TY - JOUR
T1 - Construction of a spatially gridded heat flux map based on airborne flux Measurements using remote sensing and machine learning methods
AU - Sun, Yibo
AU - Jia, Li
AU - Chen, Qiting
AU - Lin, Xingwen
AU - Sude, Bilige
AU - Quan, Zhanjun
AU - Hutjes, Ronald W.A.
PY - 2023/5/1
Y1 - 2023/5/1
N2 - Acquiring the relative truth values of land surface sensible (H) and latent (LE) heat fluxes at the regional scale is important for monitoring and simulating evapotranspiration at a global scale using satellite data or process-based models. However, it is difficult to directly obtain these values with fine spatial representation at regional scales using conventional ground-based measurement methods. Therefore, airborne measurements of turbulent flux are highly advantageous. By leveraging airborne measurements of H and LE fluxes collected in the Netherlands in August 2008, in this study we evaluated five machine learning methods for the construction of a regional gridded heat flux map, including artificial neural networks, boosted regression trees, random forest regression, deep neural networks, and support vector regression. The models were trained and tested using a dataset compiled from observations of H and LE, the digital elevation model, MODIS land surface temperature, enhanced vegetation index, and albedo data for each flux footprint. The best performing model was used to construct a regional gridded heat flux map over a case region located in the center of the Netherlands. The results shown that the support vector regression performed better than other models, with R2=0.91,RMSE=9.6W/m2 for H, and R2=0.89,RMSE=26.32W/m2 for LE. The constructed heat flux maps achieved a relative prediction error of ≤32.8% (R2>0.71) for H and ≤43.5% (R2>0.53) for LE compared to aircraft measurements. The constructed heat flux maps were then aggregated for each land cover type, providing individual estimates of source strength (52< H< 66 W/m2 and 108< LE< 218 W/m2) and spatial variability (52< σH< 66 W/m2 and 108< σLE< 218 W/m2) with an ensemble precision of < 6% (1σ). Finally, the limitations and future prospects of the current study were summarized and discussed.
AB - Acquiring the relative truth values of land surface sensible (H) and latent (LE) heat fluxes at the regional scale is important for monitoring and simulating evapotranspiration at a global scale using satellite data or process-based models. However, it is difficult to directly obtain these values with fine spatial representation at regional scales using conventional ground-based measurement methods. Therefore, airborne measurements of turbulent flux are highly advantageous. By leveraging airborne measurements of H and LE fluxes collected in the Netherlands in August 2008, in this study we evaluated five machine learning methods for the construction of a regional gridded heat flux map, including artificial neural networks, boosted regression trees, random forest regression, deep neural networks, and support vector regression. The models were trained and tested using a dataset compiled from observations of H and LE, the digital elevation model, MODIS land surface temperature, enhanced vegetation index, and albedo data for each flux footprint. The best performing model was used to construct a regional gridded heat flux map over a case region located in the center of the Netherlands. The results shown that the support vector regression performed better than other models, with R2=0.91,RMSE=9.6W/m2 for H, and R2=0.89,RMSE=26.32W/m2 for LE. The constructed heat flux maps achieved a relative prediction error of ≤32.8% (R2>0.71) for H and ≤43.5% (R2>0.53) for LE compared to aircraft measurements. The constructed heat flux maps were then aggregated for each land cover type, providing individual estimates of source strength (52< H< 66 W/m2 and 108< LE< 218 W/m2) and spatial variability (52< σH< 66 W/m2 and 108< σLE< 218 W/m2) with an ensemble precision of < 6% (1σ). Finally, the limitations and future prospects of the current study were summarized and discussed.
KW - Airborne flux measurements
KW - Footprint analysis
KW - Machine learning
KW - Regional heat flux map
KW - Upscale
U2 - 10.1016/j.agrformet.2023.109424
DO - 10.1016/j.agrformet.2023.109424
M3 - Article
AN - SCOPUS:85151086367
SN - 0168-1923
VL - 334
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
M1 - 109424
ER -