Geostationary Ocean Color Imager Research Articles

Evaluation of the Geostationary Ocean Color Imager (GOCI) to monitor the dynamic characteristics of suspension sediment in Taihu Lake

To accurately observe the dynamic concentration of total suspended matter (CTSM) by geostationary satellite, Geostationary Ocean Color Imager (GOCI), a new, regionally empirical combination algorithm, was developed based on the in situ measurement of water properties. Also, the ability of GOCI (with a 1 h temporal resolution) to capture dynamic change in CTSM was evaluated using in situ buoy-measured data. The validation results indicate that the combination algorithm (combination of two- and three-band ratio algorithms) can achieve acceptable retrieval results for CTSM, with 34.52% root mean square error percentage (RMSP) for validation data and 45.01% RMSP for GOCI match-up points. The depth of reflectance valley at 680 nm (Rrs-depth,680) is considered as an index for deciding whether to utilize a two- (Rrs-depth,680 ≤ 0.005) or three-band (Rrs-depth,680 > 0.005) algorithm in the combination algorithm. This combination algorithm overcomes some shortcomings of two- and three-band ratio algorithms of CTSM in turbid productive water. The results show that sediment resuspension will significantly change the optical properties of water and decrease the universality of the CTSM retrieval algorithm in the presence of a large area of algae. The influence of sediment resuspension on the CTSM retrieval algorithm is significant in the central lake but less so in the other four lakes studied due to lake morphology and algal biomass.

Using Remote Sensing to Track Variation in Phosphorus and Its Interaction With Chlorophyll-a and Suspended Sediment

Treatment of eutrophication and algal blooms in large shallow lakes has been constrained by the endogenetic release of nutrients from sediment resuspension despite the wellcontrolled input of nutrients from surrounding land in Taihu Lake, China. In this study, we propose a method to simplify regionally empirical algorithms for total suspended matter (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSM</sub> ), chlorophyll-a (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chl-a</sub> ), and phosphorus concentration (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> ) for the Geostationary Ocean Color Imager (GOCI) for Taihu Lake. The results show that the GOCI-derived C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSM</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chl-a</sub> , and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> match well with the in situ measured data. Based on the validated synchronous and timely observation data (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSM</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chl-a</sub> and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> ) from GOCI level-1b data, the interaction of phosphorus with algae and suspended sediment were analyzed from August 6 to August 9, 2013. The results indicate that phosphorus released from sediment resuspension is significant in Taihu Lake, with a high spatial-temporal variation. The effect of the restriction of nutrients (nitrogen and phosphorus) on algal growth varied temporally and spatially with the hydrodynamic characteristics of the lake. In most cases, restricted phosphorus in the lake is the primary reason for the algal growth, especially in open regions such as Center Lake (CL), Northwestern Lake (NWL), and Southwestern Lake (SWL). Both nitrogen and phosphorus are the restrictive factors to algal growth in Meiliang and Gonghu Bays. More attention should thus be focused on the endogenetic release of phosphorus in Taihu Lake when treating eutrophication and algal blooms in addition to controlling the input of nitrogen and phosphorus from surrounding land.

Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations

Vegetation indices are important remotely sensed metrics for ecosystem monitoring and land surface process assessment, among which Normalized Difference Vegetation Index (NDVI) has been most widely used. The newly launched Landsat 8 Operational Land Imager (OLI) sensor, together with its predecessor Landsat 7 Enhanced Thematic Mapper Plus (ETM+), provides continuous earth observations with an 8-day interval. The design improvements of the new sensor, including narrower near-infrared waveband, higher signal-to-noise ratio (SNR), and greater radiometric sensitivity highlight the need for investigating the land surface observation properties, especially its consistency with data from its predecessors and other satellite sensors. This study aims to evaluate the characteristics of Landsat 8 OLI-derived NDVI against Landsat 7 ETM+ by cross-comparison and by comparing with Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Ocean Color Imager (GOCI)-derived NDVIs as well as in-situ NDVI measurements. Simulations of Top of Atmosphere (TOA) reflectance and surface reflectance of broadleaf trees and water were conducted for Landsat 8 OLI, Landsat 7 ETM+, and MODIS in order to evaluate the impact of band pass difference on NDVI calculation. Four consecutive pairs of Landsat 8 OLI and Landsat 7 ETM+ data over China and Korea were examined, and NDVIs derived from TOA reflectance and surface reflectance by three atmospheric correction methods were evaluated. Both simulations and comparisons showed that NDVIs derived from atmospherically-corrected surface reflectance had good consistency, while the simulation showed that the agreement varied with atmospheric characteristics. The four pairs of Landsat 8 OLI and Landsat 7 ETM+ NDVI had a mean bias error within ±0.05, and R2 from 0.84 to 0.98. Vegetated land cover types were found to have better NDVI agreement than non-vegetated land cover types. Especially, Landsat 8 OLI consistently generated lower NDVI values in water area than Landsat 7 ETM+, which resulted from higher aerosol optical thickness in atmosphere. Landsat 8 OLI-derived NDVI showed better agreement with MODIS and GOCI NDVI than Landsat 7 ETM+, mainly on vegetated surfaces. Both Landsat 8 OLI and Landsat 7 ETM+ surface reflectance-derived NDVI agreed well with in-situ light emitting diode (LED) NDVI measurements at a homogeneous deciduous forest site. Landsat 8 OLI was also found to produce higher spatial variability of NDVIs than Landsat 7 ETM+ at vegetated and urban areas, but lower variability on water area. The overall good agreement between Landsat 8 OLI NDVI and Landsat 7 ETM+, MODIS and GOCI NDVIs as well as in-situ measurements ensures that it is reliable to integrate the new sensor observations with those from the multiple satellite sensors, given that the same atmospheric correction methods are applied. Furthermore, the greater NDVI contrast between vegetated areas and water areas, and the higher spatial variability of Landsat 8 OLI NDVI indicated that the new sensor has better capability in land surface process monitoring, such as land cover mapping, spatiotemporal dynamics of vegetation growth, and drought assessment.

