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

A.M. Abdi; N. Boke-Olén; H. Jin; L. Eklundh; T. Tagesson; V. Lehsten; J. Ardö

doi:10.1016/j.jag.2019.01.018

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

A.M. Abdi, N. Boke-Olén, H. Jin, L. Eklundh, T. Tagesson, V. Lehsten, J. Ardö

LUKKET: Geografi

7 Citationer (Scopus)

Abstract

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.

Originalsprog	Engelsk
Tidsskrift	International Journal of Applied Earth Observation and Geoinformation
Vol/bind	78
Sider (fra-til)	249-260
Antal sider	12
ISSN	0303-2434
DOI	https://doi.org/10.1016/j.jag.2019.01.018
Status	Udgivet - jun. 2019

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10.1016/j.jag.2019.01.018

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

abstract = "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 (G{\"o}R) 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 G{\"o}R 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.",

keywords = "Plant phenology index, PPI, Gross primary productivity, GPP, Land surface temperature, LST, Vapor pressure deficit, VPD, Drylands, Semi-arid, FLUXNET, Eddy covariance, MODIS",

author = "A.M. Abdi and N. Boke-Ol{\'e}n and H. Jin and L. Eklundh and T. Tagesson and V. Lehsten and J. Ard{\"o}",

year = "2019",

month = jun,

doi = "10.1016/j.jag.2019.01.018",

language = "English",

volume = "78",

pages = "249--260",

journal = "International Journal of Applied Earth Observation and Geoinformation",

issn = "1569-8432",

publisher = "Elsevier",

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TY - JOUR

T1 - First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems

AU - Abdi, A.M.

AU - Boke-Olén, N.

AU - Jin, H.

AU - Eklundh, L.

AU - Tagesson, T.

AU - Lehsten, V.

AU - Ardö, J.

PY - 2019/6

Y1 - 2019/6

N2 - 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.

AB - 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.

KW - Plant phenology index

KW - PPI

KW - Gross primary productivity

KW - GPP

KW - Land surface temperature

KW - LST

KW - Vapor pressure deficit

KW - VPD

KW - Drylands

KW - Semi-arid

KW - FLUXNET

KW - Eddy covariance

KW - MODIS

U2 - 10.1016/j.jag.2019.01.018

DO - 10.1016/j.jag.2019.01.018

M3 - Journal article

SN - 1569-8432

VL - 78

SP - 249

EP - 260

JO - International Journal of Applied Earth Observation and Geoinformation

JF - International Journal of Applied Earth Observation and Geoinformation

ER -

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

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