activity
20122024
most citedA Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data

240 citations · 387 across the 15 of their papers we have counts for

collaborators

15 papers

cs.AI2024★ 3 cited

AI for Extreme Event Modeling and Understanding: Methodologies and Challenges

Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs +22

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter…

cs.CV2024★ 1 cited

Bridging Remote Sensors with Multisensor Geospatial Foundation Models

Boran Han, Shuai Zhang, Xingjian Shi +1

In the realm of geospatial analysis, the diversity of remote sensors, encompassing both optical and microwave technologies, offers a wealth of distinct observational capabilities.…

cs.LG2024

Causal hybrid modeling with double machine learning

Kai-Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais +2

Hybrid modeling integrates machine learning with scientific knowledge to enhance interpretability, generalization, and adherence to natural laws. Nevertheless, equifinality and reg…

cs.CV2023★ 22 cited

Multi-modal learning for geospatial vegetation forecasting

Vitus Benson, Claire Robin, Christian Requena-Mesa +7

The innovative application of precise geospatial vegetation forecasting holds immense potential across diverse sectors, including agriculture, forestry, humanitarian aid, and carbo…

cs.LG2022★ 17 cited

Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs

Claire Robin, Christian Requena-Mesa, Vitus Benson +4

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, o…

cs.LG2021

On the Generalization of Agricultural Drought Classification from Climate Data

Julia Gottfriedsen, Max Berrendorf, Pierre Gentine +4

Climate change is expected to increase the likelihood of drought events, with severe implications for food security. Unlike other natural disasters, droughts have a slow onset and…

cs.LG2021★ 2 cited

EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task

Christian Requena-Mesa, Vitus Benson, Markus Reichstein +2

Satellite images are snapshots of the Earth surface. We propose to forecast them. We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on futu…

stat.AP2020★ 240 cited

A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data

Alvaro Moreno-Martinez, Gustau Camps-Valls, Jens Kattge +19

This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf ar…

cs.LG2020

EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts

Christian Requena-Mesa, Vitus Benson, Joachim Denzler +2

Climate change is global, yet its concrete impacts can strongly vary between different locations in the same region. Seasonal weather forecasts currently operate at the mesoscale (…

cs.LG2020★ 55 cited

A Perspective on Gaussian Processes for Earth Observation

Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge +1

Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and…

cs.CV2019

Predicting Landscapes from Environmental Conditions Using Generative Networks

Christian Requena-Mesa, Markus Reichstein, Miguel Mahecha +2

Landscapes are meaningful ecological units that strongly depend on the environmental conditions. Such dependencies between landscapes and the environment have been noted since the…

physics.ao-ph2018★ 2 cited

The FLUXCOM ensemble of global land-atmosphere energy fluxes

Martin Jung, Sujan Koirala, Ulrich Weber +7

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 FL…

stat.ML2016★ 1 cited

Maximally Divergent Intervals for Anomaly Detection

Erik Rodner, Björn Barz, Yanira Guanche +5

We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution wi…

physics.geo-ph2016

Have precipitation extremes and annual totals been increasing in the world's dry regions over the last 60 years?

Sebastian Sippel, Jakob Zscheischler, Martin Heimann +5

Daily rainfall extremes and annual totals have increased in large parts of the global land area over the last decades. These observations are consistent with theoretical considerat…

cs.CE2012★ 44 cited

Gap Filling in the Plant Kingdom---Trait Prediction Using Hierarchical Probabilistic Matrix Factorization

Hanhuai Shan, Jens Kattge, Peter Reich +3

Plant traits are a key to understanding and predicting the adaptation of ecosystems to environmental changes, which motivates the TRY project aiming at constructing a global databa…