Principal Component Analysis Based Polynomial Chaos Expansion Regression of Leaf Area Index from Polsar Imagery

Celik M., Erten E.

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belçika, 11 - 16 Temmuz 2021, ss.6096-6099 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/igarss47720.2021.9554929
  • Basıldığı Şehir: Brussels
  • Basıldığı Ülke: Belçika
  • Sayfa Sayıları: ss.6096-6099
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

Predicting biophysical parameters with high accuracy and fast speed based on remote sensing-based modeling is an attractive topic. In this context, the revisit time, coverage, and illumination condition in-dependency make the Polarimetric Synthetic Aperture Radar (PoISAR) data is an attractive tool. In this paper, one of the most studied biophysical parameters, Leaf Area Index (LAI), is chosen to assess Polynomial Chaos Expansion (PCE) regression, commonly used metamodeling due to its precise and rapid approximation performance. Experimental analysis based on AgriSAR 2009 campaign, including oat and canola, is given to validate the PCE in the regression. According to the accuracy analysis, the Pearson correlation of 88% and 95% for oat and canola, respectively, were achieved.