Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/28171
標題: 應用遙測資訊協助判釋影響水稻產量土壤限制因子之研究
Identification and Characterization of Soil Limiting Factors to Rice Yield through Remote Sensed Information
作者: 王依蘋
Wang, Yi-Ping
關鍵字: 遙測
remote sening
土壤限制因子
水稻產量
大氣改正
soil limiting factors
rice yield
atmospheric correction
出版社: 土壤環境科學系所
引用: 行政院農業委員會。2005。作物施肥手冊。行政院農業委員會。p.1-31。 吳啟南、蕭國鑫、廖子毅 。2003。SPOT衛星與Duncan資料應用於水稻產量估測。水稻精準農業體系。行政院農業委員會農業試驗所。p.43-60。 李裕娟、楊純明、張愛華。2002。施用氮肥對水稻植株氮素、葉綠素及植被反射光譜之影響。中華農業研究。51(1):1-14。 林安秋、洪黎明。1979。第二期稻作低產原因之研究(6):溫度與日照強度對水稻抽穗及結實之影響。中華農學會報。108:24-38。 林安秋、陳建山。1977。第二期稻作低產原因之研究--3.不同溫度持續對水稻之分蘖及光合作用能力之影響。中華農學會報。98:55-60。 林安秋。1977。一、二期作水稻分蘖特性及光合成能力之探討。中華農學會報。100:87-97。 林唐煌、劉振榮、陳哲俊。1998。應用SPOT衛星資料求取大氣氣溶膠光學厚度。航測及遙測學刊。3(4):1-14。 翁仁憲、陳清義。1984。臺灣水稻之光合成作用、物質生產及穀實生產特性第1 報:第一、二期作水稻之物質生產與穀實生產特性。中華農學會報。125:4-14。 許正一、蔡呈奇、陳尊賢。2003。台灣新研擬土壤管理組之歸併。土壤管理組規劃及應用研討會論文集。中華土壤肥料學會。p.21-39。台中,台灣。 郭鴻裕、劉滄棽、朱戩良、江志峰。2003。台灣現行之農田土壤管理組之歸併與利用。土壤管理組規劃及應用研討會論文集。中華土壤肥料學會。p.1-20。台中,台灣。 陳榮坤、楊純明。2002。以近地面高解析植被光譜及模擬SPOT衛星寬頻光譜估測水稻生長性狀的變化。中華農業研究。51(4):1-18。 陳榮坤、楊純明。2003。水稻植被反射光譜之特徵及變異。中華農業氣象。10:29-38。 章國威、王淑姿、申雍、羅正宗、黃鼎名、蔡和霖。2006。應用抽穗期多光譜航照影像預估水稻產量之研究。航測及遙測學刊。11(1):27-38。 黃士元、翁仁憲、陳清義。1984。臺灣水稻之光合成作用、物質生產及穀實生產特性第2 報:光合成作用之品種間差異。中華農學會報。127:18-28。 楊藹華。1983。二期作水稻光合成物質的生產、分配及與產量間的關係。中華農業研究。32(3):209-218。 劉建慧。1997。SPOT衛星影像之輻射改正。航測及遙測學刊。2(1):61-80。 潘國樑。2006。遙測學大綱。科技圖書。p.17-25。 蔡呈奇、陳尊賢、許正一、郭鴻裕。1998。台灣地區農地與坡地代表土壤的選定與其相關資料庫的建立。土壤與環境。1:73-88。台中,台灣。 Aparicio, N., D. Villegas, J. Casadesus, J.L. Araus, and C. Royo. 2000. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron. J. 92:83-91. Bach, H. 1998. Yield estimation of corn based on multitemporal LANDSAT-TM data as input for an agrometeorological Model. Journal of Optics A: Pure and Applied Optics. 7:809-825. Baez-Gonzalez, A.D., J.R. Kiniry, S.J. Maas, L.M. Tiscareno, C.J. Macias, J.L. Mendoza, C.W. Richardson, G.J. Salinas, and J.R. Manjarrez. 2005. Large-area aaize yield forecasting using leaf area index based yield model. Agron. J. 97:418-425. Balasubramanian, V., A.C. Morales, R.T. Cruz, T.M. Thiyagarajan, R. Nagarajan, M. Babu, S. Abdulrachman, and L.H. Hai. 2000. Adaptation of the chlorophyll meter (SPAD) technology for real-time N management in rice:a review, Int. Rice Res. Inst. 5:25-26. Bartels, J.M. 1996. Methods of soil analysis. Part 3. Chemical methods. p.758-760. SSSA Book Ser. 5. SSSA and ASA, Madison, WI. Bastiaanssen, W.G.M., and, S. Ali. 2003. A new crop yield forecasting model based on satellite measurements applied across the Indus Basin Pakistan. Agric. Ecosyst. Environ. 94:321-340. Birrell, S.J., K.A. Sudduth, and S.C. Borgelt. 1993. Crop yield mapping using GPS. ASAE Paper MC93-104. ASAE, St. Joseph, MI. Birrell, S.J., K.A. Sudduth, and S.C. Borgelt. 1996. Comparison of sensors and techniques for crop mapping. Comput. Electron. Agric. 14:215-233. Blackmer, T.M., J.S. Schepers, and G.E. Varvel. 1994. Light reflectance compared with other nitrogen stress measurements in corn leaves. Agron. J. 86:934-938. Blackmore, S. 2000. The interpretation of trends from multiple yield maps. Comput. Electron. Agric. 26:37-51. Boydell, B., and A.B. McBratney. 2002. Identifying potential within-field management zones from cotton-yield estimates. Precis. Agric. 3:9-23. Bremner, J.M. Total nitrogen, inorganic forms of nitrogen, organic forms of nitrogen. Pp.1149-1178. In: Black, C.A.