Uncertainty of effective roughness parameters calibrated on bare agricultural land using Sentinel-1 SAR

H.F. Benninga; R. van der Velde; Z. Su

doi:10.1109/IGARSS.2018.8518371

Uncertainty of effective roughness parameters calibrated on bare agricultural land using Sentinel-1 SAR

H.F. Benninga, R. van der Velde, Z. Su

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

2 Citations (Scopus)

13 Downloads (Pure)

Abstract

Uncertainty of roughness parameters has effect on soil moisture retrievals with backscatter models from Synthetic Aperture Radar observations. The uncertainty of soil moisture retrievals is important information for the usability of these estimates. In this paper we introduce a methodology to estimate the uncertainty of effective roughness parameters in the Integral Equation Method surface backscatter model, using a Bayesian Markov Chain Monte Carlo approach. Using Sentinel-1 imagery we demonstrate the methodology for a selected field, showing the posterior uncertainty distributions of the roughness parameters, and the effect on the backscatter model simulations and soil moisture inversions. The estimated total uncertainty of the soil moisture retrievals with the optimum parameter set is 0.043 m3/m3, which is slightly higher than the root mean square error of 0.040 m3/m3 of the retrievals compared to in situ soil moisture measurements.

Original language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publication	Observing, Uncerstanding And Forecasting The Dynamics Of Our Planet
Publisher	IEEE
Pages	108-111
Number of pages	4
ISBN (Electronic)	978-1-5386-7149-8
DOIs	https://doi.org/10.1109/IGARSS.2018.8518371
Publication status	Published - 5 Nov 2018
Event	38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018: Observing, Understanding and Forcasting the Dynamics of Our Planet - Feria Valencia Convention & Exhibition Center, Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018 Conference number: 38 https://www.igarss2018.org/

Conference

Conference	38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Abbreviated title	2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18
Internet address	https://www.igarss2018.org/

Keywords

Soil moisture
Sentinel-1
Effective roughness parameters
Uncertainty

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10.1109/IGARSS.2018.8518371

Cite this

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title = "Uncertainty of effective roughness parameters calibrated on bare agricultural land using Sentinel-1 SAR",

abstract = "Uncertainty of roughness parameters has effect on soil moisture retrievals with backscatter models from Synthetic Aperture Radar observations. The uncertainty of soil moisture retrievals is important information for the usability of these estimates. In this paper we introduce a methodology to estimate the uncertainty of effective roughness parameters in the Integral Equation Method surface backscatter model, using a Bayesian Markov Chain Monte Carlo approach. Using Sentinel-1 imagery we demonstrate the methodology for a selected field, showing the posterior uncertainty distributions of the roughness parameters, and the effect on the backscatter model simulations and soil moisture inversions. The estimated total uncertainty of the soil moisture retrievals with the optimum parameter set is 0.043 m3/m3, which is slightly higher than the root mean square error of 0.040 m3/m3 of the retrievals compared to in situ soil moisture measurements.",

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author = "H.F. Benninga and {van der Velde}, R. and Z. Su",

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note = "38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 : Observing, Understanding and Forcasting the Dynamics of Our Planet, 2018 ; Conference date: 22-07-2018 Through 27-07-2018",

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Benninga, HF, van der Velde, R & Su, Z 2018, Uncertainty of effective roughness parameters calibrated on bare agricultural land using Sentinel-1 SAR. in 2018 IEEE International Geoscience and Remote Sensing Symposium: Observing, Uncerstanding And Forecasting The Dynamics Of Our Planet. IEEE, pp. 108-111, 38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8518371

Uncertainty of effective roughness parameters calibrated on bare agricultural land using Sentinel-1 SAR. / Benninga, H.F.; van der Velde, R.; Su, Z.
2018 IEEE International Geoscience and Remote Sensing Symposium: Observing, Uncerstanding And Forecasting The Dynamics Of Our Planet. IEEE, 2018. p. 108-111.

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

TY - CHAP

T1 - Uncertainty of effective roughness parameters calibrated on bare agricultural land using Sentinel-1 SAR

AU - Benninga, H.F.

AU - van der Velde, R.

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PY - 2018/11/5

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N2 - Uncertainty of roughness parameters has effect on soil moisture retrievals with backscatter models from Synthetic Aperture Radar observations. The uncertainty of soil moisture retrievals is important information for the usability of these estimates. In this paper we introduce a methodology to estimate the uncertainty of effective roughness parameters in the Integral Equation Method surface backscatter model, using a Bayesian Markov Chain Monte Carlo approach. Using Sentinel-1 imagery we demonstrate the methodology for a selected field, showing the posterior uncertainty distributions of the roughness parameters, and the effect on the backscatter model simulations and soil moisture inversions. The estimated total uncertainty of the soil moisture retrievals with the optimum parameter set is 0.043 m3/m3, which is slightly higher than the root mean square error of 0.040 m3/m3 of the retrievals compared to in situ soil moisture measurements.

AB - Uncertainty of roughness parameters has effect on soil moisture retrievals with backscatter models from Synthetic Aperture Radar observations. The uncertainty of soil moisture retrievals is important information for the usability of these estimates. In this paper we introduce a methodology to estimate the uncertainty of effective roughness parameters in the Integral Equation Method surface backscatter model, using a Bayesian Markov Chain Monte Carlo approach. Using Sentinel-1 imagery we demonstrate the methodology for a selected field, showing the posterior uncertainty distributions of the roughness parameters, and the effect on the backscatter model simulations and soil moisture inversions. The estimated total uncertainty of the soil moisture retrievals with the optimum parameter set is 0.043 m3/m3, which is slightly higher than the root mean square error of 0.040 m3/m3 of the retrievals compared to in situ soil moisture measurements.

KW - Soil moisture

KW - Sentinel-1

KW - Effective roughness parameters

KW - Uncertainty

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ER -

Uncertainty of effective roughness parameters calibrated on bare agricultural land using Sentinel-1 SAR

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