請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/65014
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 鄭克聲 | |
dc.contributor.author | Sergio Garcia Jimenez | en |
dc.contributor.author | 賈西亞 | zh_TW |
dc.date.accessioned | 2021-06-16T23:15:23Z | - |
dc.date.available | 2022-08-01 | |
dc.date.copyright | 2012-08-10 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-08-01 | |
dc.identifier.citation | Ahmed, F.K. 1991. Measuring actual evapotranspiration from sweet corn with lysimeters in a warm, humid climate. University Microfilms International, 251p.
Allen, R.G., Pereira, L.S., Raes, D., Smith, M. 1998. Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements—FAO Irrigation and Drainage Paper 56. Food and Agriculture Organization of the United Nations FAO, 293p. Anbumozhi, V., Yamaji, E., Tabuchi, T. 1998. Rice crop growth and yield as influenced by changes in ponding water depth, water regime and fertigation level. Agricultural Water Management 37, 241-253. Central Weather Bureau. 2012. http://www.cwb.gov.tw. Seen on: July 7 2012. Chatterjee, S. 2010. Estimating evapotranspiration using remote sensing: a hybrid approach between MODIS derived enhanced vegetation index, bowen ratio system, and ground based micro-meteorological data. PhD dissertation Wright State University, 193p. Chen, C.K., Liu, C.W. 2002. Analysis of water movement in paddy rice fields (I) experimental studies. Journal of Hydrology 260, 206–215. Chen, S.K., Jang, C.S., Chen, S.M., Chen, K.H. 2011. Effect of N-fertilizer application on return flow water quality from a terraced paddy field in Northern Taiwan. Paddy Water Environ DOI 10.1007/s10333-011-0298-7. Cheng, W.L, Cheng, S.W., Yu, W.S., Cheng, S.K. 2003. Water infiltration rate in cracked paddy soil. Geoderma 117, 169–181. Choudhury, B.J., Ahmed, N.U., Idso, S.B., Reginato, R.J., Daughtry, C.S.T. 1994. Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote Sens. Environ. 50, 1-17. Clevers, J., Kooistra, L., Schaepman, M. 2008. Using spectral information from the NIR water absorption features for the retrieval of canopy water content. International Journal of Applied Observation and Geoinformation 10, 388-397. Courault, D., B. Seguin, and A. Olioso. 2005. Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches Irrigation and Drainage Systems 19: 223–249. Doorenbos, J., Kassam, A.H. 1986. Yield response to water. FAO Irrig. and Drain, Paper 33, 193p. Evett, S.R., Howell, T.A., Steiner, J.L., Cresap, J.L. 1993. Evapotranspiration by soil water balance using TDR and neutron scattering. Management of Irrigation and Drainage Systems, Proceedings of American Society of Civil Engineers, 914–921. Filella, I., Penuelas, J. 1994. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int. J. Remote sensing 15 (7), 1459-1470. Glenn, E.P., Huete, A.R., Nagler, P.L., Hirschboeck, K.K., Brown, P. 2007. Integrating remote sensing and ground methods to estimate evapotranspiration. Crit. Rev. Plant. Sci. 26 (3), 139–168. Gonzalez-Dugo, M.P., Neale, C.M.U., Mateos, L., Kustas, W.P., Prueger, J.H., Anderson M.C., Li, F. 2009. A comparison of operational remote sensing-based models for estimating crop evapotranspiration. Agricultural and Forest Meteorology 149, 1843–1853. Greenlife labors. 2012. http://www.greencoop.org.tw/. Seen on July 7 2012. Huete, A.R. 1988. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment 25, 295-309. Hunsaker, D.J., Pinter, P.J., Kimball, B.A. 2005. Wheat basal crop coefficients determined by normalized difference vegetation index. Irrig. Science 24: 1–14 Inoue, Y., Moran, M.S., Horie, T. 1998. Analysis of spectral measurements in paddy field for predicting rice growth and yield based on a simple crop simulation model. Plant Prod. Sci. 1 (4), 269-279. Jackson, R.D., Pinter Jr., P.J., Reginato, R.J., Idso, S.B. 1980. Hand – held radiometry. In: A Set of Notes Developed for Use at the Workshop on Hand-held Radiometry, Phoenix, Ariz, February 25–26. Kan, C.E., Chen, K.Y., Shih, S.F. 1997. A procedure for rotation irrigation in lowland rice. Agricultural Water Manag. 35, 109-121. Kimura, R., Okada, S., Miura H., Kamichika, M. 2004. Relationships among the leaf area index, moisture availability, and spectral reflectance in an upland rice field at field level. Agricultural Water Manag. 