Crop backscatter modeling and soil moisture estimation with support vector regression

Jelena Stamenkovic; Paolo Ferrazzoli; Leila Guerriero; Devis Tuia; Jean Philippe Thiran; Maurice Borgeaud

doi:10.1109/IGARSS.2014.6947166

Crop backscatter modeling and soil moisture estimation with support vector regression

Jelena Stamenkovic, Paolo Ferrazzoli, Leila Guerriero, Devis Tuia, Jean Philippe Thiran, Maurice Borgeaud

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Academic › peer-review

2 Citations (Scopus)

Abstract

In this paper, we used an improved version of the Tor Vergata radiative transfer model to simulate the backscattering coefficient for the L-band SAR signals over areas covered with vegetation. Fields of winter wheat, maize and sugar beet observed during the AgriSAR2006 campaign were investigated. For maize field, the presence of periodic soil surface profiles played an important role in determining the total backscattering. Soil moisture was also estimated using an inverse algorithm based on a supervised, non-parametric learning technique, v-SVR. v-SVR proved good generalization properties even with a limited number of training samples available. Dependence to the origin of training samples, as well as the influence of different features, was thoroughly considered.

Original language	English
Title of host publication	International Geoscience and Remote Sensing Symposium (IGARSS)
Publisher	IEEE
Pages	3228-3231
Number of pages	4
ISBN (Print)	9781479957750
DOIs	https://doi.org/10.1109/IGARSS.2014.6947166
Publication status	Published - 4 Nov 2014
Externally published	Yes
Event	Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 - Quebec City, Canada Duration: 13 Jul 2014 → 18 Jul 2014

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference/symposium

Conference/symposium	Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014
Country/Territory	Canada
City	Quebec City
Period	13/07/14 → 18/07/14

Keywords

Crop backscatter
soil moisture
SVR

Access to Document

10.1109/IGARSS.2014.6947166

Cite this

Stamenkovic, J., Ferrazzoli, P., Guerriero, L., Tuia, D., Thiran, J. P., & Borgeaud, M. (2014). Crop backscatter modeling and soil moisture estimation with support vector regression. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3228-3231). Article 6947166 (International Geoscience and Remote Sensing Symposium (IGARSS)). IEEE. https://doi.org/10.1109/IGARSS.2014.6947166

@inproceedings{7dadf17b7bab40839304492c4b950744,

title = "Crop backscatter modeling and soil moisture estimation with support vector regression",

abstract = "In this paper, we used an improved version of the Tor Vergata radiative transfer model to simulate the backscattering coefficient for the L-band SAR signals over areas covered with vegetation. Fields of winter wheat, maize and sugar beet observed during the AgriSAR2006 campaign were investigated. For maize field, the presence of periodic soil surface profiles played an important role in determining the total backscattering. Soil moisture was also estimated using an inverse algorithm based on a supervised, non-parametric learning technique, v-SVR. v-SVR proved good generalization properties even with a limited number of training samples available. Dependence to the origin of training samples, as well as the influence of different features, was thoroughly considered.",

keywords = "Crop backscatter, soil moisture, SVR",

author = "Jelena Stamenkovic and Paolo Ferrazzoli and Leila Guerriero and Devis Tuia and Thiran, {Jean Philippe} and Maurice Borgeaud",

year = "2014",

month = nov,

day = "4",

doi = "10.1109/IGARSS.2014.6947166",

language = "English",

isbn = "9781479957750",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "IEEE",

pages = "3228--3231",

booktitle = "International Geoscience and Remote Sensing Symposium (IGARSS)",

address = "United States",

note = "Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 ; Conference date: 13-07-2014 Through 18-07-2014",

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Stamenkovic, J, Ferrazzoli, P, Guerriero, L, Tuia, D, Thiran, JP & Borgeaud, M 2014, Crop backscatter modeling and soil moisture estimation with support vector regression. in International Geoscience and Remote Sensing Symposium (IGARSS)., 6947166, International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, pp. 3228-3231, Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014, Quebec City, Canada, 13/07/14. https://doi.org/10.1109/IGARSS.2014.6947166

Crop backscatter modeling and soil moisture estimation with support vector regression. / Stamenkovic, Jelena; Ferrazzoli, Paolo; Guerriero, Leila et al.
International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2014. p. 3228-3231 6947166 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Academic › peer-review

TY - GEN

T1 - Crop backscatter modeling and soil moisture estimation with support vector regression

AU - Stamenkovic, Jelena

AU - Ferrazzoli, Paolo

AU - Guerriero, Leila

AU - Tuia, Devis

AU - Thiran, Jean Philippe

AU - Borgeaud, Maurice

PY - 2014/11/4

Y1 - 2014/11/4

N2 - In this paper, we used an improved version of the Tor Vergata radiative transfer model to simulate the backscattering coefficient for the L-band SAR signals over areas covered with vegetation. Fields of winter wheat, maize and sugar beet observed during the AgriSAR2006 campaign were investigated. For maize field, the presence of periodic soil surface profiles played an important role in determining the total backscattering. Soil moisture was also estimated using an inverse algorithm based on a supervised, non-parametric learning technique, v-SVR. v-SVR proved good generalization properties even with a limited number of training samples available. Dependence to the origin of training samples, as well as the influence of different features, was thoroughly considered.

AB - In this paper, we used an improved version of the Tor Vergata radiative transfer model to simulate the backscattering coefficient for the L-band SAR signals over areas covered with vegetation. Fields of winter wheat, maize and sugar beet observed during the AgriSAR2006 campaign were investigated. For maize field, the presence of periodic soil surface profiles played an important role in determining the total backscattering. Soil moisture was also estimated using an inverse algorithm based on a supervised, non-parametric learning technique, v-SVR. v-SVR proved good generalization properties even with a limited number of training samples available. Dependence to the origin of training samples, as well as the influence of different features, was thoroughly considered.

KW - Crop backscatter

KW - soil moisture

KW - SVR

U2 - 10.1109/IGARSS.2014.6947166

DO - 10.1109/IGARSS.2014.6947166

M3 - Conference paper

AN - SCOPUS:84911418973

SN - 9781479957750

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 3228

EP - 3231

BT - International Geoscience and Remote Sensing Symposium (IGARSS)

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T2 - Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014

Y2 - 13 July 2014 through 18 July 2014

ER -

Crop backscatter modeling and soil moisture estimation with support vector regression

Abstract

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Conference/symposium

Keywords

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