遙測方法之土地熱通量研究

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姓名	黎梅山(Le Mai Son) 查詢紙本館藏	畢業系所	水文與海洋科學研究所
論文名稱	遙測方法之土地熱通量研究 (Land Heat Fluxes Investigation with Remote Sensing)
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摘要(中)	地表能量傳遞與轉換的過程在區域天氣、氣候和水循環中扮演著關鍵角色，而理解能量交換過程的特性對於提升許多應用的效率與效用相當重要，例如天氣預報系統、水文過程數值模擬、水資源管理、土地利用規劃、森林脆弱性評估等。長久以來，地面觀測一直被當作地表熱通量估算的逐點測量方法，但是其使用通常侷限於設定的地點，以及空間覆蓋範圍，從而導致難以在區域性的尺度操作。隨著以衛星為基礎的系統發展，空間科學和技術已成為全球觀測系統的關鍵要素，這為地-氣熱交換研究提供了高水準的時空效率、經濟效益之資料來源，根據來自遙測資料不同程度的輸入要求，已有許多不同的執行方法。以遙測資料為主要輸入，地表能量平衡模式中的物理過程方法被廣泛用於估計陸-氣界面的能量交換。然而，這些方法涉及建立複雜的物理過程，並且仍然侷限於代表地面觀測系統的設定地點。多光譜遙測技術已被普遍用於收集影響陸-氣界面傳熱過程的地表特性，利用反射率的差異，可以藉由分析光譜反射率特性來了解地表特徵。在本研究中，提出了一種新的多光譜指數，即「常態化差異潛熱指數」（NDLI），用於從衛星影像中推估潛熱通量。它利用了綠色、紅色和短波紅外光三個波段的反射率觀測數值，而這三個波段通常用於衛星地球觀測任務。NDLI首先用於淬取台灣嘉義市東部子區域內陸-氣之間的熱傳遞訊號。與現有的其他遙測指標相比，NDLI與地表模型能量平衡式（SEBAL）計算出的潛熱通量具有最高的相關性係數( r = 0.75)。隨後，對NDLI進行檢驗並推估在越南太平省的地表蒸發散 (ET)。結果表明，在98.1％的稻田中，NDLI推估的ET與SEBAL的推估結果相差不到10％。此外，NDLI推估的ET在逆境水稻區中呈現較低數值，顯示此方法之優越性。結論是，新開發的NDLI能夠量化地表的水可利用量，並且在需要陸-氣界面傳熱信息的各種實際應用中NDLI具有潛在的幫助作用。由於NDLI的方程式簡單易行，透過現有的大量衛星資料，可在其他感興趣的區域進行操作，並有助於多種實際應用
摘要(英)	Surface energy processes have an essential role in the regional weather, climate, and hydrological cycles. Understanding the characteristic of energy exchange processes is vital to improve the efficiency and effectiveness of many applications, such as water resources management, land use planning, forest vulnerability assessment, numerical modeling of hydrological processes, weather forecasting system, etc. Ground-based instrument systems have been long used as a point-wise measurement method for land heat fluxes estimation. However, their use is often restricted to a pre-defined domain and limited with spatial coverage, resulting in difficulty to operate over a regional scale. With the development of satellite-based systems, space science and technology is a key component of the global observation system, which provides a promising data source for the investigations of land-air heat exchange with high spatio-temporal efficiency and economic benefits. Different methods have been proposed and performed with varying degrees of input requirement from remotely sensed data in which the approaches of physical processes in surface energy balance models were widely used to estimate the energy exchange at the land-air interface using the remote sensing data as the primary input. However, these approaches involve the establishment of complex physical processes and still are limited to the areas with existing ground-based measurement system. Multi-spectral remote sensing has been widely utilized to collect the Earth’s surface properties that basically influence the processes of heat transfer at the land-air interface. The distinction in reflectance makes it possible to understand the earth′s surface features by analyzing its spectral reflectance signatures. In this study, a new multiple-band index, Normalized Difference Latent heat Index, is proposed for latent heat flux extraction from satellite imagery. It utilizes the reflectance observations of three channels, which are primarily used for the optical Earth observation satellites, including green, red, and shortwave-infrared. The NDLI is firstly applied to extract the information of land–air latent heat exchange over a subset region in the eastern part of Chiayi City, Taiwan. The NDLI exhibited superiority in representing the potential latent heat flux by showing the highest consistency coefficient (r = 0.75) with the corresponding predicted results from the Surface Energy Balance Algorithm for Land model (SEBAL) as compared to the other existing satellite indices. Subsequently, the NDLI is adopted to enhance the evapotranspiration estimation over rice paddy areas in the Thai Binh Province, Vietnam. Results indicated that the NDLI-derived ET differs from the derivative of SEBAL by less than 10% over 98.1% of the paddy field. Moreover, the NDLI-derived ET exhibited its superiority in revealing the low amounts of ET in the rice paddy areas under stress. It is concluded that the newly developed NDLI is a good indicator to quantify the surface moisture, evapotranspiration, and latent heat transfer at the land-air interface. The NDLI is potentially helpful for a variety of practical applications since its formula is simple and easily implemented from the abundant existing satellites
