分析 2016 年梅姬颱風期間臺灣北部沿海地區期 間葉綠素 a 與總懸浮固體之相關性

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：103

、訪客IP：3.136.22.184

姓名

江櫚源(Rafael Rivera Palma) 查詢紙本館藏

畢業系所

遙測科技碩士學位學程

論文名稱

分析 2016 年梅姬颱風期間臺灣北部沿海地區期間葉綠素 a 與總懸浮固體之相關性
(Analyzing the correlation between Chlorophyll-a and Total Suspended Solids during typhoon events (2016) in coastal areas of northern Taiwan)

相關論文

★ 評估不同數值地型資料於降雨型崩塌作用模式之應用性-以小尺度坡面之崩塌事件為例	★ 應用最大熵法於蒙古山區進行森林樹種分類
★ 利用Landsat衛星影像監測並預測中美洲瓜地馬拉首都–瓜地馬拉市之都市發展	★ 都市化與發展：對海地永續發展之意涵
★ 客家文化重點發展區之客家政策研究：以龍潭大池整體環境規劃與營造計畫為例	★ 利用多時期Landsat衛星影像進行森林砍伐之評估 -以尼加拉瓜波沙瓦生態保護區為例
★ 融合光學衛星影像及地形資訊進行崩塌地之判釋	★ 應用Sentinel-1 SAR影像進行水稻監測-以泰國中部大城府省為例
★ 都市三維結構變遷之分析-以臺灣臺北市為例	★ 應用 Sentinel-1 合成孔徑雷達資料進行地層下陷監測 - 以 2017 年泰國曼谷都會區為例
★ 利用人工神經網絡模型建立多事件為基礎之崩塌模型-以台灣玉山國家公園為例	★ 應用衛星影像於都市發展之監測與預測 ─以台灣桃園為例
★ 分析降雨及不透水面對台南水患發生之影響	★ 應用Google Earth Engine與影像分類技術於巴拉圭查科地區進行森林砍伐評估
★ 應用多時期Sentinel-1 合成孔徑雷達影像進行崩塌及淹水偵測-以印尼爪哇島Pacitan地區為例	★ 母岩裸露指標之建立並應用於崩塌判釋與監測

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

葉綠素 a (Chlorophyll-a) 是海水中生物量生產的主要指標之一，對人類生活可能有利亦可有害。首先，其為魚類和相關營養鏈的食物，但同時如果因葉綠素過多而導致之生物量增長過度以及優養化，則可能對海洋生物和漁業有害。隨著地球同步衛星的出現，我們可以利用其提供之高時間解析度影像對地表進行更詳盡的動態分析，例如颱風等特定情景下沿海地區的養分和沈積物循環的運作過程。2016 年 9 月梅姬颱風 (Typhoon Megi) 作為本研究之研究對像，造成了宜蘭地區因強降雨和狂風而遭受相當大的災害損失。本研究旨在分析
該颱風事件對沿海水域之總懸浮固體 (Total Suspended Solids, TSS) 和葉綠素 a 之濃度相關性。
本研究收集地球同步海洋彩色成像儀 (Geostationary Ocean Color Imager )之葉綠素 a 和Rrs 555 (波長為 555 nm 之反射率)影像資料，其中 Rrs 555 為用以推估總懸浮固體濃度。
本研究針對梅姬颱風侵台 (2016 年 09 月 22 日至 30)之前、中、後(各為三日)分別分析兩者之相關性變化。由於颱風期間之雲霧影響，使得影像資料之應用受限，本研究應用以一三維影像(空間與時間)修復技術來重建影像資料，以利後續分析。結果顯示，整體來說颱風前Chl-a 與 TSS 之間有較高的相關性(R2=0.5)，Chl-a 可透過 TSS 與三次多項式進行推估，而颱風後兩者相關性較低(R2=0.27)，且此相關性不僅在時間上有所變化，在距海岸、河口距離不同時亦有不同的關係變化，需要綜合颱風動態、降雨特性、河川流量等問因素進行解釋。
本研究突顯了使用之地球同步衛星資料於分析颱風等災害的生態動力過程的優勢。儘管如此，本研究仍有部分限制，特別是在時序影像中，遺失數據的重建方面應可進行改進，特別是颱風期間雲層覆蓋率很極高的情形，而針對此問題的改進或解決，或許可成為未來
針對颱風動態等相關多時序分析的研究方向。

