Leaf area index estimation with EnMAP hyperspectral data based on deep neural network
Received:March 23, 2019  Revised:December 18, 2019  download
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Author NameAffiliationE-mail
LI Xue-Ling School of Mathematical Sciences, Capital Normal University, Beijing 100048, China xueling_li@cnu.edu.cn 
DONG Ying-Ying Key laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China 
dongyy@radi.ac.cn 
ZHU Yi-Ning School of Mathematical Sciences, Capital Normal University, Beijing 100048, China  
HUANG Wen-Jiang Key laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China 
 
Abstract:Regional leaf area index (LAI) mapping is important for crop growth monitoring and yield estimation. Due to the lower accuracy and instability of statistical models for regional LAI estimation, we proposed a new deep neural network model, i.e. Small Simple Learning LAI-Net (SSLLAI-Net), based on small sample training, to achieve stable relationship between hyperspectral reflectance and LAI. The new proposed SSLLAI-Net was constructed with two convolution layers, one pooling layer and three connect layers, for which the inputs and outputs were hyperspectral reflectance and LAI estimation. Moreover, SSLLAI-Net could support small training sets. We applied SSLLAI-Net to an Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery for regional LAI mapping, in which cereals, maize, rape seed and other crops are selected as our objects. The achieved R2 values for estimated LAI of cereals, maize, rape seed and other crops were 0.95, 0.99, 0.98 and 0.90 based on small training sets with 50 samples, while for the inputs with noise, the R2 values were 0.95、0.98、0.96 and 0.89, respectively. In all, our new proposed SSLLAI-Net has high precision of regional LAI mapping, stability and noise resistance with hyperspectral remote sensing observations.
keywords:leaf area index (LAI)  hyperspectral remote sensing  EnMAP  deep neural network  SSLLAI-Net
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Copyright:《Journal of Infrared And Millimeter Waves》