Developing a two-step retrieval method for estimating total suspended solid concentration in Chinese turbid inland lakes using Geostationary Ocean Colour Imager (GOCI) imagery

Total suspended solid (TSS) concentration is an important water quality parameter. Mapping its varying distribution using satellite images with high temporal resolution is valuable for studying suspended sediment transportation and diffusion patterns in inland lakes. A total of 255 sites were used to make remote-sensing reflectance measurements and surface water sampling at four Chinese inland lakes, i.e. Taihu Lake, Chaohu Lake, Dianchi Lake, and the Three Gorges Reservoir, at different seasons. A two-step retrieval method was then developed to estimate TSS concentration for contrasting Chinese inland lakes, which is described in this article. In the first step, a cluster method was applied for water classification using eight Geostationary Ocean Colour Imager (GOCI) channel reflectance spectra simulated by spectral reflectance measured by an Analytical Spectral Devices (ASD) Inc. spectrometer. This led to the classification of the water into three classes (1, 2, and 3), each with distinct optical characteristics. Based on the water quality, spectral absorption, and reflectance, the optical features in Class 1 were dominated by TSS, while Class 3 was dominated by chl-a and the optical characteristics of Class 2 were dominated jointly by TSS and chl-a. In the second step, class-specific TSS concentration retrieval algorithms were built. We found that the band ratio Band 8/Band 4 was suitable for Class 1, while the band ratio of Band 7/Band 4 was suitable for both Class 2 and Class 3. A comprehensive determination value, combining the spectral angle mapper and Euclidean distance, was adopted to identify the classes of image pixels when the method was applied to a GOCI image. Then, based on the pixel’s class, the class-specific retrieval algorithm was selected for each pixel. The accuracy analysis showed that the performance of this two-step method was improved significantly compared to the unclassed method: the mean absolute percentage error decreased from 38.9% to 24.3% and the root mean square error decreased from 22.1 to 16.5 mg l–1. Finally, the GOCI image acquired on 13 May 2013 was used as a demonstration to map the TSS concentration in Taihu Lake with a reasonably good accuracy and highly resolved spatial structure pattern.

Satellite observation of hourly dynamic characteristics of algae with Geostationary Ocean Color Imager (GOCI) data in Lake Taihu

Phytoplankton bloom in a shallow inland eutrophic lake (Taihu Lake) is characterized by significant spatial and temporal variation and a high concentration of chlorophyll-a (Cchl-a). The observation of the rapidly changing dynamic characteristics of algae is limited by the insufficient temporal resolution of satellite data. The Geostationary Ocean Color Imager (GOCI), launched by Korea, can provide high temporal resolution satellite data to observe the hourly dynamics of algae. In this study, a simple regional NIR-red two-band empirical algorithm of Cchl-a for GOCI is proposed for Taihu Lake. Study results show that the GOCI-derived Cchl-a matches the in situ measured values well. Based on this validated algorithm of Cchl-a, we obtained the hourly maps of Cchl-a from GOCI level-1b data during the period August 6 to August 9, 2013. The spatial variation of GOCI-derived Cchl-a also matches synchronous in situ measured values well, and the temporal variation of GOCI-derived Cchl-a coincides with buoy-measured Cchl-a. The northwestern area of the lake and Meiliang Bay are worst hit by phytoplankton bloom. GOCI-derived Cchl-a revealed a clear evidence of hourly spatial and temporal variations of Cchl-a in Taihu Lake. The vertical current plays an important role in the hourly scale of spatial and temporal variations in phytoplankton. The horizontal current is important to the distribution of phytoplankton over the long term, but spatially and temporally limited in the short term.

