Abstract
The Caatinga biome, the only exclusively Brazilian biome, plays a crucial yet understudied role in regional and global carbon dynamics. Using column-averaged dry-air mole fraction of CO2 (XCO2) data from NASA’s Orbiting Carbon Observatory-2 (OCO-2) between 2015 and 2022, this study investigates spatial and temporal anomalies across distinct phytoecological biozones of the Caatinga. Anomaly detection, spatial autocorrelation (Local Moran’s I), time-series modeling (ARIMA), and correlation analyses with vegetation and climate indices (NDVI, EVI, LAI, land surface temperature, and precipitation) were applied to evaluate the biome’s carbon balance. Results reveal heterogeneous XCO2 patterns, with predominantly negative or neutral anomalies, confirming the Caatinga’s role as a carbon sink, though punctuated by localized positive anomalies indicating emission hotspots. The Savanna-Steppe and Pioneer Formation biozones exhibited the strongest seasonal and spatial clustering of positive anomalies, highlighting vulnerability to land-use pressures and climatic extremes. Forested biozones, particularly Open and Dense Ombrophilous Forests, showed increasing anomaly trends in recent years, suggesting a potential weakening of sink capacity. Correlations revealed distinct biome-specific responses: positive associations between XCO2 and precipitation in transitional and pioneer formations, and negative associations with vegetation indices in savanna areas, emphasizing hydrological control of carbon fluxes. The findings demonstrate that the Caatinga exhibits both resilience and vulnerability, with its carbon balance strongly modulated by climatic variability, vegetation structure, and anthropogenic pressures. These results underscore the biome’s strategic role in climate mitigation and the urgent need for targeted conservation and restoration policies to safeguard its carbon sequestration potential.
Data availability
The datasets generated and/or analysed during the current study are available in the GitHub repository https://github.com/arpanosso/caatinga-xco2-carbon-vulnerability. The XCO2 data used in this study were obtained from the Orbiting Carbon Observatory-2 (OCO-2) dataset, available at https://ocov2.jpl.nasa.gov/science/oco-2-data-center/.
References
Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148. https://doi.org/10.1038/nature02121 (2004).
Tam et al. Research on climate change in social psychology publications: A systematic review. Asian J. Soc. Psychol. 24(2), 117–143. https://doi.org/10.1111/AJSP.12477 (2021).
Mele, M. et al. Nature and climate change effects on economic growth: an LSTM experiment on renewable energy resources. Environ. Sci. Pollut. Res. 28(30), 41127–41134. https://doi.org/10.1007/S11356-021-13337-3 (2021).
Habibullah, M. S. et al. Impact of climate change on biodiversity loss: global evidence. Environ. Sci. Pollut. Res. 29(1), 1073–1086. https://doi.org/10.1007/S11356-021-15702-8 (2022).
Davis, S. J. et al. Future CO2 emissions and climate change from existing energy infrastructure. Science 329(5997), 1330–1333. https://doi.org/10.1126/SCIENCE.1188566 (2010).
Montzka, S. A. et al. Non-CO2 greenhouse gases and climate change. Nature 476, 43–50. https://doi.org/10.1038/nature10322 (2011).
Azevedo, T. R. et al. SEEG initiative estimates of Brazilian greenhouse gas emissions from 1970 to 2015. Sci. Data 5(1), 1–43. https://doi.org/10.1038/sdata.2018.45. (2018).
Silva Junior, C. H. L. et al. Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Sci. Adv. 6, eaba2949. https://doi.org/10.1126/sciadv.aba2949 (2020).
Costa, G. B. et al. Seasonal ecosystem productivity in a seasonally dry tropical forest (Caatinga) using flux tower measurements and remote sensing data. Remote Sens. 14(16), 3955. https://doi.org/10.3390/rs14163955 (2022).
Ferreira, R. R. et al. An assessment of the MOD17A2 gross primary production product in the Caatinga biome, Brazil. Int. J. Remote Sens. 42(4), 1275–1291. https://doi.org/10.1080/01431161.2020.1826063 (2021).
de Oliveira, M. L. et al. Remote sensing-based assessment of land degradation and drought impacts over terrestrial ecosystems in Northeastern Brazil. Sci. Total Environ. 835, 155490. https://doi.org/10.1016/j.scitotenv.2022.155490 (2022).