摘要(英)

Chlorophyll-a (Chl-a) is one of the main indicators of biomass production in ocean waters, which can be beneficial or detrimental for human activities, as they serve as food for fish and trophic chains. On the other hand, if biomass growth is excessive it can induce eutrophication, etrimental
to sea life and fishery. With the advent of geostationary satellites, now it is possible to have better temporal and spatial resolutions and thus have more data to analyze local contexts such as how nutrient and sediment cycles work in coastal areas during specific scenarios such as typhoons. In
September 2016, Typhoon Megi caused severe damage in the area of Taiwan, especially on Yilan Bay due to heavy rainfall and strong winds. This study aims to explore the effect of this typhoon on the correlation behaviors in concentrations of Total Suspended Solids (TSS) and chlorophyll-a in coastal waters during Typhoon Megi.
In this study, data of Chlorophyll-a and Rrs 555 (used for TSS estimation) were obtained from the Geostationary Ocean Color Imager (GOCI) corresponding to the period from September 22th to 31st. Corresponding to three days respectively for the period before, during, and after Typhoon Megi. Due to the cloud coverage which impedes the application of the optical data, in this study, all images were preprocessed to produce reconstructed images using a data-driven 3D (spatial and temporal) technique to reconstruct contaminated images. Results show a higher R2
value of 0.5 was obtained using pre-event scenes, and Chl-a can be effectively estimated using a cubic regression model, while a lower R2 (=0.27) is derived using data after Typhoon Megi. In addition to the variation in time, the correlation patterns varying in different coastal zones were also observed, which can be subjective to typhoon dynamics, precipitation, flow discharge, and so on.
This study shows the advantages of using geostationary satellites for the analysis of ecological dynamics in the case of disasters such as typhoons due to their higher temporal and spatial

關鍵字(中)

★ 葉綠素 a
★ 總懸浮固體
★ 地球同步海洋彩色成像儀
★ 梅姬颱風
★ 資料重建

關鍵字(英)

★ Chlorophyll-a
★ Total Suspended Solids
★ GOCI
★ Typhoon Megi
★ data reconstruction