基于GOCI影像的湖泊悬浮物浓度分类反演

PDF HTML阅读 XML下载 导出引用 引用提醒 基于GOCI影像的湖泊悬浮物浓度分类反演 DOI: 10.5846/stxb201411152264 作者: 作者单位: 南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目(41171269,41471282) Method for estimating the concentration of total suspended matter in lakes based on goci images using a classification system Author: Affiliation: Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:悬浮物直接影响到光在水体中的传播,进而影响着水生生态环境,最终决定了湖泊的初级生产力。传统的遥感反演估算模型大多是针对某一湖区进行统一建模,忽视了不同区域水体光学性质的复杂差异性,并且传统的传感器时间分辨率和空间分辨率受到一定限制。针对太湖、巢湖、滇池、洞庭湖4个湖区利用两步聚类法将高光谱模拟到GOCI影像上的波段进行分类,将水体类型分为三类,第一类水体为悬浮物主导的水体,第二类水体为悬浮物和叶绿素a共同主导的水体,第三类水体为叶绿素a主导的水体。针对不同类型水体的光学特征,分别构建了悬浮物浓度反演模型,结果表明第一类水体可以利用B7/B4,第二和第三类水体可以利用B7/(B8+B4)作为波段组合因子对悬浮物浓度进行模型构建。精度验证结果表明,分类建模后第一类和第三类水体悬浮物浓度估算精度都得到了较明显提高,第一类水体RMSE降低了9.19mg/L,MAPE降低了3%,第三类水体RMSE降低了5.63 mg/L,MAPE降低了13.97%,第二类水体精度稍有降低。最后将反演模型应用于2013年5月13日的GOCI影像,可知整体而言太湖西南部地区悬浮物浓度较高,东北部地区悬浮物浓度较低,并且从9:00到15:00,太湖南部悬浮物浓度较高的区域在逐渐缩小。 Abstract:Total suspended matter (TSM) is an important water quality indicator that can directly affect the propagation of light in water and influence the aquatic ecological environment, and ultimately determines the primary productivity of a lake. Empirical TSM concentration estimation models are often built for specific study areas, ignoring variation in the optical properties of water among diverse areas. In addition, common satellite sensors cannot be successfully used to monitor inland lakes owing to their temporal and spatial resolution. Taihu Lake, Chaohu Lake, Dianchi Lake, and Dongting Lake were selected as our study lakes, and an automatic two-step cluster method was applied for water classification based on simulated geostationary ocean color imager (GOCI) reflectance spectra. The results showed that the water samples could be classified into three types. The optical features of Water Type 1 were influenced by the TSM, the optical characteristics of Water Type 2 were influenced by both TSM and chlorophyll-a (Chl-a), and the optical properties of Water Type 3 were mainly determined by Chl-a. Estimation models were then developed for each water type using a band ratio of B7/B4 for Water Type 1 and B7/(B8 + B4) for Water Types 2 and 3 to retrieve the concentration of suspended solids. The root mean-squared errors (RMSEs) and minimum absolute percentage errors (MAPEs) of Water Type 1 were 9.19 mg/L and 3%, and those of Water Type 3 were 5.63 mg/L and 13.97%, respectively, which were significantly lower than those estimated using methods that do not consider this classification. The RMSE and MAPE of Water Type 2 were slightly higher than those estimated with the general algorithm. The diurnal variation of the TSM concentration in Taihu Lake was studied based on the GOCI data acquired on May 13, 2013 using this classification method, and the results showed that the concentration of TSM was higher in the southwest than in the northeast. In addition, the area of higher TSM concentration in the southern region of the lake was reduced from 9:00 to 15:00(Beijing Local Time). 参考文献 相似文献 引证文献

Assimilation of next generation geostationary aerosol optical depth retrievals to improve air quality simulations

AbstractPlanned geostationary satellites will provide aerosol optical depth (AOD) retrievals at high temporal and spatial resolution which will be incorporated into current assimilation systems that use low‐Earth orbiting (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) AOD. The impacts of such additions are explored in a real case scenario using AOD from the Geostationary Ocean Color Imager (GOCI) on board of the Communication, Ocean, and Meteorology Satellite, a geostationary satellite observing northeast Asia. The addition of GOCI AOD into the assimilation system generated positive impacts, which were found to be substantial in comparison to only assimilating MODIS AOD. We found that GOCI AOD can help significantly to improve surface air quality simulations in Korea for dust, biomass burning smoke, and anthropogenic pollution episodes when the model represents the extent of the pollution episodes and retrievals are not contaminated by clouds. We anticipate future geostationary missions to considerably contribute to air quality forecasting and provide better reanalyses for health assessments and climate studies.

GOCI image enhancement using an MTF compensation technique for coastal water applications.

The Geostationary Ocean Color Imager (GOCI) is the first optical sensor in geostationary orbit for monitoring the ocean environment around the Korean Peninsula. This paper discusses on-orbit modulation transfer function (MTF) estimation with the pulse-source method and its compensation results for the GOCI. Additionally, by analyzing the relationship between the MTF compensation effect and the accuracy of the secondary ocean product, we confirmed the optimal MTF compensation parameter for enhancing image quality without variation in the accuracy. In this study, MTF assessment was performed using a natural target because the GOCI system has a spatial resolution of 500 m. For MTF compensation with the Wiener filter, we fitted a point spread function with a Gaussian curve controlled by a standard deviation value (σ). After a parametric analysis for finding the optimal degradation model, the σ value of 0.4 was determined to be an optimal indicator. Finally, the MTF value was enhanced from 0.1645 to 0.2152 without degradation of the accuracy of the ocean color product. Enhanced GOCI images by MTF compensation are expected to recognize small-scale ocean products in coastal areas with sharpened geometric performance.

