CO2 emissions from peatlands exhibit substantial spatiotemporal variability, presenting challenges for identifying the underlying drivers and for accurately quantifying and modeling CO2 fluxes. Here, we integrated field measurements with Unmanned Aerial Vehicle (UAV)-based multi-sensor remote sensing to investigate soil respiration across a temperate peatland landscape. Our research addressed two key questions: (1) How do environmental factors control the spatiotemporal distribution of soil respiration across complex landscapes? (2) How do spatial and temporal peaks (i.e., hot spots and hot moments) of biogeochemical processes influence landscape-level CO2 fluxes? We find that dynamic variables (i.e., soil temperature and moisture) play significant roles in shaping CO2 flux variations, contributing 43 % to seasonal variability and 29 % to spatial variance, followed by semi-dynamic variables (i.e., Normalized Difference Vegetation Index (NDVI) and root biomass) (19 % and 24 %). Relatively static variables (i.e., soil organic carbon stock and carbon to nitrogen ratio) have a minimal influence on seasonal variation (2 %) but contribute more to spatial variance (10 %). Additionally, predicting time series of CO2 fluxes is feasible by using key environmental variables (test set: coefficient of determination (R2) = 0.74, Root Mean Square Error (RMSE) = 0.57 μmol m⁻² s⁻¹, Kling-Gupta Efficiency (KGE) = 0.77), while UAV remote sensing is an effective tool for mapping daily soil respiration (test set: R2 = 0.75, RMSE = 0.56 μmol m⁻² s⁻¹, KGE = 0.83). By the integration of in-situ high-resolution time-lapse monitoring and spatial mapping, we find that despite occurring in 10 % of the year, hot moments (i.e., periods of time which have a disproportional high CO2 fluxes compared to the surrounding) contribute 28 %–31 % of the annual CO₂ fluxes. Meanwhile, hot spots (i.e., locations which have a high CO2 fluxes)—representing 10 % of the area—account for about 20 % of CO₂ fluxes across the landscape. Our study demonstrates that integrating UAV-based remote sensing with field surveys improves the understanding of soil respiration mechanisms across timescales in complex landscapes. This will provide insights into carbon dynamics and supporting peatland conservation and climate change mitigation efforts.