Soil moisture retrieval over agricultural fields from time series multi-angular L-band radar data

Liujun Zhu; Jeffrey P. Walker; Leung Tsang; Huanting Huang; Nan Ye; Christoph Rüdiger

doi:10.1109/IGARSS.2018.8518119

Soil moisture retrieval over agricultural fields from time series multi-angular L-band radar data

Liujun Zhu, Jeffrey P. Walker, Leung Tsang, Huanting Huang, Nan Ye, Christoph Rüdiger

Civil Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

2 Citations (Scopus)

Abstract

A time series multi-angular method was presented towards combining space-borne radar data acquired from both descending and ascending orbits with different observation modes in soil moisture retrieval. Inherit from multi-temporal based retrieval methods, the method assumes time-invariant roughness and vegetation, but not requires incidence angle normalization. The numerical Maxwell model of three-dimensional simulations and distorted Born approximation (NMM3D-DBA) were used to build a set of multi-angular data cubes (3 dimension look up table). Genetic algorithm (GA) was used to minimize the difference between data cubes and radar observations with the constraint of drying down soil moisture. Evaluation based on the fifth Soil Moisture Active Passive Experiment (SMAPEx-5) dataset shows an overall root mean square error (RMSE) of 0.07 cm ³ /cm ³ at the 50-m pixel scale.

Original language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	6139-6142
Number of pages	4
ISBN (Electronic)	9781538671504
DOIs	https://doi.org/10.1109/IGARSS.2018.8518119
Publication status	Published - 31 Oct 2018
Event	IEEE International Geoscience and Remote Sensing Symposium 2018 - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018 Conference number: 38th https://ieeexplore.ieee.org/xpl/conhome/8496405/proceeding (Proceedings)

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2018-July

Conference

Conference	IEEE International Geoscience and Remote Sensing Symposium 2018
Abbreviated title	IGARSS 2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8496405/proceeding (Proceedings)

Keywords

Multi-angular
Multi-temporal
SAR
Soil moisture

Access to Document

10.1109/IGARSS.2018.8518119

Cite this

Zhu, L., Walker, J. P., Tsang, L., Huang, H., Ye, N., & Rüdiger, C. (2018). Soil moisture retrieval over agricultural fields from time series multi-angular L-band radar data. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 6139-6142). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IGARSS.2018.8518119

Zhu, Liujun ; Walker, Jeffrey P. ; Tsang, Leung et al. / Soil moisture retrieval over agricultural fields from time series multi-angular L-band radar data. 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 6139-6142 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "Soil moisture retrieval over agricultural fields from time series multi-angular L-band radar data",

abstract = " A time series multi-angular method was presented towards combining space-borne radar data acquired from both descending and ascending orbits with different observation modes in soil moisture retrieval. Inherit from multi-temporal based retrieval methods, the method assumes time-invariant roughness and vegetation, but not requires incidence angle normalization. The numerical Maxwell model of three-dimensional simulations and distorted Born approximation (NMM3D-DBA) were used to build a set of multi-angular data cubes (3 dimension look up table). Genetic algorithm (GA) was used to minimize the difference between data cubes and radar observations with the constraint of drying down soil moisture. Evaluation based on the fifth Soil Moisture Active Passive Experiment (SMAPEx-5) dataset shows an overall root mean square error (RMSE) of 0.07 cm 3 /cm 3 at the 50-m pixel scale. ",

keywords = "Multi-angular, Multi-temporal, SAR, Soil moisture",

author = "Liujun Zhu and Walker, {Jeffrey P.} and Leung Tsang and Huanting Huang and Nan Ye and Christoph R{\"u}diger",

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Zhu, L, Walker, JP, Tsang, L, Huang, H, Ye, N & Rüdiger, C 2018, Soil moisture retrieval over agricultural fields from time series multi-angular L-band radar data. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2018-July, IEEE, Institute of Electrical and Electronics Engineers, pp. 6139-6142, IEEE International Geoscience and Remote Sensing Symposium 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8518119

Soil moisture retrieval over agricultural fields from time series multi-angular L-band radar data. / Zhu, Liujun; Walker, Jeffrey P.; Tsang, Leung et al.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 6139-6142 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

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AU - Rüdiger, Christoph

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AB - A time series multi-angular method was presented towards combining space-borne radar data acquired from both descending and ascending orbits with different observation modes in soil moisture retrieval. Inherit from multi-temporal based retrieval methods, the method assumes time-invariant roughness and vegetation, but not requires incidence angle normalization. The numerical Maxwell model of three-dimensional simulations and distorted Born approximation (NMM3D-DBA) were used to build a set of multi-angular data cubes (3 dimension look up table). Genetic algorithm (GA) was used to minimize the difference between data cubes and radar observations with the constraint of drying down soil moisture. Evaluation based on the fifth Soil Moisture Active Passive Experiment (SMAPEx-5) dataset shows an overall root mean square error (RMSE) of 0.07 cm 3 /cm 3 at the 50-m pixel scale.

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Zhu L, Walker JP, Tsang L, Huang H, Ye N, Rüdiger C. Soil moisture retrieval over agricultural fields from time series multi-angular L-band radar data. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 6139-6142. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2018.8518119

Soil moisture retrieval over agricultural fields from time series multi-angular L-band radar data

Abstract

Publication series

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Keywords

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