Tracing floating green algae blooms in the Yellow Sea and the East China Sea using GOCI satellite data and Lagrangian transport simulations

In an image from the Geostationary Ocean Color Imager (GOCI) taken on 13 June 2011, a recurrent floating green algae bloom was detected around Qingdao. To detect the bloom an index based on multi-spectral band ratios using three wavelengths (555, 660, 745nm) was utilized. One month later, the GOCI images detected widespread floating green algae patches (east–west elongated) across the Yellow Sea and East China Sea (YS, ECS). The presence of the patches was confirmed from various oceanic observation cruises.Lagrangian particle tracking experiments were conducted to understand the pathway of the floating green algae patches and interpret the physical forcing factors that affect the distribution and advection of the floating green algae. The numerical simulation results indicated that dominant southerly winds during June and July 2011 were related to offshore movement of the floating green algae, especially their eastward extension in the YS/ECS. An infrequent and unusual event occurred in June 2011: a typhoon MEARI, caused the green algae to detach from the coast and initiated movement to the east. After the typhoon event, sea surface temperature recovered rapidly enough to grow the floating green algae, and wind and local current controlled the movement of the massive floating algae patches (coastal accumulation or offshore advection in the YS/ECS). Analysis of the floating green algae pixels' movement during passage of Typhoon MAON in July 2011 revealed that the floating green algae patches were significantly controlled by both ocean currents and enhanced winds. These findings suggest that the floating green algae bloom off Qingdao and in the middle of the YS and ECS in the summer of 2011 occurred due to the combined effects of recent rapid expansion of seaweed aquaculture, strong winds, and the wind patterns in blooming regions. Our combined approach, using satellite data and numerical simulations, provides a robust estimate for tracing and monitoring changes in green algae blooms on a regional scale.

천리안 위성 GOCI 영상을 이용한 남해안의 시공간적 적조변화 분석

This study deals with red tide detection by using the remote sensing imagery from the Geostationary Ocean Color Imager (GOCI), the world`s first geostationary orbit satellite, around the southern coast of Korea where the most severe red tide occurred recently. The red tide zone was determined by the available data selection from the GOCI imagery during the period of red tide occurrence and also the severe red tide zone was detected through the spatial analysis by temporal change out of the red tide zone. This study results showed that the coast in the vicinity of the Hansan and Yokji in Tongyeong-si was classified into the severe red tide zone, and that the red tide was likely to spread from the coast of Hansan and Yokji to the one of Sanyang-eub. In addition, the comparative analysis between the area of red tide occurrence, the prevention activities of Gyeongsangnam-do provincial government and the amount of the damage cost over time showed close correlation among them. It is still early to conclude that the study is showing the severe red tide zone and the spread path exactly due to various factors for red tide occurrence and activities. In order to improve the reliability of the results, the more data analysis is required.

Harmful algal bloom (HAB) in the East Sea identified by the Geostationary Ocean Color Imager (GOCI)

Harmful Cochlodinium polykrikoides blooms have frequently appeared and caused fatal harm to aquaculture in Korean coastal waters since 1995. We investigated the applicability of GOCI, the world's first Geostationary Ocean Color Imager, in monitoring the distribution and temporal movement of a harmful algal bloom (HAB) that was discovered in the East Sea near the Korean peninsula in August 2013. We identified the existence of C. polykrikoides at a maximum cell abundance of over 6000cells/mL and a chlorophyll a concentration of over 400mg/m3. In areas of C. polykrikoides blooms, GOCI remote sensing reflectance (Rrs) spectra demonstrated the typical radiometric features of a HAB, and from the diurnal variations using GOCI-derived chlorophyll concentration images, we were able to identify the vertical migration of the red tide species. We also found that the formation and propagation of the HAB had relations with cold water mass in the coastal region. GOCI can be effectively applied to the monitoring of short-term and long-term movements of red tides.