(ed.) 1965. “Methods of soil Analysis Part2”. Am. Soc. Of Agron., Inc., Madison, Wisconsin, USA. Broge, N.H., and E. Leblanc. 2000. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 76:156-172. Carr, P.M., G.R. Carlson, J.S. Jacobsen, G.A. Nielsen, and E.O. Skogley. 1991. Farming soils, not fields:A strategy for increasing fertilizer profitability. J. Prod. Agric. 4:57-61. Cassman, K.G., A. Dobermann, and D.T. Walters. 2002. Agroecosystems, nitrogen-use efficiency, and nitrogen management. Ambio. 31:132-140. Chang, K.W., Y. Shen, and J.C. Lo. 2005. Predicting rice yield using canopy reflectance measured at booting stage. Agron. J. 97:872-878. Cooley, T., G.P. Anderson, G.W. Felde, M.P. Hoke, A. Ratkowski, J. Gardner, S.M. Adler-Golden, M.W. Matthew, A. Berk, L.S. Bernstein, P.K. Acharya, D.P. Miller, and P.E. Lewis. 2002. FLAASH, a MODTRAN4-based atmospheric correction algorithm, Its application and validation. In S.S. Shen, & Lewis, P.E.(Eds.), Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery VIII. Proc. SPIE Vol. 4725(pp. 65-71). Cox, M.S., and P.D. Gerard. 2007. Soil management zone determination by yield stability analysis and classification. Agron. J. 99:1357-1365. Cui, R.X., and B.W. Lee. 2002. Spikelet number estimation model using nitrogen nutrition status and biomass at panicle initiation and heading stage of rice. Korean journal of crop science. 47:390-394. Daughtry, C.S.T., C.L. Walthall, M.S. Kim, E.B. de Colstoun, and J.E. McMurtrey. 2000. Estimating corn leaf chlorophyll content from leaf and canopy reflectance. Remote Sens. Environ. 74:229-239. De Datta, S.K. 1981. Principles and Practices of Rice Production. John Wiley & Sons, Inc. DeTar, W.R., J.V. Penner, and H.A. Funk. 2006. Airborne remote sensing to detect plant water stress in full canopy cotton. Trans. ASAE. 49:655-665. Dobermann, A., J.L. Ping, V.I. Adamchuk, G.C. Simbahan, and R.B. Ferguson. 2003. Classification of crop yield variability in irrigated production fields. Agron. J. 95:1105-1120. Doerge, T.A. 1999. Yield map interpretation. J. Prod. Agric. 12:54-61. Doraiswamy, P.C., J.L. Hatfield, T.J.Jackson, B. Akhmedov, J. Prueger, and A. Stern. 2004. Crop condition and yield simulation using Landsat and MODIS. Remote Sens. Environ. 92:548-559. Fang, H. 1998. Rice crop area estimation of an administrative division in China using remote sensing data. Int. J. Remote Sens. 17:3411-3419. Ferguson, R.B., G.W. Hergert, J.S. Schepers, C.A. Gotway, J.E. Cahoon, and T.A. Peterson. 2002. Site-specific nitrogen management of irrigated maize:Yield and soil residual nitrate effects. Soil Sci. Soc. Am. J. 66:544-553. Fleming, K.L., D.F. Heermann, and D.G. Westfall. 2004. Evaluating soil color with farmer input and apparent soil electrical conductivity for management zone delineation. Agron. J. 96:1581-1587. Fleming, K.L., D.G. Westfall, D.W. Wiens, and M.C. Brodah. 2000. Evaluating farmer developed management zone maps for variable rate fertilizer application. Precis. Agric. 2:201-215. Fraisse, C.W., K.A. Sudduth, and N.R. Kitchen. 2001. Delineation of site-specific management zones by unsupervised classification of topographic attributes and soil electrical conductivity. Trans. ASAE. 44:155-166. Franzen, D.W., A.D. Halvorson, and V.L. Hoffman. 