69, 83–100. Köksal, E.S. 2011. Hyperspectral reflectance data processing through cluster and principal component analysis for estimating irrigation and yield related indicator. Agricultural Water Manag. 98, 1317–1328. Kuo, S.F., Ho, S.S., Liu, C.W. 2006. Estimation irrigation water requirements with derived crop coefficients for upland and paddy crops in ChiaNan Irrigation Association, Taiwan. Agricultural Water Manag. 82, 433–451. MacQueen, J.B. 1967. Some Methods for classification and Analysis of Multivariate Observations. Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley, University of California Press. 1, 281-297. Moran, MS., Rahman, A.F., Washburne, J.C., Goodrich, D.C., Weltz, M.A., Kustas, W.P. 1996. Combining the Penman-Monteith equation with measurements of surface temperature and reflectance to estimate evaporation rates of semiarid grassland. Agricultural and Forest Meteorology 80, 87- 109 Olmsted, T.R. 1990. Evaluating Daily Evapotranspiration Estimation Methods: A Comparison of Potential Evapotranspiration Equations and Irrigation. University Microfilms International, 167p. Pattey, E., Strachan, I.B., Boisvert, J.B., Desjardins, R.L., McLaughlin, N.B. 2001. Detecting effects of nitrogen rate and weather on corn growth using micrometeorological and hyperspectral reflectance measurements. Agricultural and Forest Meteorology 108, 85–99 Peñuelas, J., Filella, I., Biel, C., Serrano, L., Save’, R. 1993. The reflectance at the 950- 970 nm region as an indicator of plant water status. International Journal of Remote Sensing 14, 1887- 1905. Peñuelas, J., Pinol, J., Ogaya, R., Fiella. 1997. Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). Int. J. Remote sensing 18, 2869-2875. Richards, J.A, Jia, X.P. 2006. Remote sensing digital image analysis. Third edition. Springer, 355p. Shibayama, M., Takahashi, W.,Morinaga, S., Akiyama, T. 1993. Canopy water deficit detection in paddy rice using a high resolution field spectroradiometer. Remote Sens. Environ. 45, 117–126. Stancalie, G., Marica, A., Toulios, L. 2010. Using earth observation data and CROPWAT model to estimate the actual crop evapotranspiration. Physics and Chemistry of the Earth 35, 25–30. Thenkabail, P.S., Smith, R.B., De Pau, E. 2000. Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics. Remote Sens. Environ. 71, 158–182. Thuyet, D.Q., Hien, T.Q., Watanabe, H., Saito, H., Phong, T.K., Nishimura, T. 2010. Micro paddy lysimeter for monitoring solute transport in paddy environment. Paddy Water Environ. 8, 235–245. Thorp, K.R., Dierig, D.A., French, A.N., Hunsaker, D.J. 2011. Analysis of hyperspectral reflectance data for monitoring growth and development of lesquerella. Industrial Crops and Products 33, 524–531. Tucker, C.J. 1980. Remote Sensing of Leaf Water Content in the Near Infrared. Remote Sensing of Environment 10, 23-32. Tyagi, N.K., Sharma, D.K., Luthra, S.K. 2000. Determination of evapotranspiration and crop coefficients of rice and sunflower with lysimeter. Agricultural Water Management 45, 41-54. Vu, S.H., Watanabe, H., Takagi, K. 2005. Application of FAO-56 for evaluating evapotranspiration in simulation of pollutant runoff from paddy rice field in Japan. Agricultural Water Management 76, 195–210. Wang, Y.S., Duh, J.R., Lin, K.Y., Chen, Y.L. 1996. Movement of Three s-Triazine Herbicides Atrazine, Simazine, and Ametryn in Subtropical Soils. Bull. Environ. Contam. Toxicol. 57, 743-750. Zhang, J.H., Yao, F.M., Li, B.B., Hao, Y., Hou, Y.Y. 2011. Progress in monitoring high-temperature damage to rice through satellite and ground-based optical remote sensing. Sci China Earth Sci. 54 (12), 1801–1811. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/65014 | - |
dc.description.abstract | 欲使用現地遙測的方式對作物灌溉做管理,需要先根據實驗區域情況進行現地調查並收集現地資料。 本研究的實驗區域是屬於北台灣的氣候條件,在本研究中,使用現地測量之水稻田的反射光譜和蒸發散資料,計算水稻在四個不同生長階段的植生指數(vegetation indices, VI)和一種新的光譜指數來推估適合此實驗區域的作物係數(crop coefficient, Kc)。在水稻秧苗期、營養生長期和成熟期所推估的作物係數與前人研究的建議參考值十分吻合;然而,在生殖生長期所推估的作物係數卻與前人研究有很大的差異,其原因是由於使用蒸散儀的方法無法有效的反映實際的水稻田土壤的情況。反射光譜波長範圍為350nm到1050nm,並進行平滑及一階微分處理。經由迴歸分析與群集分析反射光譜的主成分,來選出最適當的波長反射率,以比值、差值、常態化差異、和複迴歸計算新組成的植生指數。新組成之植生指數提升了與Kc值間之關聯,但由一階微分處理後新組成之植生指數沒有提升與Kc值間的關聯。 | zh_TW |
