相对误差为18.49%。模型在实测水域反演得到的叶绿素a浓度分布与MODIS叶绿素a浓度产品分布大体一致。在珠江口水域反演得到叶绿素a浓度空间分布为由西北向东南逐级递减,峰值出现在珠江口西沿岸。在长江口、渤海湾反演叶绿素a浓度空间分布均符合地理实情。研究表明HY-1C卫星CZI数据可应用于中国近海水色定量化研究。  相似文献   

8.
为了及时、准确的了解胶州湾水域总悬浮物情况,采用2001—2015年水面实测数据,选取HJ-1、MERIS、LandsatTM/ETM+三个不同卫星数据,通过ENVI软件分析计算TM/ETM数据的多波段组合与总悬浮物浓度之间的相关关系,选取相关系数最大者分别构建多元函数回归模型,对胶州湾总悬浮物浓度进行了遥感定量反演研究。结果表明,估算因子三波段模型432算法与悬浮物浓度的相关性最好(R20.8),反演精度较高,以此建立了悬浮物浓度三波段半分析+生物光学遥感反演模型,检验值R~2均高于0.85,并通过F检验法和均方根误差(RMSE)分析,证明误差的敏感性差,该模型相关系数及稳定性好。因此,三波段模型在这三种反演模型中精度最好。  相似文献   

9.
卫星水色遥感是研究北冰洋海洋生态系统及其气候变化响应的重要技术手段。本文从北冰洋卫星水色遥感产品的时空覆盖及其制约因素分析入手,通过剖析北冰洋遥感反射率、叶绿素a浓度、初级生产力等卫星关键水色产品的定量化水平及不确定性来源,凝练了未来需突破的若干关键技术。(1)7~8月是一年之中北冰洋卫星水色遥感产品空间覆盖率最高的时段(约为56%~62%),而每年10月至来年3月则不足20%,海冰覆盖(9月影响最小)、云雾干扰(4~8月影响最为严重)、低光照(9月至来年3月影响最为严重)是主要的制约因素,多星融合可在一定程度上提高水色产品的空间覆盖率。(2)4~6月是北冰洋卫星遥感反射率产品精度最高的时段,高质量数据的占比可达82%,其次为1~3月和7~9月(75%),10~12月最低(57%);基于半分析算法的溶解有机物浓度卫星遥感反演精度较高(误差约为12%),而卫星标准叶绿素a浓度产品则存在低值区高估、高值区低估的问题(相对误差为82%~112%);初级生产力遥感产品存在明显不确定性,叶绿素a浓度及其垂向分布、藻类光合作用参数等是主要的不确定性来源。(3)提升北冰洋水色遥感观测能力需突破以下关键科学技术问题:(1)高纬度海域卫星水色遥感资料的高精度处理方法;(2)基于北冰洋水体光学特性的高精度区域性水色反演模型;(3)多星协同的北冰洋水色观测技术;(4)协同卫星遥感与无人移动观测的北冰洋环境监测技术;(5)北冰洋海洋光学性质的参数化模型构建。  相似文献   

10.
悬浮泥沙作为重要水质参数,其分布和动态变化对河口及近岸的生态、环境、物质循环等都具有深远的影响。我国静止轨道高分四号(GF-4)卫星数据具有高时间和高空间分辨率的观测优势,在水色遥感上具有重大应用潜力。为探究GF-4卫星对悬浮泥沙浓度的监测能力,本文以杭州湾为研究区,构建反演模型,利用静止海洋水色成像仪进行交叉验证。结果表明,以GF-4卫星第5和第4波段遥感反射率的比值作为遥感因子建立的反演模型精度较高,决定系数为0.92,均方根误差为223.2 mg/L,平均相对误差为17.2%。交叉验证结果显示,GF-4卫星作为一种新的遥感数据源,在低浓度区与静止海洋水色成像仪反演悬浮泥沙浓度分布相似,但在高浓度区的差异随浓度增高而增大,总体可满足中国大部分海区的监测需求。  相似文献   