2000. Management zones for soil N and P levels in the Northern Great Plains. P.XXX-XXX. In P.C. Robert et al. (ed.) Proc. 5th. Int. Conf. on Precision Agriculture, Minneapolis, MN [CD-ROM]. 16-19 July 2000. ASA, CSSA, and SSSA, Madison, WI. Franzen, D.W., and T.N. Nanna. 2002. Management zone delineation methods. p. 363-377. In P.C. Robert et al. (ed.) Proc. 6th. Int. Conf. on Precision Agriculture, Minneapolis, MN [CD-ROM]. 14-17 July 2002. ASA, CSSA, and SSSA, Madison, WI. Fraser, R.S., and Y.J. Kaufman. 1985. The relative importance of aerosol scattering and absorption in remote sensing. IEEE Transactions on Geoscience and Remote Sensing. 23:625-633. Frazier, B.E., C.S. Walters, and E.M. Perry. 1997. Role of remote sensing in site-specific management. p.149-160. In F.J. Pierce and E.J. Sadler (ed.).The state of site-specific management for agriculture. ASA, CSSA, SSSA, Madison, WI. Gee, G.W., and J.W. Bauder. 1986. Particle-size analysis. p.404-408. In A. Klute et al. (ed.) Methods of Soil Analysis. Part Ι. 2nd ed. Agron. Monogr. 9. ASA and SSSA, Madison, WI. Gitelson, A.A., U. Gritz, and M.N. Merzlyak. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 160:271-282. Gitelson, A.A., Y.J. Kaufman, R. Stark, and D. Rundquist. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 80:76-87. Hansen, P.M., and J.K. Schjoerring, 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ. 86:542-553. Hatfield, J.L., A.A. Gitelson, J.S. Schepers, and C.L. Walthall. 2008. Application of spectral remote sensing for agronomic decisions. Agron. J. 100:117-131. Hong, N., J.G. White, R. Weisz, C.R. Crozier, M.L. Gumpertz, and D.K. Cassel. 2006. Remote Sensing-Informed Variable-Rate Nitrogen Management of Wheat and Corn:Agronomic and Groundwater Outcomes. Agron. J. 98:327-338. Huete, A., K. Didan, T. Miura, E.P. Rodriguez, X. Gao, and L.G. Ferreira. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83:195-213. Hutchinson, C.F. 1991. Uses of satellite data for famine early warning in sub-Saharan Africa. Int. J. Remote Sens. 12:1405-1421. Jackson, T.J., D. Chen, M. Cosh, F. Li, M. Anderson, C. Walthall, P. Doraiswamy, and E.R. Hunt. 2004. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sens. Environ. 92:475-482. Jaynes, D.B., and T.S. Colvin. 1997. Spatiotemporal variability of corn and soybean yield. Agron. J. 89:30-37. Kaufman, Y.J. 1989. The atmospheric effect on remote sensing and its correction (pp. 336-428). In G. Asrar (Ed.), Theory and applications of optical remote sensing New York:John Wiley & Sons Press. Kaufman, Y.J., and C. Sendra. 1988. Algorithm for automatic atmospheric correction to visible and near-IR imagery. Int. J. Remote Sens. 9:1357-1381. Kitchen, N.R., S.T. Drummond, E.D. Lund, K.A. Sudduth, and G.W. Buchleiter. 2003. Soil electrical conductivity and topography related to yield for three contrasting soil-crop systems. Agron. J. 95:483-495. Kumar, M., and J.L. Monteith. (1981). Remote sensing of crop growth(pp. 133-144). In Smith, H. (Ed.), Plants and the Daylight Spectrum. London:Academic Press. Ladha, J.K., A. Tirol-Padre, G.C. Punsalan, E. Castillo, U. Singh, and C. Kesava Reddy. 1998. Nondestructive estimation of shoot nitrogen in different rice genotypes. Agron. J. 90:33-40. Lamb, J.A., R.H. Dowdy, J.L. Anderson, and G.W. Rehm. 1997. Spatial and temporal stability of corn grain yields. J. Prod. Agric. 