TY - JOUR
T1 - Exploring the potential of LANDSAT-8 for estimation of forest soil CO2 efflux
AU - Crabbe, Richard Azu
AU - Janous, Dalibour
AU - Darenova, Eva
AU - Pavelka, Marian
PY - 2019/5
Y1 - 2019/5
N2 - Monitoring forest soil carbon dioxide efflux (FCO2)
is important as it contributes significantly to terrestrial ecosystem
respiration and is hence a major factor in global carbon cycle. FCO2
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 FCO2 monitoring is under-explored. To this end, this study explored the potential of LANDSAT-8 to estimate FCO2 with the specific aims of deriving land surface temperature (LST) from LANDSAT-8 and then develop FCO2 model on the basis of LANDSAT-8 LST to account for seasonal and inter-annual variations of FCO2. 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 FCO2) and Model-2 (seasonal variations of FCO2). The difference between Model-1 and Model-2 lies in their random factors; while Model-1 has ‘year’ of FCO2 measurement as a random factor, Model-2 has ‘season’ of FCO2
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 R2 = 0.26) of FCO2 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 R2 = 0.65) of variability in FCO2 and produced the least deviation from observed FCO2 (RMSE = 0.38 μmol/m2/s).
This study adds to the limited number of previous similar studies with
the aim of encouraging satellite remote sensing integration in FCO2 observation.
AB - Monitoring forest soil carbon dioxide efflux (FCO2)
is important as it contributes significantly to terrestrial ecosystem
respiration and is hence a major factor in global carbon cycle. FCO2
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 FCO2 monitoring is under-explored. To this end, this study explored the potential of LANDSAT-8 to estimate FCO2 with the specific aims of deriving land surface temperature (LST) from LANDSAT-8 and then develop FCO2 model on the basis of LANDSAT-8 LST to account for seasonal and inter-annual variations of FCO2. 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 FCO2) and Model-2 (seasonal variations of FCO2). The difference between Model-1 and Model-2 lies in their random factors; while Model-1 has ‘year’ of FCO2 measurement as a random factor, Model-2 has ‘season’ of FCO2
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 R2 = 0.26) of FCO2 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 R2 = 0.65) of variability in FCO2 and produced the least deviation from observed FCO2 (RMSE = 0.38 μmol/m2/s).
This study adds to the limited number of previous similar studies with
the aim of encouraging satellite remote sensing integration in FCO2 observation.
KW - Soil respiration
KW - Land surface temperature
KW - Fagus sylvatica
KW - Satellite remote sensing
KW - Linear mixed effect modelling
U2 - 10.1016/j.jag.2018.12.007
DO - 10.1016/j.jag.2018.12.007
M3 - Article
SN - 0303-2434
VL - 77
SP - 42
EP - 52
JO - International Journal of Applied Earth Observation and Geoinformation
JF - International Journal of Applied Earth Observation and Geoinformation
ER -