Soil moisture retrieval from time series multi-angular radar data using a dry down constraint

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

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30 Citations (Scopus)


Multi-angular and multi-temporal methods have been developed and accepted as two promising strategies for reliable soil moisture retrieval from radar data. However, the way to combine time series multi-angular data acquired from both descending and ascending orbits with different imaging modes (e.g., ScanSAR and Stripmap) remains unresolved. Consequently, a multi-temporal algorithm is proposed for soil moisture retrieval at the pixel – paddock scale (25–500 m) using time series multi-angular L-band (1.26 GHz) radar data. The method assumes time-invariant roughness and vegetation for the retrieval period together with a soil moisture dry-down constraint for noise reduction, while utilizing multi-angular data without incidence angle normalization. The Numerical Maxwell Model of Three-Dimensional simulations and distorted Born approximation (NMM3D-DBA) were used to build a set of landcover specific multi-angular look up tables (LUTs). Effective isotropic roughness values were assumed suitably able to account for the periodic features in cultivated surfaces, with values determined as part of the soil moisture retrieval. A genetic algorithm was used to minimize the difference between LUTs and time series multi-angular radar observations with the dry-down constraint. Evaluation based on the Fifth Soil Moisture Active Passive Experiment dataset (SMAPEx-5) has shown an acceptable overall root mean square error (RMSE) of 0.070 m3/m3 at the 25-m pixel scale and 0.056 m3/m3 at the paddock (field) scale (~0.1–0.5 km). Further investigations on the effect of polarization combination and time interval of radar data have confirmed the effectiveness of the proposed method for irregularly collected data with different imaging modes.

Original languageEnglish
Article number111237
Number of pages12
JournalRemote Sensing of Environment
Publication statusPublished - 15 Sept 2019


  • Multi-angular
  • Multi-temporal
  • Soil moisture
  • Synthetic aperture radar

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