基于GF-1数据的冬小麦不同生育期叶面积指数反演

Inversion of Leaf Area Index during Different Growth Stages of Winter Wheat Based on GF-1 WFV Data

叶面积指数(LAI)是作物长势监测的主要指标之一。为分析国产GF-1卫星数据在不同生育期作物长势监测中的应用价值,本文基于GF-1卫星数据构建5种常用的植被指数,并地面实测冬小麦LAI,开展不同生育期的LAI反演研究。研究发现,基于GF-1数据构建的植被指数能很好地反演冬小麦不同生育期LAI,但精度最优的指数在不同生育期存在差异。EVI构建的幂函数模型在反演全生育期LAI时表现最好,相关系数达到0. 9082;冬小麦生长前期,修正土壤信息的OSAVI指数反演精度优于NDVI、EVI、EVI2和RVI,相关系数为0. 9110;生长中期,各指数反演LAI的效果较差,仅NDVI、EVI2和OSAVI构建的线性和对数模型的相关系数达到0. 05显著水平,以EVI2构建的对数模型相关系数最高,为0. 4827;生长后期,RVI反演LAI效果最好,其指数形式模型的r=0. 8143,反演精度较高。本研究结果说明GF-1数据在作物长势遥感研究中有很大的应用前景,能有效改善中国农业遥感监测长期依赖国外数据的局面,但其LAI反演效果在区域研究中如何,反演结果与Landsat系列、SPOT系列、HJ系列等数据相比存在多大差异,还需要进一步的研究与探讨。

Leaf area index （LAI） is one of the most important parameters of crop growth condition. In order to analyze the application value of GF-1 data in crop growth monitoring during different growth stages, the LAI inversion research was carried out based on truth LAI data and five vegetation indices including NDVI,EVI,EVI2,RVI and OSAVI constructed from GF-1 data. It was found that the accuracy of LAI inversion during different growth stages of winter wheat based on GF-1 data were high. However, optimal indexes were different at different growth stages. The power function model constructed by EVI had a great correlation coefficient （r=0.9082） in the LAI inversion of the whole growth period. During earlier growth period of winter wheat, the inversion accuracy of OSAVI index with corrected soil information was better than that of NDVI, EVI, EVI2 and RVI, and the correlation coefficient was 0.9110. The correlation coefficients of linear and logarithmic models constructed by NDVI, EVI2 and OSAVI reached 0.05 significant level. The correlation coefficient of logarithmic model constructed by EVI2 was the highest, which was 0.4827. The RVI index was suitable to inversion LAI during later growth stage. The correlation coefficients of the exponential form model constructed by RVI was the highest （r=0.8143）. The results of this research showed that GF-1 data had great application potential in remote sensing research of crop growth condition, which could effectively change the situation of depending on foreign remote sensing data in the China’ s agricultural monitoring in a long time. But the application effect of the LAI inversion results in regional studies based on GF-1 data and its differences with Landsat、SPOT and HJ data need further study and discussion.