Exploring the potential of LANDSAT-8 for estimation of forest soil CO2 efflux

Abstract Monitoring forest soil carbon dioxide efflux (FCO 2 ) is important as it contributes significantly to terrestrial ecosystem respiration and is hence a major factor in global carbon cycle. FCO 2 monitoring is usually conducted by the use of soil chambers to sample various point positions, but this method is difficult to replicate at spatially large research sites. Satellite remote sensing is accustomed to monitoring environmental phenomenon at large spatial scale, however its utilisation in FCO 2 monitoring is under-explored. To this end, this study explored the potential of LANDSAT-8 to estimate FCO 2 with the specific aims of deriving land surface temperature (LST) from LANDSAT-8 and then develop FCO 2 model on the basis of LANDSAT-8 LST to account for seasonal and inter-annual variations of FCO 2 . The study was conducted over an old European beech forest ( Fagus sylvatica ) in Czech Republic. In the end, two kinds of linear mixed effect models were built; Model-1 (inter-annual variations of FCO 2 ) and Model-2 (seasonal variations of FCO 2 ). The difference between Model-1 and Model-2 lies in their random factors; while Model-1 has ‘year’ of FCO 2 measurement as a random factor, Model-2 has ‘season’ of FCO 2 measurement as a random factor. When modelling without random factors, LANDSAT-8 LST as the fixed predictor in both models was able to account for 26% (marginal R 2  = 0.26) of FCO 2 variability in Model-1 whereas it accounted for 29% in Model-2. However, the parameterisation of random effects improved the performance of both models. Model-1 was the best in that it explained 65% (conditional R 2  = 0.65) of variability in FCO 2 and produced the least deviation from observed FCO 2 (RMSE = 0.38 μmol/m 2 /s). This study adds to the limited number of previous similar studies with the aim of encouraging satellite remote sensing integration in FCO 2 observation.