RETRACTED ARTICLE: Estimating snow depth Inversion Model Assisted Vector Analysis based on temperature brightness for North Xinjiang region of China

Abstract

The measurement of snow depth based on temperature brightness with passive microwave sensing is still achallenging problem. Snow depth for the snow cover hydrological model and climate model is asignificant input parameter. Hence, this study concentrates on Inversion Model Assisted Vector Analysis (IMAV) for estimating snow depth in north Xinjiang based on the brightness of temperature. Further, the estimated set of IMAV has been hybridized to address the problem. The results suggested that for both horizontal and vertically polarized PMW radiation the IMAV outperforms SVM at 11.05, 19.6, and 38.4 GHz. If the root mean square error (RMSE) in the IMAV performance is 8 K or below, compared with anormal SVM calculation, then the average over the nine-year study period across the North Xinjiang region of China, the failure correlation coefficient is 7 or greater. Compared with SVM tests, the RMSE was decreased by more than 17% for any of the six frequencies and polarization combinations evaluated, while the anomaly coefficient was raised by more than 50%.Such results suggest that the IMAV is asuperior alternative to the SVM for subsequent use in adata assimilation system as acalculating operator.

Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

“…Another often-applied study area is the Tibetan Plateau in Asia [100][101][102][103][104][105], also sometimes as part of studies conducted over High-Mountain Asia (HMA) [106][107][108][109]. Also often examined is the Mackenzie-Basin in Canada [110][111][112][113][114] or Northwestern China [63,[115][116][117][118][119][120]. Overall, the most often-applied study area is the Northern Hemisphere [13,44,.…”