Search results for "oceanic"
showing 10 items of 642 documents
A Deep Network Approach to Multitemporal Cloud Detection
2018
We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation (MSG) satellite. The model provides pixel-level cloud maps with related confidence and propagates information in time via a recurrent neural network structure. With a single model, we are able to outline clouds along all year and during day and night with high accuracy.
Cloud detection machine learning algorithms for PROBA-V
2020
This paper presents the development and implementation of a cloud detection algorithm for Proba-V. Accurate and automatic detection of clouds in satellite scenes is a key issue for a wide range of remote sensing applications. With no accurate cloud masking, undetected clouds are one of the most significant sources of error in both sea and land cover biophysical parameter retrieval. The objective of the algorithms presented in this paper is to detect clouds accurately providing a cloud flag per pixel. For this purpose, the method exploits the information of Proba-V using statistical machine learning techniques to identify the clouds present in Proba-V products. The effectiveness of the propo…
Emulation as an Accurate Alternative to Interpolation in Sampling Radiative Transfer Codes
2018
Computationally expensive radiative transfer models (RTMs) are widely used to realistically reproduce the light interaction with the earth surface and atmosphere. Because these models take long processing time, the common practice is to first generate a sparse look-up table (LUT) and then make use of interpolation methods to sample the multidimensional LUT input variable space. However, the question arise whether common interpolation methodsperform most accurate. As an alternative to interpolation, this paper proposes to use emulation, i.e., approximating the RTM output by means of the statistical learning. Two experiments were conducted to assess the accuracy in delivering spectral outputs…
Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval
2018
The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP). Retrieving accurate atmospheric parameters from the raw data provided by IASI is a large challenge, but necessary in order to use the data in NWP models. Statistical models performance is compromised because of the extremely high spectral dimensionality and the high number of variables to be predicted simultaneously across the atmospheric column. All this poses a challenge for selecting and studying optimal models and processing schemes. Earlier work has shown non-linear models such as kernel methods and neural networks perform w…
Living in the Physics and Machine Learning Interplay for Earth Observation
2020
Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem. Inferences mean understanding variables relations, deriving models that are physically interpretable, that are simple parsimonious, and mathematically tractable. Machine learning models alone are excellent approximators, but very often do not respect the most elementary laws of physics, like mass or energy conservation, so consistency and confidence are compromised. In this paper, we describe the main challenges ahead in the field, and introduce several ways to live in the Physics and machine learning interplay: to encode differential equations from da…
Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality
2020
Global warming is leading to unprecedented changes in our planet, with great societal, economical and environmental implications, especially with the growing demand of biofuels and food. Assessing the impact of climate on vegetation is of pressing need. We approached the attribution problem with a novel nonlinear Granger causal (GC) methodology and used a large data archive of remote sensing satellite products, environmental and climatic variables spatio-temporally gridded over more than 30 years. We generalize kernel Granger causality by considering the variables cross-relations explicitly in Hilbert spaces, and use the covariance in Gaussian processes. The method generalizes the linear an…
The FLUXCOM ensemble of global land-atmosphere energy fluxes
2019
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 with remote sensing and meteorological data to estimate global gridded net radiation, latent and sensible heat and their uncertainties. The resulting FLUXCOM database comprises 147 products in two setups: (1) 0.0833° resolution using MODIS remote sensing data (RS) and (2) 0.5° resolution using remote sensing and meteorological data (RS + METEO). Within each setup we use a full factorial design across machine learning methods, forcing datasets and energy balance closure corrections. For…
Persistence of temperature and precipitation: from local to global anomalies
2021
Using detrended fluctuation analysis (DFA) we find that all continents are persistent in temperature. The scaling exponents of the southern hemisphere (SH) continents, i.e., South America (0.77) and Oceania (0.72) are somewhat higher than scaling exponents of Europe (0.70), Asia (0.69) and North America (0.64), but the scaling of Africa is by far the highest (0.86). The reason for this is the location of Africa near the equator. The scaling exponents of the precipitation are much smaller, i.e. between 0.55 (Europe) and 0.68 (North America). The scaling exponent of Europe is near that of the random noise (0.5), while the other continents are slightly persistent in precipitation. We also show…
Plume — Lid interactions during the Archean and implications for the generation of early continental terranes
2020
Abstract Many Archean terranes are interpreted to have a tectonic and metamorphic evolution that indicates intra-crustal reorganization driven by lithospheric-scale gravitational instabilities. These processes are associated with the production of a significant amount of felsic and mafic crust, and are widely regarded to be a consequence of plume-lithosphere interactions. The juvenile Archean felsic crust is made predominantly of rocks of the tonalite–trondhjemite–granodiorite (TTG) suite, which are the result of partial melting of hydrous metabasalts. The geodynamic processes that have assisted the production of juvenile felsic crust, are still not well understood. Here, we perform 2D and …
PLA based biocomposites reinforced with Posidonia oceanica leaves
2018
Abstract Biocomposites can valorize some natural products related to natural processes or crop and waste industries. In this work, leaves of Posidonia oceanica (PO), dominant sea grass in the Mediterranean Sea, were used to prepare PLA based biocomposites. Materials were prepared by melt mixing PLA with PO as filler. The morphology as well as the mechanical and thermal properties of PLA/PO composites were evaluated and related with the influence of the content and size of PO. Moreover, the experimental elastic moduli of the biocomposites was modelled employing the Halpin–Tsai (HT) model. Furthermore, in order to take into consideration the high porosity of PO leaves, the HT parameters relat…