關鍵字(中)	★ 常態化差異潛熱指數 ★ 遙測 ★ 蒸發散	關鍵字(英)	★ Normalized Difference Latent heat Index ★ Remote Sensing ★ Evapotranspiration
論文目次	LIST OF FIGURES iii LIST OF TABLES v CHAPTER I. Introduction 1 1.1. Overview 1 1.2. Literature Review 2 • Remote sensing-based land heat fluxes models 4 • Spectral signature of the Earth’s surface in remote sensing 7 • Multispectral indices in remote sensing 9 1.3. Objectives 10 CHAPTER II. Normalized Difference Latent heat Index for Remote Sensing of Land Surface Energy Fluxes 11 2.1. Introduction 11 2.2. Study Areas & Materials 13 2.2.1. Study Area 13 2.2.2. Materials 14 2.3. Methodology 16 2.3.1. Formulation of the Normalized Different Latent heat Index 18 2.3.2. Latent heat flux estimation based on the SEBAL model 22 2.4. Results 25 2.4.1. Normalized Difference Latent heat Flux 25 2.4.2. Latent heat flux distribution 32 2.4.3. Quantitative correlations between latent heat flux and investigated water-related indices based on the land cover features 33 2.4.4. Correlation between latent heat flux and water indices based on the vegetation characteristics 36 CHAPTER III. Estimation of Rice Evapotranspiration under Stress using Normalized Difference Latent heat Index 39 3.1. Introduction 39 3.2. Study Area 42 3.3. Materials and Methodology 42 3.3.1. Materials 42 3.3.2. Evapotranspiration Estimation 44 3.3.3. Paddy rice mapping and land cover classification 46 3.4. Results 48 3.4.1 NDLI & SEBAL-Derived ET maps 48 3.4.2. NDLI-Derived ET 52 3.4.3. NDLI-Derived ET over paddy rice field 58 CHAPTER IV. Discussion 67 CHAPTER V. Conclusions and Future Work 69 5.1. Conclusions 69 5.2. Future work 71 REFERENCE 73 APPENDIEX 88
參考文獻	[1] G. P. Petropoulos, Remote sensing of energy fluxes and soil moisture content: CRC Press, 2013. [2] Y.-A. Liou and S. K. Kar, "Evapotranspiration estimation with remote sensing and various surface energy balance algorithms—A review," Energies, vol. 7, pp. 2821-2849, 2014. [3] Y. Song, J. Wang, K. Yang, M. Ma, X. Li, Z. Zhang, et al., "A revised surface resistance parameterisation for estimating latent heat flux from remotely sensed data," International Journal of Applied Earth Observation and Geoinformation, vol. 17, pp. 76-84, 2012. [4] S. Consoli, G. D’Urso, and A. Toscano, "Remote sensing to estimate ET-fluxes and the performance of an irrigation district in southern Italy," Agricultural Water Management, vol. 81, pp. 295-314, 2006. [5] E. P. Glenn, A. R. Huete, P. L. Nagler, K. K. Hirschboeck, and P. Brown, "Integrating remote sensing and ground methods to estimate evapotranspiration," Critical Reviews in Plant Sciences, vol. 26, pp. 139-168, 2007. [6] Z. L. Li, R. Tang, Z. Wan, Y. Bi, C. Zhou, B. Tang, et al., "A review of current methodologies for regional evapotranspiration estimation from remotely sensed data," Sensors (Basel), vol. 9, pp. 3801-53, May 2009. [7] D. Fernández-Prieto, P. van Oevelen, Z. Su, and W. Wagner, "Advances in Earth observation for water cycle science," Hydrology and Earth System Sciences, vol. 16, pp. 543-549, 2012. [8] C. Haan, B. Barfield, and J. Hayes, "Rainfall-runoff estimation in stormwater computations," Design Hydrology and Sedimentology for Small Catchments, pp. 37-103, 1994. [9] M. F. McCabe and