Search results for "Time Serie"
showing 10 items of 261 documents
Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring
2020
Image processing entered the era of artificial intelligence, and machine learning algorithms emerged as attractive alternatives for time series data processing. Satellite image time series processing enables crop phenology monitoring, such as the calculation of start and end of season. Among the promising algorithms, Gaussian process regression (GPR) proved to be a competitive time series gap-filling algorithm with the advantage of, as developed within a Bayesian framework, providing associated uncertainty estimates. Nevertheless, the processing of time series images becomes computationally inefficient in its standard per-pixel usage, mainly for GPR training rather than the fitting step. To…
Comparison of SMOS and SMAP soil moisture retrieval approaches using tower-based radiometer data over a vineyard field
2014
International audience; The objective of this study was to compare several approaches to soil moisture (SM) retrieval using l-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30°–60°). Based on a three year data set (2010–2012), several SM retrieval approaches developed for spaceborne miss…
A multisensor fusion approach to improve LAI time series
2011
International audience; High-quality and gap-free satellite time series are required for reliable terrestrial monitoring. Moderate resolution sensors provide continuous observations at global scale for monitoring spatial and temporal variations of land surface characteristics. However, the full potential of remote sensing systems is often hampered by poor quality or missing data caused by clouds, aerosols, snow cover, algorithms and instrumentation problems. A multisensor fusion approach is here proposed to improve the spatio-temporal continuity, consistency and accuracy of current satellite products. It is based on the use of neural networks, gap filling and temporal smoothing techniques. …
Effects of dating errors on nonparametric trend analyses of speleothem time series
2012
A fundamental problem in paleoclimatology is to take fully into account the various error sources when examining proxy records with quantitative methods of statistical time series analysis. Records from dated climate archives such as speleothems add extra uncertainty from the age determination to the other sources that consist in measurement and proxy errors. This paper examines three stalagmite time series of oxygen isotopic composition (δ18O) from two caves in western Germany, the series AH-1 from the Atta Cave and the series Bu1 and Bu4 from the Bunker Cave. These records carry regional information about past changes in winter precipitation and temperature. U/Th and radiocarbon dat…
Testing the hypothesis of post-volcanic missing rings in temperature sensitive dendrochronological data
2013
a b s t r a c t The precise, annual dating control, inherent to dendrochronology, has recently been questioned through a combined analysis of tree-growth and coupled climate models (Mann et al. (2012; hereafter MAN12)) suggesting single tree-rings in temperature limited environments are missing following large volcanic events. We test this hypothesis of missing, post-volcanic rings by using a compilation of maximum late- wood density (MXD) records that are typically used for reconstructing temperature and the detection of volcanic events, together with a unique set of long instrumental station data from Europe reaching back into the early 18th century. We investigate the temporal coherence …
Experimentally induced community assembly of polypores reveals the importance of both environmental filtering and assembly history
2019
The community assembly of wood-inhabiting fungi follows a successional pathway, with newly emerging resource patches being colonised by pioneer species, followed by those specialised on later stages of decay. The primary coloniser species have been suggested to strongly influence the assembly of the later-arriving community. We created an artificial resource pulse and studied the assembly of polypores over an 11yr period to ask how the identities of the colonising species depend on the environmental characteristics and the assembly history of the dead wood unit. Our results support the view that community assembly in fungi is a highly stochastic process, as even detailed description of the …
Linking extreme seasonality and gene expression in arctic marine protists
2021
ABSTRACTAt high latitudes, strong seasonal differences in light availability affect marine organisms and restrict the timing of ecosystem processes. Marine protists are key players in Arctic aquatic ecosystems, yet little is known about their ecological roles over yearly cycles. This is especially true for the dark polar night period, which up until recently was assumed to be devoid of biological activity. A 12 million transcripts catalogue was built from 0.45-10 μm protist assemblages sampled over 13 months in a time series station in an arctic fjord in Svalbard. Community gene expression was correlated with seasonality, with light as the main driving factor. Transcript diversity and evenn…
A validity and reliability study of Conditional Entropy Measures of Pulse Rate Variability
2019
In this work, we present the feasibility to use a simpler methodological approach for the assessment of the short-term complexity of Heart Rate Variability (HRV). Specifically, we propose to exploit Pulse Rate Variability (PRV) recorded through photoplethysmography in place of HRV measured from the ECG, and to compute complexity via a linear Gaussian approximation in place of the standard model-free methods (e.g., nearest neighbor entropy estimates) usually applied to HRV. Linear PRV-based and model-free HRV-based complexity measures were compared via statistical tests, correlation analysis and Bland-Altman plots, demonstrating an overall good agreement. These results support the applicabil…
Inferring causation from time series in earth system sciences
2019
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.
A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data
2021
Growing interest in understanding microbiota dynamics has motivated the development of different strategies to model microbiota time series data. However, all of them must tackle the fact that the available data are high-dimensional, posing strong statistical and computational challenges. In order to address this challenge, we propose a Dirichlet autoregressive model with time-varying parameters, which can be directly adapted to explain the effect of groups of taxa, thus reducing the number of parameters estimated by maximum likelihood. A strategy has been implemented which speeds up this estimation. The usefulness of the proposed model is illustrated by application to a case study.