Lázaro Alonso
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SeasFire cube - a multivariate dataset for global wildfire modeling Open
Frequent, large-scale wildfires threaten ecosystems and human livelihoods globally. To effectively quantify and attribute the antecedent conditions for wildfires, a thorough understanding of Earth system dynamics is imperative. In response…
View article: Downscaling soil moisture to sub-km resolutions with simple machine learning ensembles
Downscaling soil moisture to sub-km resolutions with simple machine learning ensembles Open
Funding Information: Funding for this study was provided by the European Research Council (ERC) Synergy Grant “Understanding and Modelling the Earth System with Machine Learning (USMILE)” under the Horizon 2020 research and innovation prog…
View article: SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System
SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System Open
The SeasFire Cube is a scientific datacube for seasonal fire forecasting around the globe. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it ca…
SpectralIndices.jl: Streamlining spectral indices access and computation for Earth system research Open
Remote sensing is an essential technology in environmental science to study Earth surface processes. In optical remote sensing, spectral indices (SI) are widely used to quantify the properties of specific surface characteristics. SI mathem…
View article: Analyzing Spatio-Temporal Machine Learning Models through Input Perturbation
Analyzing Spatio-Temporal Machine Learning Models through Input Perturbation Open
The biogeoscience community has increasingly embraced the application of machine learning models across various domains from fire prediction to vegetation forecasting. Yet, as these models become more widely used, there is sometimes a gap …
View article: Hybrid Modelling: Bridging Neural Networks and Physics-Based Approaches in Terrestrial Biogeochemical Ecosystems
Hybrid Modelling: Bridging Neural Networks and Physics-Based Approaches in Terrestrial Biogeochemical Ecosystems Open
The application of automatic differentiation and deep learning approaches to tackle current challenges is now a widespread practice. The biogeosciences community is no stranger to this trend; however, quite often, previously known physical…
View article: Downscaling Soil Moisture with Simple Machine Learning Ensembles
Downscaling Soil Moisture with Simple Machine Learning Ensembles Open
Soil moisture is a key factor that influences the productivity and energy balance of ecosystems and biomes. Global soil moisture measurements have coarse native resolutions of 36km and infrequent revisits of around three days. However, the…
View article: Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model
Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model Open
Fire is a crucial factor in terrestrial ecosystems playing a role in disturbance for vegetation dynamics. Process‐based fire models quantify fire disturbance effects in stand‐alone dynamic global vegetation models (DGVMs) and their advance…
View article: SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics
SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics Open
The global occurrence, scale, and frequency of wildfires pose significant threats to ecosystem services and human livelihoods. To effectively quantify and attribute the antecedent conditions for wildfires, a thorough understanding of Earth…
View article: SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System
SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System Open
The SeasFire Cube is a scientific datacube for seasonal fire forecasting around the globe. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it ca…
View article: Multi-modal learning for geospatial vegetation forecasting
Multi-modal learning for geospatial vegetation forecasting Open
The innovative application of precise geospatial vegetation forecasting holds immense potential across diverse sectors, including agriculture, forestry, humanitarian aid, and carbon accounting. To leverage the vast availability of satellit…
View article: Modeling landscape-scale vegetation response to climate: Synthesis of the EarthNet challenge
Modeling landscape-scale vegetation response to climate: Synthesis of the EarthNet challenge Open
The biosphere displays high heterogeneity at landscape-scale. Vegetation modelers struggle to represent this variability in process-based models because global observations of micrometeorology and plant traits are not available at such fin…
View article: Modeling vegetation response to climate in Africa at fine resolution: EarthNet2023, a deep learning dataset and challenge.
Modeling vegetation response to climate in Africa at fine resolution: EarthNet2023, a deep learning dataset and challenge. Open
Droughts are a major disaster in Africa, threatening livelihoods through their influence on crop yields but also by impacting and weakening ecosystems. Modeling the vegetation state can help anticipate and reduce the impact of droughts by …
View article: Earth System Deep Learning towards a Global Digital Twin of Wildfires
Earth System Deep Learning towards a Global Digital Twin of Wildfires Open
Due to climate change, we expect an exacerbation of fire in Europe and around the world, with major wildfire events extending to northern latitudes and boreal regions [1]. In this context, it is important to improve our capabilities to ant…
View article: Towards Robust Parameterizations in Ecosystem-level Photosynthesis Models
Towards Robust Parameterizations in Ecosystem-level Photosynthesis Models Open
Photosynthesis model parameters represent vegetation properties or sensitivities of photosynthesis processes. As one of the model uncertainty sources, parameters affect the accuracy and generalizability of the model. Ideally, parameters of…
Deep Learning for Global Wildfire Forecasting Open
Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects. In t…
View article: Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs
Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs Open
Forecasting the state of vegetation in response to climate and weather events is a major challenge. Its implementation will prove crucial in predicting crop yield, forest damage, or more generally the impact on ecosystems services relevant…
View article: SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System
SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System Open
The SeasFire Cube is a scientific datacube for seasonal fire forecasting around the globe. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it ca…
View article: SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System
SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System Open
The SeasFire Cube is a scientific datacube for seasonal fire forecasting around the globe. It has been created in the context of the SeasFire project, which deals with "Earth System Deep Learning for Seasonal Fire Forecasting" and is funde…
Probing ultracold gases using photoionization fine structure Open
Photoionization of atoms immersed in an environment such as an ultracold gas is investigated. We show that the interference of two ionization pathways, one passing directly to the continuum and one accounting for scattering processes betwe…
Eigenvalue Determination for Mixed Quantum States using Overlap Statistics Open
We consider the statistics of overlaps between a mixed state and its image under random unitary transformations. Choosing the transformations from the unitary group with its invariant (Haar) measure, the distribution of overlaps depends on…
Weighted random--geometric and random--rectangular graphs: Spectral and eigenfunction properties of the adjacency matrix Open
Within a random-matrix-theory approach, we use the nearest-neighbor energy level spacing distribution $P(s)$ and the entropic eigenfunction localization length $\ell$ to study spectral and eigenfunction properties (of adjacency matrices) o…
Joint probability distributions for projection probabilities of random orthonormal states Open
A finite dimensional quantum system for which the quantum chaos conjecture applies has eigenstates, which show the same statistical properties than the column vectors of random orthogonal or unitary matrices. Here, we consider the differen…