11.
Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua and Terra satellites have unique advantages for monitoring coastal waters, owing to their high spatial resolution (250?m), short revisit period (1–2?days), and freedom from cost. An empirical retrieval model for concentration of total suspended matter (TSM) has been developed based on a statistical analysis of field surveys of TSM and remote sensing reflectance (R rs) in the Bohai Sea of China. A robust linear relationship was established between the equivalent remote sensing reflectance (converting ASD-measured R rs by spectral response function) in the 620–670?nm band (band 1) of MODIS and the concentration of TSM (R 2?=?0.95; n?=?27; RMS?=?0.512) acquired in August and September 2008. The model was validated via in situ measurements in September 2009, resulting in a mean relative error of 12.9?%. Then, the corresponding MODIS products of monthly average concentration of TSM were produced from January to December 2009. The distribution characteristics of TSM in the Bohai Sea of China are closely related with the spatial pattern and seasonal variability. This study demonstrates that the moderately high resolution of MODIS 250?m data is available for monitoring the transport and fate of materials in relatively smaller bodies of water.  相似文献   

12.
HY-1 CCD宽波段水色要素反演算法   总被引:7,自引:4,他引:3  
利用2003年春季黄海、东海区现场实测数据,建立了HY1卫星4波段CCD成像仪水色要素反演算法.由于HY1CCD的宽波段特性阻碍了黄色物质的反演,因此反演的水色要素仅包括水体表层的总悬浮物、悬浮泥沙(SS)以及叶绿素a的浓度.现场遥感反射率光谱由ASD地物波谱仪测量,对于叶绿素a的浓度利用现场萃取荧光法测量,总悬浮物、悬浮泥沙由实验室滤膜称重法获得.反演算法的拟合相关系数均大于0.88,平均相对误差在40%以下.对反演算法进行了误差灵敏度分析,结果表明对于总悬浮物、悬浮泥沙和低浊度水体中的叶绿素a的浓度反演算法能够满足日常的业务运行要求,但是对于高浊度水体中叶绿素a的浓度反演算法对某个波段组合比较敏感,仍需要进一步探讨.  相似文献   

13.
有色可溶性有机物质(Coloured Dissolved Organic Matter,CDOM)直接影响着海水的光学性质,为水体有机污染物含量遥感反演的基础参数之一。在水色遥感研究领域,一般用440nm波长位置的吸收系数ag(440)来表示其浓度,因而建立ag(440)遥感反演模式,对于掌握相关海域CDOM浓度的空间变化规律及进一步提取其他水环境参数具有重要的作用。利用2013年11月和2014年2月两个航次在珠江口海域现场采集的表观光学量及固有光学量数据,建立了基于HJ-1/CCD卫星传感器的ag(440)遥感反演模型,并应用于珠江口海域,得到该区域2012年1月至2014年6月晴天CDOM浓度空间动态分布图。研究结果表明:(1)现场测定的珠江口海域ag(440)在0.1~0.3m-1,且不同断面ag(440)呈现出一定的规律性变化;(2)利用现场实测数据对遥感模式估算值进行验证,计算出估算值的相对误差为9%,表明所建立模式具有较高的准确率;(3)遥感反演的CDOM空间分布数据与实测数据得到的分布特征基本吻合,整个珠江口及其邻近海域ag(440)的数值范围为0.07~0.31m-1,而且珠江口西部海域ag(440)高于中部和东部海域。  相似文献   

