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

Pal, Mahendra K. LU ; Ardö, Jonas LU orcid ; Eklundh, Lars LU orcid ; Cai, Zhanzhang LU ; Tagesson, Torbern LU ; Wieckowski, Aleksander LU orcid ; Buitenwerf, Robert ; Davison, Charles ; Grobler, Donvan and Munk, Michael , et al. (2024) 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 In International Geoscience and Remote Sensing Symposium (IGARSS) p.4289-4293
Abstract

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.... (More)

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.

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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
African Rangelands, GPP, LUEModel, Remote Sensing, Sentinel 2
host publication
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
series title
International Geoscience and Remote Sensing Symposium (IGARSS)
pages
5 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
conference location
Athens, Greece
conference dates
2024-07-07 - 2024-07-12
external identifiers
  • scopus:85204924264
ISBN
9798350360325
DOI
10.1109/IGARSS53475.2024.10640791
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2024 IEEE.
id
d937c694-6a97-4770-920b-4a743cd63793
date added to LUP
2024-12-03 12:20:01
date last changed
2025-04-04 15:11:32
@inproceedings{d937c694-6a97-4770-920b-4a743cd63793,
  abstract     = {{<p>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 LUE<sub>max</sub> 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.</p>}},
  author       = {{Pal, Mahendra K. and Ardö, Jonas and Eklundh, Lars and Cai, Zhanzhang and Tagesson, Torbern and Wieckowski, Aleksander and Buitenwerf, Robert and Davison, Charles and Grobler, Donvan and Munk, Michael and Senty, Paul and Brümmer, Christian and Feig, Gregor and Vanzyl, Pieter and Griffiths, Patrick}},
  booktitle    = {{IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings}},
  isbn         = {{9798350360325}},
  keywords     = {{African Rangelands; GPP; LUEModel; Remote Sensing; Sentinel 2}},
  language     = {{eng}},
  pages        = {{4289--4293}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{International Geoscience and Remote Sensing Symposium (IGARSS)}},
  title        = {{Optimizing Remote Sensing Data and Light Use Efficiency Model for Accurate Gross Primary Production Estimation in African Rangelands}},
  url          = {{http://dx.doi.org/10.1109/IGARSS53475.2024.10640791}},
  doi          = {{10.1109/IGARSS53475.2024.10640791}},
  year         = {{2024}},
}