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Torbern Tagesson

Forskare

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Optimized estimation of leaf mass per area with a 3d matrix of vegetation indices

Författare

  • Yuwen Chen
  • Jia Sun
  • Lunche Wang
  • Shuo Shi
  • Wei Gong
  • Shaoqiang Wang
  • Torbern Tagesson

Summary, in English

Leaf mass per area (LMA) is a key plant functional trait closely related to leaf biomass. Estimating LMA in fresh leaves remains challenging due to its masked absorption by leaf water in the short-wave infrared region of reflectance. Vegetation indices (VIs) are popular variables used to estimate LMA. However, their physical foundations are not clear and the generalization ability is limited by the training data. In this study, we proposed a hybrid approach by establishing a three-dimensional (3D) VI matrix for LMA estimation. The relationship between LMA and VIs was con-structed using PROSPECT-D model simulations. The three-VI space constituting a 3D matrix was divided into cubical cells and LMA values were assigned to each cell. Then, the 3D matrix retrieves LMA through the three VIs calculated from observations. Two 3D matrices with different VIs were established and validated using a second synthetic dataset, and two comprehensive experimental datasets containing more than 1400 samples of 49 plant species. We found that both 3D matrices allowed good assessments of LMA (R2 = 0.76 and 0.78, RMSE = 0.0016 g/cm2 and 0.0017 g/cm2, re-spectively for the pooled datasets), and their results were superior to the corresponding single Vis, 2D matrices, and two machine learning methods established with the same VI combinations.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publiceringsår

2021-09

Språk

Engelska

Publikation/Tidskrift/Serie

Remote Sensing

Volym

13

Issue

18

Dokumenttyp

Artikel i tidskrift

Förlag

MDPI AG

Ämne

  • Physical Geography

Nyckelord

  • 3D matrix
  • leaf mass per area
  • PROSPECT-D model
  • Vegetation index

Status

Published

Projekt

  • Carbon Sequestration and greenhouse gas emissions in (agro) Sylvopastoral Ecosystems in the Sahelian CILSS States

ISBN/ISSN/Övrigt

  • ISSN: 2072-4292