Salami, G. et al. Biomass and carbon balance in a dry tropical forest area in Northeast Brazil. An. Acad. Bras. Cienc. 95(4), e20191250. https://doi.org/10.1590/0001-3765202320191250 (2023).
Hakkarainen, J. et al. J. Direct space-based observations of anthropogenic CO2 emission areas from OCO-2. Geophys. Res. Lett. 43(21), 11400–11406. https://doi.org/10.1002/2016GL070885 (2016).
Araújo, S. et al. Hot spots and anomalies of CO2 over eastern Amazonia, Brazil: A time series from 2015 to 2018. Environmental.
Gatti, L. V. et al. Amazonia as a carbon source linked to deforestation and climate change. Nature 595, 388–393. https://doi.org/10.1038/s41586-021-03629-6 (2021).
Gloor, M. et al. The carbon balance of South America: a review of the status, decadal trends and main determinants. Biogeosciences 9, 5407–5430. https://doi.org/10.5194/bg-9-5407-2012 (2012).
Le Dumont, J. et al. Deep learning applied to CO2 power plant emissions quantification using simulated satellite images. Geosci. Model. Dev. 17, 1995–2014. https://doi.org/10.5194/gmd-17-1995-2024 (2024).
Biederman, J. A. et al. CO2 exchange and evapotranspiration across dryland ecosystems of southwestern North America. Glob Change Biol. 23, 1372–1391. https://doi.org/10.1111/gcb.13686 (2017).
Jin, Z. et al. A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system. Earth Syst. Sci. Data. 16, 2857–2876. https://doi.org/10.5194/essd-16-2857-2024 (2024).
Crowell, S. et al. The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network. Atmos. Chem. Phys. 19, 9797–9831. https://doi.org/10.5194/acp-19-9797-2019 (2019).
Chevallier, F. et al. Large CO2 emitters as seen from satellite: comparison to a gridded global emission inventory. Geophys. Res. Lett. 49(5). e2021GL097540 (2022).
Lian, J. et al. Analysis of temporal and spatial variability of atmospheric CO2 concentration within Paris from the GreenLITE™ laser imaging experiment. Atmos. Chem. Phys. 19, 13809–13825. https://doi.org/10.5194/acp-19-13809-2019 (2019).
Zheng, B. et al. Observing carbon dioxide emissions over China’s cities and industrial areas with the orbiting carbon Observatory-2. Atmos. Chem. Phys. 20, 8501–8510. https://doi.org/10.5194/acp-20-8501-2020 (2020).
Kuhlmann, G. et al. Quantifying CO2 emissions of a city with the copernicus anthropogenic CO2 monitoring satellite mission. Atmos. Meas. Tech. 13, 6733–6751. https://doi.org/10.5194/amt-13-6733-2020 (2020).
Zhu, Y. et al. The correlation between urban form and carbon emissions: A bibliometric and literature review. Sustainability 15(18), 13439. https://doi.org/10.3390/su151813439 (2023).
Chandra, N. et al. Estimated regional CO2 flux and uncertainty based on an ensemble of atmospheric CO2 inversions. Atmos. Chem. Phys. 22, 9215–9243. https://doi.org/10.5194/acp-22-9215-2022 (2022).
Worden, M. et al. Inferred drought-induced plant allocation shifts and their impact on drought legacy at a tropical forest site. Glob Change Biol. 30, e17287. https://doi.org/10.1111/gcb.17287 (2024).
Wunch, D. et al. Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON. Atmos. Meas. Tech. 10, 2209–2238. https://doi.org/10.5194/amt-10-2209-2017 (2017).
O’Dell, C. W. et al. Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 using the version 8 ACOS algorithm. Atmos. Meas. Tech. 11, 6539–6576. https://doi.org/10.5194/amt-11-6539-2018 (2018).
Lyapustin, A. et al. Scientific impact of MODIS C5 calibration degradation and C6 + improvements. Atmos. Meas. Tech. 7, 4353–4365. https://doi.org/10.5194/amt-7-4353-2014 (2014).
IBGE. Biomas E Sistema Costeiro-Marinho Do Brasil (Instituto Brasileiro de Geografia e Estatística, 2019).
Silva, P. F. et al. Seasonal patterns of carbon dioxide, water and energy fluxes over the Caatinga and grassland in the semi-arid region of Brazil. J. Arid Environ. 147, 71–82. https://doi.org/10.1016/j.jaridenv.2017.09.003 (2017).
