
Lars Eklundh
Professor

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