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
摘要： 通过人为控制灌溉水平，在冬小麦3个发育期(孕穗、开花、乳熟)测定了冠层光谱和叶片含水量(leaf water content，LWC)。针对每期数据，结合偏最小二乘回归和迭代特征去除，建立了基于诊断波段的LWC回归模型。结果表明，叶片水分的光谱响应及反演精度受小麦生长状态的影响。在孕穗、开花和乳熟3个发育阶段，回归模型中光谱数据的最佳利用形式分别为对数光谱、导数光谱和反射率光谱;重要光谱区间为SWIR，NIR和SWIR;模型交叉验证决定系数(R2CV)为0.750，0.889和0.696。研究结论对今后监测冬小麦旱情和开发作物水分遥感产品具有重要的指导作用。
关键词：叶片含水量;高光谱;偏最小二乘回归
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.
Key words：Leaf water content;Hyperspectral;Partial least Square regression
王圆圆，李贵才，张立军，范锦龙. 利用偏最小二乘回归从冬小麦冠层光谱提取叶片含水量 [J]. 光谱学与光谱分析, 2010, 30(04): 1070-1074.
WANG Yuan-yuan, LI Gui-cai, ZHANG Li-jun, FAN Jin-long. Retrieval of Leaf Water Content of Winter Wheat from Canopy Hyperspectral Data Using Partial Least Square Regression. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(04): 1070-1074.
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