Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning

Volume: 231, Pages: 110959 - 110959
Published: Sep 1, 2019
Abstract
Leaf mass per area (LMA) and leaf equivalent water thickness (EWT) are key leaf functional traits providing information for many applications including ecosystem functioning modeling and fire risk management. In this paper, we investigate two common conclusions generally made for LMA and EWT estimation based on leaf optical properties in the near-infrared (NIR) and shortwave infrared (SWIR) domains: (1) physically-based approaches estimate EWT...
Paper Details
Title
Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning
Published Date
Sep 1, 2019
Volume
231
Pages
110959 - 110959
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