A comprehensive comparison of machine learning and feature selection methods for maize biomass estimation using sentinel-1 sar, sentinel-2 vegetation indices, and biophysical variables

HIGHLIGHTS

• who: Chi Xu et al. from the School of Geographical Sciences, Northeast Normal University, Changchun, China have published the article: A Comprehensive Comparison of Machine Learning and Feature Selection Methods for Maize Biomass Estimation Using Sentinel-1 SAR, Sentinel-2 Vegetation Indices, and Biophysical Variables, in the Journal: (JOURNAL) of 10/08/2018
• what: The authors evaluate the ability of multi-temporal S-1 and S-2 data to estimate maize biomass and explore improving the accuracy of biomass estimation. In this study, different types of SAR and optical indices were identified and regressed this . . .
  

   

  

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