E. F. Wood, "Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors," Remote Sensing of Environment, vol. 105, pp. 271-285, 2006. [10] S. Ahmad, A. Kalra, and H. Stephen, "Estimating soil moisture using remote sensing data: A machine learning approach," Advances in Water Resources, vol. 33, pp. 69-80, 2010. [11] I. Mariotto and V. P. Gutschick, "Non-lambertian corrected albedo and vegetation index for estimating land evapotranspiration in a heterogeneous semi-arid landscape," Remote Sensing, vol. 2, pp. 926-938, 2010. [12] A. N. French, J. G. Alfieri, W. P. Kustas, J. H. Prueger, L. E. Hipps, J. L. Chávez, et al., "Estimation of surface energy fluxes using surface renewal and flux variance techniques over an advective irrigated agricultural site," Advances in Water Resources, vol. 50, pp. 91-105, 2012. [13] W. Bastiaanssen, M. Menenti, R. A. Feddes, and A. A. M. Holtslag, "A remote sensing surface energy balance algorithm for land (SEBAL) - 1. Formulation," Journal of Hydrology, vol. 212, pp. 198-212, Dec 1998. [14] W. Bastiaanssen, H. Pelgrum, J. Wang, Y. Ma, J. Moreno, G. Roerink, et al., "A remote sensing surface energy balance algorithm for land (SEBAL).: Part 2: Validation," Journal of Hydrology, vol. 212, pp. 213-229, 1998. [15] K. B. Wilson, D. D. Baldocchi, M. Aubinet, P. Berbigier, C. Bernhofer, H. Dolman, et al., "Energy partitioning between latent and sensible heat flux during the warm season at FLUXNET sites," Water Resources Research, vol. 38, pp. 30-1-30-11, 2002. [16] A. Green, K. McAneney, and M. Astill, "Surface-layer scintillation measurements of daytime sensible heat and momentum fluxes," Boundary-Layer Meteorology, vol. 68, pp. 357-373, 1994. [17] W. M. L. Meijninger, Surface fluxes over natural landscapes using scintillometry: Wageningen Universiteit, 2003. [18] S. M. Liu, Z. W. Xu, W. Wang, Z. Jia, M. Zhu, J. Bai, et al., "A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem," Hydrology and Earth System Sciences, vol. 15, pp. 1291-1306, 2011. [19] H. Thunnissen and G. Nieuwenhuis, "A simplified method to estimate regional 24-h evapotranspiration from thermal infrared data," Remote Sensing of Environment, vol. 31, pp. 211-225, 1990. [20] F. Caparrini, F. Castelli, and D. Entekhabi, "Mapping of land-atmosphere heat fluxes and surface parameters with remote sensing data," Boundary-Layer Meteorology, vol. 107, pp. 605-633, 2003. [21] M. M. Rahman and W. Zhang, "Review on estimation methods of the Earth’s surface energy balance components from ground and satellite measurements," Journal of Earth System Science, vol. 128, p. 84, 2019. [22] T. Y. Chang, Y.-A. Liou, C. Y. Lin, S. C. Liu, and Y. C. Wang, "Evaluation of surface heat fluxes in Chiayi plain of Taiwan by remotely sensed data," International Journal of Remote Sensing, vol. 31, pp. 3885-3898, 2010. [23] R. Tang, Z.-L. Li, K.-S. Chen, Y. Jia, C. Li, and X. Sun, "Spatial-scale effect on the SEBAL model for evapotranspiration estimation using remote sensing data," Agricultural and Forest Meteorology, vol. 174, pp. 28-42, 2013. [24] Y. Ma, S. Liu, F. Zhang, J. Zhou, Z. Jia, and L. Song, "Estimations of regional surface energy fluxes over heterogeneous oasis–desert surfaces in the middle reaches of the Heihe River during HiWATER-MUSOEXE," IEEE Geoscience and Remote Sensing Letters, vol. 12, pp. 671-675, 2015. [25] J. D. Kalma, T. R. McVicar, and M. F. McCabe, "Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data," Surveys in Geophysics, vol. 29, pp. 421-469, 2008. [26] R. Jackson, R. Reginato, and S. Idso, "Wheat canopy temperature: a practical tool for evaluating water requirements," Water Resources Research, vol. 13, pp. 651-656, 1977. [27] V. Caselles, J. Sobrino, and C. Coll, "On the use of satellite thermal data for determining evapotranspiration in partially vegetated areas," International