10:410-414. Lee, Y.J., C.M. Yang, K.W. Chang, and Y. Shen. 2008. Field test of the simple spectral index using 735nm in mapping nitrogen status of rice canopy. Agron. J. 100:205-212. Liang, S. 2004. Quantitative Remote Sensing of Land Surfaces (pp. 534). New York:John Wiley & Sons, Inc. Liang, S., H. Fang, and M. Chen. 2001. Atmospheric Correction of Landsat ETM+ Land Surface Imagery:I. Methods. IEEE Transactions on Geoscience and Remote Sensing. 39:2490-2498. Lobell, D.B., G.P. Asner, J.I. Ortiz-Monasterio, and T.L. Benning. 2003. Remote sensing of regional crop production in the Yaqui Valley, Mexico:estimates and uncertainties. Agriculture, Ecosystems, and Environment. 94:205-220. Macdonald, R.B., and F.G. Hall. 1980. Global crop forecasting. Available URL:http://www.sciencemag.org/cgi/content/abstract/208/4445/670 Machado, S., E.D. Bynum, Jr., T.L. Archer, R.J. Lascano, L.T. Wilson, J. Bordovsky, E. Segarra, K. Bronson, D.M. Nesmith, and W. Xu. 2002. Spatial and temporal variability of corn growth and grain yield:Implications for site-specific farming. Crop Sci. 42:1564-1576. Mae, T. 1997. Physiological nitrogen efficiency in rice:Nitrogen utilization, photosynthesis, and yield potential. Plant Soil. 196:201-210. Mausbach, M.J., D.J. Lytle, and L.D. Spivey. 1993. Application of soil survey information to soil specific farming. p.57-68. In P.C. Robert et al. (ed.) Soil specific crop management. Proc. Int. Conf., Minneapolis, MN. 14-16 Apr. 1992. ASA, CSSA, and SSSA, Madison, WI. McBratney, A.B., B.M. Whelan, J.A. Taylor, and M.J. Pringle. 2000. A management opportunity index for precision agriculture. In P.C. Robert et al. (ed.) Proc. 5th. Int. Conf. on Precision Agriculture, Minneapolis, MN [CD-ROM]. 16-19 July 2000. ASA, CSSA, and SSSA, Madison, WI. Mclean, E.O. 1982, Soil pH and lime requirement. p.199-224. In A.L. Page et al. (ed.) Methods of soil analysis. Part II. 2nd ed. Agron. Monog. 5. ASA and SSSA, Madison, WI. Moran, M.S., Y. Inoue, and E.M. Barns. 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ. 61:319-346. Moulin, S., A. Bondeau, and R. Delecolle. 1998. Combining agricultural crop models and satellite observations:from field to regional scales. International Journal of Remote Sensing. 19(6):1021-1036. Mulla, D.J. and J.S. Schepers. 1997. Key processes and properties for site-specific soil and crop management. p.1-18. In F.J. Pierce and E.J. Sadler (ed.).The state of site-specific management for agriculture. ASA, CSSA, SSSA, Madison, WI. Ntanos, D.A., and S.D. Koutroubas. 2002. Dry matter and N accumulation and translocation for Indica and Japonica rice under Mediterranean conditions. Field Crops Research. 74:93-101. Okamoto, K., and M. Fukuhara. 1996. Estimation of paddy rice field area using the area ratio of categories in each pixel of Landsat TM. International Journal of Remote Sensing. 9:1735-1749. Osborne, S.L., J.S. Schepers, and M.R. Schlemmer. 2004. Detecting nitrogen and phosphorus stress in corn using multi-spectral imagery. Communications in Soil Science and Plant Analysis. 35:505-516. Peng, S., F.V. García, R.C. Lzza, and K.G. Cassman. 1993. Adjustment for specific leaf weight improves chlorophyll meter’s estimate of rice leaf nitrogen concentration. Agron. J. 85:987-990. Pierce, F.J., N.W. Anderson, T.S. Colvin, J.K. Schueller, D.S. Humburg, and N.B. McLauglin. 