利用MODIS衛星監測地表蒸發散行為

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：156

、訪客IP：3.145.97.248

姓名

姚登元(Teng-Yeng Yao) 查詢紙本館藏

畢業系所

太空科學研究所

論文名稱

利用MODIS衛星監測地表蒸發散行為
(Monitor land surface evaporation by using MODIS data)

相關論文

★ 2.4GHz無線傳輸系統於遙測與GPS數據整合之研製	★ 2.4GHz之無線電波室內傳播通道特性量測與分析
★ K波段地面鏈路降雨衰減效應之研究	★ 多層非均勻介質之微波散射模擬分析
★ Ka 波段地面鏈路降雨效應與植被遮蔽效應之研究	★ 地面遙測影像雷達發射與接收模組之設計
★ 合成孔徑雷達之移動目標物速度估測研究	★ 小波轉換於合成孔徑雷達干涉相位雜訊之研究
★ Ka波段台灣地區降雨及地面環境傳播特性研究	★ 雨滴粒徑分佈應用於Ka波段降雨衰減估計之研究
★ 全偏極合成孔徑雷達非監督式目標分類與極化方位角偏移效應估算之研究	★ 全偏極合成孔徑雷達於目標分類之研究
★ 影像融合技術應用於地表分類之探討	★ 應用共軛梯度演算法在掃描式合成孔徑雷達目標物特徵增強處理
★ 台灣北部地區Ka波段降雨衰減模式之研究	★ 雨滴粒徑與植被遮蔽效應對Ka波段電波衰減影響之探討

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)

摘要
地表蒸發散量 (Evapotranspiration，簡稱ET)，又稱潛熱(Latent Heat)，單位為W/m2，於氣候動力學及生態系的生產率研究是不可或缺的因子之一，因為蒸發散資訊可表為記錄地表能量傳遞的過程之重要參數。雖然已有許多方式去估計蒸發散量，但需要氣象環境參數作輔助，故會受到地面觀測站分佈的影響，而且利用環境參數所反演的蒸發散量沒有辦法代表大區域範圍的平均。因此希望透過衛星遙測資料分析，將可有效取得環境大面積的蒸發散情形。
本篇研究以台灣為測區，使用Aqua/MODIS的資料與Neshida在2003年所提出的蒸發散比率（Evaporation fraction, EF）演算法反演全台的EF分布圖，EF為ET除上可用能量（Q），Q為可感熱與潛熱的總和。
驗證部分是看以MODIS衛星所反演的EF與嘉義跟宜蘭測站蒸發皿觀測值的相關性，並且將所反演出的EF乘上可用能量（Q）得到ET值與利用地面測站氣象環境參數反演出來的ET值做比較。2003~2005年嘉義測站使用環境參數與MODIS的影像資料所反演之蒸發散量的相關係數為0.083；2003~2005年嘉義測站利用MODIS觀測資料反演之EF與測站每日蒸發皿觀測量的相關係數為0.297。2003~2005年宜蘭測站使用環境參數與MODIS的影像資料所反演之蒸發散量的相關係數為0.502；2003~2005年宜蘭測站利用MODIS觀測資料反演之EF與測站每日蒸發皿觀測量的相關係數為0.740。結果顯示出兩種不同特性的資料在比對上有一定的困難，不管在時間以及空間的分布上，故需要進一步的研究佐證。

摘要(英)

Abstract
Evapotranspiration (ET, or latent heat flux) is one of the critical factors for understanding the climate dynamics and the terrestrial ecosystem productivity because of its close relation to energy transfer process. Although there are many approaches to estimate ET, most of the existing techniques of ET estimation require surface meteorological observations. Thus the area coverage of ET estimation is limited by the density of ground observation network, and it is difficult, if not impossible, to estimate ET at regional to global scale by means of traditional meteorological observations. Therefore, remote sensing is the one of the best solution for estimating ET at large scale.
In this study, we used MODIS data on board NASA’s AQUA satellite over the Taiwan Island to map the distribution of evaporation fraction (EF). Validation was made by calculating the correlation coefficient between EF and the daily basin observation in Chiayi and Ilan stations. Quantitative comparison of ET between the satellite derived and the surface meteorological observation was also made. During the years of 2003 to 2005, the correlation coefficients were 0.083 and 0.502 in Chiayi and Inlan, respectively, while the correlation coefficients between EF and the daily basin observation in Chiayi is 0.297 and 0.740 in Inlan.
Study indicated that direct comparison between these two types of observation may be difficult due to their distinctive observing characteristics of spatial-temporal patterns, among others. It should be further investigated.

