## 5 Conclusions  
The present study employed the hyperspectral satellite remotely sensed vegetation phonological processes indices and re-analysed climate datasets to analyse the seasonal and inter-annual GPP dynamics, the interaction of ecosystem function types and the effects of climate characteristics on GPP variability by using different LUE models. Thereafter, this study compared the employed models to indicate which among them presents reliability and higher accuracy in GPP simulation in the GLR-SSA. According to the results, this area indicated that: (i) the seasons between December and May (Dec-Jan-Feb and Mar-Apr-May) were characterized by higher GPP values (\(\geq\)80 g C\(\cdot\) m\({}^{-2}\cdot\)month\({}^{-1}\)); (ii) GPP increased during 2002-2003, 2011-2013 and 2015-2016, while other years were characterized by the interchanges of increasing and decreasing trends; (iii) evergreen broadleaf forests (EVGB) had higher GPP, while annual grass and crop lands had lower GPP; (iv) higher GPP values were obtained under wet conditions, for example, the humid zones of the Democratic Republic of Congo (DRC) and Mozambique, while dry conditions exhibited lower GPP values, as in the case of the semi-arid and arid zones of the northern part of Kenya and the southern part of Zambia and Zimbabwe closer to the Kalahari Desert (Figure 4), which proved that climate has a great control on GPP variations in this region. The evaluation of model reliability indicated that the CASA model is more reliable than other LUE models to a certain terrestrial ecosystem because it requires the adjustment of the parameters and their optimization with the underpinned local terrestrial ecosystems. The present study may provide insightful science-based information to identify the regional ecological functions (i.e., carbon fluxes) and their relationships with climate variability. Therefore, this study may help contribute to a better understanding of the different interactions between different ecological functions along the tropical regions. B.C. designed and supervised the research; A.K. collected and processed data and analysed the results; B.C. and A.K. wrote the manuscript; F.W., W.N., A.D., Y.H., H.Z. and L.G. provided tools and technical assistance. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China (42141018 & 41977404), National Key R&D Program of China (2018 YEA0606001, 2017 YFA0604301 and 2017 YFA0604302), and Strategic Priority Research Program of Chinese Academy of Sciences (XDA 20030302). The MODIS-NDVI (MOD13A1), MODIS-FPAR (MOD15A2H) and MODIS-surface reflectance (MOD09A1) datasets are available at Earth-data for NASA search engine's official website [[https://search.earthdata.nasa.gov/search](https://search.earthdata.nasa.gov/search)]([https://search.earthdata.nasa.gov/search](https://search.earthdata.nasa.gov/search)) (accessed on 9 May 2021). Land cover data provided by the European Space Agency Annual Global Climate Change Initiative, Land Cover (CCI-LC), maps at 300 m spatial resolution spanning a period of 24 years (1992-2015) (ESA-CCI) can be freely downloaded at [[http://maps.elie.ucl.ac.be/CCI/viewer/download/](http://maps.elie.ucl.ac.be/CCI/viewer/download/)]([http://maps.elie.ucl.ac.be/CCI/viewer/download/](http://maps.elie.ucl.ac.be/CCI/viewer/download/)) (accessed on 9 May 2021).