摘要
高光谱遥感能快速无损获取植被冠层信息,是实现作物长势实时监测的重要技术。为研究不同氮磷水平下冬小麦不同生育时期叶面积指数高光谱遥感监测模型,提高叶面积指数高光谱监测精度,该研究连续5 a定位测定黄土高原旱地不同氮磷水平和不同冬小麦品种各生育时期冠层光谱反射率与叶面积指数,通过相关分析、回归分析等统计方法,构建不同生育时期冬小麦叶面积指数监测模型。结果表明：不同氮磷水平下,冬小麦叶面积指数随施肥量增加呈递增趋势,随生育时期改变呈抛物线趋势变化;随着氮磷供应量的增加,冠层光谱反射率在可见光波段显著降低2%-5%（P〈0.05）,在近红外波段显著增加4%-10%（P〈0.05）;不同生育时期叶面积指数与优化土壤调整植被指数、增强型植被指数Ⅱ、新型植被指数、修正归一化差异植被指数、修正简单比值植被指数均达极显著相关（P〈0.01）;拔节期、孕穗期、抽穗期、灌浆期和成熟期叶面积指数分别与优化土壤调整植被指数、增强型植被指数Ⅱ、增强型植被指数Ⅱ、修正归一化差异植被指数和修正简单比值植被指数拟合效果较好,决定系数分别为0.952、0.979、0.989、0.960和0.993;以不同年份独立数据验证模型表明,所建预测模型均有较好的验证结果,相对误差分别为13.0%、13.5%、12.8%、12.6%和14.0%,均方根误差分别为：0.313、0.336、0.316、0.316、0.324。因此,优化土壤调整植被指数、增强型植被指数Ⅱ、增强型植被指数Ⅱ、修正归一化差异植被指数和修正简单比值植被指数能有效评价拔节期、孕穗期、抽穗期、灌浆期和成熟期冬小麦叶面积指数。同时,叶面积指数分段监测模型较统一监测模型精度有所改善。该结果为实现不同肥力水平下冬小麦不同生育时期长势精确监测提供理论依据和技术支撑。
Hyperspectral remote sensing can rapidly and nondestructively acquire vegetation canopy information. It is an important real time technology to monitor and manage crop growth. Leaf area index（LAI） is a key parameter for crop growth evaluation and yield prediction. The objectives of this study were to establish wheat LAI estimation model based on winter wheat（Triticum aestivum L.） canopy hyperspectral reflectance with different rates of nitrogen or phosphorus application, and to improve the forecast precision of the LAI estimation model at different growth stages of winter wheat on the Loess Plateau of China. The experiments were carried out during 2009-2014 at Northwest AF University, Yangling, China. The treatments included different winter wheat varieties with various drought resistance grown under five nitrogen fertilizer application rates（0, 75, 150, 225 and 300 kg N/hm^2） and four phosphorus application rates（0, 60, 120 and 180 kg P2O5/hm^2） LAI and canopy hyperstpectral reflectance of different varieties under fertilizer treatments were measured at jointing, booting, heading, grain filling and maturity stage, respectively. Then LAI monitoring models at different growth stages of winter wheat were constructed by using correlation analysis, regression analysis. The results showed that LAI of wheat was increased with increase in nitrogen and phosphorus application rate at different growth stages, and LAI from jointing to maturity showed a parabolic curve, and the maximum LAI of wheat was at heading stage. When nitrogen or phosphorus application was sufficient, the canopy hyperspectral reflectance of wheat was reduced 3%-5% in the visible wavelength（P〈0.05）, and increased 4%-10% in the near infrared wavelength（P〈0.05）. There were significant（P〈0.01） correlation between Optimized Soil Adjusted Vegetation Index（OSAVI）, Enhanced Vegetation Index Ⅱ（EVI2）, New Vegetation Index（NVI）, Modified Normalized Difference Vegetation Index（m NDVI）, and Modified Simple Ratio Index（m SRI） to LAI, the range of correlation coefficient were from 0.852 to 0.987 at different growth stages. Monitoring models based on OSAVI, EVI2, EVI2, m NDVI and m SRI produced better estimation for LAI at jointing, booting, heading, grain filling and maturity respectively, and R2 were respectively 0.952, 0.979, 0.989, 0.960 and 0.993（P〈0.01）. Meanwhile, compared the predicted value and measured value to verify reliability and applicability of the model, relative error of the measured value and predicted value were 13.0%, 13.5%, 12.8%, 12.6% and 0.14.0%, and root mean square error were 0.313, 0.336, 0.316, 0.316 and 0.324, at jointing, booting, heading, grain filling and maturity stage, respectively. Therefore, vegetation indices of OSAVI, EVI2, EVI2, m NDVI and m SRI was the most suitable indeces for monitoring winter wheat LAI at jointing, booting, heading, grain filling and maturity, respectively. There was high prediction precision with different vegetation indices monitoring LAI of winter wheat at different growth stages. These conclusions has important implications for monitoring LAI of winter wheat in large area on the Loess Plateau. Meanwhile, there is a high prediction accuracy of monitoring model based on the different vegetation indices at different growth stages of winter wheat.This result provides technical support for growth monitoring of winter wheat at different fertility and different growth stages for farmers..
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2014年第24期141-150,共10页
Transactions of the Chinese Society of Agricultural Engineering
基金
863计划(2013AA102902)
国家自然科学基金(31071374
30771280)
关键词
农作物
遥感
监测
冬小麦
叶面积指数
高光谱遥感
监测模型
crops
remote sensing
monitoring
winter wheat
leaf area index
hyperspectral remote sensing
monitoring model
