Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/28062
標題: 圖繪田間水稻含氮狀態之遙感探測技術研究
Remote Sensing Techniques to Map Nitrogen Status of Rice Plants within Fields
作者: Lee, Yuh-Jyuan
李裕娟
關鍵字: Rice (Oryza sativa L.)
Canopy nitrogen status
dR/dλ│735
Remote sensing technique
Hyperspectral image
Narrowband flilter
Leaf anatomy
Canopy reflectance
Total chlorophyll content
水稻
植被(冠)含氮狀態
dR/dλ│735
遙測技術
高光譜影像
窄波段濾片
葉片解剖
植被反射比
葉綠素總量
出版社: 土壤環境科學系所
引用: Adamsen, F.J., P.J. Pinter, Jr., E.M. Barnes, R.L. LaMorte, G.W. Wall, S.W. Leavitt, and B.A. Kimball. 1999. Measuring wheat senescence using a digital camera. Crop Sci. 39:719-724. Baret, F., S. Jacquemoud, G. Guyot, and C. Leprieur. 1992. Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands. Remote Sens. Environ. 41:133-142. 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. Blackmer, T.M., J.S. Schepers, G.E. Varvel, and E.A. Walter-Shea. 1996. Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agron. J. 88:1-5. Blackmore, B.S., P.N. Wheeler, J. Moris, R.M. Moris, and R.J.A. Jones. 1995. The role of precision farming in sustainable agriculture: A European perspective. p.777-793. In: Site-specific management for agricultural systems. P.C. Robert, R.H. Rust, and W.E. Larson (eds.). ASA, CSSA, SSSA, WI. Boese, S.R., and N.P.A. Hunner. 1990. Effect of growth temperature and temperature shifts on spinach leaf morphology and photosynthesis. Plant Physiol. 94:1830-1836. Bouman, B.A.M. 1995. Crop modeling and remote sensing for yield prediction. Netherlands J. Agric. Sci. 43:143-161. Bremner, J.M. 1996. Nitrogen-total. p.1085-1122. In: Methods of soil analysis. Part 3. Chemical methods. D.L. Sparks et al. (eds.). SSSA Book Ser. 5. SSSA and ASA, Madison, WI. 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. Buléon, A., P. Colonna, V. Planchot, and S. Ball. 1998. Structural processes during starch granule hydration by synchrotron radiation microdiffraction. Intl. J. Biol. Macromol. 23:85-112. Buresh, R.J., E.G. Castillo, and S.K. DE Datta. 1993. Nitrogen losses in puddle soils as affected by timing of water deficit and nitrogen fertilizer. Plant Soil 157:197-206. Cassanova, D., G.F. Epema, and J. Goudriaan. 1998. Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crops Res. 55:83-92. Cassman, K.G., A. Dobermann, and D.T. Walters. 2002. Agroecosystems, nitrogen-use efficiency, and nitrogen management. Ambio. 31:132-140. Castelnuovo, R. 1995. Environmental concerns driving site-specific management in agriculture. p.867-880. In: Site-specific management for agricultural systems. P.C. Robert, R.H. Rust, and W.E. Larson (eds.). ASA, CSSA, SSSA, WI. Charles-Edwards, D.A., J. Charles-Edwards, and F.I. Sant. 1974. Leaf photosynthetic activity in six temperate grass varieties grown in contrasting light and temperature environments. J. Exp. Bot. 25:715-724. Chen, R.-K., and C.-M. Yang. 2005. Determining optimal timing for using LAI and NDVI to predict rice yield. J. Photogramm. Remote Sens. 10(3):239-254. Collins, W. 1978. Remote sensing of crop type and maturity. Photo. Eng. Remote Sens. 26:43-55. 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 J. Crop Sci. 47:390-394. Cui, R.X., M.H. Kim, J.H. Kim, H.S. Lee, and B.W. Lee. 2002. Determination of critical nitrogen concentration and dilution curve for rice growth. Korean J. Crop Sci. 47:127-131. Daughtry, C.S.T., C.L. Walthall, M.S. Kim, E. Brown de Colstoun, and J.E. McMurtrey. 2000. Estimating corn foliar chlorophyll content from leaf and canopy reflectance. Remote Sens. Environ. 