dc.description.abstract | Management of crop irrigation by using field-based remote sensing requires background data obtained from field experiments carried out under local conditions. In this study, spectral and evapotranspiration data were used to evaluate well-known vegetation indices (VI) and new spectral indicators for estimating current crop coefficient (Kc) in rice paddy under northern Taiwan climatic conditions. Estimated Kc values for the initial, development and late growth stages were very close to those suggested in previous research works, however, the Kc values at the mid-stage greatly differ from previous Kc values, possibly due to lysimeter method did not perfectly match the paddy soil heat conditions in the field. Spectral reflectance (Refl) values were smoothed and first-order derivative spectra (ρ) were calculated for each individual wavelength between 350 and 1050nm. Based on regression relationship analysis and principal component analysis (PCA) of grouped data, the most appropriate wavelengths were selected and new combinations were calculated by using rationing, differencing, normalized differencing and multiple regression. New indicators improve the degree of correlation between Kc and VI while no significant improvements were found for new wavelengths combinations derived from the first-order derivative. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T23:15:23Z (GMT). No. of bitstreams: 1 ntu-101-R99622042-1.pdf: 3093740 bytes, checksum: 798b5a20e6ad2dbc08e84d628d589200 (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | Abstract i
摘要 ii Contents iii List of Figures v List of Tables vii Chapter 1 Introduction 1 1.1 Statement of the problem 1 1.2 Research objectives 3 1.2.1 General aim 3 1.2.2 Specific aims 3 Chapter 2 Concepts 4 2.1 Evapotranspiration process 4 2.2 Factors affecting evapotranspiration 5 2.3 Measuring evapotranspiration 6 2.3.1 Soil Water Balance (SWB) 7 2.3.2 The FAO Penman-Monteith method 9 2.4 Source and characteristics of remote sensing data 12 2.4.1 Computing evapotranspiration by field-based reflectance 15 Chapter 3 Literature Review 17 3.1 Estimation of ET in rice paddy from field-based remote sensing data 17 3.2 Estimation of ET in vegetation from field-based remote sensing data 19 Chapter 4 Materials and Methods 24 4.1 Experimental site 24 4.2 Meteorological data 27 4.3 Paddy rice characteristics 27 4.4 Selected methods for Kc estimation 31 4.4.1 Estimation of ETo using CROPWAT8.0 31 4.4.2 Estimation of ETc using SWB 31 4.4.3 Crop coefficient (Kc) derivation 32 4.5 Field-based reflectance data collection 35 4.6 Previously defined vegetation indices (VI) 36 4.6.1 The Simple Ratio (SR) 37 4.6.2 Normalized Difference Vegetation Index (NDVI) 37 4.6.3 Soil Adjusted Vegetation Index (SAVI) 38 4.6.4 Water Index (WI) 38 4.7Single narrow band reflectance processing 39 4.7.1 Linear regression analysis 40 4.7.2 Cluster and principal component analysis PCA 41 4.7.3 Selected wavelength combinations 41 4.7.4 Evaluation of new wavelength combination 43 Chapter 5 Results and Discussion 45 5.1 Meteorological factors 45 5.2 Estimated reference evapotranspiration ETo 50 5.3 Estimated reference crop evapotranspiration ETc 52 5.4 Comparison between ETo and ETc 57 5.5 Comparison between ETc and FAO-56 simulated ETc 59 5.6 Derived crop coefficients (Kc) 61 5.7 Field-based rice paddy reflectance profile 64 5.8 Relations between previous vegetation indices and Kc 67 5.9 Single narrow band reflectance process 73 5.10 Cluster and principal components analysis 81 Chapter 6 Conclusions 84 References 86 Annex 91 | |
dc.language.iso | en | |
dc.title | 應用現地遙測技術推估水稻蒸發散量 | zh_TW |
dc.title | Estimating Rice Paddy Evapotranspiration using Field-based Remote Sensing | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 吳瑞賢,黃文政,蘇元風 | |
dc.subject.keyword | 現地遙測,作物蒸發散量,水稻田,植生指數,土水平衡, | zh_TW |
dc.subject.keyword | field-based remote sensing,crop evapotranspiration,rice paddy,vegetation index,soil water balance, | en |
dc.relation.page | 97 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2012-08-03 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
顯示於系所單位: | 生物環境系統工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-101-1.pdf 目前未授權公開取用 | 3.02 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。