Soil Moisture Inversion Via Semiempirical and Machine Learning Methods With Full-Polarization Radarsat-2 and Polarimetric Target Decomposition Data: A Comparative Study
Soil Moisture Inversion Via Semiempirical and Machine Learning Methods With Full-Polarization Radarsat-2 and Polarimetric Target Decomposition Data: A Comparative Study
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DOI：
10.1109/access.2020.3035235
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Hüseyın Acar;M. S. Özerdem;Emrullah Acar
中科院分区：
文献类型：
--
作者：
Hüseyın Acar;M. S. Özerdem;Emrullah Acar
本文采用半经验模型和机器学习方法，从Radarsat-2卫星和极化目标分解数据中反演表层土壤水分。所使用的半经验模型和机器学习技术分别是oh(1992)、Dubois(1995)、oh(2004)和广义回归神经网络(GRNN)、最小二乘支持向量机(LS-SVM)、极限学习机(ELM)、基于核的极限学习机(KELM)和基于自适应网络的模糊推理系统(ANFIS)。此外，本研究还使用了Yamaguchi、van Zyl、Freeman-Durden、H/A/α和Cloude极化目标分解方法。在土壤水分反演方面，首先对裸露和中度植被覆盖的两个不同日期的Radarsat-2图像进行了预处理。然后，从地面测量点对应的经预处理的SAR图像像素中提取Sigma零系数和极化分解分量作为特征向量。最后，将西格玛零系数用于半经验反演模型，并将西格玛零系数和极化分解分量作为机器学习方法的输入。半经验模型的最优精度为13.01vol.%和17.91vol.裸土和中度植被土壤的均方根误差(RMSE)分别为：%。机器学习技术的最佳准确率为4.04vol.%和2.72vol.分别为两个日期的%RMSE。结果表明，机器学习技术的性能远远好于半经验模型。
In this article, surface soil moisture was retrieved from Radarsat-2 and polarimetric target decomposition data by using semiempirical models and machine learning methods. The semiempirical models and machine learning techniques employed were Oh (1992), Dubois (1995), Oh (2004) and Generalized Regression Neural Network (GRNN), Least Squares – Support Vector Machine (LS-SVM), Extreme Learning Machine (ELM), Kernel based Extreme Learning Machine (KELM), Adaptive Network based Fuzzy Inference System (ANFIS), respectively. In addition, Yamaguchi, van Zyl, Freeman-Durden, H/A/ $\alpha $ and Cloude polarimetric target decomposition methods were used in this study. For soil moisture inversion, firstly, preprocessing was applied to the Radarsat-2 image of two different dates with bare and moderately vegetated soil. Then, sigma nought coefficients and the polarimetric decomposition components were extracted as feature vector from preprocessed SAR image pixels corresponding to ground measured points. Lastly, sigma nought coefficients were used in semiempirical inversion models, and sigma nought coefficients and polarimetric decomposition components were used as input to machine learning methods. The best accuracy results for semiempirical models were 13.01 vol. % and 17.91 vol. % Root Mean Square Error (RMSE) for bare and moderately vegetated soil, respectively. The best accuracy for machine learning techniques were 4.04 vol. % and 2.72 vol. % RMSE for two dates, respectively. The results indicated that the machine learning techniques performed much better than the semiempirical models.