74:229-239. Demetriades-Shah, T.H., M.D. Steven, and J.A. Clark. 1990. High resolution derivative spectra in remote sensing. Remote Sens. Environ. 33:55-64. Diker, K., and W.C. Bausch. 2003. Potential use of nitrogen reflectance index to estimate plant parameters and yield of maize. Biosys. Eng. 85:437-447. Dobermann, A., M.F. Pampolino, and H.U. Neue. 1995. Spatial and temporal variability of transplanted rice at the field scale. Agron. J. 87:712-720. 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. Fowler, D.B., L.V. Gusta, and N.J. Tyler. 1981. Selection for winter hardiness in wheat. III. Screening methods. Crop Sci. 21:896-901. Galloway, J.N., and E.B. Cowling. 2002. Reactive nitrogen and the world: 200 years of change. Ambio. 31: 64-71. Gilabert, M.A., S. Gandia, and J. Melia. 1996. Analyses of spectral-biophysical relationships for a corn canopy. Remote Sens. Environ. 55:11-20. Graeff, S., and W. Claupein. 2003. Quantifying nitrogen status of corn (Zea mays L.) in the field by reflectance measurements. Eur. J. Agron. 19:611-618. Guyot, G. 1990. Optical properties of vegetation canopies. p.19-43. In Applications of remote sensing in agriculture. M.D. Steven and J.A. Clark (eds.). Butterworths, London. 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. Hanway, J.I. 1962. Corn growth and composition in relation to soil fertility: I. Growth of different plant parts and relation between leaf weight and grain yield. Agron. J. 57:7-12. Heilman, J.L., E.T. Kanemasu, J.O. Bagley, and V.P. Rasmussen. 1977. Evaluating soil moisture and yield of winter wheat in the Great Plains using Landsat data. Remote Sens. Environ. 6:315-326. Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25:295-309. Hunner, N.P.A., J.P. Palta, P.H. Li, and J.V. Carter. 1981. Anatomical changes in leaves of Puma rye in response to growth at cold-hardening temperatures. Bot. Gaz. 142:55-62. Inoue, Y., M.S. Moran, and T. Horie. 1998. Analysis of spectral measurements in paddy field for predicting rice growth and yield based on a simple crop simulation model. Plant Prod. Sci. 1:269-279. Jaynes, D.B., T.S. Colvin, and J. Ambuel. 1995. Yield mapping by electromagnetic induction. p.383-394. In: Site-specific management for agricultural systems. P.C. Robert, R.H. Rust, and W.E. Larson (eds.). ASA, CSSA, SSSA, WI. Jones, H.G., J.M. Anderson, and R. Casa. 2000. Multispectral and multiangular remote sensing of crop canopy stress. Aspects Appl. Biol. 60:155-162. Karlen, D.L., E.J. Sadler, and C.R. Camp. 1987. Dry matter, nitrogen, phosphorus, and potassium accumulation rates by corn on Norfolk loamy sand. Agron. J. 79:649-656. Knipling, E.B. 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens. Environ. 1:155-159. Lee, Y.-J., C.-M. Yang, and A.-H. Chang. 2002. Changes of nitrogen and chlorophyll contents and spectral characteristics to the application of nitrogen fertilizer in rice plants. (in Chinese with English abstract) J. Agric. Res. China 51:1-14. Lin, A.C. 1979. Comparison on the photosynthesis under community conditions between the first and second crops of rice. (in Chinese) p.85-90. In: Proceedings of the symposium on the causes of low yield of the second crop rice in Taiwan and the measures for improvement. Taiwan Agricultural Research Institute, 7-8 June 1978. Published by National Science Council, Taipei. Lord, D., R.L. Desjardins, P.A. Dubé, and E.J. Brach. 1985. Variations of crop canopy spectral reflectance measurements under changing sky conditions. Photogramm. Eng. Remote Sens. 51:689-695. Louwerse, W., and W. Zeerde. 