論文目次

摘要................................................ i
ABSTRACT............................................ ii
TABLE OF CONTENTS................................... iv
LIST OF FIGURES..................................... vi
LIST OF TABLES ................................... viii
CHAPTER 1 INTRODUCTION .............................. 1
CHAPTER 2 LITERATURE REVIEW ......................... 3
2.1 TOTAL SUSPENDED SOLIDS (TSS) AND TYPHOON EVENTS IN TAIWAN............................................... 3
2.2 SATELLITE OCEAN COLOR OBSERVATION OF COASTAL AREAS................................................ 5
2.3 TOTAL SUSPENDED SOLIDS ESTIMATION ..................................................... 9
2.4 CHLOROPHYLL-a ESTIMATION.......................................... 11
2.5 RELATION BETWEEN TOTAL SUSPENDED SOLIDS AND CHLOROPHYLL-A................................................... 13
CHAPTER 3 STUDY AREA AND DATA................................................ 17
3.1 STUDY AREA AND TYPHOON EVENT............................................... 17
3.1.1 STUDY AREA................................................ 17
3.1.2 TYPHOON EVENTS .................................................... 22
3.2 GEOSTATIONARY OCEAN COLOR IMAGER.............................................. 23
3.2.1 SPECIFICATIONS ............................... 23
3.2.2 CHLOROPHYLL-a PRODUCT......................... 25
3.2.3 PREPROCESSING ................................ 26
3.2.4 DATA COLLECTION............................... 27
CHAPTER 4. METHOD................................... 28
4.1 WORKFLOW ....................................... 28
4.2 DATA RESTORATION................................ 30
4.3 TOTAL SUSPENDED SOLIDS ESTIMATION .............. 31
4.4 CORRELATION ANALYSIS............................ 32
CHAPTER 5 RESULTS .................................. 33
5.1 GOCI CHLOROPHYLL-a AND TOTAL SUSPENDED SOLIDS.............................................. 33
5.2. CORRELATION ANALYSIS........................... 37
5.2.1. SPATIAL CORRELATION ......................... 37
5.2.2. TEMPORAL CORRELATION ........................ 40
5.3. CORRELATION MODELS............................. 42
5.4. RELATIONSHIPS BETWEEN CHL-a AND TSS ........... 44
CHAPTER 6 DISCUSSION................................ 46
6.1. CORRELATIONS BETWEEN CHL-a AND TSS ............ 46
6.2. THE EFFECT OF TTS ON CHL- a DURING TYPHOON MEGI................................................ 47
6.3. CHANGES OF CHL- a AND TSS CORRELATION IN COASTAL AREAS .................................................... 50
6.4. POTENTIAL EFFECTS OF TYPHOON DYNAMICS............................................ 53
6.5. POTENTIAL FOR RIVER PLUME DEFINITION .................................................... 54
6.6. LIMITATIONS OF THE STUDY AND FUTURE WORK................................................ 56
6.7. APPLICATION OF THE STUDY............................................... 57
CHAPTER 7 CONCLUSIONS .................................................... 59
REFERENCES.......................................... 62

