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Estimating Fraction of Photosynthetically Active Radiation of Corn with Vegetation Indices and Neural Network from Hyperspectral Data

查看全文 作　　者：YANG [1]Fei;ZHU [1]Yunqiang;ZHANG [2]Jiahua;YAO [3]Zuofang 高影响力作者 机构地区：[1]The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;[2]The Laboratory of Remote Sensing and Climate Information, Chinese Academy of Meteorological Sciences, Beifing 100081, China;[3]The Beijing National Technology Transfer Center of Chinese Academy of Sciences, Beijing 100086高影响力机构 出　　处：《Chinese Geographical Science》索引2012年第22卷第1期,共12页高影响力期刊 基　　金：Under the auspices of National Key Research Program of Global Change Research (No.2010CB951302);National Natural Science Fundation of China (No.40771146);China Postdoctoral Science Foundation Funded Project (No.07Z7601MZ1) 摘　　要：The fraction of photosynthetically active radiation(FPAR) is a key variable in the assessment of vegetation productivity and land ecosystem carbon cycles.Based on ground-measured corn hyperspectral reflectance and FPAR data over Northeast China,the correlations between corn-canopy FPAR and hyperspectral reflectance were analyzed,and the FPAR estimation performances using vegetation index(VI) and neural network(NN) methods with different two-band-combination hyperspectral reflectance were investigated.The results indicated that the corncanopy FPAR retained almost a constant value in an entire day.The negative correlations between FPAR and visible and shortwave infrared reflectance(SWIR) bands are stronger than the positive correlations between FPAR and near-infrared band reflectance(NIR).For the six VIs,the normalized difference vegetation index(NDVI) and simple ratio(SR) performed best for estimating corn FPAR(the maximum R2 of 0.8849 and 0.8852,respectively).However,the NN method esti-mated results(the maximum R2 is 0.9417) were obviously better than all of the VIs.For NN method,the two-band combinations showing the best corn FPAR estimation performances were from the NIR and visible bands;for VIs,however,they were from the SWIR and NIR bands.As for both the methods,the SWIR band performed exceptionally well for corn FPAR estimation.This may be attributable to the fact that the reflectance of the SWIR band were strongly controlled by leaf water content,which is a key component of corn photosynthesis and greatly affects the absorption of photosynthetically active radiation(APAR),and makes further impact on corn-canopy FPAR. 关 键 词：高光谱反射率 光合有效辐射 神经网络方法 玉米冠层 植被生产力 数据估算 归一化差异植被指数 指数和
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