1977. Photosynthesis, transpiration and leaf morphology of Phaseolus vulgaris and Zea mays grown at different irradiance in artificial and sunlight. Photosynthetica 11:11-21. Martens, D.A. 2001. Nitrogen cycling under different soil management systems. Adv. Agron. 70:143-197. Mashima, S.I., N. Matsumoto, and O. KENJIRO. 1999. Nitrogen flow associated with agricultural practices and environmental risk in Japan. Soil Sci. Plant Nutr. 45:881-889. Mauser, W., and H. Bach. 1995. Imaging spectroscopy in hydrology and agriculture-determination of model parameters. p.261-283. In: Imaging spectrometry- a tool for environment observations. J. Hill and J. Megier (eds.). Kluwer Academic Publishing, Dordrecht, The Netherlands. McBurney, T. 1992. The relationship between leaf thickness and plant water potential. J. Exp. Bot. 43:327-335. Moran, M.S., Y. Inoue, and E.M. Barnes. 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ. 61:319-346. Moreno-Sotomayor, A., A. Weiss, E.T. Paparozzi, and T.J. Arkebauer. 2002. Stability of leaf anatomy and light response curves of field grown maize as a function of age and nitrogen status. J. Plant Physiol. 159:819-826. Muchow. R.C., T.R. Sinclair, and T.R. Bennet. 1990. Temperature and solar radiation effects on potential maize yield across locations. Agron. J. 82:338-343. Myers, R.J.K. 1978. Nitrogen and phosphorus nutrition of dry-land grain sorghum at Katherine, Northern Territory: I. Effect of rate of nitrogen fertilizer. Aust. J. Exp. Agric. Anim. Husb. 18:554-563. 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 Res. 74:93-101. Nguyen, H.T., and B.-W. Lee. 2006. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. Eur. J. Agron. 24:349-356. Nobel, P.S., L. Zaragoza, and W. Smith. 1975. Relation between mesophyll surface area, photosynthetic rate, and illumination level during development for leaves of Plectranthus parviflorus Henckel. Plant Physiol. 55:1067-1070. 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 Res. 74:93-101. Pan, W.L., D.R. Huggins, G.L. Malzer, C.L. Douglas, and J.L. Smith. 1997. Field heterogeneity in soil-plant nitrogen relations: Implications for site-specific management. p.81-89. In: The State of site specific management for agriculture. F.J. Pierce and E.J. Sadler (eds.). ASA, CSSA, SSSA, WI. Peng, S.B., F.V. Garcia, R.C. Laza, and K.G. Cassman. 1993. Adjustment for specific leaf weight improves chlorophyll meter's estimate pf rice leaf nitrogen concentration. Agron. J. 85:987-990. Roberts, A.C.B., and J.M. Anderson. 1999. Shallow water bathymetry using integrated airborne multi-spectral remote sensing. Intl. J. Remote Sens. 20:497-510. Sass, J.E. 1958. Botanical microtechnique. 3rd ed. Iowa State College Press, Ames, Iowa. 228pp. Schowengerdt, R.A. 1997. Remote sensing. Models and methods for image processing. p.346-353. Academic Press, New York. Shanahan, J.F., J.S. Schepers, D.D. Francis, G.E. Varvel, W.W. Wilhelm, J.M. Tringe, M.R. Schemmer, and D.J. Major. 2001. Use of remote imagery to estimate corn grain yield. Agron. J. 93:583-589. Shen, Y., J.C. Lo, and S.P. Cheng. 2000. Development of remote sensing techniques to identify nitrogen status of paddy rice. (in Chinese with English abstract) Chinese J. Agromet. 7: 23-32. Shen, Y., J.T. Wu, J.H. Chen, C.F. Chiang, J.C. Liu, and C.H. Wang. 2003. Locating soil limiting factors as assisted by remote sensing images. (in Chinese) p.81-88. In: Rice precision farming system. C.M. Yang and C.Y. Lin (eds.). Taiwan Agricultural Research Institute, Taichung Hsien, Taiwan ROC. 