Journal ArticleDOI
Estimating Soil Moisture With the Support Vector Regression Technique
Reads0
Chats0
TLDR
An experimental analysis of the application of the ε-insensitive support vector regression (SVR) technique to soil moisture content estimation from remotely sensed data at field/basin scale provides useful indications for building soil moisture estimation processors for upcoming satellites or near-real-time applications.Abstract:
This letter presents an experimental analysis of the application of the e-insensitive support vector regression (SVR) technique to soil moisture content estimation from remotely sensed data at field/basin scale. SVR has attractive properties, such as ease of use, good intrinsic generalization capability, and robustness to noise in the training data, which make it a valid candidate as an alternative to more traditional neural-network-based techniques usually adopted in soil moisture content estimation. Its effectiveness in this application is assessed by using field measurements and considering various combinations of the input features (i.e., different active and/or passive microwave measurements acquired using various sensor frequencies, polarizations, and acquisition geometries). The performance of the SVR method (in terms of estimation accuracy, generalization capability, computational complexity, and ease of use) is compared with that achieved using a multilayer perceptron neural network, which is considered as a benchmark in the addressed application. This analysis provides useful indications for building soil moisture estimation processors for upcoming satellites or near-real-time applications.read more
Citations
More filters
Journal ArticleDOI
Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
TL;DR: The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities from remote sensing data using machine learning methods.
Journal ArticleDOI
Data-Driven Decision Making in Precision Agriculture: The Rise of Big Data in Agricultural Systems
TL;DR: The research dedicated to applications of data science techniques, and especially machine learning techniques, in relevant agricultural systems are reviewed, which reveal opportunities and promising areas of use.
Journal ArticleDOI
Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale
TL;DR: A new look at soil moisture retrieval in vegetated areas considering the needs of practical applications is taken, and an advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture.
Journal ArticleDOI
Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT2 images and hydrological modeling
Giacomo Bertoldi,Stefano Della Chiesa,Claudia Notarnicola,Luca Pasolli,Georg Niedrist,Ulrike Tappeiner +5 more
TL;DR: In this paper, the spatial patterns of surface soil moisture of alpine meadows and pastures in the Matsch/Mazia Valley in the Italian Alps by comparing estimations from three different sources of information: (I) RADARSAT 2 synthetic aperture radar (SAR) images; (II) simulations by using the GEOtop hydrological model and (III) ground observations, derived from a network of fixed stations and field campaigns with mobile devices.
Journal ArticleDOI
A multiple criteria active learning method for support vector regression
Begum Demir,Lorenzo Bruzzone +1 more
TL;DR: The proposed active learning method selects iteratively the most informative as well as representative unlabeled samples to be included in the training set by jointly evaluating three criteria: relevancy, diversity, and density of samples.
References
More filters
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Microwave Scattering and Emission Models and their Applications
TL;DR: First-order radiative transfer solution passive sensing formulation of the surface scattering problem surface model and special cases ranges validity of the IEM model matrix doubling formulations for scattering and emission scattering and emissions models for snow and sea ice comparisons of model predictions with backscattering and emission measurements from snow and ice.
Journal ArticleDOI
Backscattering from a randomly rough dielectric surface
TL;DR: A backscattering model for scattering from a randomly rough dielectric surface is developed and both like- and cross-polarized scattering coefficients are obtained that satisfy reciprocity and contain only multiple scattering terms.
Book
Neural Computing - An Introduction
Russell Beale,Thomas Jackson +1 more
TL;DR: The perceptron: a vectorial perspective The perceptron learning rule: proof Limitations of perceptrons The end of the line?