EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Epistemic and aleatoric uncertainty maps in high resolution biophysical parameter retrieval 

Laura Martínez-Ferrer1, Álvaro Moreno-Martínez1,7, Jordi Muñoz-Marí1, Emma Izquierdo-Verdiguier2, Manuel Campos-Taberner3, Javier García-Haro3, Marco Maneta4, Nathaniel Robinson5, Nicholas Clinton6, John Kimball7, Steven W. Running7, and Gustau Camps-Valls1
Laura Martínez-Ferrer et al.
  • 1Universitat de València, Image Processing Laboratory, Paterna, Spain (
  • 2Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU), Austria
  • 3UV-Environmental Remote Sensing Group (UV-ERS), Universitat de València, Spain
  • 4Department of Geosciences, University of Montana, USA
  • 5Panthera, New York, NY, USA
  • 6Google, Inc., Mountain View, CA, USA
  • 7Numerical Terradynamic Simulation Group (NTSG), University of Montana, Missoula, USA

Biophysical parameters such as the Leaf Area Index (LAI), Fraction Absorbed Photosynthetically Active Radiation (FAPAR) and Canopy Water Content (CWC) are key inputs for ecological, meteorological and agricultural applications and models. Moreover, LAI and FAPAR are considered Essential Climate Variables (ECVs) which are feasible for global climate observation. Within this context, there are two main issues to achieve a reliable biophysical variable retrieval: cloud contamination and oversimplified uncertainties from the operational products. We propose a methodology based on a hybrid method which inverts a radiative transfer model (PROSAIL) with artificial neural networks (ANN) to produce 30m resolution continuous time series of biophysical variables (FAPAR, LAI, FVC, CWC, CCC) over large areas. To obtain gap free input reflectance data, we used a cloud optimized fusion algorithm (HISTARFM) combining MODIS and Landsat information. In addition, HISTARFM provides realistic uncertainty estimates along with the fused reflectances. This valuable information allows us to carry out an exhaustive uncertainty analysis considering the aleatoric uncertainty (data error) that needs to be propagated through the ANN, and the epistemic uncertainty (model error). We validate our biophysical retrieval with operational MODIS and Copernicus products. This study is performed over the contiguous US (CONUS) area with Google Earth Engine (GEE). The proposed retrieval methodology combined with the unprecedented GEE computational power allows to obtain high spatial resolution biophysical products and realistic uncertainty estimates to capture the needed spatial detail and adequately monitor croplands and heterogeneous vegetated landscapes at very broad scales.

How to cite: Martínez-Ferrer, L., Moreno-Martínez, Á., Muñoz-Marí, J., Izquierdo-Verdiguier, E., Campos-Taberner, M., García-Haro, J., Maneta, M., Robinson, N., Clinton, N., Kimball, J., Running, S. W., and Camps-Valls, G.: Epistemic and aleatoric uncertainty maps in high resolution biophysical parameter retrieval , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15196,, 2021.