基于低空无人机成像光谱仪影像估算棉花叶面积指数

农作物叶面积指数(leaf area index,LAI)遥感监测具有快速、无损的优势。该文以低空无人机作为遥感平台,使用新型成像光谱仪获取的农田高光谱影像数据对棉花LAI进行反演。利用影像高光谱分辨率的特点,针对传统固定波段植被指数(fixed-bandvegetation index,F_VI)进行改进,通过动态搜索相应植被指数定义所使用波段范围内的反射率极值的方法,计算与各类植被指数对应的极值植被指数(extremum vegetation index,E_VI)。分别以原始全波段光谱反射率、连续投影算法(successive projections algorithm,SPA)提取的有效波段反射率以及各类F_VI和E_VI作为自变量,使用最小二乘和偏最小二乘(partial least squares,PLS)回归等方法构建LAI遥感估算模型。结果显示:1)以植被指数为自变量的模型估算效果(验证R2最高为0.85)优于以光谱反射率作为自变量的模型(验证R2最高为0.59);2)使用E_VI作为自变量能够显著提高LAI的估测精度(验证R2最大提高了0.11);3)使用PLS回归算法结合多个E_VI建立的LAI-E_VIs-PLS模型精度最高。使用LAI-E_VIs-PLS模型对棉花地块高光谱影像进行反演,制作棉花LAI空间分布图,取得良好的估算结果(验证R2=0.88,RMSE=0.29),为农作物LAI遥感监测提供了新的技术手段。

Use of hyperspectral images from UAV-based imaging spectroradiometer to estimate cotton leaf area index

Abstract: Remote sensing technology can be used to estimate the leaf area index (LAI) value of crops rapidly and harmlessly. This study collected the hyperspectral images of cotton field using a new type of imaging spectroradiometer (UHD185), which has 138 channels from visible to near-infrared band range with high spatial resolution. The weight of UHD185 is less than 500 g and it can be easily mounted on a multi-rotor unmanned aerial vehicle (UAV), which makes it possible to acquire hyperspectral ortho images of crops at different spatial scales in any growth stage according to the requirements of crop growth monitoring. Cotton LAI values of 80 sampling points were measured by the SUNSCAN canopy analyzer during the UAV campaign, and the spectral information of cotton canopy at each point was extracted from the hyperspectral images after atmospheric and radiometric correction. The aim was to seek a new method to build the hyperspectral estimation model of cotton LAI using hyperspectral images. In the processing of multispectral image data, the bands used to calculate vegetation index (VI) are usually fixed. However, there are many other bands that can be also used to calculate VIs in hyperspectral images and these bands may contain important information about the ground objects. In this research, the extremal reflectance in the range of certain bands was chosen to calculate the extremum vegetation index (E_VI), intending to get the most significant vegetation feature of each pixel. Four VIs i.e. ratio vegetation index (RVI), difference vegetation index (DVI), normalized differential vegetation index (NDVI), and green-band normalized differential vegetation index (GNDVI), which are closely related to vegetation coverage, were selected to estimate the cotton LAI value. Original spectral reflectance, selected spectral reflectance by successive projections algorithm (SPA), fixed-band vegetation index (F_VI) and E_VI were taken as independent variables respectively to build hyperspectral cotton LAI estimation models using least squares and partial least squares (PLS) regression methods. The results indicated: 1) When using spectral reflectance to estimate cotton LAI, the SPA successfully simplified the model and improved the accuracy at the same time; 2) Models containing VIs had better predictive ability than those which used spectral reflectance as independent variables; (3) RVI was the best VI to estimate cotton LAI when using single VI as parameter; (4) E_VI improved the accuracy of LAI estimation models significantly compared with F_VI; (5) The LAI - E_VIs - PLS model, which takes multiple E_VIs as independent variables and uses PLS regression method, had the highest accuracy and best predictive ability (R2=0.85, RMSE=0.02). The cotton LAI estimation map was made by resolving each pixel in the cotton hyperspectral images using the LAI - E_VIs - PLS model. The result was validated by the measured LAI and showed good precision (R2=0.88, RMSE=0.29). Therefore, this new method to monitor the LAI of crops is feasible