Rodrigues, J. A. et al. Spatial-temporal dynamics of Caatinga vegetation cover by remote sensing in the Brazilian semiarid region. Dyna 87(215), 109–117. https://doi.org/10.15446/dyna.v87n215.8785 (2020).
Gomes, L. et al. Impacts of fire frequency on net CO2 emissions in the Cerrado savanna vegetation. Fire 7(8), 280. https://doi.org/10.3390/fire7080280 (2024).
Moro, M. F. et al. A phytogeographical metaanalysis of the semiarid Caatinga domain in Brazil. Bot. Rev. 82, 91–148. https://doi.org/10.1007/s12229-016-9164-z (2016).
O’Dell, C. W. et al. Improved retrievals of carbon dioxide from OCO-2. Atmos. Meas. Tech. 11, 6539–6574. https://doi.org/10.5194/amt-11-6539-2018 (2018).
O’Dell, C. W. et al. The ACOS CO2 retrieval algorithm – Part 1: description and validation. Atmos. Meas. Tech. 5, 99–121. https://doi.org/10.5194/amt-5-99-2012 (2012).
Kiel, M. et al. How bias correction goes wrong: measurement of XCO2 affected by erroneous surface pressure estimates. Atmos. Meas. Tech. 12, 2241–2259. https://doi.org/10.5194/amt-12-2241-2019 (2019).
Crisp, D. et al. The OCO-2 instrument: calibration and performance. Atmos. Meas. Tech. 10, 59–81. https://doi.org/10.5194/amt-10-59-2017 (2017).
Kataoka, F. et al. The cross-calibration of spectral radiances and cross-validation of CO2 estimates from GOSAT and OCO-2. Remote Sens. 9, 1158. https://doi.org/10.3390/rs9111158 (2017).
NASA/GSFC. OCO-2 Level 2 bias-corrected Lite Files, Version 10r. Goddard Earth Sciences Data and Information Services Center (GES DISC). https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_10r/summary (2023).
Gorelick, N. et al. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2 (2002).
Schulz, K. et al. Grazing deteriorates the soil carbon stocks of Caatinga forest ecosystems in Brazil. For. Ecol. Manag. 367, 62–70. https://doi.org/10.1016/j.foreco.2016.02.011 (2016).
Wan, Z. New refinements and validation of the MODIS land-surface temperature/emissivity products. Remote Sens. Environ. 140, 36–45. https://doi.org/10.1016/j.rse.2013.08.027 (2014).
Abatzoglou, J. T. et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data. 5, 170191. https://doi.org/10.1038/sdata.2017.191 (2018).
R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (R Foundation for Statistical Computing, 2025).
Costa, G. B. et al. Seasonal ecosystem productivity in a seasonally dry tropical forest (Caatinga) using flux tower measurements and remote sensing data. Remote Sens. 14, 3955. https://doi.org/10.3390/rs14163955 (2022).
Mendes, K. R. et al. Interannual variability of energy and CO2 exchanges in a remnant area of the Caatinga biome under extreme rainfall conditions. Sustainability 15(13), 10085. https://doi.org/10.3390/su151310085 (2023).
Botía, S. et al. Combined CO2 measurement record indicates Amazon forest carbon uptake is offset by savanna carbon release. Atmos. Chem. Phys. 25, 6219–6255 (2025).
Mendes, K. R. et al. Seasonal variation in net ecosystem CO2 exchange of a Brazilian seasonally dry tropical forest. Sci. Rep. 10, 9454. https://doi.org/10.1038/s41598-020-66415-w (2020).
da Costa, L. M., Davitt, A., Volpato, G., Costa de Mendonça, G., Panosso, A. R. & La Scala Jr., N. A comparative analysis of GHG inventories and ecosystems carbon absorption in Brazil. Sci. Total Environ. 958, 177932. https://doi.org/10.1016/j.scitotenv.2024.177932 (2025).
Mendes, K. R. et al. The Caatinga dry tropical forest: a highly efficient carbon sink in South America. Agric. Meteorol. 369, 110573. https://doi.org/10.1016/j.agrformet.2025.110573 (2025).
Silva LFdS, Pessoa, L. G. M. et al. Changes in soil C, N, and P concentrations and stocks after Caatinga natural regeneration of degraded pasture areas in the Brazilian semiarid region. Sustainability 16(20), 8737. https://doi.org/10.3390/su16208737 (2024).
