GNSS-IR model of snow depth estimation combining wavelet transform with sliding window

• Main Authors: BIAN Shaofeng, ZHOU Wei, LIU Lilong, LI Houpu, LIU Bei
• Format: Article
• Language: zho
• Published: Surveying and Mapping Press 2020-09-01
• Series: Acta Geodaetica et Cartographica Sinica
• Subjects: global navigation satellite system interferometric reflectometry; wavelet transform; sliding window; snow depth estimation; land surface roughness
• Online Access: http://xb.sinomaps.com/article/2020/1001-1595/2020-9-1179.htm

Currently, GNSS interferometric reflectometry technology has become a high-precision method for monitoring land surface snow depth. Aiming at the problems of signal separation and random estimation biases, we developed a GNSS-IR refined model with multi-satellite fusion for snow depth estimation combining wavelet transform with sliding window. The common polynomial method was replaced by discrete wavelet transform to obtain the high-quality SNR sequences of the reflected signals which can calculate the reflected height of GPS antenna. Then, these reflected heights from SNR observations of multi-satellite were effectively selected and averaged using the sliding window under a constrained threshold. The refined model was established using GNSS observations for snow season from 2016 to 2017, and then the snow depth datasets of both PBO H₂O and SNOTEL were regarded as reference to verify the performance of the refined model. The results show that there is a high agreement between snow depths derived from the refined model and in situ measurements, and the RMSE is 10 cm. Compared with the results of a single satellite, the accuracy and the stability of the refined model with multi-satellite fusion are obviously better. In terms of RMSE, the accuracy of the refined model has been improved by 50% when compared with PBO H₂O dataset. In addition, taking into consideration that land surface roughness is an error factor, a relative RMSE value of snow depth estimations corrected by a new datum of the reflection height is approximately 4 cm, and the correlation coefficient between snow depth estimations and in situ measurements reaches 0.98.