Journal of Remote Sensing, vol. 13, pp. 2669-2682, 1992. [28] T. N. Carlson and M. J. Buffum, "On estimating total daily evapotranspiration from remote surface temperature measurements," Remote Sensing of Environment, vol. 29, pp. 197-207, 1989. [29] M. Wild, D. Folini, C. Schär, N. Loeb, E. G. Dutton, and G. König-Langlo, "The global energy balance from a surface perspective," Climate Dynamics, vol. 40, pp. 3107-3134, 2013. [30] C. Cammalleri, G. Ciraolo, G. La Loggia, and A. Maltese, "Daily evapotranspiration assessment by means of residual surface energy balance modeling: A critical analysis under a wide range of water availability," Journal of Hydrology, vol. 452, pp. 119-129, 2012. [31] S. S. Anapalli, T. R. Green, K. N. Reddy, P. H. Gowda, R. Sui, D. K. Fisher, et al., "Application of an energy balance method for estimating evapotranspiration in cropping systems," Agricultural Water Management, vol. 204, pp. 107-117, 2018. [32] L. H. Kantha and C. A. Clayson, Small scale processes in geophysical fluid flows vol. 67: Elsevier, 2000. [33] M. Raupach and J. Finnigan, "′Single-layer models of evaporation from plant canopies are incorrect but useful, whereas multilayer models are correct but useless′: discuss," Australian Journal of Plant Physiology (Australia), 1988. [34] R. Tang, Z.-L. Li, Y. Jia, C. Li, K.-S. Chen, X. Sun, et al., "Evaluating one-and two-source energy balance models in estimating surface evapotranspiration from Landsat-derived surface temperature and field measurements," International Journal of Remote Sensing, vol. 34, pp. 3299-3313, 2013. [35] M. Menenti and B. Choudhury, "Parameterization of land surface evapotranspiration using a location dependent potential evapotranspiration and surface temperature range," Exchange processes at the land surface for a range of space and time scales, vol. 212, pp. 561-568, 1993. [36] G. Roerink, Z. Su, and M. Menenti, "S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance," Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, vol. 25, pp. 147-157, 2000. [37] B. Bansouleh, A. Karimi, and H. Hesadi, "Evaluation of SEBAL and SEBS Algorithms in the Estimation of Maize Evapotranspiration," International Journal of Plant & Soil Science, vol. 6, pp. 350-358, 2015. [38] R. G. Allen, M. Tasumi, and R. Trezza, "Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model," Journal of Irrigation and Drainage Engineering, vol. 133, pp. 380-394, 2007. [39] J. M. Norman and F. Becker, "Terminology in thermal infrared remote sensing of natural surfaces," Agricultural and Forest Meteorology, vol. 77, pp. 153-166, 1995. [40] M. Anderson, J. Norman, G. Diak, W. Kustas, and J. Mecikalski, "A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing," Remote Sensing of Environment, vol. 60, pp. 195-216, 1997. [41] W. P. Kustas and J. M. Norman, "A two‐source approach for estimating turbulent fluxes using multiple angle thermal infrared observations," Water Resources Research, vol. 33, pp. 1495-1508, 1997. [42] K. Coulson and D. W. Reynolds, "The spectral reflectance of natural surfaces," Journal of Applied Meteorology and Climatology, vol. 10, pp. 1285-1295, 1971. [43] J. B. Adams, "Visible and near‐infrared diffuse reflectance spectra of pyroxenes as applied to remote sensing of solid objects in the solar system," Journal of Geophysical Research, vol. 79, pp. 4829-4836, 1974. [44] J. Dymond, J. Shepherd, and J. Qi, "A simple physical model of vegetation reflectance for standardising optical satellite imagery," Remote Sensing of Environment, vol. 75, pp. 350-359, 2001. [45] J. C. Price and W. C. Bausch, "Leaf area index estimation from visible and near-infrared reflectance data," Remote Sensing of Environment, vol. 52, pp. 55-65, 1995. [46] L. Han, "Spectral reflectance with varying suspended sediment