1997. Yield mapping. p. 211-244. In F.J. Pierce and E.J. Sadler (ed.).The state of site-specific management for agriculture. ASA, CSSA, and SSSA, Madison, WI. Powers, J.F., R. Wiese, and D. Flowerday. 2000. Managing nitrogen for water quality – lessons from management system evaluation. J. Environ. Qual. 29:355-366. Richter, R., D. Schläpfer, and A. Müller. 2006. An automatic atmospheric correction algorithm for visible/NIR imageries. International Journal of Remote Sensing. 27:2077-2085. Schepers, A.R., J.F. Shanahan, M.A. Liebig, J.S. Schepers, S.H. Johnson, and A. Luchiari, Jr. 2004. Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agron. J. 96:195-203. Serrano, L., I. Filella, and J. Penuelas. 2000. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Sci. 40:723-731. Shibayama, M., and T. Akiyama. 1986. A spectroradiometer for field use:VI. Radiometric estimation for chlorophyll index of rice canopy. Jpn. J. Crop Sci. 55:433-438. Singh, B., Y. Singh, J.K. Ladha, K.F. Bronson, V. Balasubramanian, J. Singh, and C.S. Khind. 2002. Chlorophyll meter– and leaf color chart–based nitrogen management for rice and wheat in northwestern India. Agron. J. 94:821-829. StatSoft. 2001. STATISTICA (data analysis software system). Version 6. StatSoft, Tulsa, OK. Thenkabail, P.S. 2002. Optimal hyperspectral narrowbands for discriminating agricultural crops. Remote Sensing Reviews. 20(4):257-291. Thenkabail, P.S., R.B. Smith, and E. De Pauw. 2000. Hyperspectral vegetation indices and their relationships with agricultural crops. Remote Sens. Environ. 71:158-182. Thornton, P.K., W.T. Bowen, A.C. Ravelo, P.W. Wilkens, G. Farmer, J. Brock, and J.E. Brink. 1997. Estimating millet production for famine early warning:An application of crop simulation modeling using satellite and ground-based data in Burkina Faso. Agricultural and Forest Meteorology. 83:95-112. Tiffany, D.G., K. Foord, and V. Eidman. 2000. Grower paths to profitable usage of precision agriculture technologies. In P.C. Robert et al. (ed.) Proc. 5th. Int. Conf. on Precision Agriculture, Minneapolis, MN [CD-ROM]. 16-19 July 2000. ASA, CSSA, and SSSA, Madison, WI. Turner, F.T., and M.F. Jund. 1991. Chlorophyll meter to predict nitrogen topdress requirement for semidwarf rice. Agron. J. 83:926-928. Wiegand, C.L., D.E. Richardson, D.E. Escobar, and J.H. Everitt. 1994. Photographic and video graphic observations for determining and mapping the response of cotton to soil salinity. Remote Sens. Environ. 49:212-223. Wollenhaupt, N.C., D.J. Mulla, and C.A. Gotway Crawford. 1997. Soil sampling and interpolation techniques for mapping spatial variability of soil properties. In F.J. Pierce and E.J. Sadler (eds.). The State of Site Specific Management for Agriculture. pp. 19-53. ASA, CSSA, and SSSAJ, Madison, WI. Xue, L., W. Cao, W. Luo, T. Dai, and Y. Zhu. 2004. Monitoring leaf nitrogen status in rice with canopy spectral reflectance. Agron. J. 96:135-142. Yoshida, S., and F.T. Parao. 1976. Climatic influence on yield and yield components of lowland rice in the tropics. In:Climate and Rice. pp. 471-494. IRRI, Los Baños, Philippines. Zhao, D., K.R. Reddy, V.G. Kakani, J.J. Read, and S. Koti. 2005. Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton. Agron. J. 97:89-98.