關鍵字(中)

★ 蒸發散比率
★ 地表蒸發散

關鍵字(英)

★ ET
★ EF
★ MODIS

論文目次

摘要 I
Abstract II
目錄 III
表目錄 IV
圖目錄 V
第一章緒論 1
1.1 研究背景與目的 1
1.2 文獻回顧 2
第二章基本理論 4
2.1 蒸發散的物理傳輸機制 4
2.2 基本理論方程式 5
2.2.1 擴散方程式 ( Fick’s First Law ) 5
2.2.2 水氣(潛熱)傳輸方程式 6
2.2.3可感熱傳輸方程式 11
2.3 大氣不穩定的修正方程式 12
第三章 NASA EF演算法簡介與反演方法 18
3.1 蒸發散比率（EF） 18
3.2 EF雙來源線性模型 21
3.3 估計核心變數 22
3.3.1 植被覆蓋率（fveg） 22
3.3.2 植被蒸發散比率（EFveg） 23
3.3.3 裸露土蒸發散比率（EFsoil） 27
3.4 估計基本變數 30
3.4.1 估計大氣溫度（Ta） 30
3.4.2 輻射成分、土壤熱通量與可用能量 30
3.5 估計溫暖邊界 34
第四章研究結果 38
4.1 影像資料簡介 38
4.2 研究測區 40
4.3 EF演算法靈敏度測試 41
4.4 反演結果與驗證 54
第五章結論與建議 73
5.1 結論 73
5.2 建議 73
參考文獻 75
符號表 79