concentrations in clear and algae-laden waters," Photogrammetric Engineering and Remote Sensing, vol. 63, pp. 701-705, 1997. [47] E. R. Stoner and M. Baumgardner, "Characteristic variations in reflectance of surface soils," Soil Science Society of America Journal, vol. 45, pp. 1161-1165, 1981. [48] L. Weidong, F. Baret, G. Xingfa, T. Qingxi, Z. Lanfen, and Z. Bing, "Relating soil surface moisture to reflectance," Remote Sensing of Environment, vol. 81, pp. 238-246, 2002. [49] N. D. Duong, "Water body extraction from multi spectral image by spectral pattern analysis," International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 39, p. B8, 2012. [50] M. Okhrimenko, C. Coburn, and C. Hopkinson, "Multi-spectral lidar: Radiometric calibration, canopy spectral reflectance, and vegetation vertical SVI profiles," Remote Sensing, vol. 11, p. 1556, 2019. [51] A. Chappell, T. M. Zobeck, and G. Brunner, "Using bi-directional soil spectral reflectance to model soil surface changes induced by rainfall and wind-tunnel abrasion," Remote Sensing of Environment, vol. 102, pp. 328-343, 2006. [52] J. Rouse Jr, R. Haas, J. Schell, and D. Deering, "Paper A 20," in Third Earth Resources Technology Satellite-1 Symposium: The Proceedings of a Symposium Held by Goddard Space Flight Center at Washington, DC on December 10-14, 1973: Prepared at Goddard Space Flight Center, 1974, p. 309. [53] S. K. McFeeters, "The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features," International Journal of Remote Sensing, vol. 17, pp. 1425-1432, 1996. [54] H. Xu, "Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery," International Journal of Remote Sensing, vol. 27, pp. 3025-3033, 2006. [55] B.-C. Gao, "NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space," Remote Sensing of Environment, vol. 58, pp. 257-266, 1996. [56] J. Lacaux, Y. Tourre, C. Vignolles, J. Ndione, and M. Lafaye, "Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal," Remote Sensing of Environment, vol. 106, pp. 66-74, 2007. [57] F. Cian, M. Marconcini, and P. Ceccato, "Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data," Remote Sensing of Environment, vol. 209, pp. 712-730, 2018. [58] J. Smith, The Facts on File dictionary of weather and climate: Infobase Publishing, 2014. [59] M. C. Anderson, R. G. Allen, A. Morse, and W. P. Kustas, "Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources," Remote Sensing of Environment, vol. 122, pp. 50-65, Jul 2012. [60] U. W. Liaqat and M. Choi, "Surface energy fluxes in the Northeast Asia ecosystem: SEBS and METRIC models using Landsat satellite images," Agricultural and Forest Meteorology, vol. 214, pp. 60-79, Dec 15 2015. [61] R. G. Allen, L. S. Pereira, T. A. Howell, and M. E. Jensen, "Evapotranspiration information reporting: I. Factors governing measurement accuracy," Agricultural Water Management, vol. 98, pp. 899-920, Apr 2011. [62] M. V. Kongo, G. W. P. Jewitt, and S. A. Lorentz, "Evaporative water use of different land uses in the upper-Thukela river basin assessed from satellite imagery," Agricultural Water Management, vol. 98, pp. 1727-1739, Sep 2011. [63] Y.-A. Liou and A. W. England, "A land-surface process/radiobrightness model with coupled heat and moisture transport for freezing soils," IEEE Transactions on Geoscience and Remote Sensing, vol. 36, pp. 669-677, 1998. [64] A. A. Almhab and I. Busu, "Estimation of Evapotranspiration with Modified SEBAL model using landsat-TM and NOAA-AVHRR images in arid mountains area," in Modeling & Simulation, 2008. AICMS 08. Second Asia International Conference on, 2008, pp. 350-355. [65] T. Y. Chang, Y. C. Wang, C. C. Feng, A. D. Ziegler, T. W. Giambelluca, and Y.