基于GF-1/2卫星数据的冬小麦叶面积指数反演

Inversion of leaf area index of winter wheat based on GF-1/2 image

叶面积指数(leaf area index,LAI)是监测作物生长状况的重要参数,准确、快速、大面积估算LAI不仅有助于更好地监测农作物,而且也有助于其在建模、总体作物管理及精准农业中的应用。本研究为了利用国产遥感影像快速、大面积反演冬小麦LAI,以GF-1/2影像作为数据源,提取常用植被指数,结合不同生育期(起身期、拔节期、开花期)实测LAI数据,建立反演冬小麦LAI的单变量和多变量经验模型,并对其进行验证。结果表明,GF-1起身期、GF-1拔节期以及GF-1开花期提取的植被指数中,MSR(modified simple ratio)、GNDVI(green normalized difference vegetation index)、EVI(enhanced vegetation index)与LAI间的相关系数最大,分别为0.708、0.671和0.743,说明这些植被指数与冬小麦LAI间的相关性较显著;GF-1不同生育期的反演模型相比,基于拔节期GNDVIGF-1建立的二次多项式模型和基于开花期EVIGF-1、GSRGF-1(green simple ratio)、NDVIGF-1(normalized difference vegetation index)建立的PLSR(partial least squares regression)模型R2最大,均为0.783,PLSR模型的RMSE小于二次多项式模型,说明该多变量模型的稳定性优于单变量模型;同一个生育期不同影像相比,基于GF-2的NDVIGF-2建立的二次多项式模型和基于NDVIGF-2、MSRGF-2、SAVIGF-2(soil-adjusted vegetation index)建立的PLSR模型精度高于基于GF-1的2种模型,R2分别为0.768和0.809;不同生育期反演的LAI分布图表明,LAI反演值与实测LAI值基本吻合。以上研究结果说明国产高分辨率遥感影像在农作物生理参数反演中有一定的应用价值,可以为以后的相关研究提供一定的参考。

Leaf area index(LAI)is an important parameter for monitoring crop growth,and an important input parameter for crop yield prediction model,hydrological and climatic models.LAI can be used in field crop growth monitoring and verification of remote sensing products.Therefore,accurate,rapid and large-scale estimation of LAI is not only conducive to better monitoring crops,but also conducive to its application in modeling,crop management and precision agriculture.Remote sensing technique has become a promising method to detect and monitor the crop LAI due to its many advantages.In this paper,Banjiequan Village,Wumachang Township,Qitai County,Xinjiang,China was selected as the study area.In order to rapidly and extensively retrieve LAI of winter wheat using domestic remote sensing images,17 common vegetation indices were extracted from the GF-1/2 images,which was synchronized with field sampling,and observation at intervals of 20 m in the east-west direction and 30 m in the north-south direction in a 130 m×420 m block.A total of 78 sampling points were taken from a small area of 4 rows×50 cm.Sampling width was measured by tape ruler and coordinates of sampling points were given by GPS.Based on the vegetation indices extracted from GF-1/2 image and LAI data measured at erecting stage,jointing stage and flowering stage,we established univariate(linear,exponential,power,quadratic polynomials)and multivariate(partial least squares regression,PLSR)empirical models for inversion of winter wheat LAI,and validated them.The correlation coefficients of LAI with MSR(modified simple ratio),GNDVI(green normalized difference vegetation index),EVI(enhanced vegetation index)extracted from the erecting,jointing and flowering stages of GF-1 were the maximum,which were 0.708,0.671,and 0.743,respectively,indicating that the correlation between these vegetation indices and LAI of winter wheat was significant.The univariate model R2 based on MSRGF-1,NDVIGF-2(normalized difference vegetation index),GNDVIGF-1 at jointing stage and EVIGF-1 at flowering stage were all greater than 0.7.Compared with different image data at the same growth stage,the quadratic polynomial model based on NDVIGF-2 and PLSR model based on NDVIGF-2,MSRGF-2,and SAVIGF-2(soil-adjusted vegetation index)were more precise than those based on GF-1,with R2 of 0.768 and 0.809 respectively.Compared with the models with the same data(GF-1)at different growth stages,the quadratic polynomial model based on GNDVIGF-1 at jointing stage and the PLSR model based on EVIGF-1,GSRGF-1(green simple ratio)and NDVIGF-1 at flowering stage had the maximum value of R2,which was 0.783.The RMSE of the PLSR model was smaller than that of the quadratic polynomial model,indicating the stability of the multivariate model was better than univariate model.Analyzing the LAI distribution maps inverted from different growth stages,it was found that the LAI inversion values basically coincided with the measured LAI values.The above results show that the domestic high-resolution remote sensing image has certain application value in crop physiological parameter inversion,and provides some references for related researches in the future.