Find in Library

Monitoring tropical and subtropical forest soil CO₂ emission efflux (<i>FSCO</i>₂) is crucial for understanding the global carbon cycle and terrestrial ecosystem respiration. In this study, we addressed the challenge of low spatiotemporal resolution in <i>FSCO</i>₂ monitoring by combining data fusion and model methods to improve the accuracy of quantitative inversion. We used time series Landsat 8 <i>LST</i> and <i>MODIS LST</i> fusion images and a linear mixed effect model to estimate <i>FSCO</i>₂ at watershed scale. Our results show that modeling without random factors, and the use of Fusion <i>LST</i> as the fixed predictor, resulted in 47% (marginal <i>R</i>² = 0.47) of <i>FSCO</i>₂ variability in the Monthly random effect model, while it only accounted for 19% of <i>FSCO</i>₂ variability in the Daily random effect model and 7% in the Seasonally random effect model. However, the inclusion of random effects in the model’s parameterization improved the performance of both models. The Monthly random effect model that performed optimally had an explanation rate of 55.3% (conditional <i>R</i>² = 0.55 and t value > 1.9) for <i>FSCO</i>₂ variability and yielded the smallest deviation from observed <i>FSCO</i>₂. Our study highlights the importance of incorporating random effects and using Fusion <i>LST</i> as a fixed predictor to improve the accuracy of <i>FSCO</i>₂ monitoring in tropical and subtropical forests.