光谱学与光谱分析 |
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Retrieval of Leaf Water Content of Winter Wheat from Canopy Hyperspectral Data Using Partial Least Square Regression |
WANG Yuan-yuan, LI Gui-cai, ZHANG Li-jun, FAN Jin-long |
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration(LRCVES/CMA), Beijing 100081, China |
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Abstract Accurate estimation of leaf water content (LWC) from remote sensing can assist in determining vegetation physiological status, and further has important implications for drought monitoring and fire risk evaluation. This paper focuses on retrieving LWC from canopy spectra of winter wheat measured with ASD FieldSpec Pro. The experimental plots were treated with five levels of irrigation (0, 200, 300, 400 and 500 mm) in growing season, and each treatment had three replications. Canopy spectra and LWC were collected at three wheat growth stages (booting, flowering, and milking). The temporal variations of LWC, spectral reflectance, and their correlations were analyzed in detail. Partial least square regression embedded iterative feature-eliminating was designed and employed to obtain diagnostic bands and build prediction models for each stage. The results indicate that LWC decreases quickly along with the winter wheat growth. The mean values of LWC for the three stages are respectively 338.49%, 269.65%, and 230.90%. The spectral regions correlated strongly with LWC are 1 587-1 662 and 1 692-1 732 nm (booting), 617-687 and 1 447-1 467 nm (flowering), and 1 457-1 557 nm (milking). As far as the LWC prediction models are concerned, the optimum modes of spectral data are respectively logarithmic, 1st order derivative and plain reflectance. The diagnostic bands detected by PLS are from SWIR, NIR, and SWIR. Retrieval accuracy at the flowering stage is the highest (R2cv=0.889) due to the enhancement of leaf water information at canopy scale via multiple scattering. At the booting and milking stage, accuracies are relatively lower (R2cv=0.750, 0.696), because the retrieval of LWC is negatively affected by soil background and dry matter absorption respectively. This research demonstrated clearly that the spectral response and retrieval of LWC has distinct temporal characteristics, which should not be neglected when developing remote sensing product of crop water content in the future.
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Received: 2009-05-12
Accepted: 2009-08-13
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Corresponding Authors:
WANG Yuan-yuan
E-mail: wangyuany@cma.gov.cn
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[1] Ceccato P, Flasse S, Tarantola S, et al. Remote Sensing of Environment, 2001, 77: 22. [2] Zarco-Tejada P L J, Rueda C A, Ustin S L. Remote Sensing of Environment, 2003, 85: 109. [3] Cheng Y B, Zarco-Tejada P J, Riano D, et al. Remote sensing of Environment, 2006, 105: 354. [4] Gao B C. Remote Sensing of Environment, 1996, 58: 257. [5] Tucker C J. Remote Sensing of Environment, 1980, 10: 23. [6] Hunt E R, Rock B N. Remote Sensing of Environment, 1989, 30: 43. [7] Pierce L L, Running S W, Riggs G A, Photogrammetric Engineering and Remote Sensing, 1990, 56: 579. [8] Bowman W D. Remote Sensing of Environment,1989, 30: 249. [9] Green R O, Conel J E, Roberts D A. Summaries of the Fourth Annual JPL Airborne Geoscience Workshop, Oct. 25-29, 1993, Washington, DC, vol. 1, 73. [10] Roberts D A, Green R O, Adams J B. Remote Sensing of Environment, 1997, 62: 223. [11] Penuelas J, Pinol J, Ogaya R, et al. International Journal of Remote Sensing, 1997, 18: 2869. [12] WANG Ji-hua, ZHAO Chun-jiang, GUO Xiao-wei, et al(王纪华,赵春江,郭晓维,等). Acta Agriculaturae Boreali-Sinica(华北农学报),2000,15(4):68. [13] Eitel J U H, Gessler P E, Smith A M S, et al. Forest Ecology and Management, 2006, 229: 170. [14] Serrano L, Ustin S L, Roberts D A, et al. Remote Sensing of Environment, 2000, 74: 570. [15] Colombo R, Meroni M, Marchesi A, et al. Remote sensing of Environment, 2008, 112: 1820. [16] JI Hai-yan, WANG Peng-xin, YAN Tai-lai(吉海彦,王鹏新,严泰来). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007,27(3):514. [17] De Santis A, Vaughan P, Chuvieco E, et al. Journal of Geophysical Research, 2006, 111, G04S03. [18] Yebra M, Chuvieco E, Riano D. Agricultural and Forest Meterology, 2008, 148: 523. [19] WANG Hui-wen(王惠文). Partial Least Squares Regression Method and Application(偏最小二乘回归方法及其应用). Beijing: National Defense Industry Press(北京:国防工业出版社),1999. 200. [20] Smith M L, Martin M E, Plourde L, et al. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6): 1332. [21] LIU Liang-yun, WANG Ji-hua, ZHANG Yong-jiang, et al(刘良云, 王纪华, 张永江, 等). Journal of Remote Sensing(遥感学报), 2007, 11(3): 289. [22] Myneni R B, Hall F G, Sellers P J, et al. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33: 481. [23] Riano D, Vaughan P, Chuvieco E, et al. IEEE Transaction on Geoscience and Remote Sensing, 2005, 43: 819.
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