0000000000627666
AUTHOR
G. Camps-vails
Development of an earth observation processing chain for crop bio-physical parameters at local scale
This paper proposes a full Earth observation processing chaing for biophysical parameter estimation at local scales. In particular, we focus on the Leaf Area Index (LAI) as an essential climate variable required for the monitoring and modeling of land surfaces at local scale. The main goal of this study is tied to the use of optical satellite images to retrieve Earth Observation (EO) biophysical parameters able to describe the spatio-temporal changes in agro-ecosystems at local scale. The objective of this work is two-fold: (i) to set up and update the EO products processing chain at high resolution (local) scale; and (ii) derive multitemporal LAI maps at 30 m resolution to be fed into a cr…
Biophysical parameter retrieval with warped Gaussian processes
This paper focuses on biophysical parameter retrieval based on Gaussian Processes (GPs). Very often an arbitrary transformation is applied to the observed variable (e.g. chlorophyll content) to better pose the problem. This standard practice essentially tries to linearize/uniformize the distribution by applying non-linear link functions like the logarithmic, the exponential or the logistic functions. In this paper, we propose to use a GP model that automatically learns the optimal transformation directly from the data. The so-called warped GP regression (WGPR) presented in [1] models output observations as a parametric nonlinear transformation of a GP. The parameters of such prior model are…
A Support Vector Domain Description Approach to Supervised Classification of Remote Sensing Images
This paper addresses the problem of supervised classification of remote sensing images in the presence of incomplete (nonexhaustive) training sets. The problem is analyzed according to two different perspectives: 1) description and recognition of a specific land-cover class by using single-class classifiers and 2) solution of multiclass problems with single-class classification techniques. In this framework, we analyze different one-class classifiers and introduce in the remote sensing community the support vector domain description method (SVDD). The SVDD is a kernel-based method that exhibits intrinsic regularization ability and robustness versus low numbers of high-dimensional samples. T…
Biophysical parameter estimation with adaptive Gaussian Processes
We evaluate Gaussian Processes (GPs) for the estimation of biophysical parameters from acquired multispectral data. The standard GP formulation is used, and all hyperparameters (kernel parameters and noise variance) are optimized by maximizing the marginal likelihood. This gives rise to a fully-adaptive GP to data characteristics, both in terms of signal and noise properties. The good numerical results in the estimation of oceanic chlorophyll concentration and leaf membrane state confirm GPs as adequate, alternative non-parametric methods for biophysical parameter estimation. GPs are also analyzed by scrutinizing the predictive variance, the estimated noise variance, and the relevance of ea…
Operational cloud screening service for Sentinel-2 image time series
This paper deals with the development and implementation of a cloud screening algorithm for image time series, with the focus on the forthcoming Sentinel-2 satellites to be launched under the ESA Copernicus Programme. The proposed methodology is based on kernel ridge regression and exploits the temporal information to detect anomalous changes that correspond to cloud covers. The huge data volumes to be processed when dealing with high temporal, spatial, and spectral resolution datasets motivate the implementation of the algorithm within distributed computer resources. In consequence, an operational cloud screening service has been specifically designed and implemented in the frame of the Se…