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Field-Scale Soil Moisture Retrieval Using PALSAR-2 Polarimetric Decomposition and Machine Learning
Article

Decomposition-Based Soil Moisture Estimation Using UAVSAR Fully Polarimetric Images

1
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1417466191, Iran
2
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
3
Agriculture and Agri-Food Canada, 960 Carling Ave., Ottawa, ON K1A 0C6, Canada
*
Author to whom correspondence should be addressed.
Agronomy 2021, 11(1), 145; https://doi.org/10.3390/agronomy11010145
Received: 6 December 2020 / Revised: 6 January 2021 / Accepted: 8 January 2021 / Published: 14 January 2021
Polarimetric decomposition extracts scattering features that are indicative of the physical characteristics of the target. In this study, three polarimetric decomposition methods were tested for soil moisture estimation over agricultural fields using machine learning algorithms. Features extracted from model-based Freeman–Durden, Eigenvalue and Eigenvector based H/A/α, and Van Zyl decompositions were used as inputs in random forest and neural network regression algorithms. These algorithms were applied to retrieve soil moisture over soybean, wheat, and corn fields. A time series of polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired during the Soil Moisture Active Passive Experiment 2012 (SMAPVEX12) field campaign was used for the training and validation of the algorithms. Three feature selection methods were tested to determine the best input features for the machine learning algorithms. The most accurate soil moisture estimates were derived from the random forest regression algorithm for soybeans, with a correlation of determination (R2) of 0.86, root mean square error (RMSE) of 0.041 m3 m−3 and mean absolute error (MAE) of 0.030 m3 m−3. Feature selection also impacted results. Some features like anisotropy, Horizontal transmit and Horizontal receive (HH), and surface roughness parameters (correlation length and RMS-H) had a direct effect on all algorithm performance enhancement as these parameters have a direct impact on the backscattered signal. View Full-Text
Keywords: soil moisture; agriculture; random forest; neural network; SMAPVEX12; UAVSAR; polarimetric decomposition soil moisture; agriculture; random forest; neural network; SMAPVEX12; UAVSAR; polarimetric decomposition
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MDPI and ACS Style

Akhavan, Z.; Hasanlou, M.; Hosseini, M.; McNairn, H. Decomposition-Based Soil Moisture Estimation Using UAVSAR Fully Polarimetric Images. Agronomy 2021, 11, 145. https://doi.org/10.3390/agronomy11010145

AMA Style

Akhavan Z, Hasanlou M, Hosseini M, McNairn H. Decomposition-Based Soil Moisture Estimation Using UAVSAR Fully Polarimetric Images. Agronomy. 2021; 11(1):145. https://doi.org/10.3390/agronomy11010145

Chicago/Turabian Style

Akhavan, Zeinab, Mahdi Hasanlou, Mehdi Hosseini, and Heather McNairn. 2021. "Decomposition-Based Soil Moisture Estimation Using UAVSAR Fully Polarimetric Images" Agronomy 11, no. 1: 145. https://doi.org/10.3390/agronomy11010145

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