14.
A bio-optical dataset collected during the 1998?C2007 period in the Yellow and East China Seas (YECS) was used to provide alternative empirical ocean-color algorithms in the retrieval of chlorophyll-a (Chl-a), total suspended matter (TSM), and colored dissolved organic matter (CDOM) absorption coefficients at 440 nm (ag440). Assuming that remote-sensing reflectance (Rrs) could be retrieved accurately, empirical algorithms for TChl (regionally tuned Tassan??s Chl-a algorithm) in case-1 waters (TChl2i in case-2 waters), TTSM (regionally tuned Tassan??s TSM algorithm), and Tag440 or Cag440 (regionally tuned Tassan??s or Carder??s ag440 algorithm) were able to retrieve Chl-a, TSM, and ag440 with uncertainties as high as 35, 46, and 35%, respectively. Applying the standard SeaWiFS Rrs, TChl was not viable in the eastern part of the YECS, which was associated with an inaccurate SeaWiFS Rrs retrieval because of improper atmospheric correction. TChl behaved better than other algorithms in the turbid case-2 waters, although overestimation was still observed. To retrieve more reliable Chl-a estimates with standard SeaWiFS Rrs in turbid water (a proxy for case-2 waters), we modified TChl for data with SeaWiFS normalized water-leaving radiance at 555 nm (nLw555) > 2 mW cm?2 ??m?1 sr?1 (TChl2s). Finally, with standard SeaWiFS Rrs, we recommend switching algorithms from TChl2s (for case-2 waters) to MOCChl (SeaWiFS-modified NASA OC4v4 standard algorithm for case-1 waters) for retrieving Chl-a, which resulted in uncertainties as high as 49%. To retrieve TSM and ag440 using SeaWiFS Rrs, we recommend empirical algorithms for TTSM (pre-SeaWiFS-modified form) and MTag440 or MCag440 (SeaWiFS Rrs-modified forms of Tag440 or Cag440). These could retrieve with uncertainties as high as 82 and 52%, respectively.  相似文献   

15.
Optical remote sensing data is now being used systematically for marine ecosystem applications, such as the forcing of biological models and the operational detection of harmful algae blooms. However, applications are hampered by the incompleteness of imagery and by some quality problems. The Data Interpolating Empirical Orthogonal Functions methodology (DINEOF) allows calculation of missing data in geophysical datasets without requiring a priori knowledge about statistics of the full dataset and has previously been applied to SST reconstructions. This study demonstrates the reconstruction of complete space–time information for 4 years of surface chlorophyll a (CHL), total suspended matter (TSM) and sea surface temperature (SST) over the Southern North Sea (SNS) and English Channel (EC). Optimal reconstructions were obtained when synthesising the original signal into 8 modes for MERIS CHL and into 18 modes for MERIS TSM. Despite the very high proportion of missing data (70%), the variability of original signals explained by the EOF synthesis reached 93.5% for CHL and 97.2% for TSM. For the MODIS TSM dataset, 97.5% of the original variability of the signal was synthesised into 14 modes. The MODIS SST dataset could be synthesised into 13 modes explaining 98% of the input signal variability. Validation of the method is achieved for 3 dates below 2 artificial clouds, by comparing reconstructed data with excluded input information. Complete weekly and monthly averaged climatologies, suitable for use with ecosystem models, were derived from regular daily reconstructions. Error maps associated with every reconstruction were produced according to Beckers et al. (2006). Embedded in this error calculation scheme, a methodology was implemented to produce maps of outliers, allowing identification of unusual or suspicious data points compared to the global dynamics of the dataset. Various algorithm artefacts were associated with high values in the outlier maps (undetected cloud edges, haze areas, contrails, and cloud shadows). With the production of outlier maps, the data reconstruction technique becomes also a very efficient tool for quality control of optical remote sensing data and for change detection within large databases.  相似文献   

16.
Based on field experiments and analysis, the study examined the spectral characteristic and spatial variability of turbidity in the Pearl River Estuary by using the EO-1 ALI satellite imagery collected on December 18, 2005. A negative regression model (turbidity = −439.52 × R (570) + 22.913, R2 = 0.9042, n = 11) between the in-situ turbidity and the reflectance at 570 nm (maximum correlation spectral band between 350 and 2500 nm), resulting from increasing of organic matters in suspended solids, was built and applied to ALI band 4 (0.525–0.605 nm). Simple in-water spectral pairs calibration method of bright and dark targets provided the good atmospheric correction of ALI with a root mean square error of 0.00061, and mean absolute percentage error of 2.04%. The study also found the seawater turbidity is a more accurate indicator of Chl_a concentration (R2 = 0.7442) than TSS (R2 = 0.7061). Also, there is a large correlation between TSS and the turbidity (R2 = 0.86, N = 22) for Modaomen watercourse. The model-deduced turbidity distribution from ALI band 4 exhibited distinctive spatial variability of turbidity in the dry season, accordant with seasonal in-situ investigation. The ALI data provides accurate estimates of the mean water clarity conditions in the PRE (RMSE = 1.878 and MAPE = 11.7%) and has potential importance for water quality monitoring of optical remote sensing in the similar estuaries and its future operation.  相似文献   

