不同生育期水稻叶面积指数的高光谱遥感估算模型

Estimation of Rice Canopy LAI with Different Growth Stages Based on Hyperspectral Remote Sensing Data

2011年和2012年通过大田试验,利用便携式野外光谱仪实测水稻冠层不同生育时期的高光谱数据,同时使用SUNSCAN冠层分析系统采集水稻冠层叶面积指数(LAI);采用光谱微分技术和统计分析技术,分别分析高光谱反射率及其植被指数与LAI之间的关系,建立LAI估算模型并进行模拟结果对比。结果表明:水稻抽穗-成熟期,利用光谱值的对数形式对LAI值的模拟效果较好,分蘖-抽穗期利用光谱反射率模拟LAI变化过程的效果不理想。在利用各种植被指数估算LAI方法中,水稻分蘖-抽穗期以修改型土壤调整植被指数MSAVI[758,805]对LAI的估算效果最好,模拟值与实测值的相关系数通过了0.01水平的显著性检验(R=0.7754),估算精度较高。在抽穗-成熟期,也以修改型土壤调整植被指数MSAVI[758,817]对LAI的模拟效果最好,模拟值与实测值的相关系数通过了0.01水平的显著性检验(R=0.6488),估算精度较高。说明修改型土壤调整植被指数(MSAVI)能更好地模拟水稻不同生育期的叶面积指数,按照分蘖-抽穗期、抽穗-成熟期两个生育阶段分别建立水稻冠层LAI的高光谱估算模型能够提高LAI估算的准确度,研究结果也证实了分生育阶段建模的必要性。

To explore the relationship between hyperspectral reflectance, vegetation indexes and LAI, the experiment was conducted from 2011 and 2012. Rice canopy hyperspectral data was measured at different growth stages by using the ASD Field Spec Hand Held portable field spectrometer, rice canopy leaf area index （LAI） was collected at the same time by using SUNSCAN canopy analysis system. LAI estimation model was established and the simulation results were compared. The results showed that LAI was better simulated by spectral log form heading stage to maturity stage, but could not simulated by reflectance during the stage of tillering to heading. Among all of vegetation indexes estimation methods, LAI was best simulated by MSAV1 （modified soil-adjusted vegetation index） [758, 805], the correlation coefficient between simulating data and testing data was significant （R=0.7754）. From the heading stage to maturity stage, LAI was best simulated by MSAVI [758, 817], the correlation coefficient between simulating data and testing data was significant （R=0.6488）. The results indicated that MSAVI could simulated LAI of rice at different growth stages.