First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
The importance of semi-arid ecosystems in the global carbon cycle as sinks for CO2 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 CO2 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 (R2), root-mean-square error (RMSE), and Bayesian information criterion (BIC). The GöR model produced R2 = 0.73, RMSE = 1.45 g C m−2 d−1, and BIC = 678; the T-G model produced R2 = 0.68, RMSE = 1.57 g C m−2 d−1, and BIC = 707; the MOD17 model produced R2 = 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 (R2 = 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.
|Tidsskrift||International Journal of Applied Earth Observation and Geoinformation|
|Status||Udgivet - 2019|