Construction of a spatially gridded heat flux map based on airborne flux Measurements using remote sensing and machine learning methods

Yibo Sun*, Li Jia, Qiting Chen, Xingwen Lin, Bilige Sude, Zhanjun Quan, Ronald W.A. Hutjes

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number109424
JournalAgricultural and Forest Meteorology
Volume334
DOIs
Publication statusPublished - 1 May 2023

Keywords

  • Airborne flux measurements
  • Footprint analysis
  • Machine learning
  • Regional heat flux map
  • Upscale

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