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Article
Open AccessExploring interactions between socioeconomic context and natural hazards on human population displacement
Climate change is leading to more extreme weather hazards, forcing human populations to be displaced. We employ explainable machine learning techniques to model and understand internal displacement flows and p...
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Article
Open AccessImproving air quality assessment using physics-inspired deep graph learning
Existing methods for fine-scale air quality assessment have significant gaps in their reliability. Purely data-driven methods lack any physically-based mechanisms to simulate the interactive process of air pol...
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Article
Publisher Correction: Causal inference for time series
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Article
Causal inference for time series
Many research questions in Earth and environmental sciences are inherently causal, requiring robust analyses to establish whether and how changes in one variable cause changes in another. Causal inference prov...
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Article
Inference over radiative transfer models using variational and expectation maximization methods
Earth observation from satellites offers the possibility to monitor our planet with unprecedented accuracy. Radiative transfer models (RTMs) encode the energy transfer through the atmosphere, and are used to m...
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Article
Open AccessInferring causal relations from observational long-term carbon and water fluxes records
Land, atmosphere and climate interact constantly and at different spatial and temporal scales. In this paper we rely on causal discovery methods to infer spatial patterns of causal relations between several ke...
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Chapter and Conference Paper
The Kernelized Taylor Diagram
This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely used Taylor diagram, which is ...
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Article
Open AccessPredicting regional coastal sea level changes with machine learning
All ocean basins have been experiencing significant warming and rising sea levels in recent decades. There are, however, important regional differences, resulting from distinct processes at different timescale...
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Article
Open AccessEmergent vulnerability to climate-driven disturbances in European forests
Forest disturbance regimes are expected to intensify as Earth’s climate changes. Quantifying forest vulnerability to disturbances and understanding the underlying mechanisms is crucial to develop mitigation an...
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Article
Open AccessUnderstanding deep learning in land use classification based on Sentinel-2 time series
The use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to i...
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Article
Open AccessThe Low Dimensionality of Development
The World Bank routinely publishes over 1500 “World Development Indicators” to track the socioeconomic development at the country level. A range of indices has been proposed to interpret this information. For ...
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Chapter
Machine Learning Methods for Spatial and Temporal Parameter Estimation
Monitoring vegetation with satellite remote is of paramount relevance to understand the status and health of our planet. Accurate and constant monitoring of the biosphere has large societal, economical, and ...
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Article
Open AccessInferring causation from time series in Earth system sciences
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rar...
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Article
Open AccessThe FLUXCOM ensemble of global land-atmosphere energy fluxes
Although a key driver of Earth’s climate system, global land-atmosphere energy fluxes are poorly constrained. Here we use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers ...
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Article
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods
An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of...
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Article
Deep learning and process understanding for data-driven Earth system science
Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominat...
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Chapter
Advances in Kernel Machines for Image Classification and Biophysical Parameter Retrieval
Remote sensing data analysis is knowing an unprecedented upswing fostered by the activities of the public and private sectors of geospatial and environmental data analysis. Modern imaging sensors offer the nec...
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Chapter and Conference Paper
Automatic Emulation by Adaptive Relevance Vector Machines
This paper introduces an automatic methodology to construct emulators for costly radiative transfer models (RTMs). The proposed method is sequential and adaptive, and it is based on the notion of the acquisiti...
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Article
Compensatory water effects link yearly global land CO2 sink changes to temperature
A study of how temperature and water availability fluctuations affect the carbon balance of land ecosystems reveals different controls on local and global scales, implying that spatial climate covariation driv...
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Chapter and Conference Paper
Fair Kernel Learning
New social and economic activities massively exploit big data and machine learning algorithms to do inference on people’s lives. Applications include automatic curricula evaluation, wage determination, and ris...
