The carbon cycle of tropical terrestrial vegetation plays a vital role in the storagernand exchange of atmospheric carbon dioxide (CO2). However, large uncertaintiesrnsurround the impacts of land-use change, climate warming, the frequencyrnof droughts, and CO2 fertilization. This culminates in poorly quantified carbonrnstocks and carbon fluxes even for the major ecosystems in Africaâ€™s carbon cyclern(savannas and tropical evergreen forests). Contributors to this uncertaintyrnare the sparsity of (micro-)meteorological observations across Africaâ€™s vast landrnarea, a lack of sufficient ground-based observation networks and validationrndata for CO2, and incomplete representation of important processes in numericalrnmodels. Satellite retrievals are strongly influenced by land-use changes,rncloud cover, and aerosol loading. Moreover, Africa is a continent with wide extremesrnin surface type (which ranges from desert, rainforest, and Savannah) andrnaerosol loading. Therefore, the comparison of satellite observations with modelrnand available in-situ observations will be useful to prove the performance ofrnsatellites and show how these systematic errors vary geographically over therncontinent. In this thesis, GOSAT column-averaged dry-air mole fraction of carbonrndioxide (XCO2) is compared with the NOAA CT2016 and six flask observationsrnacross Africa using five years of data covering the period from May 2009 tornApril 2014. Besides, XCO2 from OCO-2 is compared with NOAA CT16NRT17rnand eight flask observations across Africa using two years of data covering thernperiod from January 2015 to December 2016. The analysis shows that the XCO2rnfrom GOSAT is higher than XCO2 simulated by CT2016 by 0.28 _ 1.05 ppm,rnwhereas OCO-2 XCO2 is lower than CT16NRT17 by 0.34 _ 0.9 ppm on Africanrnlandmass on average. The mean correlations of 0.83 _ 0.12 and 0.60 _ 0.41 andrnan average RMSD of 2.30 _ 1.45 and 2.57 _ 0.89 ppm are found between thernmodel and the respective datasets from GOSAT and OCO-2 implying the existencernof a reasonably good agreement between CT and the two satellites overrnAfricaâ€™s land region. However, significant variations were observed in some regions.rnFor example, OCO-2 XCO2 is lower than that of CT16NRT17 by up to 3rnppm over some regions in North Africa (e.g., Egypt, Libya, and Mali ) whereasrnit exceeds CT16NRT17 XCO2 by 2 ppm over Equatorial Africa (10_S - 10_N).rnrnThis regional difference is also noted in the comparison of model simulationsrnand satellite observations with flask observations over the continent. For example,rnCT shows a better sensitivity in capturing flask observations over sitesrnlocated in Northern Africa. In contrast, satellite observations have better sensitivityrnin capturing flask observations in lower altitude island sites. CT2016rnshows a high spatial mean of seasonal mean RMSD of 1.91 ppm during DJFrnfrom GOSAT, while CT16NRT17 shows RMSD of 1.75 ppm during MAM fromrnOCO-2. On the other hand, the low RMSD of 1.00 and 1.07 ppm during SON inrnmodel XCO2 from GOSAT and OCO-2, respectively, indicate better agreementrnduring autumn. The model simulation and satellite observations exhibit similarrnseasonal cycles of XCO2 with a small discrepancy over Southern Africa (35_ -rn10_S) and during wet seasons over all regions. Two remotely sensed vegetationrnproducts that have been shown to correlate highly with Gross Primary Productivityrn(GPP): Sun-Induced Fluorescence (SIF) and Near-Infrared Reflectancernof vegetation (NIRv) are also analyzed to further understand the dynamics ofrncarbon dioxide flux. A comparison against flux tower observations of daytimepartitionedrnNet Ecosystem Exchange (NEE) from six major biomes in Africarnshows that SIF and NIRv reproduce the seasonal patterns of GPP well, resultingrnin correlation coefficients of >0.9 (N=12 months, 4 sites) over savannas in thernnorthern and southern hemisphere. These coefficients are slightly higher thanrnfor the widely used MPI-BGC GPP products and Enhanced Vegetation Indexrn(EVI). Similar to SIF signals in the neighbouring Amazon, peak productivity occursrnin