參考文獻

[1] International Ocean Colour Coordinating Group [IOCCG]. Earth Observations in Support of Global Water Quality Monitoring, IOCCG Report Series No. 17. Dartmouth, NS: IOCCG. 2018.
[2] Environmental Protection Administration Executive Yuan, R.O.C. Taiwan. Sea area environmental classification and marine environmental quality standards. 2018.
[3] Luo, Y.; Liu, J.-W.; Wu, J.-W.; Yuan, Z.; Zhang, J.-W.; Gao, C.; Lin, Z.-Y. Comprehensive Assessment of Eutrophication in Xiamen Bay and Its Implications for Management Strategy in Southeast China. Int. J. Environ. Res. Public Health 2022, 19, 13055. https://doi.org/10.3390/ ijerph192013055. 2022.
[4] Agarwal, N. Sharma, R. Thapliyal, P. Gangwar, R. Kumar, P. Kumar, R. Geostationary Satellite-Based Observations for Ocean Applications. Current Science. 117. 506. 10.18520/cs/v117/i3/506-515. 2019.
[5] Klemas, Victor. Resolution requirements for coastal applications of new geostationary satellites. Proceedings of MTS/IEEE OCEANS, 2005. 2005. 227 -233 Vol. 1. 10.1109/OCEANS.2005.1639767. 2005.
[6] Romdani, Andhy & Chen, Jia-Lin & Chien, Hwa & Jing-Hua, Lin & Liao, ChingYuan & Hou, Cheng-Chien. Downdrift Port Siltation Adjacent to a River Mouth: Effects of Mesotidal Conditions and Typhoon. Journal of Waterway, Port, Coastal,
and Ocean Engineering. 149. 10.1061/JWPED5.WWENG-1940. 2023.
63
[7] Chau, P.M.; Wang, C.-K.; Huang, A.-T. The Spatial-Temporal Distribution of GOCI-Derived Suspended Sediment in Taiwan Coastal Water Induced by Typhoon Soudelor. Remote Sens. 2021, 13, 194. 2021.
[8] Chen, C.W.; Oguchi, T.; Hayakawa, Y.S.; Saito, H.; Chen, H.; Lin, G.W.; Wei, L.W.; Chao, Y.C. Sediment yield during typhoon events in relation to landslides, rainfall, and catchment areas in Taiwan. Geomorphology 2018, 303, 540–548. 2018.
[9] Hung, C.; Lin, G.-W.; Kuo, H.-L.; Zhang, J.-M.; Chen, C.-W.; Chen, H. Impact of an Extreme Typhoon Event on Subsequent Sediment Discharges and RainfallDriven Landslides in Affected Mountainous Regions of Taiwan. Geofluids 2018, 2018, 1–11. 2018.
[10] Chien, H.; Chiang, W.S.; Kao, S.J.; Liu, J.T.; Liu, K.K.; Liu, P.L.F. Sediment Dynamics observed in the Jhoushuei River and Adjacent Coastal Zone in Taiwan
Strait. Oceanography 2011, 24, 122–131. 2011.
[11] Kao, S. J., S. Jan, S. C. Hsu, T. Y. Lee, and M. Dai. Sediment budget in the Taiwan Strait with high fluvial sediment inputs from mountainous rivers: New observations and synthesis. Terr. Atmos. Ocean. Sci., 19, 525-546, doi:
10.3319/TAO.2008.19.5.525. 2008.
[12] Chen WB, Liu WC, Kimura N, Hsu MH. Particle release transport in Danshuei River estuarine system and adjacent coastal ocean: a modeling assessment. Environ Monit Assess. 2010 Sep;168(1-4):407-28. doi: 10.1007/s10661-009-1123-2. Epub 2009 Aug 13. PMID: 19680754. 2010.
[13] Huang, C.; Liu, Y.; Luo, Y.; Wang, Y.; Liu, X.; Zhang, Y.; Zhuang, Y.; Tian, Y. Improvement and Assessment of Ocean Color Algorithms in the Northwest 64 Pacific Fishing Ground Using Himawari-8, MODIS-Aqua, and VIIRS-SNPP. Remote Sens. 2022, 14, 3610. https://doi.org/10.3390/ rs14153610. 2022.
[14] Groom, S., Sathyendranath, S., Ban, Y., Bernard, S., Brewin, R., Brotas, V., Brockmann, C., Chauhan, P., Choi, J., Chuprin, A., Ciavatta, S., Cipollini, P., Donlon, C., Franz, B., He, X., Hirata, T., Jackson, T., Kampel, M., Krasemann, H., … Wang, M. Satellite Ocean Colour: Current Status and Future Perspective. In Frontiers in Marine Science (Vol. 6). Frontiers Media SA. https://doi.org/10.3389/fmars.2019.00485. 2019.
