Soil Moisture Retrieval of Winter Wheat Fields Based on Random Forest Regression Using Quad-Polarimetric SAR Images

LI Pingxiang; LIU Zhiqu; YANG Jie; SUN Weidong; LI Minyi; REN Yexian

doi:10.13203/j.whugis20160531

Volume 44 Issue 3

Mar. 2019

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Geomatics and Information Science of Wuhan University > 2019 > 44(3): 405-412. > DOI: 10.13203/j.whugis20160531

LI Pingxiang, LIU Zhiqu, YANG Jie, SUN Weidong, LI Minyi, REN Yexian. Soil Moisture Retrieval of Winter Wheat Fields Based on Random Forest Regression Using Quad-Polarimetric SAR Images[J]. Geomatics and Information Science of Wuhan University, 2019, 44(3): 405-412. DOI: 10.13203/j.whugis20160531

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Soil Moisture Retrieval of Winter Wheat Fields Based on Random Forest Regression Using Quad-Polarimetric SAR Images

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2.
Deqing iSpatial Co. Ltd, Deqing 313200, China

Funds:

The National Natural Science Foundation of China 41771377

The National Natural Science Foundation of China 41601355

The National Natural Science Foundation of China 91438203

The National Natural Science Foundation of China 41501382

the GF Satellite Program from State Administration of Science, Technology and Industry for National Defense of China 03-Y20A10-9001-15/16

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Author Bio:
LI Pingxiang, professor, specializes in the theories and methods of polarimetric SAR. E-mail: pxli@whu.edu.cn
Corresponding author:
LIU Zhiqu, PhD candidate. E-mail:meloqu@qq.com
Received Date: March 05, 2018
Published Date: March 04, 2019

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Abstract

Abstract

Soil moisture has great significance in the researches of hydrology, meteorology and agriculture yield estimation. The quad-polarimetric SAR images can provide a lot of polarimetric features, the significance of the features in surface parameter retrieval have attracted attentions in previous researches with no final conclusions because of the complexity of terrain scattering. In this paper, random forest regression (RFR) is used for both soil moisture retrieval and the importance evaluation of polarimetric features of Radarsat-2 images in winter wheat fields. According to the score of importance, feature selection and combination are done for modelling. We evaluate the retrieval accuracy of models with different feature combinations. The results show that models of important features selected by RFR have RMSE(root mean square error) less than 6% which are better results compared to traditional models; when compared with support vector regression and artifical neural networks, the RFR also shows best retrieval accuracies, which proves that RFR is suitable for soil moisture retrieval and feature selection. The high retrieval accuracies of LBC-CPD(linear backscatter coefficients-Cloude-Pottier decomposition) and LBC-CPR(linear backscatter coefficients-circular polarimetric ratio) indicates these features can improve the retrieval accuracy of soil moisture.
- polarimetric SAR,
- soil moisture,
- random forest regression,
- support vector regression,
- artificial neural networks

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Soil Moisture Retrieval of Winter Wheat Fields Based on Random Forest Regression Using Quad-Polarimetric SAR Images

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