Freitas ICd, Alves, M. A. et al. Soil carbon and nitrogen stocks under agrosilvopastoral systems with different arrangements in a transition area between Cerrado and Caatinga biomes in Brazil. Agronomy 12(12), 2926. https://doi.org/10.3390/agronomy12122926 (2022).
Viana-Lima, A. Y. et al. From overgrazed land to forests: assessing soil health in the Caatinga biome. J. Environ. Manage. 374, 124022. https://doi.org/10.1016/j.jenvman.2024.124022 (2025).
Rocha, W. et al. Drought and fire affect soil CO2 efflux and use of non-structural carbon by roots in forests of Southern Amazonia. For. Ecol. Manage. 585, 122584. https://doi.org/10.1016/j.foreco.2025.122584 (2025).
Medeiros, R. et al. Remote sensing phenology of the Brazilian Caatinga and its environmental drivers. Remote Sens. 14(11), 2637. https://doi.org/10.3390/rs14112637 (2022).
GONGGA Model Intercomparison Project. Global Carbon Sink Variability Report (Chinese Academy of Sciences, 2025).
Butz, A. et al. Toward accurate CO2 and CH4 observations from GOSAT. Geophys. Res. Lett. 38, L14802. https://doi.org/10.1029/2011GL047393 (2011).
Mendes, K. R. et al. The Caatinga dry tropical forest: a highly efficient carbon sink in South America. Agric. For. Meteorol. 369, 110573. https://doi.org/10.1016/j.agrformet.2025.110573 (2025).
Taylor, T. E. et al. Orbiting carbon observatory (OCO-2) instrument performance and calibration. Atmos. Meas. Tech. 13, 123–140. https://doi.org/10.5194/amt-13-123-2020 (2020).
Helmholtz Centre for Environmental Research-UFZ, et al et al. The long-term consequences of forest fires on the carbon fluxes of a tropical forest. Appl. Sci. 11(10), 4696. https://doi.org/10.3390/app11104696 (2021).
Niu, Y. et al. Variations in diurnal and seasonal net ecosystem carbon dioxide exchange in a semiarid sandy grassland ecosystem in China’s Horqin sandy land. Biogeosciences 17, 6309–6324. https://doi.org/10.5194/bg-17-6309-2020 (2020).
Ramos, D. et al. Front. Environ. Sci. 11:1275844. doi:https://doi.org/10.3389/fenvs.2023.1275844 (2023).
de Araujo, M. D. et al. Seasonal ecosystem productivity in a seasonally dry tropical forest (Caatinga) using flux tower measurements and remote sensing data. Remote Sens. 14(16), 3955. https://doi.org/10.3390/rs14163955 (2022).
Fischer, R. et al. The Long-term consequences of forest fires on the carbon fluxes of a tropical forest in Africa. Appl. Sci. 11(10), 4696. https://doi.org/10.3390/app11104696 (2021).
Silva, T. S. F. et al. Vegetation structure and phenology in Brazilian dry forests using remote sensing. Biotropica 49, 640–651. https://doi.org/10.1111/btp.12415 (2017).
Medeiros, R. et al. Remote sensing phenology of the Brazilian Caatinga and its environmental drivers. Remote Sens. 14, 2637. https://doi.org/10.3390/rs14112637 (2022).
Zou, L. et al. Assessing the temporal response of tropical dry forests to droughts using remote sensing. Remote Sens. 12, 2341. https://doi.org/10.3390/rs12142341 (2020).
Barbosa, H. A., Kumar, T. V. L., Paredes, F., Elliott, S. & Ayuga, J. G. Assessment of Caatinga response to drought using Meteosat-SEVIRI normalized difference vegetation index (2008–2016). ISPRS J. Photogramm Remote Sens. 148, 235–252. https://doi.org/10.1016/j.isprsjprs.2018.12.014 (2019).
Acknowledgments
The authors acknowledge the Universidade do Estado de Minas Gerais (UEMG) for covering the Article Processing Charge (APC) associated with the publication of this article.
Author information
Authors and Affiliations
Contributions
L.J.S., L.M.C., R.B., A.R.P., T.T.C.P., C.G.M., and N.L.S.J. contributed equally to the writing of the manuscript, preparation of the figures, and revision of the text. All authors reviewed and approved the final version of the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Silva, L.J., da Costa, L.M., de Oliveira Bordonal, R. et al. Predominantly positive XCO2 anomalies in the Caatinga biome highlight carbon vulnerability. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37629-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-37629-1