17.
An optical model is developed for the remote sensing of coloured dissolved organic matter (CDOM) in a wide range of waters within coastal and open ocean environments. The absorption of CDOM (denoted as ag) is generally considered as an exponential form model, which has two important parameters – the slope S and absorption of CDOM at a reference wavelength ag(λ0). The empirical relationships for deriving these two parameters are established using in-situ bio-optical datasets. These relationships use the spectral remote sensing reflectance (Rrs) ratio at two wavelengths Rrs(670)/Rrs(490), which avoids the known atmospheric correction problems and is sensitive to CDOM absorption and chlorophyll in coastal/ocean waters. This ratio has tight relationships with ag(412) and ag(443) yielding correlation coefficients between 0.77 and 0.78. The new model, with the above parameterization applied to independent datasets (NOMAD SeaWiFS match-ups and Carder datasets), shows good retrievals of the ag(λ) with regression slopes close to unity, little bias and low mean relative and root mean square errors. These statistical estimates improve significantly over other inversion models (e.g., Linear Matrix-LM and Garver-Siegel-Maritorena-GSM semi-analytical models) when applied to the same datasets. These results demonstrate a good performance of the proposed model in both coastal and open ocean waters, which has the potential to improve our knowledge of the biogeochemical cycles and processes in these domains.  相似文献   

18.
悬浮物含量及其时空分布是河口海岸环境中关心的热点问题。2016年2月16日,欧洲航天局发射了新一代海洋水色传感器(OLCI),该传感器具有良好的时空及光谱分辨率。本研究结合2017年7月杭州湾同步采样数据,对比了6种大气校正算法和8种悬浮物浓度(TSM)估算模型,遴选和分析了适宜于杭州湾和OLCI数据的大气校正方法和TSM估算模型,验证了OLCI数据二级产品精度和适用性。结果表明:(1)基于紫外光谱的大气校正算法(UVAC)精度最高,同步4个采样点的大气校正平均相对误差(MAPE)分别为34.21%、13.11%、5.92%和20.28%。在除Oa1以外的14个波段的MAPE均值为15.23%,Oa4至Oa10波段的MAPE低于8%;(2)基于Oa16/Oa5的波段比值模型,具有良好的建模(MAPE为16.49%,RMSE为50.92 mg/L)和验证(MAPE为19.08%,RMSE为19.29 mg/L)精度及模型稳健性;(3)基于C2RCC算法的固有光学量和TSM含量产品及OLCI二级TSM含量产品在杭州湾精度较差,不适用于杭州湾TSM和固有光学量遥感监测应用;(4)空间上,TSM在杭州湾中部区域含量较低,在杭州湾南岸和湾口区域含量较高。  相似文献   

