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

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

2 Citations (Scopus)


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 languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781538671504
Publication statusPublished - 31 Oct 2018
EventIEEE International Geoscience and Remote Sensing Symposium 2018 - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018
Conference number: 38th (Proceedings)

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)


ConferenceIEEE International Geoscience and Remote Sensing Symposium 2018
Abbreviated titleIGARSS 2018
Internet address


  • Multi-angular
  • Multi-temporal
  • SAR
  • Soil moisture

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