현장관측용 분광 광도계의 상대 검교정 시스템 개발

천리안해양관측위성(Geostationary Ocean Color Imager, GOCI)이 2010년 6월에 발사된 이후, 영상 자료의 보정과 검증을 위한 여러 차례의 현장 관측이 한반도 주변에서 수행되었다. 한국해양과학기술원 해양위성센터(Korea Ocean Satellite Center, KOSC)에서는 Analytical Spectral Devices (ASD)사의 분광 광도계 FieldSpec3나 TriOS사의 분광 광도계 RAMSES와 같은 현장관측장비의 특성 변화를 확인하기 위하여 미국국립표준기술원(National Institute of Standards and Technology, NIST)의 표준화 절차를 거친 광원과 표준 분광 광도계를 이용하여 각각의 현장 관측 마다 기기의 성능을 측정하였다. 본 논문에서는 해양위성센터에 구축된 광학 실험실과 현장관측 분광 광도계의 상대적 복사 검교정 방법에 대해서 소개하고 있다. 광학 실험실은 98% 이상의 광원 균질성을 지니는 20인치 적분구(USS-2000S, LabSphere)와 360 nm 부터 1100 nm 까지 1.6 nm 파장 간격으로 측정이 가능한 표준 분광 광도계(MCPD9800, Photal), 그리고 <TEX>${\pm}0.1mm$</TEX>의 편평도를 가지는 광학테이블(<TEX>$3600{\times}1500{\times}800mm^3$</TEX>)을 기본으로 구성되어 있다. 실험실 내부는 정확한 검교정 실험을 위하여 일정한 온습도를 유지하고 있으며, 동일한 광원에 동일한 위치에서 표준과 현장관측용 분광 광도계를 동시에 측정하는 방법을 기본으로 한다. 해양위성센터가 보유하고 있는 ASD 를 측정한 결과, 현장관측용 분광광도계의 결과가 푸른 가시광 영역에서 미세한 차이가 측정 시 마다 나타나는 것을 확인하였고, 더불어 1년간의 상대 검교정 실험에 따르면 평균적으로 4.41% 정도의 파장별 광특성이 변화하는 것을 확인하였다. 이러한 결과는 측정 정확도를 유지하고, GOCI 자료의 신뢰도를 확보하기 위하여 지속적인 검교정 실험을 수행해야 하는 이유를 보여주고 있다. After launching the Geostationary Ocean Color Imager (GOCI) on June 2010, field campaigns were performed routinely around Korean peninsula to collect in-situ data for calibration and validation. Key measurements in the campaigns are radiometric ones with field radiometers such as Analytical Spectral Devices FieldSpec3 or TriOS RAMSES. The field radiometers must be regularly calibrated. We, in the paper, introduce the optical laboratory built in KOSC and the relative calibration method for in-situ measurement spectroradiometer. The laboratory is equipped with a 20-inch integrating sphere (USS-2000S, LabSphere) in 98% uniformity, a reference spectrometer (MCPD9800, Photal) covering wavelengths from 360 nm to 1100 nm with 1.6 nm spectral resolution, and an optical table (<TEX>$3600{\times}1500{\times}800mm^3$</TEX>) having a flatness of <TEX>${\pm}0.1mm$</TEX>. Under constant temperature and humidity maintainance in the room, the reference spectrometer and the in-situ measurement instrument are checked with the same light source in the same distance. From the test of FieldSpec3, we figured out a slight difference among in-situ instruments in blue band range, and also confirmed the sensor spectral performance was changed about 4.41% during 1 year. These results show that the regular calibrations are needed to maintain the field measurement accuracy and thus GOCI data reliability.

GOES Imager Shows Diurnal Changes of a &lt;italic&gt;Trichodesmium erythraeum&lt;/italic&gt; Bloom on the West Florida Shelf

The advantages of geostationary observations of sediment plumes and phytoplankton blooms have been reported for coastal waters in the southern North Sea and west Pacific. So far, similar observations have not been possible for the Gulf of Mexico where blooms of Trichodesmium erythraeum often occur. Here, using data collected by the Geostationary Operational Environmental Satellite (GOES) Imager, we document diurnal changes of a Trichodesmium bloom first identified by the Moderate Resolution Imaging Spectroradiometer (MODIS). Despite the low-signal-to-noise ratio ( ~ 46 : 1 for typical ocean radiance), the 550-750-nm band revealed clear patterns of Trichodesmium mats floating on the ocean surface and their temporal changes between 14:15 and 22:30 GMT on May 22, 2004. Normalization of the delineated bloom against the ocean background provided an effective atmospheric correction that enabled quantification of the changes in bloom size (i.e., area) and bloom intensity over the course of a day. The area coverage increased by about eightfold from midmorning (14-15 GMT) to reach its maximum around 18:30 GMT, whereas the mean intensity of the bloom area increased by ~ 22% from midmorning to 17:30 GMT. In the afternoon, while the bloom area remained relatively stable on the water surface, bloom intensity sharply decreased. These temporal patterns may be caused by physical aggregation and/or vertical migration of the Trichodesmium cells, and they agree well with the diurnal changes of a harmful algal bloom of the dinoflagellate Prorocentrum donghaiense in the East China Sea observed by the Geostationary Ocean Color Imager.

Geostationary satellite observations of dynamic phytoplankton photophysiology

AbstractSince June 2010, the Geostationary Ocean Color Imager (GOCI) has been collecting the first diurnally resolved satellite ocean measurements. Here GOCI retrievals of phytoplankton chlorophyll concentration and fluorescence are used to evaluate daily to seasonal changes in photophysiological properties. We focus on nonphotochemical quenching (NPQ) processes that protect phytoplankton from high light damage and cause strong diurnal cycles in fluorescence emission. This NPQ signal varies seasonally, with maxima in winter and minima in summer. Contrary to expectations from laboratory studies under constant light conditions, this pattern is highly consistent with an earlier conceptual model and recent field observations. The same seasonal cycle is registered in fluorescence data from the polar‐orbiting Moderate Resolution Imaging Spectroradiometer Aqua satellite sensor. GOCI data reveal a strong correlation between mixed layer growth irradiance and fluorescence‐derived phytoplankton photoacclimation state that can provide a path for mechanistically accounting for NPQ variability and, subsequently, retrieving information on iron stress in global phytoplankton populations.

