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OBSERVING SOIL MOISTURE CHANGE USING C-BAND INTERFEROMETRY USING MACHINE LEARNING REGRESSION. / Mira, Nuno Cirne; Catalão, João; Nico, Giovanni.

International Geoscience and Remote Sensing Symposium (IGARSS). 2021. стр. 6343-6346.

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Mira, NC, Catalão, J & Nico, G 2021, OBSERVING SOIL MOISTURE CHANGE USING C-BAND INTERFEROMETRY USING MACHINE LEARNING REGRESSION. в International Geoscience and Remote Sensing Symposium (IGARSS). стр. 6343-6346, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Бельгия, 11/07/21. https://doi.org/10.1109/IGARSS47720.2021.9554692

APA

Mira, N. C., Catalão, J., & Nico, G. (2021). OBSERVING SOIL MOISTURE CHANGE USING C-BAND INTERFEROMETRY USING MACHINE LEARNING REGRESSION. в International Geoscience and Remote Sensing Symposium (IGARSS) (стр. 6343-6346) https://doi.org/10.1109/IGARSS47720.2021.9554692

Vancouver

Mira NC, Catalão J, Nico G. OBSERVING SOIL MOISTURE CHANGE USING C-BAND INTERFEROMETRY USING MACHINE LEARNING REGRESSION. в International Geoscience and Remote Sensing Symposium (IGARSS). 2021. стр. 6343-6346 https://doi.org/10.1109/IGARSS47720.2021.9554692

Author

Mira, Nuno Cirne ; Catalão, João ; Nico, Giovanni. / OBSERVING SOIL MOISTURE CHANGE USING C-BAND INTERFEROMETRY USING MACHINE LEARNING REGRESSION. International Geoscience and Remote Sensing Symposium (IGARSS). 2021. стр. 6343-6346

BibTeX

@inproceedings{c4b82e19fb924e238c339497ce5ef767,
title = "OBSERVING SOIL MOISTURE CHANGE USING C-BAND INTERFEROMETRY USING MACHINE LEARNING REGRESSION",
abstract = "The observation of soil moisture is fundamental for several climate sciences. Remote sensing had proved that it is possible to observe soil moisture from both Synthetic Aperture Radar (SAR) and SAR interferometry (InSAR) observables. This paper shows the use of machine learning regression algorithms to estimate soil moisture change using the InSAR coherence and phase and the soil type. Random Forest Regression and Extra-Tree and Bagging Regression were used. The purpose is to evaluate the improvement gain with the inclusion of “non-conventional” data such as the soil type on the estimation of soil moisture variations in time. The results point out that the inclusion of the soil type improves the estimation with coefficient of determination - R2 up to 72%.",
keywords = "Interferometric Coherence, Machine Learning, SAR Interferometry (InSAR), Soil Moisture, Synthetic Aperture Radar (SAR)",
author = "Mira, {Nuno Cirne} and Jo{\~a}o Catal{\~a}o and Giovanni Nico",
year = "2021",
month = jan,
day = "1",
doi = "10.1109/IGARSS47720.2021.9554692",
language = "English",
pages = "6343--6346",
booktitle = "International Geoscience and Remote Sensing Symposium (IGARSS)",
note = " 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS ; Conference date: 11-07-2021 Through 16-07-2021",

}

RIS

TY - GEN

T1 - OBSERVING SOIL MOISTURE CHANGE USING C-BAND INTERFEROMETRY USING MACHINE LEARNING REGRESSION

AU - Mira, Nuno Cirne

AU - Catalão, João

AU - Nico, Giovanni

PY - 2021/1/1

Y1 - 2021/1/1

N2 - The observation of soil moisture is fundamental for several climate sciences. Remote sensing had proved that it is possible to observe soil moisture from both Synthetic Aperture Radar (SAR) and SAR interferometry (InSAR) observables. This paper shows the use of machine learning regression algorithms to estimate soil moisture change using the InSAR coherence and phase and the soil type. Random Forest Regression and Extra-Tree and Bagging Regression were used. The purpose is to evaluate the improvement gain with the inclusion of “non-conventional” data such as the soil type on the estimation of soil moisture variations in time. The results point out that the inclusion of the soil type improves the estimation with coefficient of determination - R2 up to 72%.

AB - The observation of soil moisture is fundamental for several climate sciences. Remote sensing had proved that it is possible to observe soil moisture from both Synthetic Aperture Radar (SAR) and SAR interferometry (InSAR) observables. This paper shows the use of machine learning regression algorithms to estimate soil moisture change using the InSAR coherence and phase and the soil type. Random Forest Regression and Extra-Tree and Bagging Regression were used. The purpose is to evaluate the improvement gain with the inclusion of “non-conventional” data such as the soil type on the estimation of soil moisture variations in time. The results point out that the inclusion of the soil type improves the estimation with coefficient of determination - R2 up to 72%.

KW - Interferometric Coherence

KW - Machine Learning

KW - SAR Interferometry (InSAR)

KW - Soil Moisture

KW - Synthetic Aperture Radar (SAR)

UR - http://www.scopus.com/inward/record.url?scp=85129904581&partnerID=8YFLogxK

U2 - 10.1109/IGARSS47720.2021.9554692

DO - 10.1109/IGARSS47720.2021.9554692

M3 - Conference contribution

AN - SCOPUS:85129904581

SP - 6343

EP - 6346

BT - International Geoscience and Remote Sensing Symposium (IGARSS)

T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

Y2 - 11 July 2021 through 16 July 2021

ER -

ID: 114329484