Assessment of Methods for Passive Microwave Snow Cover Mapping Using FY-3C/MWRI Data in China

Xiaojing Liu,Lingmei Jiang,Gongxue Wang,Shengli Wu,Shirui Hao,Jianwei Yang

doi:10.3390/rs10040524

Abstract

Ongoing information on snow and its extent is critical for understanding global water and energy cycles. Passive microwave data have been widely used in snow cover mapping given their long-time observation capabilities under all-weather conditions. However, assessments of different passive microwave (PMW) snow cover area (SCA) mapping algorithms have rarely been reported, especially in China. In this study, the performances of seven PMW SCA mapping algorithms were tested using in situ snow depth measurements and a one-kilometer Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover product over China. The selected algorithms are the FY3 algorithm, Grody’s algorithm, the South China algorithm, Kelly’s algorithm, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm. During the test period, most algorithms performed reasonably well. The overall accuracy of all algorithms is higher than 0.895 against in situ observations and higher than 0.713 against the IMS product. In general, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm had poor performance during the test. Their misclassification errors were larger than those of the remaining algorithms. Grody’s algorithm, the South China algorithm and Kelly’s algorithm had higher positive predictive values and lower omission errors than those of the others. The errors of these three algorithms were mainly caused by variations in commission errors. Comparing to Grody’s algorithm, the South China algorithm and Kelly’s algorithm, the FY3 algorithm presented a conservative snow cover estimation to balance the problem between snow identification and overestimation. As a result, the overall accuracy of the FY3 algorithm was the highest of all the tested algorithms. The accuracy of all algorithms tended to decline with a decreased snow cover fraction as well as SD < 5 cm. All tested algorithms have severe omission errors over barren land and grasslands. The results shown in this study contribute to ongoing efforts to improve the performance and applicability of PMW SCA algorithms.

Highlights

Snow cover is an important geophysical parameter for understanding global climate change, the radiation budget and the water cycle [1,2]
To test and compare the performance of the seven algorithms, the Snow cover area (SCA) derived from passive microwave (PMW) SCA mapping algorithms were quantitatively evaluated using in situ snow depth (SD) observations and the one-kilometer Ice Mapping System (IMS) snow cover product
Dry snow records of in situ observations were used for analysis because the seven tested algorithms are mostly for dry snow discrimination

Summary

Introduction

Snow cover is an important geophysical parameter for understanding global climate change, the radiation budget and the water cycle [1,2]. Snow cover area (SCA) monitoring using optical and microwave sensors has been reported for decades [4]. A number of snow cover detection algorithms using optical sensors have been developed since the 1980s [5,6,7,8,9]. Snow cover maps derived from optical sensors are strongly influenced by observation conditions such as cloud obscuration and solar illumination. Inaccessibility in cloud cover and weak sun exposure regions greatly limit the applicability of optical SCA products in regional and global applications [4]

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Conclusion