-A. Liou, "Estimation of Root Zone Soil Moisture Using Apparent Thermal Inertia With MODIS Imagery Over a Tropical Catchment in Northern Thailand," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, pp. 752-761, 2012. [66] T. T. Shi, D. X. Guan, J. B. Wu, A. Z. Wang, C. J. Jin, and S. J. Han, "Comparison of methods for estimating evapotranspiration rate of dry forest canopy: Eddy covariance, Bowen ratio energy balance, and Penman‐Monteith equation," Journal of Geophysical Research: Atmospheres, vol. 113, 2008. [67] G. Paul, P. H. Gowda, P. V. Prasad, T. A. Howell, S. A. Staggenborg, and C. M. Neale, "Lysimetric evaluation of SEBAL using high resolution airborne imagery from BEAREX08," Advances in Water Resources, vol. 59, pp. 157-168, 2013. [68] R. G. Allen, L. S. Pereira, D. Raes, and M. Smith, "FAO Irrigation and drainage paper No. 56," Rome: Food and Agriculture Organization of the United Nations, vol. 56, p. e156, 1998. [69] Z. Sun, B. Wei, W. Su, W. Shen, C. Wang, D. You, et al., "Evapotranspiration estimation based on the SEBAL model in the Nansi Lake Wetland of China," Mathematical and Computer Modelling, vol. 54, pp. 1086-1092, 2011. [70] J. Ramos, C. Cratchley, J. Kay, M. Casterad, A. Martinez-Cob, and R. Dominguez, "Evaluation of satellite evapotranspiration estimates using ground-meteorological data available for the Flumen District into the Ebro Valley of NE Spain," Agricultural Water Management, vol. 96, pp. 638-652, 2009. [71] W. Bastiaanssen, "SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey," Journal of Hydrology, vol. 229, pp. 87-100, 2000. [72] A. Bhandari, A. Kumar, and G. Singh, "Feature extraction using Normalized Difference Vegetation Index (NDVI): a case study of Jabalpur city," Procedia Technology, vol. 6, pp. 612-621, 2012. [73] R. Niclòs, J. M. Galve, J. A. Valiente, M. J. Estrela, and C. Coll, "Accuracy assessment of land surface temperature retrievals from MSG2-SEVIRI data," Remote Sensing of Environment, vol. 115, pp. 2126-2140, 2011. [74] X. Yu, X. Guo, and Z. Wu, "Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method," Remote Sensing, vol. 6, pp. 9829-9852, 2014. [75] Y.-A. Liou and A. W. England, "Annual temperature and radiobrightness signatures for bare soils," IEEE Transactions on Geoscience and Remote Sensing, vol. 34, pp. 981-990, 1996. [76] M. Govender, K. Chetty, and H. Bulcock, "A review of hyperspectral remote sensing and its application in vegetation and water resource studies," Water Sa, vol. 33, pp. 145-151, 2007. [77] S. Horion, H. Eerens, B. Tychon, and P. Ozer, "Monitoring of the 2003 summer drought in Belgium with the NDWI applied on spot-vegetation data," in Proceedings of the 2nd International Vegetation User Conference. 1998-2004: 6 years of operational activities., 2005, pp. 217-222. [78] L. Ji, L. Zhang, B. K. Wylie, and J. Rover, "On the terminology of the spectral vegetation index (NIR− SWIR)/(NIR+ SWIR)," International Journal of Remote Sensing, vol. 32, pp. 6901-6909, 2011. [79] R. Allen, M. Tasumi, and R. Trezza, "SEBAL (Surface Energy Balance Algorithms for Land)—Advanced Training and Users Manual—Idaho Implementation (Version 1.0)," Disponível: ftp://ftp. funceme. br/Cospar_Funceme_2010/C LASS_DAY_04, vol. 11, 2002. [80] D. Autovino, M. Minacapilli, and G. Provenzano, "Modelling bulk surface resistance by MODIS data and assessment of MOD16A2 evapotranspiration product in an irrigation district of Southern Italy," Agricultural Water Management, vol. 167, pp. 86-94, 2016. [81] Y.-A. Liou, J. F. Galantowicz, and A. W. England, "A land surface process/radiobrightness model with coupled heat and moisture transport for prairie grassland," IEEE Transactions on Geoscience and Remote Sensing, vol. 37, pp. 1848-1859, 1999. [82] W. Bastiaanssen, E. Noordman, H. Pelgrum, G. Davids, B. Thoreson, and R. Allen, "SEBAL model with remotely sensed data to improve water-resources management under actual field conditions," Journal of Irrigation and Drainage Engineering, vol. 131, pp. 85-93, 2005. [83] C. A. Paulson, "The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer," Journal of Applied Meteorology, vol. 9, pp. 857-861, 1970. [84] E. K. Webb, "Profile relationships: The log‐linear range, and extension to strong stability," Quarterly Journal of the Royal Meteorological Society, vol. 96, pp. 67-90, 1970. [85] Y.