參考文獻

參考文獻
1. 陳亦穎，「發展遙測資料反演可感熱與潛熱通量之研究」，國立中央大學水文科學研究所碩士論文，2004年。
2. Brutsaert, W. and Stricker, H., An advection-aridity approach to estimate actual regional evapotranspiration. Water Resources Research, 15, 443-450, 1979.
3. Businger, J.A., J.C. Wyngaard, Y. Izumi and E.F. Bradley, “Flux-profile relationships in the atmospheric surface layer”, J. Atmos. Sci., Vol.28, pp.181-189, 1971.
4. Carlson, T., N., Gillies, R., R., and Schmugge, T. J., An interpretation of methodologies for indirect measurement of soil water content, Agric. For. Meteorol., 77, 191-205, 1995.
5. Choudhury, B., J., Ahmed, N.,.U., Idso, S. B., Reginato, R., J., and Daughtry, C., S., T., Relations between evaporation coefficients and vegetation indices studied by model simulations, Remote Sens. Environ., 50, 1-17, 1994.
6. Crago, R. D., Comparison of the evaporative fraction and the Priestley-Taylor a for parameterizing daytime evaporation. Water Resources Research, 32(5), 1403-1409, 1996.
7. De Bruin, H.A. R., A model for the Priestley-Taylor parameter α. Journal of Applied Meteorology, 22, 572-578, 1983.
8. Dingman, S. L., “Physical hydrology. ”, Prentice Hall, pp.642, 2002, Book.
9. Dyer, A.J, “A review of flux–profile relationships.”, Boundary-Layer Meteorol., Vol.3, pp363-372, 1974.
10. Gillies, R. R., Carlson, T. N., Cui, J., Kustas, W. P., and Humes, K. S., A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature. Int. J. Remote Sensing, 18(15), 3145-3166, 1997.
11. Granger, R. J. and Gray, D. M., Evaporation from natural nonsaturated surfaces. Journal of Hydrology, 111, 21-29, 1989.
12. Hipps, L. E., Or, D., and Neale, C. M. U., Spatial structure and scaling of surface fluxes in a Great Basin ecosystem. in "Scaling Up in Hydrology Using Remote Sensing," Stewart et al. eds., John Wiley & Sons, 113-125, 1996.
13. Idso, S. B., Aase, J. K., ans Jackson, R. D., Net radiation - soil heat flux relations as influenced by soil water content variations. Boundary-Layer Meteorology, 9, 113-122, 1975.
14. Jackson, R. D., Idso, S. B., Reginato, R. J., and Pinter, P. J., Canopy temperature as a crop water stress indicator. Water Resources Research, 17(4), 1133-1138, 1981.
15. Jarvis, P. G., The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philosophical Transactions of the Royal Society of London, Series B, 273, 593-610, 1976.
16. Jiang, L., Islam, S., Estimation of surface evaporation map over southern great plains using remote sensing data. Water Resources Research, 37(2), 329-340, 2001.
17. Kelliher, F. M., Leuning, R., Raupach, M. R., and Schulze, E-D., Maximum conductance for evaporation from global vegetation types. Agric. For. Meteorol., 73, 1-16, 1995.
18. K. Nishida, R. R. Nemani, S. W. Running, and J. M. Glassy, “An operational remote sensing algorithm of land surface evaporation,” J. Geophys. Res., vol. 108 (D9),4270-4284, 2003.
19. K. Nishida, R. R. Nemani, J. M. Glassy, and S. W. Running, “Development of an evapotranspiration index from Aqua/MODIS for monitoring surface moisture status.” IEEE Trans. Geosci. Remote Sensing., 41 (2), pp: 493-501, 2003.
20. Kondo, J., Meteorology of Water Environment, Asakura-shoten, 350pp., Tokyo, 1994.
21. Kondo, J., Atmospheric Science near the Ground Surface, University of Tokyo Press, 324pp., Tokyo, 2000.
22. Kosugi, Y., Leaf-scale analysis of the CO2 and H2O exchange processes between trees and atmosphere. Ph. D. dissertation to Kyoto University, Kyoto, Japan, 1996.
23. Moran, M. S., Jackson, R. D., Raymond, L. H., Gay, L. W., and Slater, P. N., Mapping surface energy balance components by combining Landsat Thematic Mapper and ground-based meteorological data. Remote Sens. Environ., 30, 77-87, 1989.
24. Moran, M. S., Clarke, T. R., Inoue, Y., and Vidal, A., Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sens. Environ., 49, 246-263, 1994.
25. Nemani, R. R., and Running, S. W., Estimation of regional surface resistance to evapotranspiration from NDVI and thermal-IR AVHRR data. Journal of Applied Meteorology, 28, 276-284, 1989.
26. Priestley, C. H. B. and Taylor, R. J., On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100, 81-92, 1972.
27. Prince, S. D. and Goward, S. N., Global primary production: a remote sensing approach. Journal of Biogeography, 22, 815-835, 1995.
28. Shuttleworth, W. J., Gurney, R. J., Hsu, A. Y., and Ormsby, J. P., FIFE: the variation in energy partition at surface flux sites. IAHS Publication, 186, 67-74, 1989.
29. Spencer, J. W., Fourier series representation of the position of the sun. Search, Vol. 2, p. 272, 1971.
30. Sugita, M., and Brutsaert, W., Daily evaporation over a region from lower boundary-layer profiles measured with radiosondes. Water Resources Research, 27(5), 747-752, 1991.
31. Tanaka, H., Ohta, T., Hiyama, T., and Maximov, T.C., Seasonal variation of photosynthesis and transpiration properties of a boreal deciduous forest: Analysis using a single layer canopy model. Journal of Japanese Forest Society, 82(3), 259-267, 2000.
32. Toda, M., Ohte, N., Tani, M., Tanaka, H., Musiake, K., Aoki, M., Boonyawat, S., Diurnal and seasonal variations of CO2 exchange processes over typical land covers in tropical monsoon region. Journal of Japan Society of Hydrology and Water Resources. 13(4), 276-290, 2000.
33. White, M. A,, Thornton, P. E., Running, S. W., and Nemani, R. R., Parameterization and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model: net primary production controls. Earth Interactions, 4, Paper 3, 2000.
34. Yaglom, A. M., “Comments on wind and temperature flux-profile relationships”, Boundary Layer Metelorol., vol.11, pp.89-102, 1977.

指導教授

陳錕山(Kun-Shan Chen)

審核日期

2006-7-22

推文