苏伟,侯宁,李琪,张明政,赵晓凤,蒋坤萍.基于Sentinel-2遥感影像的玉米冠层叶面积指数反演[J].农业机械学报,2018,49(1):151-156.
SU Wei,HOU Ning,LI Qi,ZHANG Mingzheng,ZHAO Xiaofeng,JIANG Kunping.Retrieving Leaf Area Index of Corn Canopy Based on Sentinel-2 Remote Sensing Image[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(1):151-156.
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基于Sentinel-2遥感影像的玉米冠层叶面积指数反演   [下载全文]
Retrieving Leaf Area Index of Corn Canopy Based on Sentinel-2 Remote Sensing Image   [Download Pdf][in English]
投稿时间:2017-04-25  
DOI:10.6041/j.issn.1000-1298.2018.01.019
中文关键词:  Sentinel-2遥感影像  玉米冠层  叶面积指数  红边波段  光谱指数
基金项目:国家自然科学基金项目(41371327、41671433)
作者单位
苏伟 中国农业大学 
侯宁 中国农业大学 
李琪 中国土地勘测规划院 
张明政 中国农业大学 
赵晓凤 中国农业大学 
蒋坤萍 中国农业大学 
中文摘要: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0.5342,均方根误差为0.2885。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。
SU Wei, HOU Ning, LI Qi, ZHANG Mingzheng, ZHAO Xiaofeng and JIANG Kunping
China Agricultural University,China Agricultural University,China Land Surveying and Planning Institute,China Agricultural University,China Agricultural University and China Agricultural University
Key Words:Sentinel-2 remote sensing image  corn canopy  leaf area index  red-edge bands  spectral indices
Abstract: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 0.5342 and RMSE of 0.2885. Therefore, linear regression model of NDSI(783,705) was recommended as the most legible model for estimating LAI of corn. The red-edge 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.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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