-A. Liou and A. W. England, "A land surface process radiobrightness model with coupled heat and moisture transport in soil," IEEE Transactions on Geoscience and Remote Sensing, vol. 36, pp. 273-286, Jan 1998. [86] I. Campos, J. González-Piqueras, A. Carrara, J. Villodre, and A. Calera, "Estimation of total available water in the soil layer by integrating actual evapotranspiration data in a remote sensing-driven soil water balance," Journal of Hydrology, vol. 534, pp. 427-439, 2016. [87] A. Z. Wang, T. Y. Chang, and Y.-A. Liou, "Terrain correction for increasing the evapotranspiration estimation accuracy in a mountainous watershed," IEEE Geoscience and Remote Sensing Letters, vol. 7, pp. 352-356, 2010. [88] C.-H. Cheng, F. Nnadi, and Y.-A. Liou, "A regional land use drought index for Florida," Remote Sensing, vol. 7, pp. 17149-17167, 2015. [89] Y.-A. Liou and G. M. Mulualem, "Spatio–temporal Assessment of Drought in Ethiopia and the Impact of Recent Intense Droughts," Remote Sensing, vol. 11, p. 1828, 2019. [90] M. Dorjsuren, Y.-A. Liou, and C.-H. Cheng, "Time series MODIS and in situ data analysis for Mongolia drought," Remote Sensing, vol. 8, p. 509, 2016. [91] G. M. Mulualem and Y.-A. Liou, "Application of Artificial Neural Networks in Forecasting a Standardized Precipitation Evapotranspiration Index for the Upper Blue Nile Basin," Water, vol. 12, p. 643, 2020. [92] G. Bartzas, D. Vamvuka, and K. Komnitsas, "Comparative life cycle assessment of pistachio, almond and apple production," Information Processing in Agriculture, vol. 4, pp. 188-198, 2017. [93] J. Martínez-Fernández, A. González-Zamora, N. Sánchez, A. Gumuzzio, and C. Herrero-Jiménez, "Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index," Remote Sensing of Environment, vol. 177, pp. 277-286, 2016. [94] V. Parkash and S. Singh, "A Review on Potential Plant-Based Water Stress Indicators for Vegetable Crops," Sustainability, vol. 12, p. 3945, 2020. [95] X. Wang, C. Zhao, N. Guo, Y. Li, S. Jian, and K. Yu, "Determining the canopy water stress for spring wheat using canopy hyperspectral reflectance data in loess plateau semiarid regions," Spectroscopy Letters, vol. 48, pp. 492-498, 2015. [96] J. Cho and T. Oki, "Application of temperature, water stress, CO 2 in rice growth models," Rice, vol. 5, p. 10, 2012. [97] V. C. Patil, K. A. Al-Gaadi, R. Madugundu, E. H. Tola, S. Marey, A. Aldosari, et al., "Assessing agricultural water productivity in desert farming system of Saudi Arabia," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, pp. 284-297, 2015. [98] W. Bastiaanssen, D. J. Molden, and I. W. Makin, "Remote sensing for irrigated agriculture: examples from research and possible applications," Agricultural Water Management, vol. 46, pp. 137-155, 2000. [99] M. Mkhwanazi, J. L. Chavez, and A. A. Andales, "SEBAL-A: A remote sensing ET algorithm that accounts for advection with limited data. Part I: Development and validation," Remote Sensing, vol. 7, pp. 15046-15067, 2015. [100] A. L. Ruhoff, A. R. Paz, W. Collischonn, L. E. Aragao, H. R. Rocha, and Y. S. Malhi, "A MODIS-based energy balance to estimate evapotranspiration for clear-sky days in Brazilian tropical savannas," Remote Sensing, vol. 4, pp. 703-725, 2012. [101] P. Wagle, N. Bhattarai, P. H. Gowda, and V. G. Kakani, "Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 128, pp. 192-203, 2017. [102] J. Kiptala, Y. Mohamed, M. L. Mul, and