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

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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 languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publicationObserving, Uncerstanding And Forecasting The Dynamics Of Our Planet
PublisherIEEE
Pages108-111
Number of pages4
ISBN (Electronic)978-1-5386-7149-8
DOIs
Publication statusPublished - 5 Nov 2018
Event38th IEEE International Geoscience and Remote Sensing Symposium 2018: Observing, Understanding and Forcasting the Dynamics of Our Planet - Feria Valencia Convention & Exhibition Center, Valencia, Spain
Duration: 22 Jul 201827 Jul 2018
Conference number: 38
https://www.igarss2018.org/

Conference

Conference38th IEEE International Geoscience and Remote Sensing Symposium 2018
Abbreviated titleIGARSS 2018
CountrySpain
CityValencia
Period22/07/1827/07/18
Internet address

Fingerprint

roughness
synthetic aperture radar
agricultural land
soil moisture
backscatter
methodology
Markov chain
imagery
parameter
simulation
effect

Keywords

  • Soil moisture
  • Sentinel-1
  • Effective roughness parameters
  • Uncertainty

Cite this

Benninga, H. F., 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 (pp. 108-111). IEEE. https://doi.org/10.1109/IGARSS.2018.8518371
Benninga, H.F. ; van der Velde, R. ; Su, Z. / Uncertainty of effective roughness parameters calibrated on bare agricultural land using Sentinel-1 SAR. 2018 IEEE International Geoscience and Remote Sensing Symposium: Observing, Uncerstanding And Forecasting The Dynamics Of Our Planet. IEEE, 2018. pp. 108-111
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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 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 proceedingChapterAcademicpeer-review

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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.

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Benninga HF, van der Velde R, Su Z. 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. 2018. p. 108-111 https://doi.org/10.1109/IGARSS.2018.8518371