[15] Valdés, L., Lomas, M.W. New light for ship-based time series. In: What are Marine Ecological Time Series telling us about the ocean? A status report, pp. 11–17. Ed. by T. D. O′Brien, L. Lorenzoni, K. Isensee, and L. Valdés. IOC
UNESCO, IOC Technical Series, No. 129. 297 pp. 2017.
[16] Lopez-Betancur, D., Moreno, I., Guerrero-Mendez, C., Saucedo-Anaya, T., González, E., Bautista-Capetillo, C., & González-Trinidad, J. (2022).Convolutional Neural Network for Measurement of Suspended Solids and Turbidity. Applied Sciences, 12(12), 6079. MDPI AG. Retrieved from
http://dx.doi.org/10.3390/app12126079. 2022.
[17] Bin Omar, A., & Bin MatJafri, M. Turbidimeter Design and Analysis: A Review on Optical Fiber Sensors for the Measurement of Water Turbidity.Sensors, 9(10), 8311–8335. MDPI AG. Retrieved from http://dx.doi.org/10.3390/s91008311. 2009.
[18] Zhao, J., Zhang, F., Chen, S., Wang, C., Chen, J., Zhou, H., & Xue, Y. Remote Sensing Evaluation of Total Suspended Solids Dynamic with Markov Model: A
Case Study of Inland Reservoir across Administrative Boundary in South China. 65 Sensors, 20(23), 6911. MDPI AG. Retrieved from http://dx.doi.org/10.3390/s20236911. 2020.
[19] Wang, Chongyang & Chen, Shuisen & li, Dan & Wang, Danni & Liu, Wei & Yang, Ji. A Landsat-based model for retrieving total suspended solids concentration of estuaries and coasts in China. Geoscientific Model Development. 4347-4365. 10.5194/gmd-10-4347-2017. 2017.
[20] Yang, X., Mao, Z., Huang, H., & Zhu, Q. (2016). Using GOCI Retrieval Data to Initialize and Validate a Sediment Transport Model for Monitoring Diurnal Variation of SSC in Hangzhou Bay, China. Water, 8(3), 108. MDPI AG. Retrieved from http://dx.doi.org/10.3390/w8030108. 2016.
[21] He, X.; Bai, Y.; Pan, D.; Huang, N.; Dong, X.; Chen, J.; Chen, C.-T.A.; Cui, Q. Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters. Remote Sens. Environ. 2013, 133, 225–239. 2013.
[22] Moon, J.E.; Park, Y.J.; Ryu, J.H.; Choi, J.K.; Ahn, J.H.; Min, J.E.; Son, Y.B.; Lee, S.J.; Han, H.J.; Ahn, Y.H. Initial validation of GOCI water products against
in situ data collected around Korean peninsula for 2010–2011. Ocean Sci. J. 2012, 47, 261–277. 2012.
[23] Franklin, J. B., Sathish, T., Vinithkumar, N. V., & Kirubagaran, R. A novel approach to predict chlorophyll-a in coastal-marine ecosystems using multiple linear regression and principal component scores. In Marine Pollution Bulletin
(Vol. 152, p. 110902). Elsevier BV. https://doi.org/10.1016/j.marpolbul.2020.110902. 2020.
[24] Pan, J., Huang, L., Devlin, A., & Lin, H. Quantification of Typhoon-Induced Phytoplankton Blooms Using Satellite Multi-Sensor Data. Remote Sensing, 10(2),
318. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs10020318. 2018.
[25] Ying Ying Tang, D., Wayne Chew, K., Ting, H.-Y., Sia, Y.-H., Gentili, F. G., Park, Y.-K., Banat, F., Culaba, A. B., Ma, Z., & Loke Show, P. Application of
regression and artificial neural network analysis of Red-Green-Blue image components in prediction of chlorophyll content in microalgae. In Bioresource Technology (Vol. 370, p. 128503). Elsevier BV.
https://doi.org/10.1016/j.biortech.2022.128503. 2023.
[26] Su, H., Lu, X., Chen, Z., Zhang, H., Lu, W., & Wu, W. (2021). Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning. Remote Sensing, 13(4), 576. MDPI AG. Retrieved from