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. StatSoft. 2001. STATISTICA (data analysis software system). Version 6. StatSoft, Tulsa, OK. Steven, M.D., T.J. Malthus, T.H. Demetriades-Shah, F.M. Danson., and J.A. Clark. 1990. High-spectral resolution indices for crop stress. p.209-227. In: Applications of remote sensing in agriculture. M.D. Steven and J.A. Clark (eds.). Butterworths, London. Sun, X., and J.M. Anderson. 1993. A spatially variable light-frequency-selective component-based, airborne pushbroom imaging spectrometer for the water environment. Photogramm. Engin. Remote Sens. 59: 399-406. Takebe, M., T. Yoneyma, K. Inada, and T. Murakami. 1990. Spectral reflectance ratio of rice canopy for estimating crop nitrogen status. Plant Soil 122:295-297. Thenkabail, P.S. 2002. Optimal hyperspectral narrowbands for discriminating agricultural crops. Remote Sens. Rev. 20(4):257-291. Thenkabail, P.S., A.D. Ward, and J.G. Lyon. 1994. Impacts of agricultural management practices on soybean and corn crops evident in ground-truth data and thematic mapper vegetation indices. Transac. Amer. Soc. Agric. Engin. 37:989-995. 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. Thomas, J.R., and G.F. Oerther. 1972. Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agron. J. 64:11-13. Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8:127-150. Wolf, P.R. 1983. Elements of photogrammetry. 2nd ed. p. 226-263. McGraw-Hill, New York. 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. Yang, C.-M., and M.-R. Su. 1997. Analysis of reflectance spectrum of rice canopy. (Chinese with English abstract) Chinese J. Agromet. 4:87-95. Yang, C.M., K.W. Chang, M.H. Yin, and H.M. Huang. 1998. Methods for the determination of the chlorophylls and their derivatives. Taiwania 43:116-122. Yang, C.-M., B.-K. Shen, J.-Z. Yu, C.-T. Lo, and J.-T. Wu. 2003a. Estimating chlorophyll and nitrogen status in plants of Amaranthus mangostanus with chlorophyll meter. (in Chinese with English abstract) J. Agric. Res. China 52:105-116. Yang, C.-M., J.-T. Wu, B.-K. Shen, J.-Z. Yu, C.-T. Lo, and Y. Shen. 2003b. Estimating growth and nitrogen status in plants of Amaranthus mangostanus with canopy spectral characteristics. J. Agric. Res. China 52:268-290. Yang, C.-M., and R.-K. Chen. 2004. Modeling rice growth using hyperspectral reflectance data. Crop Sci. 44:1283-1290. Yang, C.-M., and R.-K. Chen. 2005. Potential for using FORMOSAT-II data simulated by hyperspectral reflectance to estimate growth and predict yield of rice. J. Agric. Assoc. China 54:54-69. Yang, C.-M., and R.-K. Chen. 2007. Differences in growth estimation and yield prediction of rice crop using satellites data simulated from near ground hyperspectral reflectance. J. Photogram. Remote Sens. 12:93-105.
摘要: Nitrogen (N) is the most important nutrient to increase and stabilize yield of paddy rice, and the spatial distribution of N status of rice plants within field is the primary information needed for precision management of N fertilizer. A better yield production and grain quality can be expected by applying suitable amounts of N fertilizer at right time to the right place site-specifically with N map. This research was first to investigate changes in plant N concentration and leaf total chlorophyll content upon applying different rates of N fertilizer during rice (Oryza sativa L. cv. Tainung 67) growth, and then to examine whether the alterations in leaf internal structure and morphology related to variation in N