P. Van der Zaag, "Mapping evapotranspiration trends using MODIS and SEBAL model in a data scarce and heterogeneous landscape in Eastern Africa," Water Resources Research, vol. 49, pp. 8495-8510, 2013. [103] K. Guan, Z. Li, and L. N. Rao, "Measuring Rice Yield from Space: The Case of Thai Binh Province, Viet Nam," Asian Development Bank Economics Working Paper Series, 2018. [104] K. Guan, Z. Li, L. N. Rao, F. Gao, D. Xie, N. T. Hien, et al., "Mapping Paddy Rice Area and Yields Over Thai Binh Province in Viet Nam From MODIS, Landsat, and ALOS-2/PALSAR-2," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, pp. 2238-2252, 2018. [105] K. Geeen and L. Tashman, "Percentage error: What denominator?," Foresight: The International Journal of Applied Forecasting, pp. 36-40, 2009. [106] P. Dao and Y.-A. Liou, "Object-based flood mapping and affected rice field estimation with Landsat 8 OLI and MODIS data," Remote Sensing, vol. 7, pp. 5077-5097, 2015. [107] Y.-A. Liou, H.-C. Sha, T.-M. Chen, T.-S. Wang, Y.-T. Li, Y.-C. Lai, et al., "Assessment of Disaster Losses in Rice Paddy Field and Yield after Tsunami Induced by the 2011 Great East Japan Earthquake," Journal of Marine Science and Technology, vol. 20, pp. 618-623, 2012. [108] E. B. Knipling, "Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation," Remote Sensing of Environment, vol. 1, pp. 155-159, 1970. [109] J. G. Barr, M. S. DeLonge, and J. D. Fuentes, "Seasonal evapotranspiration patterns in mangrove forests," Journal of Geophysical Research: Atmospheres, vol. 119, pp. 3886-3899, 2014. [110] S. S. Tong, J. P. Deroin, T. L. Pham, and X. C. Cao, "Estimation of Surface Parameters of Tidal Flats Using Sentinel-1A SAR Data in the Northern Coast of Vietnam," in International Conference on Geo-Spatial Technologies and Earth Resources, 2017, pp. 65-88. [111] B. Hadiwijaya, S. Pepin, P.-E. Isabelle, and D. F. Nadeau, "The Dynamics of Transpiration to Evapotranspiration Ratio under Wet and Dry Canopy Conditions in a Humid Boreal Forest," Forests, vol. 11, p. 237, 2020. [112] D. Sun and R. T. Pinker, "Case study of soil moisture effect on land surface temperature retrieval," IEEE Geoscience and Remote Sensing Letters, vol. 1, pp. 127-130, 2004. [113] M. Pablos, M. Piles, N. Sanchez, V. González-Gambau, M. Vall-Llossera, A. Camps, et al., "A sensitivity study of land surface temperature to soil moisture using in-situ and spaceborne observations," in 2014 IEEE Geoscience and Remote Sensing Symposium, 2014, pp. 3267-3269. [114] L. Wang and J. J. Qu, "Satellite remote sensing applications for surface soil moisture monitoring: A review," Frontiers of Earth Science in China, vol. 3, pp. 237-247, 2009. [115] W. Zhu, A. Lv, S. Jia, and L. Sun, "Development and evaluation of the MTVDI for soil moisture monitoring," Journal of Geophysical Research: Atmospheres, vol. 122, pp. 5533-5555, 2017. [116] Y.-A. Liou, J.-F. Galantowicz, and A.-W. England, "A land surface process/radiobrightness model with coupled heat and moisture transport for prairie grassland," IEEE Transactions on Geoscience and Remote Sensing, vol. 37, pp. 1848-1859, 1999. [117] T. J. Jackson, "III. Measuring surface soil moisture using passive microwave remote sensing," Hydrological Processes, vol. 7, pp. 139-152, 1993. [118] Y.-A. Liou, M. S. Le, and H. Chien, "Normalized Difference Latent Heat Index for Remote Sensing of Land Surface Energy Fluxes," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, pp. 1423-1433, Mar 2019. [119] Y.-A. Liou and M. S. Le, "Rapid Identification of Evapotranspiration Features Using Normalized Difference Latent Heat Index (NDLI)." in IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 1915-1918.
指導教授	劉說安錢樺(Yuei-An Liou Hwa Chien)	審核日期	2021-1-25
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