摘要
叶面积指数是描述玉米冠层结构的重要参数之一,决定玉米冠层的光合作用、呼吸作用、蒸腾和碳循环等生物物理过程,因此精确反演叶面积指数对玉米长势监测具有重要意义。以河北省保定市的涿州市、高碑店市、定兴县为研究区,利用Sentinel-2遥感影像和LAI-2000地面同步实测数据进行玉米冠层叶面积指数反演,使用归一化差异光谱指数和比值型光谱指数两类指数,构建了单变量和多变量玉米冠层叶面积指数反演模型,通过决定系数(R2)和均方根误差(RMSE)筛选出最佳模型。研究结果表明,由NDSI(783,705)构建的单变量模型为最优反演模型,其决定系数为0.534 2,均方根误差为0.288 5。因此,基于Sentinel-2遥感影像利用植被指数反演玉米冠层叶面积指数的方法可作为判断玉米长势状况的初步判断依据。
Leaf area index is one of the important parameters to describe the canopy structure of corn,which determines the biophysical processes of corn canopy photosynthesis,respiration,transpiration and carbon cycle. Therefore,retrieval of leaf area index is of great significance to corn growth monitoring. The Sentinel-2 remote sensing image and LAI-2000 ground synchronous data were used to retrieve the leaf area index of corn canopy. Normalized difference spectral index（NDSI） and ratio spectral index（RSI） were extracted to build the univariate and multivariate empirical models. The best LAI retrieving models were identified based on the best combinations of coefficient of determination（R2） and root mean square error（RMSE）. Finally,spatial distributions of LAI in the study area were mapped through the optimal retrieve model. Results showed that all spectral indices tested were significantly correlated with LAI of corn,and the correlation between spectral indices built with red-edge bands and LAI was higher than that built without red-edge bands. Validation analysis result indicated that although the accuracy of the multivariate empirical model was high,its ability to predict LAI was poor. Linear regression model of NDSI（783,705）most accurately explained retrieval of LAI of corn,with R2 of 0. 534 2 and RMSE of 0. 288 5. Therefore,linear regression model of NDSI（783,705）was recommended as the most legible model for estimating LAI of corn. The rededge bands confirmed from Sentinel-2 remote sensing image improved the accuracy of retrieving the LAI of corn. Moreover,the results also provided a powerful evidence to develop the Sentinel-2 remote sensing image and red-edge bands application in retrieving the LAI of corn.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2018年第1期151-156,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(41371327
41671433)
