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Lars Eklundh

Professor

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Optimizing Remote Sensing Data and Light Use Efficiency Model for Accurate Gross Primary Production Estimation in African Rangelands

Författare

  • Mahendra K. Pal
  • Jonas Ardö
  • Lars Eklundh
  • Zhanzhang Cai
  • Torbern Tagesson
  • Aleksander Wieckowski
  • Robert Buitenwerf
  • Charles Davison
  • Donvan Grobler
  • Michael Munk
  • Paul Senty
  • Christian Brümmer
  • Gregor Feig
  • Pieter Vanzyl
  • Patrick Griffiths

Summary, in English

This paper focuses on the meticulous selection of optimal remote sensing and climate datasets for Gross Primary Productivity (GPP) estimation in African rangelands. Utilizing Eddy Covariance Flux Tower data, we refine data selection and employ a Light Use Efficiency (LUE) model, with Sentinel 2 for photosynthetically active vegetation quantification, MODIS for Photosynthetically Active Radiation (PARin), and ERA5 Land reanalysis for climatic variables. The Eddy Covariance-based LUE-GPP model is identified as superior compare to other LUE based GPP models and further enhanced through fine-tuning LUEmax and climate scalars. Footprint analysis determines a 500m footprint size, aligning with literature recommendations. Comparative analyses with various LUE models reveal ECLUE's superiority. Statistical validations affirm key parameter selections, leading to a reliable LUE-based GPP model tailored for African rangelands. The proposed model contributes to accurate GPP assessment, essential for informed environmental stewardship in these critical ecosystems.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • LU profilområde: Naturbaserade framtidslösningar
  • MERGE: ModElling the Regional and Global Earth system

Publiceringsår

2024

Språk

Engelska

Sidor

4289-4293

Publikation/Tidskrift/Serie

International Geoscience and Remote Sensing Symposium (IGARSS)

Dokumenttyp

Konferensbidrag

Förlag

IEEE - Institute of Electrical and Electronics Engineers Inc.

Ämne

  • Physical Geography
  • Remote Sensing

Nyckelord

  • African Rangelands
  • GPP
  • LUEModel
  • Remote Sensing
  • Sentinel 2

Conference name

2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024

Conference date

2024-07-07 - 2024-07-12

Conference place

Athens, Greece

Aktiv

Published

ISBN/ISSN/Övrigt

  • ISBN: 9798350360325