High temporal resolution aerosol retrieval using Geostationary Ocean Color Imager: application and initial validation

The Geostationary Ocean Color Imager (GOCI) provides multispectral imagery of the East Asia region hourly from 9:00 to 16:00 local time (GMT+9) and collects multispectral imagery at eight spectral channels (412, 443, 490, 555, 660, 680, 745, and 865 nm) with a spatial resolution of 500 m. Thus, this technology brings significant advantages to high temporal resolution environmental monitoring. We present the retrieval of aerosol optical depth (AOD) in northern China based on GOCI data. Cross-calibration was performed against Moderate Resolution Imaging Spectrometer (MODIS) data in order to correct the land calibration bias of the GOCI sensor. AOD retrievals were then accomplished using a look-up table (LUT) strategy with assumptions of a quickly varying aerosol and a slowly varying surface with time. The AOD retrieval algorithm calculates AOD by minimizing the surface reflectance variations of a series of observations in a short period of time, such as several days. The monitoring of hourly AOD variations was implemented, and the retrieved AOD agreed well with AErosol RObotic NETwork (AERONET) ground-based measurements with a good R2 of approximately 0.74 at validation sites at the cities of Beijing and Xianghe, although intercept bias may be high in specific cases. The comparisons with MODIS products also show a good agreement in AOD spatial distribution. This work suggests that GOCI imagery can provide high temporal resolution monitoring of atmospheric aerosols over land, which is of great interest in climate change studies and environmental monitoring.

Satellite multi-sensor mapping of suspended particulate matter in turbid estuarine and coastal ocean, China

In this work, five ocean-colour sensors, the Moderate Resolution Imaging Spectroradiometer aboard the Terra satellite (Terra MODIS), Moderate Resolution Imaging Spectroradiometer aboard the Aqua satellite (Aqua MODIS), Medium Range Imaging Spectrometer aboard the Environmental Satellite (Envisat MERIS), Medium Resolution Spectral Imager aboard the FY-3 satellite (FY-3 MERSI), and Geostationary Ocean Colour Imager (GOCI), were selected to examine the compatibility of an algorithm proposed for suspended particulate matter (SPM) retrieval and concordance of satellite products retrieved from different ocean-colour sensors. The results could effectively increase revisit frequency and complement a temporal gap of time series satellites that may exist between on-orbit and off-orbit. Using in situ measurements from 17 cruise campaigns between 2004 and 2012, the SPM retrieval algorithm was recalibrated so as to be universal and adapted for multi-sensor retrievals. An inter-comparison of multi-sensor-derived products showed that GOCI-derived SPM and Envisat MERIS-derived SPM had the best fitting on a 1:1 scatterplot, with a statistic regression slope of 0.9617 and an intercept of 0.0041 (in units of g l–1), respectively. SPM products derived from three sensors with nearly synchronous transit, Envisat MERIS, Terra MODIS, and FY-3 MERSI, exhibited excellent accordance with mean differences of 0.056, 0.057, and 0.013 g l–1 in three field fixed stations, respectively, in the Yangtze estuary. Terra MODIS-derived SPM with GOCI-derived SPM, except in the high SPM waters of Hangzhou Bay, and Aqua MODIS-derived SPM with GOCI-derived SPM, except in the moderate SPM waters of the South Branch and south of the Subei Coast, showed a good correspondence. Meanwhile, synchronous multi-sensor-derived SPM with concurrent in situ SPM time series observed in fixed field stations mostly displayed a good correspondence. Results suggest that the algorithm is feasible and compatible for SPM retrieval by multiple sensors.

Application of the Geostationary Ocean Color Imager (GOCI) to estimates of ocean surface currents

AbstractThe Geostationary Ocean Color Imager (GOCI) can be utilized efficiently to observe subtle changes in oceanic environments under cloud‐free conditions because it receives ocean color images around the Korean Peninsula hourly, for 8 h a day. Here we investigated the applicability of the GOCI for estimating hourly variations in ocean surface currents, which provide significant information on seawater circulation for fisheries, shipping controls, and more. Ocean surface currents were deduced from eight images of GOCI‐derived total suspended matter (TSM) from highly turbid coastal waters and images of chlorophyll concentration (CHL) for relatively clear waters. The results showed that GOCI TSM‐derived ocean surface currents can effectively estimate and represent fast tidal currents, as well as flood and ebb tides on the west coast of Korea, in comparison with in situ measurements. GOCI‐derived CHL scenes successfully illustrated currents moving along boundaries where warm and cold seawaters mix, in addition to mesoscale currents such as the East Korea Warm Current (EKWC) in the East Sea of Korea. Satellite‐based sea surface temperature and sea surface height images supported the reliability of GOCI‐derived ocean surface currents in the East Sea.

