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20202025
most citedMachine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sources

247 citations · 609 across the 16 of their papers we have counts for

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Showing physics.ao-phShow all

5 papers · 1 filter

physics.ao-ph202047 cited

A global Canopy Water Content product from AVHRR/Metop

Francisco Javier García-Haro, Manuel Campos-Taberner, Álvaro Moreno +8

Spatially and temporally explicit canopy water content (CWC) data are important for monitoring vegetation status, and constitute essential information for studying ecosystem-climat…

physics.ao-ph20208 cited

Nonlinear Complex PCA for spatio-temporal analysis of global soil moisture

Diego Bueso, Maria Piles, Gustau Camps-Valls

Soil moisture (SM) is a key state variable of the hydrological cycle, needed to monitor the effects of a changing climate on natural resources. Soil moisture is highly variable in…

physics.ao-ph20201 cited

Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality

Miguel Morata-Dolz, Diego Bueso, Maria Piles +1

Global warming is leading to unprecedented changes in our planet, with great societal, economical and environmental implications, especially with the growing demand of biofuels and…

physics.ao-ph20201 cited

Estimation of vegetation loss coefficients and canopy penetration depths from SMAP radiometer and IceSAT lidar data

M. Baur, T. Jagdhuber, M. Link +3

In this study the framework of the - model is used to derive vegetation loss coefficients and canopy penetration depths from SMAP multi-temporal retrievals of vegetation opti…

physics.ao-ph202035 cited

Nonlinear PCA for Spatio-Temporal Analysis of Earth Observation Data

Diego Bueso, Maria Piles, Gustau Camps-Valls

Remote sensing observations, products and simulations are fundamental sources of information to monitor our planet and its climate variability. Uncovering the main modes of spatial…