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Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning

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Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning

dc.contributor.author	Martínez Ferrer, Laura
dc.contributor.author	Moreno Martínez, Álvaro
dc.contributor.author	Campos Taberner, Manuel
dc.contributor.author	García Haro, Francisco Javier
dc.contributor.author	Muñoz Marí, Jordi
dc.contributor.author	Running, Steven W.
dc.contributor.author	Kimball, John
dc.contributor.author	Clinton, Nicholas
dc.contributor.author	Camps Valls, Gustau
dc.date.accessioned	2023-06-15T07:45:15Z
dc.date.available	2023-06-16T04:45:06Z
dc.date.issued	2022	es_ES
dc.identifier.citation	Martínez-Ferrer, L., Moreno-Martínez, Á., Campos-Taberner, M., García-Haro, F. J., Muñoz-Marí, J., Running, S. W., ... & Camps-Valls, G. (2022). Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning. Remote Sensing of Environment, 280, 113199.	es_ES
dc.identifier.uri	https://hdl.handle.net/10550/87910
dc.description.abstract	The estimation of biophysical variables is at the core of remote sensing science, allowing a close monitoring of crops and forests. Deriving temporally resolved and spatially explicit maps of parameters of interest has been the subject of intense research. However, deriving products from optical sensors is typically hampered by cloud contamination and the trade-off between spatial and temporal resolutions. In this work we rely on the HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to generate long gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectances. An artificial neural network is trained on PROSAIL inversion to predict monthly biophysical variables at 30 m spatial resolution with associated, realistic uncertainty bars. We emphasize the need for a more thorough analysis of uncertainty, and propose a general and scalable approach to combine both epistemic and aleatoric uncertainties by exploiting Monte Carlo (MC) dropout techniques from the trained artificial network and the propagation of HISTARFM uncertainties through the model, respectively. A model recalibration was performed in order to provide reliable uncertainties. We provide new high resolution products of several key variables to quantify the terrestrial biosphere: Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Canopy Water Content (CWC) and Fractional Vegetation Cover (FVC) are at 30 m Landsat spatial resolution and over large continental areas. Two study areas are considered: the large heterogeneous but moderately cloud covered contiguous United States, and the homogeneous but largely cloud covered Amazonia. The produced vegetation products largely agree with the test dataset (R = 0.90, RMSE = 0.80 m2/m2 and ME = 0.12 m2/m2 for LAI, and R = 0.98, RMSE = 0.07 and ME = 0.01 for FAPAR) providing low error and high accuracy. Additionally, the validation considers a thorough comparison with operational and largely validated medium resolution products, such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Copernicus Global Land Service. Our products presented a good agreement and consistency with both MODIS (R = 0.84 and R = 0.85 for LAI and FAPAR, respectively) and Copernicus (R = 0.92 and R = 0.91 for LAI and FAPAR, respectively). To foster a wider adoption and reproducibility of the methodology we provide an application in GEE and source code at:https://github.com/IPL-UV/ee_BioNet/	es_ES
dc.language.iso	en	es_ES
dc.publisher	Elsevier	es_ES
dc.subject	MODIS	es_ES
dc.subject	landsat	es_ES
dc.subject	downscaling	es_ES
dc.subject	biophysical parameter estimation	es_ES
dc.subject	uncertainty	es_ES
dc.subject	neural networks	es_ES
dc.title	Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning	es_ES
dc.type	journal article	es_ES
dc.subject.unesco	UNESCO::CIENCIAS TECNOLÓGICAS	es_ES
dc.identifier.doi	10.1016/j.rse.2022.113199	es_ES
dc.accrualmethod	CI	es_ES
dc.embargo.terms	0 days	es_ES
dc.type.hasVersion	VoR	es_ES
dc.rights.accessRights	open access	es_ES