http://dx.doi.org/10.3390/rs13040576. 2021.
[27] Hu, C., Lee Z., and Franz, B.A. Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys.
Res., 117, C01011, doi:10.1029/2011JC007395. 2012.
[28] O′Reilly, J.E., & Werdell, P. J. Chlorophyll algorithms for ocean color sensors - OC4, OC5 & OC6. Remote Sensing of Environment, 229, 32-47. doi: 10.1016/j.rse.2019.04.021. 2019.
[29] Xiao, W., Wang, L., Laws, E., Xie, Y., Chen, J., Liu, X., Chen, B., & Huang, B. Realized niches explain spatial gradients in seasonal abundance of phytoplankton groups in the South China Sea. In Progress in Oceanography (Vol.
162, pp. 223–239). Elsevier BV. https://doi.org/10.1016/j.pocean.2018.03.008.
2018.
[30] Huang, Y.-G., Yang, H.-F., Jia, J.-J., Li, P., Zhang, W.-X., Wang, Y. P., Ding, Y.-F., Dai, Z.-J., Shi, B.-W., & Yang, S.-L. Declines in suspended sediment concentration and their geomorphological and biological impacts in the Yangtze
River Estuary and adjacent sea. In Estuarine, Coastal and Shelf Science (Vol. 265, p. 107708). Elsevier BV. https://doi.org/10.1016/j.ecss.2021.107708. 2022.
[31] Chen, C., Mao, Z., Tang, F., Han, G., & Jiang, Y. Declining riverine sediment input impact on spring phytoplankton bloom off the Yangtze River Estuary from
17-year satellite observation. In Continental Shelf Research (Vol. 135, pp. 86–91). Elsevier BV. https://doi.org/10.1016/j.csr.2017.01.012. 2017.
[32] Wang, Y., Chen, J., Zhou, F., Zhang, W., & Hao, Q. Spatial and Temporal Variations of Chlorophyll a and Primary Productivity in the Hangzhou Bay. Journal of Marine Science and Engineering, 10(3), 356. MDPI AG. Retrieved from http://dx.doi.org/10.3390/jmse10030356. 2022.
[33] Guo, K., Zou, T., Jiang, D., Tang, C., & Zhang, H. (). Variability of Yellow River turbid plume detected with satellite remote sensing during water-sediment regulation. In Continental Shelf Research (Vol. 135, pp. 74–85). Elsevier BV. https://doi.org/10.1016/j.csr.2017.01.017. 2017.
[34] Wang, T., & Zhang, S. Effect of Summer Typhoon Linfa on the Chlorophylla Concentration in the Continental Shelf Region of Northern South China Sea. Journal of Marine Science and Engineering, 9(8), 794. MDPI AG. Retrieved from
http://dx.doi.org/10.3390/jmse9080794. 2021.
[35] Lu, Z., & Gan, J. Controls of seasonal variability of phytoplankton blooms in the Pearl River Estuary. In Deep Sea Research Part II: Topical Studies in 68 Oceanography (Vol. 117, pp. 86–96). Elsevier BV. https://doi.org/10.1016/j.dsr2.2013.12.011. 2015.
[36] Tseng, Y.-H.; Lu, C.-Y.; Zheng, Q.; Ho, C.-R. Characteristic Analysis of Sea Surface Currents around Taiwan Island from CODAR Observations. Remote Sens. 2021, 13, 3025. https://doi.org/10.3390/ rs13153025. 2021.
[37] He, Q., Zhan, H., Xu, J., Cai, S., Zhan, W., Zhou, L., & Zha, G. Eddy-induced chlorophyll anomalies in the western South China Sea. Journal of Geophysical Research: Oceans, 124, 9487– 9506. https://doi.org/10.1029/2019JC015371. 2019.
[38] Hsu, P.-C., Lu, C.-Y., Hsu, T.-W., & Ho, C.-R. Diurnal to Seasonal Variations in Ocean Chlorophyll and Ocean Currents in the North of Taiwan Observed by Geostationary Ocean Color Imager and Coastal Radar. Remote Sensing, 12(17), 2853. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs12172853. 2020.
[39] Hung, C., Shih, M.-F., & Lin, T.-Y. The Climatological Analysis of Typhoon Tracks, Steering Flow, and the Pacific Subtropical High in the Vicinity of Taiwan and the Western North Pacific. Atmosphere, 11(5), 543. MDPI AG. Retrieved
