Zobrazeno 1 - 10
of 918
pro vyhledávání: '"REICHSTEIN, MARKUS"'
Autor:
Camps-Valls, Gustau, Fernández-Torres, Miguel-Ángel, Cohrs, Kai-Hendrik, Höhl, Adrian, Castelletti, Andrea, Pacal, Aytac, Robin, Claire, Martinuzzi, Francesco, Papoutsis, Ioannis, Prapas, Ioannis, Pérez-Aracil, Jorge, Weigel, Katja, Gonzalez-Calabuig, Maria, Reichstein, Markus, Rabel, Martin, Giuliani, Matteo, Mahecha, Miguel, Popescu, Oana-Iuliana, Pellicer-Valero, Oscar J., Ouala, Said, Salcedo-Sanz, Sancho, Sippel, Sebastian, Kondylatos, Spyros, Happé, Tamara, Williams, Tristan
In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. However, the latter
Externí odkaz:
http://arxiv.org/abs/2406.20080
In the realm of geospatial analysis, the diversity of remote sensors, encompassing both optical and microwave technologies, offers a wealth of distinct observational capabilities. Recognizing this, we present msGFM, a multisensor geospatial foundatio
Externí odkaz:
http://arxiv.org/abs/2404.01260
Autor:
Cohrs, Kai-Hendrik, Varando, Gherardo, Carvalhais, Nuno, Reichstein, Markus, Camps-Valls, Gustau
Hybrid modeling integrates machine learning with scientific knowledge to enhance interpretability, generalization, and adherence to natural laws. Nevertheless, equifinality and regularization biases pose challenges in hybrid modeling to achieve these
Externí odkaz:
http://arxiv.org/abs/2402.13332
Autor:
Benson, Vitus, Robin, Claire, Requena-Mesa, Christian, Alonso, Lazaro, Carvalhais, Nuno, Cortés, José, Gao, Zhihan, Linscheid, Nora, Weynants, Mélanie, Reichstein, Markus
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 satellite imagery f
Externí odkaz:
http://arxiv.org/abs/2303.16198
Autor:
Robin, Claire, Requena-Mesa, Christian, Benson, Vitus, Alonso, Lazaro, Poehls, Jeran, Carvalhais, Nuno, Reichstein, Markus
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 for socio-
Externí odkaz:
http://arxiv.org/abs/2210.13648
Autor:
Gottfriedsen, Julia, Berrendorf, Max, Gentine, Pierre, Reichstein, Markus, Weigel, Katja, Hassler, Birgit, Eyring, Veronika
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 depend on various external factors, making drought detection in climat
Externí odkaz:
http://arxiv.org/abs/2111.15452
Autor:
Dechant, Benjamin, Kattge, Jens, Pavlick, Ryan, Schneider, Fabian D., Sabatini, Francesco M., Moreno-Martínez, Álvaro, Butler, Ethan E., van Bodegom, Peter M., Vallicrosa, Helena, Kattenborn, Teja, Boonman, Coline C.F., Madani, Nima, Wright, Ian J., Dong, Ning, Feilhauer, Hannes, Peñuelas, Josep, Sardans, Jordi, Aguirre-Gutiérrez, Jesús, Reich, Peter B., Leitão, Pedro J., Cavender-Bares, Jeannine, Myers-Smith, Isla H., Durán, Sandra M., Croft, Holly, Prentice, I. Colin, Huth, Andreas, Rebel, Karin, Zaehle, Sönke, Šímová, Irena, Díaz, Sandra, Reichstein, Markus, Schiller, Christopher, Bruelheide, Helge, Mahecha, Miguel, Wirth, Christian, Malhi, Yadvinder, Townsend, Philip A.
Publikováno v:
In Remote Sensing of Environment 1 September 2024 311
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 future weather. EarthNet2021 is a large dataset suitable for training deep
Externí odkaz:
http://arxiv.org/abs/2104.10066
Autor:
Bogdanovich, Ekaterina, Brenning, Alexander, Reichstein, Markus, De Polt, Kelley, Guenther, Lars, Frank, Dorothea, Orth, René
Publikováno v:
In International Journal of Disaster Risk Reduction January 2024 100
Autor:
Moreno-Martinez, Alvaro, Camps-Valls, Gustau, Kattge, Jens, Robinson, Nathaniel, Reichstein, Markus, van Bodegom, Peter, Kramer, Koen, Cornelissen, J. Hans C., Reich, Peter, Bahn, Michael, Niinemets, Ulo, Peñuelas, Josep, Craine, Joseph, Cerabolini, Bruno E. L., Minden, Vanessa, Laughlin, Daniel C., Sack, Lawren, Allred, Brady, Baraloto, Christopher, Byun, Chaeho, Soudzilovskaia, Nadejda A., Running, Steven W.
Publikováno v:
Remote Sensing of Environment, Volume 218, 1 December 2018, Pages 69-88
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 area, leaf dry matter content, leaf nitrogen and phosphorus content per
Externí odkaz:
http://arxiv.org/abs/2012.06417