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Haolu Shang, Li Jia, and Massimo Menenti

observation gaps of the PDBT time series. Noise-free daily PDBT, vertical brightness temperature (BT), and NDVI are derived from the Harmonic Analysis of Time Series (HANTS) algorithm of their raw data time series. The vegetation transmission function is obtained from the regression between NDVI and its dependent variable PDBT for flooded paddy fields, under the assumption that the land surface temperature and PEED of the flooded rice is constant during its growing season. The quasi-linear relationship

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Mustafa Gokmen, Zoltan Vekerdy, Maciek W. Lubczynski, Joris Timmermans, Okke Batelaan, and Wouter Verhoef

the total amount of snow cover days from MOD10A2 product in 2005–06 (between October and April). The data of in situ snow measurements were obtained from DSI (unpublished data). The snow measurements were conducted on monthly basis ( Fig. 3 ) by DSI between October and May, recording the average snow depth, snow water equivalent, and snow density values. In the multivariate regression analysis, we used the maximum snow depth and corresponding SWE values (usually occur in April/May) as the yearly

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Donghai Zheng, Rogier van der Velde, Zhongbo Su, Martijn J. Booij, Arjen Y. Hoekstra, and Jun Wen

) from 20 May 2009 to 17 May 2010 is used for this analysis. The dataset includes eddy covariance (EC) measurements and profile measurements of wind, temperature, and humidity. The bulk MOST formulation is used in combination with these micrometeorological measurements to derive values for z 0m , z 0h , and kB −1 . Subsequently, these z 0m , z 0h , and kB −1 values are utilized together with the H measurements to assess the performance of the various z 0m and z 0h or kB −1 schemes

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