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

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.

Development of a Snow Depth Estimation Algorithm over China for the FY-3D/MWRI

“…The estimated snow depth was retrieved with different algorithms. To remove the scattering signals of frozen ground, cold desert, and rainfall, this study applied Li's snow cover identification method [43] based on Liu et al's (2018) assessment of snow cover mapping methods [44]. It also should be noted that in this study the validation was conducted with brightness temperatures at 10.65, 18.7, 36.5, and 89 GHz from FY-3C/MWRI.…”

The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China

“…(1) PMW Snow Cover Mapping Algorithms Microwave scattering from dry snow results in a positive Tb gradient between low-and high-frequency channels. However, other scattering materials such as precipitation, deserts, and frozen ground also produce a similar spectral response to snow, which makes distinguishing snow from other land cover types difficult [41][42][43]. Most PMW snow cover detection algorithms are based on a decision tree classification approach.…”

“…Most PMW snow cover detection algorithms are based on a decision tree classification approach. According to available channels of sensors and the assessment work in [43], only two PMW mapping algorithms were selected for consistency testing in this paper. The classification criteria of the two algorithms are shown in Table 3.…”

“…To illustrate the influence of snow cover on Tb consistency between SSM/I and SSMIS, in this study, these pairwise pixels were separated into two groups, snow cover vs. snow-free. Li's snow detection method was employed to determine snow or non-snow based on the assessment work in [43]. Figure 3 (left) shows that the Tb from SSMIS at 19 GHz tends to be lower than the SSM/I observation for each land cover type, with a large mean bias (approximately −2.8 K to −4.6 K) and mode bias (approximately −2.5 K to −4 K).…”

Recent Progress in Quantitative Land Remote Sensing in China

“…Although 40 articles cannot comprehensively characterize different aspects of quantitative land remote sensing in China, they clearly represent the current level of research in this area by Chinese scientists. These papers are related to various satellite data products, such as incident solar radiation [38][39][40], chlorophyll fluorescence [41], surface directional reflectance [42][43][44], aerosol optical depth [45], albedo [46,47], land surface temperature [48][49][50], upward longwave radiation [51], leaf area index [52][53][54][55], fractional vegetation cover [56], forest biomass [57], precipitation [58], evapotranspiration [59][60][61], freeze/thaw [62], snow cover [63], vegetation productivity [64][65][66][67][68], phenology [69,70], biodiversity indicators [71], drought monitoring [72], forest disturbance [55], air-quality monitoring [73], sensor design [74], and sampling strategy [75] for validation with in situ measurements. Most of these papers are based on optical-thermal remotely-sensed observations, but a few papers are also based on microwave [62,63] and Lidar…”