Crop-Growth Driven Forward-Modeling of Sentinel-1 Observables Using Machine-Learning

Tina Nikaein; Vineet Kummer; Susan Steele-Dunne; Paco Lopez Dekker

doi:10.1109/IGARSS46834.2022.9883154

Crop-Growth Driven Forward-Modeling of Sentinel-1 Observables Using Machine-Learning

Tina Nikaein, Vineet Kummer, Susan Steele-Dunne, Paco Lopez Dekker

Mathematical Geodesy and Positioning

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

This paper presents an approach to implement a forward model for Sentinel-1 copol and crosspol backscatter and coherence using crop bio-geophysical parameters namely leaf area index, biomass, canopy height, soil moisture and root zone moisture as inputs for the maize. These required input parameters are generated using Decision Support System for Agrotechnology Transfer (DSSAT), one of the state-of-the-art crop growth models. The predicted SAR signal is generated using Support Vector Regression (SVR) over all the maize fields in an agricultural region, Flevoland, Netherlands. The correlation between simulated signal and observed signal is evaluated.

Original language	English
Title of host publication	Proceedings of the IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Place of Publication	Danvers
Publisher	IEEE
Pages	5961-5964
Number of pages	4
ISBN (Electronic)	978-1-6654-2792-0
ISBN (Print)	978-1-6654-2793-7
DOIs	https://doi.org/10.1109/IGARSS46834.2022.9883154
Publication status	Published - 2022
Event	IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium - Kuala Lumpur, Malaysia Duration: 17 Jul 2022 → 22 Jul 2022

Conference

Conference	IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Country/Territory	Malaysia
City	Kuala Lumpur
Period	17/07/22 → 22/07/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Crop
DSSAT
Sentinel-1
SAR
simulation
forward-model

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Crop-Growth_Driven_Forward-Modeling_of_Sentinel-1_Observables_Using_Machine-LearningFinal published version, 2.38 MB

Cite this

@inproceedings{8e71bae3246a4620a4793dad9c25a6ea,

title = "Crop-Growth Driven Forward-Modeling of Sentinel-1 Observables Using Machine-Learning",

abstract = "This paper presents an approach to implement a forward model for Sentinel-1 copol and crosspol backscatter and coherence using crop bio-geophysical parameters namely leaf area index, biomass, canopy height, soil moisture and root zone moisture as inputs for the maize. These required input parameters are generated using Decision Support System for Agrotechnology Transfer (DSSAT), one of the state-of-the-art crop growth models. The predicted SAR signal is generated using Support Vector Regression (SVR) over all the maize fields in an agricultural region, Flevoland, Netherlands. The correlation between simulated signal and observed signal is evaluated.",

keywords = "Crop, DSSAT, Sentinel-1, SAR, simulation, forward-model",

author = "Tina Nikaein and Vineet Kummer and Susan Steele-Dunne and {Lopez Dekker}, Paco",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium ; Conference date: 17-07-2022 Through 22-07-2022",

year = "2022",

doi = "10.1109/IGARSS46834.2022.9883154",

language = "English",

isbn = "978-1-6654-2793-7",

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booktitle = "Proceedings of the IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",

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address = "United States",

}

Nikaein, T, Kummer, V, Steele-Dunne, S & Lopez Dekker, P 2022, Crop-Growth Driven Forward-Modeling of Sentinel-1 Observables Using Machine-Learning. in Proceedings of the IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, Danvers, pp. 5961-5964, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17/07/22. https://doi.org/10.1109/IGARSS46834.2022.9883154

Crop-Growth Driven Forward-Modeling of Sentinel-1 Observables Using Machine-Learning. / Nikaein, Tina; Kummer, Vineet; Steele-Dunne, Susan et al.
Proceedings of the IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Danvers: IEEE, 2022. p. 5961-5964.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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AB - This paper presents an approach to implement a forward model for Sentinel-1 copol and crosspol backscatter and coherence using crop bio-geophysical parameters namely leaf area index, biomass, canopy height, soil moisture and root zone moisture as inputs for the maize. These required input parameters are generated using Decision Support System for Agrotechnology Transfer (DSSAT), one of the state-of-the-art crop growth models. The predicted SAR signal is generated using Support Vector Regression (SVR) over all the maize fields in an agricultural region, Flevoland, Netherlands. The correlation between simulated signal and observed signal is evaluated.

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BT - Proceedings of the IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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Crop-Growth Driven Forward-Modeling of Sentinel-1 Observables Using Machine-Learning

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Keywords

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