status. The relations of leaf total chlorophyll content, internal structure and morphological characteristics, and plant N concentration to the canopy reflectance behavior were further studied. As the results shown, applying N fertilizer from 0 to 180 kg N ha-1, with 60 kg N ha-1 intervals, changed leaf total chlorophyll and plant N concentration measured in the panicle initiation/formation stage. Plant applied with higher N rates tended to have higher amounts of leaf total chlorophyll and absorbed more light in the visible region of incident solar radiation. The mean reflectance of BLUE (425-490 nm), GREEN (490-560 nm) and RED (640-740 nm) wavebands showed a negative relationship with leaf total chlorophyll, a decreased in reflectance with the increase of chlorophyll content. A diversity of anatomical and morphological characteristics of leaves was observed to be modulated by N concentration in rice plants grown in both first and second cropping seasons. Leaf thickness increased progressively with increasing plant N concentration, and rice plants grown in second crop had a tendency to have thicker leaf blades than those grown in first crop with the same N concentration. The extent of leaf rolling was relieved by the increasing N status and a linear relationship between value of leaf rolling index (LRI) and aboveground N concentration was fitted. Changes in bulliform/mesophyll ratio to aboveground N concentration were a quadratic function, the ratio increased to a plateau and decreased thereafter. Leaf water content (LWC) also changed in a curvilinear trend in the measured range of aboveground N concentration, and leaves of plants grown in first crop had higher LWC than those plants grown in second crop under the same N level. A loose distribution and packing pattern of starch granules was found in the parenchyma cells of plants with higher N status relative to those of lower N ones. Application of varied rates of N fertilizer also affected canopy reflectance behavior of different wavebands. The mean reflectance of waveband at BLUE, GREEN and RED decreased while NIR (740-1100 nm) increased with the increasing aboveground N concentration and LRI. Secondly, as the spatial distribution of canopy N map within a field is the primary information needed for precision management of N fertilizer, this research developed a simple spectral index (SI) using the first derivative values of canopy reflectance spectra at 735 nm (dR/dλ│735) to assess N concentration of rice plants and validated the applicability of a simplified imaging system based on the derived spectral model from the N─dR/dλ│735 to assess N concentration of rice plants and validated the applicability of a simplified imaging system based on the derived spectral model from the N─dR/dλ│735 relationship in mapping canopy N status within fields from two remote sensing platforms. Results showed that values of dR/dλ│735 were linearly related to plant N concentrations measured at the panicle initiation/formation stage of first crop in 2001. The N─dR/dλ│735 relationship (R2 = 0.679, P < 0.001) was better fitted than the N─NDVI (normalized difference vegetation index) relationship (R2 = 0.471, P < 0.010), and remained valid (R2 = 0.514, P < 0.001) when more data from different cropping seasons in varied years and locations were pooled . The composite regression model provided fair results (r = 0.554, P < 0.010) in validation test with another datasets collected from different crops and locations. The ratio-based SIs SRVI (simple ratio