8,432 research outputs found
A perspective on gaussian processes for earth observation
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 Gaussian processes (GPs) in particular has
attained outstanding results in the estimation of bio-geo-physical variables
from the acquired images at local and global scales in a time-resolved manner.
GPs provide not only accurate estimates but also principled uncertainty
estimates for the predictions, can easily accommodate multimodal data coming
from different sensors and from multitemporal acquisitions, allow the
introduction of physical knowledge, and a formal treatment of uncertainty
quantification and error propagation. Despite great advances in forward and
inverse modelling, GP models still have to face important challenges that are
revised in this perspective paper. GP models should evolve towards data-driven
physics-aware models that respect signal characteristics, be consistent with
elementary laws of physics, and move from pure regression to observational
causal inference.Comment: 1 figur
Statistical Physics in Meteorology
Various aspects of modern statistical physics and meteorology can be tied
together. The historical importance of the University of Wroclaw in the field
of meteorology is first pointed out. Next, some basic difference about time and
space scales between meteorology and climatology is outlined. The nature and
role of clouds both from a geometric and thermal point of view are recalled.
Recent studies of scaling laws for atmospheric variables are mentioned, like
studies on cirrus ice content, brightness temperature, liquid water path
fluctuations, cloud base height fluctuations, .... Technical time series
analysis approaches based on modern statistical physics considerations are
outlined.Comment: Short version of an invited paper at the XXIth Max Born
symposium,Ladek Zdroj, Poland; Sept. 200
Fusion of hyperspectral and ground penetrating radar to estimate soil moisture
In this contribution, we investigate the potential of hyperspectral data
combined with either simulated ground penetrating radar (GPR) or simulated
(sensor-like) soil-moisture data to estimate soil moisture. We propose two
simulation approaches to extend a given multi-sensor dataset which contains
sparse GPR data. In the first approach, simulated GPR data is generated either
by an interpolation along the time axis or by a machine learning model. The
second approach includes the simulation of soil-moisture along the GPR profile.
The soil-moisture estimation is improved significantly by the fusion of
hyperspectral and GPR data. In contrast, the combination of simulated,
sensor-like soil-moisture values and hyperspectral data achieves the worst
regression performance. In conclusion, the estimation of soil moisture with
hyperspectral and GPR data engages further investigations.Comment: This work has been accepted to the IEEE WHISPERS 2018 conference. (C)
2018 IEE
Deep Gaussian processes for biogeophysical parameter retrieval and model inversion
Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently,
different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical
inversion with in situ data that often results in problems with extrapolation outside the study area; and the most
widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine
learning algorithms, are applied to invert RTM simulations. We will focus on the latter. Among the different
existing algorithms, in the last decade kernel based methods, and Gaussian Processes (GPs) in particular, have
provided useful and informative solutions to such RTM inversion problems. This is in large part due to the
confidence intervals they provide, and their predictive accuracy. However, RTMs are very complex, highly
nonlinear, and typically hierarchical models, so that very often a single (shallow) GP model cannot capture
complex feature relations for inversion. This motivates the use of deeper hierarchical architectures, while still
preserving the desirable properties of GPs. This paper introduces the use of deep Gaussian Processes (DGPs) for
bio-geo-physical model inversion. Unlike shallow GP models, DGPs account for complicated (modular, hierarchical) processes, provide an efficient solution that scales well to big datasets, and improve prediction accuracy over their single layer counterpart. In the experimental section, we provide empirical evidence of performance for the estimation of surface temperature and dew point temperature from infrared sounding data, as well
as for the prediction of chlorophyll content, inorganic suspended matter, and coloured dissolved matter from
multispectral data acquired by the Sentinel-3 OLCI sensor. The presented methodology allows for more expressive forms of GPs in big remote sensing model inversion problems.European Research Council (ERC)
647423Spanish Ministry of Economy and Competitiveness
TIN2015-64210-R
DPI2016-77869-C2-2-RSpanish Excellence Network
TEC2016-81900-REDTLa Caixa Banking Foundation (Barcelona, Spain)
100010434
LCF-BQ-ES17-1160001
SciTech News Volume 71, No. 1 (2017)
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Scale-aware neural calibration for wide swath altimetry observations
Sea surface height (SSH) is a key geophysical parameter for monitoring and
studying meso-scale surface ocean dynamics. For several decades, the mapping of
SSH products at regional and global scales has relied on nadir satellite
altimeters, which provide one-dimensional-only along-track satellite
observations of the SSH. The Surface Water and Ocean Topography (SWOT) mission
deploys a new sensor that acquires for the first time wide-swath
two-dimensional observations of the SSH. This provides new means to observe the
ocean at previously unresolved spatial scales. A critical challenge for the
exploiting of SWOT data is the separation of the SSH from other signals present
in the observations. In this paper, we propose a novel learning-based approach
for this SWOT calibration problem. It benefits from calibrated nadir altimetry
products and a scale-space decomposition adapted to SWOT swath geometry and the
structure of the different processes in play. In a supervised setting, our
method reaches the state-of-the-art residual error of ~1.4cm while proposing a
correction on the entire spectral from 10km to 1000kComment: 8 pages, 7 figures, Preprin
Comparing surface-soil moisture from the SMOS mission and the ORCHIDEE land-surface model over the Iberian Peninsula
The aim of this study is to compare the surface soil moisture (SSM) retrieved from ESA's Soil Moisture and Ocean Salinity mission (SMOS) with the output of the ORCHIDEE (ORganising Carbon and Hydrology In Dynamic EcosystEm) land surface model forced with two distinct atmospheric data sets for the period 2010 to 2012. The comparison methodology is first established over the REMEDHUS (Red de Estaciones de MEDiciĂłn de la Humedad def Suelo) soil moisture measurement network, a 30 by 40. km catchment located in the central part of the Duero basin, then extended to the whole Iberian Peninsula (IP). The temporal correlation between the in-situ, remotely sensed and modelled SSM are satisfactory (r. >. 0.8). The correlation between remotely sensed and modelled SSM also holds when computed over the IP. Still, by using spectral analysis techniques, important disagreements in the effective inertia of the corresponding moisture reservoir are found. This is reflected in the spatial correlation over the IP between SMOS and ORCHIDEE SSM estimates, which is poor (Âż. ~. 0.3). A single value decomposition (SVD) analysis of rainfall and SSM shows that the co-varying patterns of these variables are in reasonable agreement between both products. Moreover the first three SVD soil moisture patterns explain over 80% of the SSM variance simulated by the model while the explained fraction is only 52% of the remotely sensed values. These results suggest that the rainfall-driven soil moisture variability may not account for the poor spatial correlation between SMOS and ORCHIDEE products.Peer ReviewedPostprint (published version
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