Crop Height Estimation of Corn from Multi-Year RADARSAT-2 Polarimetric Observables Using Machine Learning

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Title: Crop Height Estimation of Corn from Multi-Year RADARSAT-2 Polarimetric Observables Using Machine Learning
Authors: Xie, Qinghua | Wang, Jinfei | Lopez-Sanchez, Juan M. | Peng, Xing | Liao, Chunhua | Shang, Jiali | Zhu, Jianjun | Fu, Haqiang | Ballester-Berman, J. David
Research Group/s: Señales, Sistemas y Telecomunicación
Center, Department or Service: Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática
Keywords: Crop height | RADARSAT-2 | Corn | Synthetic Aperture Radar (SAR) | PolSAR | Machine learning | RFR | SVR | Agriculture
Knowledge Area: Teoría de la Señal y Comunicaciones
Issue Date: 23-Jan-2021
Publisher: MDPI
Citation: Xie Q, Wang J, Lopez-Sanchez JM, Peng X, Liao C, Shang J, Zhu J, Fu H, Ballester-Berman JD. Crop Height Estimation of Corn from Multi-Year RADARSAT-2 Polarimetric Observables Using Machine Learning. Remote Sensing. 2021; 13(3):392.
Abstract: This study presents a demonstration of the applicability of machine learning techniques for the retrieval of crop height in corn fields using space-borne PolSAR (Polarimetric Synthetic Aperture Radar) data. Multi-year RADARSAT-2 C-band data acquired over agricultural areas in Canada, covering the whole corn growing period, are exploited. Two popular machine learning regression methods, i.e., Random Forest Regression (RFR) and Support Vector Regression (SVR) are adopted and evaluated. A set of 27 representative polarimetric parameters are extracted from the PolSAR data and used as input features in the regression models for height estimation. Furthermore, based on the unique capability of the RFR method to determine variable importance contributing to the regression, a smaller number of polarimetric features (6 out of 27 in our study) are selected in the final regression models. Results of our study demonstrate that PolSAR observables can produce corn height estimates with root mean square error (RMSE) around 40–50 cm throughout the growth cycle. The RFR approach shows better overall accuracy in corn height estimation than the SVR method in all tests. The six selected polarimetric features by variable importance ranking can generate better results. This study provides a new perspective on the use of PolSAR data in retrieving agricultural crop height from space.
Sponsor: This research was funded in part by the National Natural Science Foundation of China (Grant No. 41804004, 41820104005, 41531068, 41904004), the Canadian Space Agency SOAR-E program (Grant No. SOAR-E-5489), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUG190633), and the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund under project TEC2017-85244-C2-1-P.
ISSN: 2072-4292
DOI: 10.3390/rs13030392
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Peer Review: si
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