vegetation index) (R2 = 0.519, P < 0.001), R810/R560 (R2 = 0.453, P < 0.001), NDVI (R2 = 0.355, P < 0.001), and (R1100-R660)/(R1100+R660) (R2 = 0.111, P < 0.010) were also correlated with plant N concentration to a varied extent. Based on the as aforementioned N─NDVI relationship, a simplified imaging system, including an Electrim EDC-1000L monochrome camera, a Canon PHF6 1.4 lens, a set of Andover bandpass filters (730 nm and 740 nm), and an Advantech PCA6751 single board computer, was finally assembled and mounted on a mobile lifter and a helicopter to take spectral images of rice canopies for mapping the N status within fields. Results indicated that the system unit was able to provide field maps of canopy N status with reasonable accuracy (r = 0.465 to 0.912, RMSE = 0.100 to 0.550) from both remote sensing platforms. It appears that spatial information of N status obtained from this research may have a potential used for variable-rate applications of N fertilizer during the panicle initiation/formation stage. The validation tests on a variety of stress identification indices derived from ground spectroradiometer measurements can also be easily performed with the established simplified imaging system following the process such as this research.
氮素是提高和穩定水稻生產最重要的營養元素之一,田間水稻植株含氮狀態的空間分佈則係精準施用氮肥最主要的圖繪資訊。經由田間稻株含氮狀態空間分佈圖繪,將不等量氮肥於特定時間施用於農田特定位置的精準做法,將可預期獲得較佳之產量與米質。本研究首先調查田間栽培水稻生育期間施用不等量氮肥造成稻株植體含氮濃度及葉片葉綠素總量之改變,接著檢視葉片內部構造及形態隨著含氮狀態變化形成之差異,再進一步探討葉片葉綠素總量、內部構造與形態特徵、以及植體含氮濃度對植被反射比行為之影響。根據試驗結果,發現介於0-180 kg N ha-1氮肥施用範圍,不等氮肥施用量將改變穗啟始期-形成期量測之植體含氮濃度及葉片葉綠素總量,稻株施用高氮肥量者具有較高植體含氮濃度及葉片葉綠素總量,而且吸收較多入射太陽光之可見光波段。在可見光不同波段反射比與葉片葉綠素總量的關係上,藍光(425-490 nm)、綠光(490-560 nm)及紅光(640-740 nm)波段之平均反射比皆與葉綠素總量呈現直線負相關,即各波段反射比隨葉綠素總量上升而下降。無論一、二期作稻株,葉片之許多解剖與形態特徵均顯示受到植體含氮濃度之影響。當植體含氮濃度增加時,葉片厚度隨著增厚,在相同植體含氮濃度下生長於二期作之稻株葉片厚度大於生長於一期作之稻株葉片厚度。高含氮量稻株之葉片捲曲程度較小,具有較高之葉片捲曲指數(LRI),而LRI與地上部植體含氮濃度之間呈現直線正相關。Bulliform/mesophyll ratio與地上部植體含氮濃度關係適用於一元二次函數,此一比值隨著氮濃度增加而上升,到達一高原期後則下降。葉片水分含量(LWC)亦伴隨地上部植體含氮濃度的增加呈現曲線變化,而在同一含氮濃度水準下,一期作稻株之LWC較二期作稻株之LWC高。高含氮狀態稻株之葉片薄壁細胞的澱粉粒分佈較為鬆散,排列也較不緊密。施用不等量氮肥另影響水稻植被不同波段反射比之表現,藍光、綠光及紅光波段之平均反射比隨著稻株含氮濃度與LRI測值增加而下降,近紅外光(740-1100 nm)波段之平均反射比則反之。由於田間水稻植被含氮狀態提供氮肥精準管理所必需,本研究乃繼續探究水稻植被反射光譜與植體氮營養狀況之關係,以建立適用於評估稻株含氮狀態之簡易光譜指數(spectral index, SI)。經分析2001年一期稻作之幼穗起始期-穗形成期間植被光譜範圍(350-1100 nm)各窄波段反射比與植體含氮濃度之關係,發現735 nm波段反射比之一次微分值(dR/dλ│735)與植體含氮濃度之關係,發現735 nm波段反射比之一次微分值(dR/dλ│735)與植體含氮濃度呈線性正相關(R2 = 0.679, P < 0.001),可利用作為估測稻株含氮狀態之光譜指數,且其相關性優於標準差植被指數(NDVI)與植體含氮濃度之線性相關(R2 = 0.471, P = 0.0023)。續而納入分別於臺北、臺中、嘉義、屏東等不同地區進行之兩年期量測資料,顯示以N─dR/dλ│735關係建立的稻株含氮狀態遙測模式之決定係數(R2)達到0.514的極顯著水準。經再以臺北、嘉義和屏東三個試區不同期作的735 nm波段一次微分值與稻株含氮濃度做驗證,其相關係數(r)值仍可達到0.554的顯著水準。此外,比值型光譜指數諸如簡易比植被指數(simple ratio vegetation index, SRVI; R2 = 0.519, P < 0.001)、R810/R560 (R2 = 0.453, P < 0.001)、NDVI (R2 = 0.355, P < 0.001)及(R1100-R660)/(R1100+R660) (R2 = 0.111, P < 0.010)等,與植體含氮濃度之間亦發現具有不等程度的相關性。據此試驗結果,顯見本研究所建立的水稻族群含氮狀態分佈遙測模式,確可利用於快速鑑別穗起始期-形成期間稻株氮營養狀態。而以此遙測模式為基礎構建之簡易型影像拍攝系統,則試驗證明可以作為測定及圖繪田間水稻族群含氮狀態分佈等用途。此項簡易型光譜影像拍攝系統由電子式單彩相機(EDC-1000L)、鏡頭(Canon PHF6 1.4 lens)、窄波段濾片(Andover bandpass filters 730 nm及740 nm)及工業用單版電腦(Advantech PCA6751)組成,並內建自行開發需用的軟體程式。將本研究建立之稻株含氮狀態遙測模式及簡易型影像拍攝系統,依照田間作業需求掛載於高空作業車或直昇機上,發現圖繪之田間水稻族群含氮狀態分佈均獲得合理估測之結果(r=0.620 to 0.912, RMSE= 0.100 to 0.151)。顯然的,經由本研究方法圖繪之穗起始期-形成期稻株含氮狀態空間變異分佈具有使用於可變率氮肥施用之應用潛力,所構建之簡易型影像拍攝系統,亦能延伸應用於其他近地面光譜資料建立之逆境鑑別指數驗證之用。
URI: http://hdl.handle.net/11455/28062
其他識別: U0005-0506200710470300
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0506200710470300
Appears in Collections:土壤環境科學系

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.