First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems
(2019) In International Journal of Applied Earth Observation and Geoinformation 78. p.249-260- Abstract
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... (More)
(Less)
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.
- author
- Abdi, A. M. LU ; Boke-Olén, N. LU ; Jin, H. LU ; Eklundh, L. LU ; Tagesson, T. LU ; Lehsten, V. LU and Ardö, J. LU
- organization
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Drylands, Eddy covariance, Remote sensing, Earth observation, Gross primary productivity, Land surface temperature, Africa, Plant phenology index
- in
- International Journal of Applied Earth Observation and Geoinformation
- volume
- 78
- pages
- 12 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85062888348
- ISSN
- 1569-8432
- DOI
- 10.1016/j.jag.2019.01.018
- project
- Global Savannah Phenology: Integrating Earth Observation, Ecosystem Modeling, and PhenoCams
- language
- English
- LU publication?
- yes
- id
- 29b684b8-7638-4bb6-b4c2-9bfc81300c40
- date added to LUP
- 2019-03-21 08:44:48
- date last changed
- 2023-01-03 22:00:09
@article{29b684b8-7638-4bb6-b4c2-9bfc81300c40, abstract = {{<p><br> The importance of semi-arid ecosystems in the global carbon cycle as sinks for CO <br> <sub>2</sub><br> 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 <br> <sub>2</sub><br> 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 <br> <sup>2</sup><br> ), root-mean-square error (RMSE), and Bayesian information criterion (BIC). The GöR model produced R <br> <sup>2</sup><br> = 0.73, RMSE = 1.45 g C m <br> <sup>−2</sup><br> d <br> <sup>−1</sup><br> , and BIC = 678; the T-G model produced R <br> <sup>2</sup><br> = 0.68, RMSE = 1.57 g C m <br> <sup>−2</sup><br> d <br> <sup>−1</sup><br> , and BIC = 707; the MOD17 model produced R <br> <sup>2</sup><br> = 0.49, RMSE = 1.98 g C m <br> <sup>−2</sup><br> d <br> <sup>−1</sup><br> , and BIC = 800. The PPI-based GPP model was able to capture the magnitude of EC GPP better than the other tested models (R <br> <sup>2</sup><br> = 0.77, RMSE = 1.32 g C m <br> <sup>−2</sup><br> d <br> <sup>−1</sup><br> , 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. <br> </p>}}, author = {{Abdi, A. M. and Boke-Olén, N. and Jin, H. and Eklundh, L. and Tagesson, T. and Lehsten, V. and Ardö, J.}}, issn = {{1569-8432}}, keywords = {{Drylands; Eddy covariance; Remote sensing; Earth observation; Gross primary productivity; Land surface temperature; Africa; Plant phenology index}}, language = {{eng}}, pages = {{249--260}}, publisher = {{Elsevier}}, series = {{International Journal of Applied Earth Observation and Geoinformation}}, title = {{First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems}}, url = {{http://dx.doi.org/10.1016/j.jag.2019.01.018}}, doi = {{10.1016/j.jag.2019.01.018}}, volume = {{78}}, year = {{2019}}, }