247 citations · 631 across the 20 of their papers we have counts for
16 papers · 1 filter
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…
Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation
Anna Mateo-Sanchis, Maria Piles, Jordi Muñoz-Marí +3
Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation remote sensing data provides…
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…
Nonlinear Distribution Regression for Remote Sensing Applications
Jose E. Adsuara, Adrián Pérez-Suay, Jordi Muñoz-Marí +3
In many remote sensing applications one wants to estimate variables or parameters of interest from observations. When the target variable is available at a resolution that matches…
Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sources
S. Salcedo-Sanz, P. Ghamisi, M. Piles +7
This paper reviews the most important information fusion data-driven algorithms based on Machine Learning (ML) techniques for problems in Earth observation. Nowadays we observe and…
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…
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…
SMAP-based retrieval of vegetation opacity and albedo
Dara Entekhabi, Alexandra Konings, Maria Piles +1
Over land the vegetation canopy affects the microwave brightness temperature by emission, scattering and attenuation of surface soil emission. The questions addressed in this study…
Preliminary assessment of an integrated SMOS and MODIS application for global agricultural drought monitoring
N. Sánchez, A. González-Zamora, J. Martínez-Fernández +5
An application of the Soil Moisture Agricultural Drought Index (SMADI) at the global scale is presented. The index integrates surface soil moisture from the SMOS mission with land…
Comparison of downscaling techniques for high resolution soil moisture mapping
Sabah Sabaghy, Jeffrey Walker, Luigi Renzullo +19
Soil moisture impacts exchanges of water, energy and carbon fluxes between the land surface and the atmosphere. Passive microwave remote sensing at L-band can capture spatial and t…
Remote sensing of vegetation dynamics in agro-ecosystems using SMAP vegetation optical depth and optical vegetation indices
M. Piles, D. Chaparro, D. Entekhabi +3
The ESA's SMOS and the NASA's SMAP missions, launched in 2009 and 2015, respectively, are the first two missions having on-board L-band microwave sensors, which are very sensitive…
Explicit Granger causality in kernel Hilbert spaces
Diego Bueso, Maria Piles, Gustau Camps-Valls
Granger causality (GC) is undoubtedly the most widely used method to infer cause-effect relations from observational time series. Several nonlinear alternatives to GC have been pro…
Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis
J. Emmanuel Johnson, Valero Laparra, Maria Piles +1
Information theory is an excellent framework for analyzing Earth system data because it allows us to characterize uncertainty and redundancy, and is universally interpretable. Howe…
Learning drivers of climate-induced human migrations with Gaussian processes
Jose M. Tarraga, Maria Piles, Gustau Camps-Valls
In the current context of climate change, extreme heatwaves, droughts, and floods are not only impacting the biosphere and atmosphere but the anthroposphere too. Human populations…
Living in the Physics and Machine Learning Interplay for Earth Observation
Gustau Camps-Valls, Daniel H. Svendsen, Jordi Cortés-Andrés +7
Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem. Inferences mean understanding variables…
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…