Mr. Sadegh Ranjbar Profile

Sadegh Ranjbar

at Univ of Tehran

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SPIE Journal Paper | 8 March 2021

Machine learning inversion approach for soil parameters estimation over vegetated agricultural areas using a combination of water cloud model and calibrated integral equation model

Sadegh Ranjbar, Arastou Zarei, Mahdi Hasanlou, Mehdi Akhoondzadeh, Jalal Amini, Meisam Amani

JARS, Vol. 15, Issue 01, 018503, (March 2021) https://doi.org/10.1117/12.10.1117/1.JRS.15.018503

KEYWORDS: Data modeling, Synthetic aperture radar, Computer simulations, Agriculture, Soil science, Machine learning, Vegetation, Polarization, Performance modeling, Calibration

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Estimating volumetric soil moisture (Mv) and surface roughness (S) are the key parameters for numerous agricultural and hydrological applications. Although these two parameters can be effectively retrieved from synthetic aperture radar (SAR) data, the presence of vegetation can negatively affect the results. A method was proposed to accurately estimate Mv and S over vegetated agricultural areas. The method was based on applying the machine learning inversion approach along with SAR data to invert a combination of the parameterized water cloud model (PWCM) and the calibrated integral equation model (CIEM). The soil backscattered component in water cloud model (WCM) was generated by CIEM to be applied to the WCM parameterization and dataset simulation. Three machine learning algorithms, including the support vector regression (SVR), multi-output SVR (MSVR), and artificial neural network (ANN), were employed to model the relationship between the simulated dataset variables. The genetic algorithm was also applied to optimize the models’ parameters. The inversion technique results demonstrated that the MSVR and ANN had the highest accuracy in estimating Mv and S due to their better structures. The SMAPVEX-16 in situ dataset, along with three Sentinel-1 images, was applied to evaluate the accuracy of the WCM parameterization and the proposed method for Mv and S estimation. The accuracies of the PWCM in the VV and VH polarizations of Sentinel-1 C-band data were reasonable for VWC < 2.5 kg / m2 [root-mean-square error (RMSE) = 1.44 and 1.77 dB, respectively]. Additionally, it was observed that the trained SVR, MSVR, and ANN had similar results for different VWC values. In summary, the proposed method had high potential in vegetated agricultural areas with VWC < 2.5 kg / m2, for which the RMSEs were 4 to 7 vol. % and 0.35 to 0.46 cm depending on the VWC values in retrieving Mv and S, respectively.

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