Climate change is expected to increase the likelihood of drought events, with severe implications for food security.
We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather.
no code implementations • 11 Dec 2020 • Alvaro Moreno-Martinez, Gustau Camps-Valls, Jens Kattge, Nathaniel Robinson, Markus Reichstein, Peter van Bodegom, Koen Kramer, J. Hans C. Cornelissen, Peter Reich, Michael Bahn, Ulo Niinemets, Josep Peñuelas, Joseph Craine, Bruno E. L. Cerabolini, Vanessa Minden, Daniel C. Laughlin, Lawren Sack, Brady Allred, Christopher Baraloto, Chaeho Byun, Nadejda A. Soudzilovskaia, Steven W. Running
The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits.
Applications Applied Physics
Here, we define high-resolution Earth surface forecasting as video prediction of satellite imagery conditional on mesoscale weather forecasts.
Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet.
We demonstrate that for many purposes the generated landscapes behave as real with immediate application for global change studies.
no code implementations • 11 Dec 2018 • Martin Jung, Sujan Koirala, Ulrich Weber, Kazuhito Ichii, Fabian Gans, Gustau-Camps-Valls, Dario Papale, Christopher Schwalm, Gianluca Tramontana, Markus Reichstein
Here we use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate net radiation, latent and sensible heat and their uncertainties.
We present new methods for batch anomaly detection in multivariate time series.