19.
This paper presents three years (1998–2000) of chlorophyll a (chl a) data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) for Case 2 waters of Chesapeake Bay and the middle Atlantic bight (MAB) to describe phytoplankton dynamics on seasonal to interannual time scales. We used extensive data on inherent and apparent optical properties in conjunction with satellite retrievals to: (1) characterize the bio-optical properties of the study area relevant to processing and interpreting SeaWiFS data; (2) test the applicability of the SeaWiFS bio-optical algorithm (OC4v.4) for the estuarine and coastal waters; (3) evaluate the accuracy of the SeaWiFS remote sensing reflectance (RRS) and chl a products on regional and seasonal bases using in situ observations. The characteristically strong absorption by chromophoric dissolved organic matter (acdom) and non-pigmented particulate matter (ad) in estuarine and coastal waters contributed to overestimates of chl a using OC4v.4 applied to in situ radiances for the Bay (mean ratio 1.42±1.20) and the MAB (2.60±1.36). Values of RRS from SeaWiFS in the blue region of the spectrum were low compared to in situ RRS, suggesting that uncertainties remain in atmospheric correction. Direct comparisons of SeaWiFS retrievals of chl a with in situ chl a for the Bay showed larger biases and uncertainties (mean ratio 1.97±1.85) than for chl a estimated from OC4v.4 applied to in situ RRS. The larger biases were attributed to errors in SeaWiFS radiances and the larger uncertainties to time-space “aliasing” of satellite observations and in situ measurements. To reduce the time differences between SeaWiFS and in situ data, we compared chl a obtained from continuous underway fluorometric measurements on selected ship tracks to SeaWiFS chl a and showed that SeaWiFS captured phytoplankton dynamics in much of the Bay. The agreement of SeaWiFS chl a with in situ chl a was strongest in the mid- (regions 3, 4) to lower Bay (regions 1, 2), and deteriorated toward the upper Bay (regions 5, 6), in part due to a reduction of sensitivity and an increase of noise for SeaWiFS products in the highly absorbing, low RRS waters of the upper Bay. A three-year time-series of SeaWiFS and in situ data showed that SeaWiFS accurately and reliably captured seasonal and interannual variability of chl a associated with variations of freshwater flow. Significant short-term variability of chl a in summer that was unresolved with shipboard data was detected in the SeaWiFS time-series and the implications are discussed. The overall performance of SeaWiFS in the mid- to lower Bay and the MAB, combined with high spatial (∼1 km2) and temporal (∼100 clear scenes per year) resolution, indicate current SeaWiFS products are valuable for quantifying seasonal to interannual variability of chl a in estuarine and coastal waters.  相似文献   

20.
This paper provides initial validation results for GOCI-derived water products using match-ups between the satellite and ship-borne in situ data for the period of 2010?C2011, with a focus on remote-sensing reflectance (R rs ). Match-up data were constructed through systematic quality control of both in situ and GOCI data, and a manual inspection of associated GOCI images to identify pixels contaminated by cloud, land and inter-slot radiometric discrepancy. Efforts were made to process and quality check the in situ R rs data. This selection process yielded 32 optimal match-ups for the R rs spectra, chlorophyll a concentration (Chl_a) and colored dissolved organic matter (CDOM), and with 20 match-ups for suspended particulate matter concentration (SPM). Most of the match-ups are located close to shore and thus the validation should be interpreted limiting to near-shore coastal waters. The R rs match-ups showed the mean relative errors of 18?C33% for the visible bands with the lowest 18?C19% for the 490 nm and 555 nm bands and 33% for the 412 nm band. Correlation for the R rs match-ups was high in the 490?C865 nm bands (R2=0.72?C0.84) and lower in the 412 nm band (R2=0.43) and 443 nm band (R2=0.66). The match-ups for Chl_a showed a low correlation (<0.41) although the mean absolute percentage error was 35% for the GOCI standard Chl_a. The CDOM match-ups showed an even worse comparison with R2<0.2. These match-up comparison for Chl_a and CDOM would imply the difficulty to estimate Chl_a and CDOM in near-shore waters where the variability in SPM would dominate the variability in R rs . Clearly, the match-up statistics for SPM was better with R2=0.73 and 0.87 for two evaluated algorithms, although GOCI-derived SPM overestimated low concentration and underestimated high concentration. Based on this initial match-up analysis, we made several recommendations -1) to collect more offshore under-water measurements of the R rs data, 2) to include quality flags in level-2 products, 3) to introduce an ISRD correction in the GOCI processing chain, 4) to investigate other types of in-water algorithms such as semianalytical ones, and 5) to investigate vicarious calibration for GOCI data and to maintain accurate and consistent calibration of field radiometric instruments.  相似文献   

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