Abstract
The impacts of climate on spatiotemporal variations of eco-physiological and bio-physical factors have been widely explored in previous research, especially in dry areas. However, the understanding of gross primary productivity (GPP) variations and its interactions with climate in humid and semi-humid areas remains unclear. Based on hyperspectral satellite remotely sensed vegetation phenology processes and related indices and the re-analysed climate datasets, we investigated the seasonal and inter-annual variability of GPP by using different light-use efficiency (LUE) models including the Carnegie-Ames-Stanford Approaches (CASA) model, vegetation photosynthesis models (VPMChl and VPMCanopy) and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products (MOD17A2H) during 2001–2020 over the Great Lakes region of Sub-Saharan Africa (GLR-SSA). The models’ validation against the in situ GPP-based upscaled observations (GPP-EC) indicated that these three models can explain 82%, 79% and 80% of GPP variations with root mean square error (RMSE) values of 5.7, 8.82 and 10.12 g C·m−2·yr−1, respectively. The spatiotemporal variations of GPP showed that the GLR-SSA experienced: (i) high GPP values during December-May; (ii) high annual GPP increase during 2002–2003, 2011–2013 and 2015–2016 and annual decreasing with a marked alternation in other years; (iii) evergreen broadleaf forests having the highest GPP values while grasslands and croplands showing lower GPP values. The spatial correlation between GPP and climate factors indicated 60% relative correlation between precipitation and GPP and 65% correction between surface air temperature and GPP. The results also showed high GPP values under wet conditions (in rainy seasons and humid areas) that significantly fell by the rise of dry conditions (in long dry season and arid areas). Therefore, these results showed that climate factors have potential impact on GPP variability in this region. However, these findings may provide a better understanding of climate implications on GPP variability in the GLR-SSA and other tropical climate zones.
Highlights
Carbon uptake through the photosynthesis process by a terrestrial ecosystem termed gross primary productivity (GPP) [1,2] is the way CO2 enters into the biosphere from the atmosphere [3,4]
Different diagnostic and prognostic models were developed and operated to estimate the spatial and temporal patterns of GPP variability [1,5,6,7]. These models are grouped into four categories based on their fundamental theories: enzymes kinetic process-based model, the light-use efficiency (LUE) or production efficiency models, machine learning models based on eddy covariance (EC), and other measurements statistically based on sun-induced chlorophyll fluorescence (SIF) models, respectively [7,8,9,10]
High values of GPP were found in the lower plateau of the Democratic Republic of Congo (DRC) and the areas closer to Indian
Summary
Carbon uptake through the photosynthesis process by a terrestrial ecosystem termed gross primary productivity (GPP) [1,2] is the way CO2 enters into the biosphere from the atmosphere [3,4]. Different diagnostic and prognostic models were developed and operated to estimate the spatial and temporal patterns of GPP variability [1,5,6,7]. These models are grouped into four categories based on their fundamental theories: enzymes kinetic process-based model, the light-use efficiency (LUE) or production efficiency models, machine learning models based on eddy covariance (EC), and other measurements statistically based on sun-induced chlorophyll fluorescence (SIF) models, respectively [7,8,9,10]
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