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We quantified site representativeness using Voronoi volumes within an environmental feature space and implemented an iterative pruning strategy to identify a representative\u2010optimal subset (1363 sites). Using process\u2010based models as benchmarks, we developed a Representative\u2010optimal Model (RM) to simulate global R\n                    <jats:sub>S<\/jats:sub>\n                    dynamics (1982\u20132022). Compared to the Full\u2010set model (FM), the RM significantly enhanced spatial consistency with benchmarks and improved alignment with FLUXNET observations and atmospheric CO\n                    <jats:sub>2<\/jats:sub>\n                    fluctuations. Neglecting representativeness led to a 6.4\u2009PgC\u2009year\n                    <jats:sup>\u22121<\/jats:sup>\n                    overestimation of global R\n                    <jats:sub>S<\/jats:sub>\n                    (94.1 vs. 100.5\u2009PgC\u2009year\n                    <jats:sup>\u22121<\/jats:sup>\n                    ), while simultaneously underestimating its long\u2010term trend and interannual variability. Arid zones contributed &gt;\u200950% of this downward revision, reflecting corrected biases in water\u2010limited regions. These findings demonstrate that optimizing existing dataset representativeness can effectively mitigate systematic biases and refine global carbon budget accounting. While expanding observational networks remains a long\u2010term priority, we advocate routinely integrating spatial representativeness optimization into data\u2010driven workflows to immediately correct structural sampling biases.\n                  <\/jats:p>","DOI":"10.1111\/gcb.70956","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T10:45:58Z","timestamp":1780483558000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatially\u2010Uneven Observations Cause Overestimation of Global Soil Respiration"],"prefix":"10.1111","volume":"32","author":[{"given":"Li","family":"Cao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction Beijing Normal University  Beijing China"},{"name":"Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science Beijing Normal 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