Volume 40 Issue 4
Jul.  2020
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LUO Liming, BAI Weihua, SUN Yueqiang, XIA Junming. GNSS-R Sea Surface Wind Speed Inversion Based on Tree Model Machine Learning Method[J]. Journal of Space Science, 2020, 40(4): 595-601. doi: 10.11728/cjss2020.04.595
Citation: LUO Liming, BAI Weihua, SUN Yueqiang, XIA Junming. GNSS-R Sea Surface Wind Speed Inversion Based on Tree Model Machine Learning Method[J]. Journal of Space Science, 2020, 40(4): 595-601. doi: 10.11728/cjss2020.04.595

GNSS-R Sea Surface Wind Speed Inversion Based on Tree Model Machine Learning Method

doi: 10.11728/cjss2020.04.595
  • Received Date: 2018-12-05
  • Rev Recd Date: 2020-05-25
  • Publish Date: 2020-07-15
  • GNSS-R is a new technique based on GNSS satellite reflection signals, and it can be applied to the inversion of sea surface wind field. The traditional GNSS-R technology inversion of sea surface wind field mainly has waveform matching and experience function. The waveform matching method is time-consuming and computationally intensive; the empirical function method often uses only a small amount of physical observations, which causes waste of additional information and loss of certain inversion precision. The accuracy of the traditional method of wind speed inversion is about 2m·s-1. In order to improve the inversion accuracy of sea surface wind speed, the tree model algorithm decision tree, random forest and GBDT commonly used in the field of machine learning are introduced to predict the sea surface wind speed. The training set and the verification set are constructed by using GNSS-R and ECMWF data. The training set is used for model learning, and the verification set is used to test the inversion effect of the model. The prediction error of decision tree and random forest is about 0.6m·s-1, and the prediction error of GBDT and other algorithms is about 2m·s-1, which meets the requirements of wind speed inversion. Compared with the traditional GNSS-R inversion method, the machine learning tree model algorithm performs better and has stable performance and less error on the verification set. Therefore, the machine learning tree model algorithm can be applied to the sea surface wind speed inversion.

     

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