정지궤도 천리안위성 해양관측센서 GOCI의 Tasseled Cap 변환계수 산출연구

이 연구에서는 Geostationary Ocean Color Imager(GOCI) 센서에 적용할 수 있는 고유의 Tasseled Cap Transformation(TCT) 계수를 제시하고 있다. TCT는 다중밴드 센서 자료로부터 지표의 특성을 분석하는 전통적인 영상변환 방법 중 하나로 새로운 다중밴드 광학센서가 관측을 시작하는 경우 센서의 특성 차이로 인하여 각각의 육상관측 위성센서에 적합한 TCT 계수들이 장기 분석을 통하여 수립되어야 한다. GOCI 센서는 해양관측이 주 목적으로 개발되었으나 영상의 상당 부분은 육지를 관측하고 있으며 밴드 구성은 육지관측에도 일반적으로 이용되는 Visible-Near InfraRed(VNIR) 영역의 정보를 포함하고 있다. 또한 GOCI 센서의 높은 시간 해상도는 지표의 일별 변화의 관측에도 유용하게 사용될 수 있다. 이러한 장점을 이용하여 GOCI 센서에 대한 고유한 TCT가 제공된다면 GOCI 센서의 관측범위 내에서 준 실시간으로 지표변화에 대한 분석과 해석이 가능할 것이다. TCT는 일반적으로 "Brightness", "Greenness", "Wetness"의 세 가지 정보를 포함하지만, ShortWave InfraRed(SWIR) 파장대역이 없는 GOCI 센서의 경우에는 "Wetness"의 정보를 얻을 수 없다. GOCI 센서의 높은 시간 해상도의 활용을 극대화하기 위해서는 "Wetness"의 정보가 제공되어야 한다. "Wetness"의 정보를 얻기 위해 GOCI 주성분 분석(Principal Component Analysis: PCA) 공간을 MODIS TCT 공간에 선형 회귀하는 방법이 사용되었다. 이 연구에서 산출된 GOCI TCT 계수는 정지궤도의 특성에 의해 관측 시간대별로 다른 변환계수를 가질 수 있다. 이 차이를 알아보기 위하여 GOCI TCT 자료와 MODIS TCT 자료 사이의 상관관계가 비교되었다. 그 결과, "Brightness"와 "Greenness"는 4시 자료, "Wetness"는 2시 자료의 변환계수가 선택되었다. 최종적으로 산출된 변환계수의 적절성을 평가하기 위하여 GOCI TCT 자료는 MODIS TCT 영상 및 여러 육상 파라미터들과 비교되었다. GOCI TCT 영상은 MODIS TCT 영상보다 지표 피복의 분류가 더 세밀하게 표현되었으며, GOCI TCT 공간의 지표 피복 분포도 유의미한 결과를 보여줬다. 또한 GOCI TCT의 "Brightness", "Greenness", "Wetness" 자료는 Albedo(<TEX>$R^2$</TEX> = 0.75), Normalized Difference Vegetation Index(NDVI) (<TEX>$R^2$</TEX> = 0.97), Normalized Difference Moisture Index(NDMI) (<TEX>$R^2$</TEX> = 0.77)와 각각 비교적 높은 상관관계가 나타났다. 이러한 결과들은 적절한 TCT 계수의 산출이 이루어졌다는 것을 보여준다. The objective of this study is to determine Tasseled Cap Transformation (TCT) coefficients for the Geostationary Ocean Color Imager (GOCI). TCT is traditional method of analyzing the characteristics of the land area from multi spectral sensor data. TCT coefficients for a new sensor must be estimated individually because of different sensor characteristics of each sensor. Although the primary objective of the GOCI is for ocean color study, one half of the scene covers land area with typical land observing channels in Visible-Near InfraRed (VNIR). The GOCI has a unique capability to acquire eight scenes per day. This advantage of high temporal resolution can be utilized for detecting daily variation of land surface. The GOCI TCT offers a great potential for application in near-real time analysis and interpretation of land cover characteristics. TCT generally represents information of "Brightness", "Greenness" and "Wetness". However, in the case of the GOCI is not able to provide "Wetness" due to lack of ShortWave InfraRed (SWIR) band. To maximize the utilization of high temporal resolution, "Wetness" should be provided. In order to obtain "Wetness", the linear regression method was used to align the GOCI Principal Component Analysis (PCA) space with the MODIS TCT space. The GOCI TCT coefficients obtained by this method have different values according to observation time due to the characteristics of geostationary earth orbit. To examine these differences, the correlation between the GOCI TCT and the MODIS TCT were compared. As a result, while the GOCI TCT coefficients of "Brightness" and "Greenness" were selected at 4h, the GOCI TCT coefficient of "Wetness" was selected at 2h. To assess the adequacy of the resulting GOCI TCT coefficients, the GOCI TCT data were compared to the MODIS TCT image and several land parameters. The land cover classification of the GOCI TCT image was expressed more precisely than the MODIS TCT image. The distribution of land cover classification of the GOCI TCT space showed meaningful results. Also, "Brightness", "Greenness", and "Wetness" of the GOCI TCT data showed a relatively high correlation with Albedo (<TEX>$R^2$</TEX> = 0.75), Normalized Difference Vegetation Index (NDVI) (<TEX>$R^2$</TEX> = 0.97), and Normalized Difference Moisture Index (NDMI) (<TEX>$R^2$</TEX> = 0.77), respectively. These results indicate the suitability of the GOCI TCT coefficients.