the wet season coinciding with peak soil moisture, and is followed by anrninitial decline during the early dry season that reverses when light availabilityrnpeaks. This suggests similar leaf dynamics are at play. Spatially, SIF and NIRvrnshow a strong linear relation (R >0.9, N=250+ pixels) with multiyear MPI-BGCrnGPP even within single biomes. Both MPI-BGC GPP and EVI show saturationrnrelative to peak NIRv and SIF signals during high productivity months, whichrnsuggests that GPP in the most productive regions of Africa might be larger thanrnsuggested. Africaâ€™s biome integrated productivity is strongly controlled by thernseasonality of soil moisture, with a weak influence of light availability superimposed,rnindicating that the biome productivity of Africa strongly dependsrnon spatiotemporal drivers. Therefore, an understanding of the spatiotemporalrnecosystem dynamics together with its relation to meteorological variables isrnparamount to quantify the responsiveness of the carbon cycle to climate variability.rnFor that reason, an Empirical Ensemble Mode Decomposition (EEMD)rnwas applied on 17 years monthly time series of natural CO2 flux covering thernperiod from January 2000 to December 2016. The EEMD depicts natural CO2rnflux has six periodicities over tropical Africa corresponding to seasonal, interannual,rnand decadal-scale variabilities which are likely driven by atmospheric andrnoceanic processes. Seasonal variabilities at quasi-3 months, quasi-6 months, andrnquasi-12 months contribute about 91.41% of the variability of natural CO2 flux,rnsuggesting that CO2 flux has a strong variability at the seasonal scale. Moreover,rnhigh atmospheric CO2 flux was observed during warm and dry conditions. Precipitationrnis found to be a dominating driver of CO2 flux at the seasonal scalernover the west coast of tropical Africa and East Africa. In addition to the sixrnperiodicities, the application of EEMD to a monthly time series of CO2 flux indicatesrnthe existence of either a nonlinear downward trend or a possible multidecadalrnperiodicity that cannot be captured by the limited length of the currentrndata set. The later is more likely as revealed by a slight reversal at the beginningrnof 2013. Moreover, analysis of different regions of tropical Africa showsrnreduced CO2 uptake over most regions since 2000, with exception for tropicalrnNorth Africa which is found to have increased CO2 uptake most likely due tornenhanced vegetation which exceeds deforestation. At the interannual scale, arnquasi-2 year and quasi-5 year fluctuations were obtained from the EEMD withrna contribution of 6.93% to the total CO2 flux variability. This interannual fluctuationrnhas a significant correlation with NiÃ±o 3.4 index, El NiÃ±o induced temperature,rnprecipitation, soil moisture, and enhanced vegetation index. A significantrnpositive correlation between a warming temperature and interannual CO2rnflux over tropical North Africa and rainforest regions suggests that temperaturernis the major driver of CO2 fluctuation at the interannual scale over thesernregions. Conversely, over Western and Tropical East Africa, precipitation wasrnfound as the most dominant driver. The anomalously high interannual CO2rnflux was found in response to strong El NiÃ±o (NiÃ±o 3.4 index greater than 1.0)rnin the years 2009 and 2015/16 over most of Equatorial Africa. During the peakrnof 2015/16 El NiÃ±o, tropical Africa releases 0.2 mol/m2/month CO2 into thernatmosphere due to interannual variability. The strongest (0.5 mol/m2/month)rncontribution was from the tropical rainforest, most likely driven by the risingrntemperature. Besides, Ethiopian highlands also release 0.4 mol/m2/month CO2rnflux due to dry and warm conditions during this strong El NiÃ±o event. ThernCO2 flux mean over 17 years (2000-2016) shows that tropical Africa is a net CO2rnsink (-7.02 gC/m2/year). However, during the 2015/16 El NiÃ±o years, tropicalrnAfrica releases 29.12 gC/m2/year leading to 487.49 TgC/year which is twicernthe estimated carbon flux of Africa (240 Tg C yÃƒÂ€Â€Â€1 ) for the period covering fromrn2000 to 2005.