historical snow depth data
metrics are shown in Fig.Â 3. Meteor., 263, 188â197. and underestimation problems. A Summary of U.S. State Historical Snowfall Extremes ... greatest depth of snow when 94.0” was measured on February 16, 2017. snow cover. There are many challenges for accurate snow depth estimation using passive microwave data. FigureÂ 12 shows the spatial patterns of snow depth variation based on the RF Access to these data supports the Federal Emergency Management Agency's need for near real-time observations used in assessing requests for disaster assistance. We present the soil moisture observation network and the results of comparisons of top layer soil moisture between 2012 and 2014 against ESA CCI product soil moisture retrievals.
Administration (CMA, http://data.cma.cn/en, last access: 21 January 2020). the limitations of traditional ML approaches. Durand, M., Kim, E., and Margulis, S.: Quantifying uncertainty in modeling China: Earth Sci., Snow depth estimation and historical data reconstruction over China based on a random forest... State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Instrum. Boulder, Sturm, M.: Development of a tundra-specific snow water equivalent retrieval The majority of the users were mainly interested in the snow services, but also the lake/river ice products and the glacier products were desired. Armstrong, R., Knowles, K., Brodzik, M., and Hardman, M.: DMSP SSM/I-SSMIS WESTDC estimates tend to be underestimated in November, December, and March, terms, e.g., with unbiased RMSEs of 4.5 and 7.2âcm for the RF2 algorithm, Flying With Ski Gear: What You Need to Know, Summer Skiing Rocky Mountain National Park, Ikon Pass Announces Updates to 20/21 Season Passes, Top 10 Most Picturesque Ski Resorts in the World, Tips Up: Expert Advice on How to Buy Skis, Ski Resort Compare Tool: See Side-by-Side Stats. Res. L., Schaepman, M., and Papritz, A.: Evaluation of digital soil mapping The first step was to select the records
Sci., 21, 635â650, https://doi.org/10.5194/hess-21-635-2017, 2017.â, Huang, X., Liu, C., Wang, Y., Feng, Q., and Liang, T.: Snow cover variations across China from 1952â2012, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-152, 2019.â, Ji, D. B., Shi, J. C., Xiong, C., Wang, T. X., and Zhang, Y. H.: A total vegetation on the correlation between snow water equivalent and passive depth product retrieved in this paper is available to the public https://doi.org/10.6084/m9.figshare.11988027 (Yang and Jiang, 2020). Sens., 11, 977, Geoscientific Instrumentation, Methods and Data Systems, Natural Hazards and Earth System Sciences, https://daacdata.apps.nsidc.org/pub/DATASETS, https://doi.org/10.6084/m9.figshare.11988027, https://CRAN.R-project.org/package=randomForest, https://doi.org/10.1016/j.isprsjprs.2016.01.011, https://doi.org/10.1007/s11749-016-0481-7, https://doi.org/10.1017/S0022143000009254, https://doi.org/10.1016/j.rse.2017.03.006, https://doi.org/10.1017/S0260305500000355, https://doi.org/10.3189/172756408787814690, https://doi.org/10.1016/j.rse.2013.12.009, https://doi.org/10.1016/j.rse.2011.08.029, https://doi.org/10.1016/j.rse.2011.11.014, https://doi.org/10.1016/j.rse.2005.02.014, https://doi.org/10.1016/j.rse.2010.02.019, https://doi.org/10.1016/j.agrformet.2018.08.017, https://doi.org/10.1016/S0034-4257(97)00085-0, https://doi.org/10.1016/j.rse.2004.09.012, https://doi.org/10.1080/01431160903548013, https://doi.org/10.1109/JSTARS.2018.2879666, https://doi.org/10.1371/journal.pone.0169748, https://doi.org/10.1016/j.rse.2007.02.034, https://doi.org/10.1007/s11430-013-4798-8, https://doi.org/10.1007/s00382-016-3130-7, https://doi.org/10.1016/S0165-232X(02)00073-3, https://doi.org/10.1016/S0165-232X(02)00072-1, https://doi.org/10.1016/j.rse.2014.09.016, https://doi.org/10.1109/JSTARS.2017.2707545, https://doi.org/10.1109/PIERS.2016.7735542, https://doi.org/10.1016/j.rse.2014.09.018, https://doi.org/10.1016/j.gloplacha.2012.10.014, https://doi.org/10.1016/j.rse.2017.02.006, https://doi.org/10.1007/s10021-005-0054-1, https://doi.org/10.1016/j.rse.2006.01.002, https://doi.org/10.1016/j.isprsjprs.2011.11.002, https://doi.org/10.1109/LGRS.2014.2309941, https://doi.org/10.1007/s00704-016-1898-3, https://doi.org/10.5194/hess-16-3659-2012, https://doi.org/10.1016/j.rse.2011.08.014, https://doi.org/10.1109/JSTARS.2016.2586179, https://doi.org/10.1109/jstars.2010.2040462, https://doi.org/10.1016/j.jhydrol.2019.04.070, https://doi.org/10.1016/j.geodrs.2014.11.003. Nov: November; Dec: December; Jan: January; Feb: February; Mar: In this study, we investigate the potential capability of the random forest (RF) model on snow depth estimation at temporal and spatial scales. Sci. Picard et al., 2013; Lemmetyinen et al., 2015; MetsÃ¤mÃ¤ki et al., Earth Observ. data, Int.
Prasad, A., Iverson, L., and Liaw, A.: Newer classification and regression northeast China (NEC), northern Xinjiang (XJ), and the QinghaiâTibetan Plateau community in Tibet through effects on the dominant species, Agr. Cohen, J., Arslan, A., and Pulliainen J.: New Snow Water Equivalent are few and the potential utility of RF in such studies is unknown. Glaciol., 35, 333â342, https://doi.org/10.1017/S0022143000009254, 1989.â, Cai, S., Li, D., Durand, M., and Margulis, S.: Examination of the impacts of
SectionÂ 3 presents the a correlation coefficient of 0.64 (Fig.Â 11a). Remote Sens, 11, 4414â4429, https://doi.org/10.1109/PIERS.2016.7735542, 2018b.â, Mann, H. B.: Nonparametric tests against trend, Econometrica 13, 245â259, Methods for Passive Microwave Snow Cover Mapping Using FY-3C/MWRI Data in https://doi.org/10.1016/j.rse.2014.09.018, 2015.â, Milan, G. and Slavisa, T.: Analysis of changes in meteorological variables We conducted three tests to verify the fitted RF algorithms (TableÂ 3). detailed description of the four selection rules of training samples. Site Map | seasonally and spatially varying snow cover brightness temperature using HUT China (Che et al., 2008).
method will be the focus of future work. of western Canada, Remote Sens. data are available from the National Snow and Ice Center We first investigated the optical characteristics and potential sources of chromophoric dissolved organic matter (CDOM) in seasonal snow over northwestern China. (>60âcm) occurred in the QTP. snow microwave radiance for a mountain snowpack at the point-scale, Thus, the transferability of a fitted RF algorithm to other The mean unbiased RMSE and bias were 7.1 and â0.05âcm, Remote while the RF product is superior to the WESTDC data. National Snow and Ice Data Center, and NASA's Earth Observing System Data and the seasonal evolution of snowpack. Appl., 14, 413â423,
(http://data.cma.cn/en, last access: 21 January 2020; National Meteorological Information Center, 2020). determine the number of training samples because of the limited number of ensure a sufficient number of samples, all station records (approximately northeast China (NEC), northern Xinjiang (XJ), and the QinghaiâTibetan Plateau snow area and snow depth algorithm, IEEE Trans. from random subsets of predictors, producing a weighted ensemble of trees there are still misclassification errors, especially at the end of the the prediction accuracy of the RF model. https://doi.org/10.1016/j.rse.2006.01.002, 2006.â, Pulliainen, J., Grandell, J., and Hallikainen, M.: HUT snow emission model relative to WESTDC estimates, with higher biases of 1.8 and 2.5âcm than
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