GOCI 위성영상과 HYSPLIT 모델을 이용한 화산재 확산경로 예측 - 2013년 9월 17일 분화된 사쿠라지마 화산을 중심으로 -

일본 가고시마에 위치하고 있는 사쿠라지마 화산은 세계에서 가장 활발하게 활동하고 있는 화산 중 하나이다. 2013년 8월 18일 사쿠라지마 화산은 최근 들어 가장 큰 규모의 분화를 하였고, 분화에 의해 발생한 화산재는 일본뿐만 아니라 국내 유입에 대한 우려를 야기하였다. 본 연구에서는 사쿠라지마 화산분출물이 한반도에 미치는 영향을 분석하기 위하여 수치실험을 수행하였다. 화산재 확산 경로를 예측하기 위하여 HYSPLIT 확산모델과 UM 기상자료를 이용하였으며, 연구대상일은 화산재 유입이 예상되는 규모로 분화한 2013년 9월 17일이다. 또한 천리안 위성영상을 이용하여 화산재를 탐지하였고, 모의실험 결과와 비교를 통해 검증을 수행하였다. 그 결과, HYSPLIT 모델기반의 화산재 확산 예측 경로와 GOCI 위성 영상에서 탐지한 화산재 분포지역과 63.52%가 일치하는 것을 보였다. Mt. SAKRAJIMA in southern Kagosima, japan is one of the most active volcanoes in the world. On 18 August 2013, the SAKRAJIMA volcano recently went into the largest scaled eruption with a huge plume of volcanic ash. Therefore, the concern arises if this considerable amount of ashes might flow into the Korea peninsula as well as Japan. In this paper, we performed numeric experiment to analyze how volcanic product resulted from the SAKRAJIMA volcano has impacted on Korea. In order to predict the spread pathway of ash, HYSPLIT model and UM data has been used and 17th September 2013 has been selected as observation date since it is expected that the volcanic ash would flow into the South Korea. In addition, we have detected ash dispersion by using optical Communication, Ocean and Meteorological Satellite- Geostationary Ocean Color Imager (COMS-GOCI) images. As the results, we come to a very satisfactory conclusion that the spread pathway of volcanoes based on HYSPLIT model are matched 63.52 % with ash dispersion area detected from GOCI satellites image.

천리안 위성영상에 감지된 사쿠라지마 화산분화와 지자기 변동 분석 연구 - 2013년 8월 18일 분화를 중심으로 -

2013년 8월 18일 일본의 사쿠라지마 화산에서 비교적 큰 규모의 분화가 발생하였다. 이에 본 연구에서는 천리안 위성 자료를 이용하여 화산분화 다음날 주변 지역의 화산재를 감지하였으며, 기상청 청양 지자기 관측소 자료와 함께 일본 지자기 관측소 자료를 이용하여 지자기 변동에 대해 분석하였다. 먼저, 지자기 관측 자료를 이용하여 주성분 분석을 수행하고 관측 자료의 재구성 자료를 구축하였다. 재구성된 자료는 웨이블릿 기반 셈블런스 분석을 수행하였다. 다음으로는 지자기 관측 자료의 고유값 분석을 수행하고 Kp 지수와의 웨이블릿 기반 셈블런스 필터링을 통해서 태양의 영향을 최소화하였다. 분석결과에서는 전체적으로 화산 발생 시점에서 이벤트가 발생하는 것을 확인 할 수 있었다. 다만, 일부 지자기 관측소의 경우 화산이 아닌 다른 영향을 받았을 가능성을 배제 할 수 없다. 이 연구에서는 국내 연구로는 드물게 화산 분화에 의한 지자기 영향을 분석하였으며 향후 지진 화산 연구에 도움이 될 수 있을 것으로 기대한다. On Aug 18, 2013, Sakurajima volcano in Japan erupted on a relatively large-scale. Geostationary Ocean Color Imager (GOCI) had used to detect volcanic ash in the surrounding area on the next day of this eruption. The geomagnetic variation has been analyzed using geomagnetic data from Cheongyang observatory in Korea and several geomagnetic observatories in Japan. First, we reconstruct geomagnetic data by principal component analysis and conduct semblance analysis by wavelet transform. Secondly, we minimize the error of solar effect by using wavelet based semblance filtering with Kp index. As a result of this study, we could confirm that the geomagnetic variation usually occur at the moment of Sakurajima volcano eruption. However, we cannot rule out the possibilities that it could have been impacted by other factors besides volcanic eruption in other variation's cases. This research is an exceptional study to analyze geomagnetic variation related with abroad volcanic eruption uncommonly in Korea. Moreover, we expect that it can help to develop further study of geomagnetic variation involved in earthquake and volcanic eruption.