from http://dx.doi.org/10.3390/atmos11050543. 2020.
[40] Hsu, P.-C.; Lee, H.-J.; Lu, C.-Y. Impacts of the Kuroshio and Tidal Currents on the Hydrological Characteristics of Yilan Bay, Northeastern Taiwan. Remote
Sens. 2021, 13, 4340. https://doi.org/ 10.3390/rs13214340. 2021.
[41] Pandey, R. S., & Liou, Y.-A. Typhoon strength rising in the past four decades. In Weather and Climate Extremes (Vol. 36, p. 100446). Elsevier BV. https://doi.org/10.1016/j.wace.2022.100446. 2022.
[42] National Disaster Prevention Technology Center. (2016). Typhoon Megi Disaster Report. Taiwan.
[43] Hong Kong Observatory. (2017). Tropical Cyclones in 2016.[44] Zhang, H., Liu, X., Wu, R., Liu, F., Yu, L., Shang, X., Qi, Y., et al. Ocean Response to Successive Typhoons Sarika and Haima (2016) Based on Data
Acquired via Multiple Satellites and Moored Array. Remote Sensing, 11(20), 2360. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs11202360. 2019.
[45] Yin, W., & Huang, D. Applications of geostationary satellite data in the study of ocean and coastal short-term processes. In Remote Sensing of Ocean and Coastal Environments (pp. 139–154). Elsevier. https://doi.org/10.1016/b978-0-12-819604-5.00009-3. 2021.
[46] Jeon, Ho-Kun & Cho, Hongyeon. Missing Pattern Analysis of the GOCI-I Optical Satellite Image Data. Ocean and Polar Research. 44. 179-190. 10.4217/OPR.2022009. 2022.
[47] Moon, K. Park, Y. Ishizaka J. Evaluation of chlorophyll retrievals from geostationary ocean color imager (GOCI) for the north-east Asian region. Remote Sens Environ 184:482–495. doi:10.1016/j.rse.2016.07.031. 2016.
[48] Choi, J.-K., Park, Y. J., Ahn, J. H., Lim, H.-S., Eom, J., and Ryu, J.-H. GOCI, the world′s first geostationary ocean color observation satellite, for the monitoring
of temporal variability in coastal water turbidity, J. Geophys. Res., 117, C09004, doi:10.1029/2012JC008046. 2012.
[49] Wang, M. Ahn, J-H. Jiang, L. Shi, W. Son, S. Park, Y-J. Ryu, J-H. Ocean color products from the Korean Geostationary Ocean Color Imager (GOCI). Optics express. 21. 3835-3849. 10.1364/OE.21.003835. 2013.
[50] Concha, J., Mannino, A., Franz, B., & Kim, W. Uncertainties in the Geostationary Ocean Color Imager (GOCI) Remote Sensing Reflectance for Assessing Diurnal Variability of Biogeochemical Processes. Remote Sensing, 11(3), 295. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs11030295. 2019.
[51] O′Reilly, J.E., and 24 Coauthors. SeaWiFS Postlaunch Calibration and Validation Analyses, Part 3. NASA Tech. Memo. 2000-206892, Vol. 11, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, 49 pp.
2000.
[52] Mobley, C.; Werdell, P.; Franz, B.; Ahmad, Z.; Bailey, S. Atmospheric Correction for Satellite Ocean Color Radiometry; Technical Report NASA/TM2016-217551; NASA Goddard Space Flight Center: Greenbelt, MD, USA. 2016.
[53] Gordon, H.R.; Wang, M. Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: A preliminary algorithm. Appl. Opt. 1994, 33, 443–452. 1994.
[54] Bailey, S.W.; Franz, B.A.; Werdell, P.J. Estimation of near-infrared waterleaving reflectance for satellite ocean color data processing. Opt. Express 2010, 18, 7521–7527. 2010.
[55] Ahmad, Z.; Franz, B.A.; McClain, C.R.; Kwiatkowska, E.J.; Werdell, J.; Shettle, E.P.; Holben, B.N. (2010). New aerosol models for the retrieval of aerosol
