Meny

Javascript verkar inte påslaget? - Vissa delar av Lunds universitets webbplats fungerar inte optimalt utan javascript, kontrollera din webbläsares inställningar.
Du är här

First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems

Författare:
  • A. M. Abdi
  • N. Boke-Olén
  • H. Jin
  • L. Eklundh
  • T. Tagesson
  • V. Lehsten
  • J. Ardö
Publiceringsår: 2019
Språk: Engelska
Sidor: 249-260
Publikation/Tidskrift/Serie: International Journal of Applied Earth Observation and Geoinformation
Volym: 78
Dokumenttyp: Artikel i tidskrift
Förlag: Elsevier

Abstract english


The importance of semi-arid ecosystems in the global carbon cycle as sinks for CO
2
emissions has recently been highlighted. Africa is a carbon sink and nearly half its area comprises arid and semi-arid ecosystems. However, there are uncertainties regarding CO
2
fluxes for semi-arid ecosystems in Africa, particularly savannas and dry tropical woodlands. In order to improve on existing remote-sensing based methods for estimating carbon uptake across semi-arid Africa we applied and tested the recently developed plant phenology index (PPI). We developed a PPI-based model estimating gross primary productivity (GPP) that accounts for canopy water stress, and compared it against three other Earth observation-based GPP models: the temperature and greenness (T-G) model, the greenness and radiation (GöR) model and a light use efficiency model (MOD17). The models were evaluated against in situ data from four semi-arid sites in Africa with varying tree canopy cover (3–65%). Evaluation results from the four GPP models showed reasonable agreement with in situ GPP measured from eddy covariance flux towers (EC GPP) based on coefficient of variation (R
2
), root-mean-square error (RMSE), and Bayesian information criterion (BIC). The GöR model produced R
2
= 0.73, RMSE = 1.45 g C m
−2
d
−1
, and BIC = 678; the T-G model produced R
2
= 0.68, RMSE = 1.57 g C m
−2
d
−1
, and BIC = 707; the MOD17 model produced R
2
= 0.49, RMSE = 1.98 g C m
−2
d
−1
, and BIC = 800. The PPI-based GPP model was able to capture the magnitude of EC GPP better than the other tested models (R
2
= 0.77, RMSE = 1.32 g C m
−2
d
−1
, and BIC = 631). These results show that a PPI-based GPP model is a promising tool for the estimation of GPP in the semi-arid ecosystems of Africa.

Keywords

  • Physical Geography
  • Drylands
  • Eddy covariance
  • FLUXNET
  • GPP
  • Gross primary productivity
  • Land surface temperature
  • LST
  • MODIS
  • Plant phenology index
  • PPI
  • Semi-arid
  • Vapor pressure deficit
  • VPD

Other

Published
  • ISSN: 1569-8432
niklas
E-post: niklas [dot] boke_olen [at] cec [dot] lu [dot] se

Postdoc

Centrum för miljö- och klimatforskning (CEC)

+462228969

E230

Ekologihuset, Sölvegatan 37, Lund

50

Institutionen för naturgeografi och ekosystemvetenskap
Lunds universitet
Sölvegatan 12
223 62 Lund
Sverige

Hantering av personuppgifter