摘要: In Taiwan, rice is the most important food, and is also the biggest area crop. Even in the same climate and field management, spatial distribution diverse always appear on the field paddy rice yield. It demonstrated that there were in the existence of limiting paddy rice yield factors in soil, we called it “soil limiting factors”. Because field research and soil characteristics surveying consume time and money, it is hard to identify or characterize soil limiting factors in rice yield. Farmers always use excessive fertilizer to raise rice yield, but some studies prove that the lost of fertilizer not only pollute the ecological environment, but also harm the human health indirectly. Our purpose is using the establishment of remote sensed information to identify the soil limiting factors, to suggest farmers appropriate field management methods, to maintain high yield and reduce pollution. The study is divided into five chapters. In chapter 1, we explain our purpose. In chapter 2, we study about atmospheric correction protocols using FLAASH to retrieve effectively surface reflectance from SPOT imageries for regions which have large extends of paddy rice fields are presented. Examples are given to demonstrate that the proposed protocols work well under various atmospheric and surface conditions. In chapter 3, we use SPOT satellite imageries to forecast large-area rice yield, the possibility of forecasting rice yield rate in Taiwan with a high degree of accuracy using existing optical remote sensing data sources in combination with an empirical regression type yield model has been proven. In chapter 4, through remote sensed yield information, spatiotemporal trend maps of yield classification were first determined for a 200ha paddy rice fields under conventional two-cropping system in central Taiwan. Soil and plant samples were then collected from areas of different yield classes. Through statistical analysis and interpretations based on the observed differences in soil characteristics and rice yield component performances. In chapter 5 we believed that the systematic approach developed in this study has the potential to expedite the work of identification and characterization of the yield limiting factors in other paddy rice grown area because multiple year/crop season yield maps, usually were not available, can now be retrieved from historical satellite images.
水稻為台灣地區最重要的糧食作物,也是種植面積最大的作物。然而即使在相同氣候、田間管理的情況下,田間水稻產量普遍存在有空間分布不均勻的狀況,顯示在土壤中應該有限制水稻產量的因子存在,本研究稱該因子為「土壤限制因子」。受限於人力、物力和時間等因素,有關產量和土壤特性之田間調查資訊相當有限,難以判釋土壤限制因子。然而,農民往往期望藉由提高肥料施用量以促進產量增加收益,但過量的施肥未必能提高產量,卻一定會污染生態環境,也會間接危害人體健康。 本研究之目的為利用衛星遙測影像具有進行大面積快速調查的優勢,研究根據SPOT波段範圍劃分GRN、RED、NIR三波段,發展一套由衛星影像大氣改正、進行大面積水稻產量推估,以及透過多年期產量空間分佈圖,規劃適當土壤和植體採樣調查點,以判釋田間土壤限制因子的技術。 第二章利用最大分蘗期(MT)至孕穗期(BT)波段反射率穩定的水稻作為探針,建立衛星影像大氣校正的流程,以獲得地表各波段正確的反射率。研究結果顯示,在MT至BT階段水稻NIR/RED主要分佈於8.5< NIR/RED<23,水稻地真光譜皆符合經驗式NIR/GRN = 2.367 + 0.467 NIR/RED的特性,作為台中、嘉義樣區多期作的BT影像大氣改正的依據,並篩選不變目標點,進一步驗證地真實際的反射率和影像大氣改正後的反射率在GRN、RED、NIR三波段的方均根誤差分別為0.008、0.018、0.017,且多數相對誤差値皆在10%以內,驗證使用已知的水稻植被光譜特性作為大氣改正改正之方法確實可行。 在第三章將七年的田間試驗所建立的產量推估模式,運用於試驗樣區孕穗期衛星影像之水稻產量推估,並展示推估結果的準確度;建立一、二期作的產量推估模式,並實際應用於台灣北、中和南部八處試驗樣點衛星影像的產量推估,其與兩個期作實際產量的平均絕對誤差分別是7.7%和13.0%,顯示本研究所建立的產量推估方法能準確地獲得大面積產量空間分佈資訊。 在第四章結合衛星影像和地面的調查資料,找出穩定高產、低產的區域,探討導致樣區水稻產量的時空變異分佈和可能的土壤限制因子。研究以外埔鄉200公頃的水稻田區為例,透過三年六期作衛星影像處理和地面的調查資料,尋找當地的土壤限制因子,結果顯示外埔土壤砂粒含量高的區域,氮肥淋洗損失大,一、二期作穀粒產量主要依局地淋洗損失程度而異。而若土壤中黏粒含量高,且氮肥施用過量,可能會因葉面積過多所產生之遮陰和呼吸消耗的影響,反而導致二期作產量較一期作減少。 透過本論文所發展的SPOT影像大氣改正技術、水稻產量遙測推估模式和判釋田間土壤限制因子之技術,若能搭配台灣各區域歷年或持續被資源衛星拍攝記錄之水稻孕穗期的衛星影像,將有助於快速獲得其他稻作區域產量的空間分佈資訊。若再結合相關土壤特性分析,應能有效率的找出低產因子,進而可透過適當的田間管理規劃,達到維持高產、降低污染的目標。
URI: http://hdl.handle.net/11455/28171
其他識別: U0005-0802201014482900
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0802201014482900
Appears in Collections:土壤環境科學系

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