optical thickness and normalized water-leaving radiances from the SeaWiFS and MODIS sensors over coastal regions and open oceans. Appl. Opt. 2010, 49, 5545–5560. 2010.
71
[56] Morel, A.; Antoine, D.; Gentili, B. Bidirectional reflectance of oceanic waters: Accounting for Raman emission and varying particle scattering phase function.
Appl. Opt. 2002, 41, 6289–6306. 2002.
[57] Choi, M., Lim, H., Kim, J., Lee, S., Eck, T. F., Holben, B. N., Garay, M. J., Hyer, E. J., Saide, P. E., and Liu, H. Validation, comparison, and integration of
GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign, Atmos. Meas. Tech., 12, 4619–4641, https://doi.org/10.5194/amt-12-4619-2019. 2019.
[58] Wang, G., Garcia, D., Liu, Y., de Jeu, R., & Johannes Dolman, A. A threedimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations. In Environmental Modelling &
Software (Vol. 30, pp. 139–142). Elsevier BV. https://doi.org/10.1016/j.envsoft.2011.10.015. 2012.
[59] Garcia, D. Robust smoothing of gridded data in one and higher dimensions with missing values. In Computational Statistics & Data Analysis (Vol. 54, Issue 4, pp. 1167–1178). Elsevier BV. https://doi.org/10.1016/j.csda.2009.09.020.
2010.
[60] Xu, Y., & Chen, J. Remote sensing and buoy based monitoring of chlorophyll a in the Yangtze Estuary reveals nutrient-limited status dynamics: A case study
of typhoon. In Frontiers in Marine Science (Vol. 9). Frontiers Media SA. https://doi.org/10.3389/fmars.2022.1017936. 2022.
[61] Zheng, Z.-W.; Chen, Y.-R. (2022). Influences of Tidal Effect on Upper Ocean Responses to Typhoon Passages Surrounding Shore Region off Northeast Taiwan.
J. Mar. Sci. Eng. 2022, 10, 1419. https://doi.org/ 10.3390/jmse10101419. 2022.
[62] Piton, V., Herrmann, M., Marsaleix, P., Duhaut, T., Ngoc, T. B., Tran, M. C., Shearman, K., & Ouillon, S. Influence of winds, geostrophy and typhoons on the
seasonal variability of the circulation in the Gulf of Tonkin: A high-resolution 3D regional modeling study. In Regional Studies in Marine Science (Vol. 45, p. 101849). Elsevier BV. https://doi.org/10.1016/j.rsma.2021.101849. 2021. 2021
[63] Beckers, J. M., & Rixen, M. EOF calculations and data filling from incomplete oceanographic datasets. Journal of Atmospheric and Oceanic Technology, 20(12), 1839-1856. 2003.
[64] Alvera-Azcárate, A., Barth, A., & Weisberg, R. H. Use of singular value decomposition to examine the completeness of an oceanographic database. Journal of Atmospheric and Oceanic Technology, 24(9), 1577-1583. 2007.
[65] Kondrashov, D., & Ghil, M. Spatio-temporal filling of missing points in geophysical data sets. Nonlinear Processes in Geophysics, 13(2), 151-159. 2006.
[66] Hocke, K., & Kämpfer, N. Sudden stratospheric warmings seen in radiosonde data. Journal of Geophysical Research: Atmospheres, 114(D10). 2009.
[67] Ćatipović, L., Matić, F., & Kalinić, H. Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey. In Journal of Marine Science and Engineering (Vol. 11, Issue 2, p. 340). MDPI AG. https://doi.org/10.3390/jmse11020340. 2023.
[68] Hsu, P.-C., Lu, C.-Y., Hsu, T.-W., & Ho, C.-R. Diurnal to Seasonal Variations in Ocean Chlorophyll and Ocean Currents in the North of Taiwan Observed by Geostationary Ocean Color Imager and Coastal Radar. In Remote Sensing (Vol. 12, Issue 17, p. 2853). MDPI AG. https://doi.org/10.3390/rs12172853.
[69] Central Weather Bureau. Typhoon Megi official report. 2016

指導教授

姜壽浩(Chiang Shou-Hao)

審核日期

2023-8-17

推文