Ure 5b, most leads had less than 0.1 had been normally significantly less than 1 km. Certainly, asas shown in Figure 5b, most leads had significantly less than km2km2 of location, which accounts tiny a tiny portion entire 25 25 km25 cells.grid cells. 0.1 of region, which accounts to get a for portion of the with the complete 25 grid km Hence, it can be affordable that the DMS-based lead Cefalonium medchemexpress detection and AMSR-based TIC were not extremely Therefore, it is actually affordable that the DMS-based lead detection and AMSR-based TIC had been not correlated (R 0.21, FigureFigure eight), since narrow leads are hardly detected bycoarse reshighly correlated (R 0.21, 8), for the reason that narrow leads are hardly detected by the the coarse olution satellite information [14,40]. One example is, we identified that most the majority of AMSR-based TIC resolution satellite data [14,40]. By way of example, we located that of AMSR-based TIC along the track was zero and AMSR-based SIC was one hundred even thoughthough the DMS images along the track was zero and AMSR-based SIC was 100 even the DMS pictures clearly showed leads in that location. region. clearly showed leads in thatFigure 8. Scatter plot in between DMS-based lead fraction (this study) and AMSR-based TIC. Figure 8. Scatter plot amongst DMS-based lead fraction (this study) and AMSR-based TIC.Figure 9 shows the lead fractions and related dynamic and thermodynamic variables Figure 9 shows the lead fractions and associated dynamic and thermodynamic variables at the scale of 25 km on the similar days that DMS photos were taken from 2012 to 2018. Within the scale of 25 km around the exact same days that DMS pictures had been taken from 2012 to 2018. In at common, the lead fractions did not show significant correlation with any single auxiliary Pyridaben Anti-infection variable or kinetic property from sea ice motion data. This really is affordable for the reason that (1) these ancillary data have 25 km spatial resolution, that is a great deal coarser than the spatial resolution with the DMS image; (2) the DMS images have only 500 m of width, representing only a little portion along the Laxon Line; and (3) the formation of sea ice leads final results in the accumulative and complicated effects of several dynamic and thermodynamic variables, rather than just one variable. Though the DMS images have different spatial scale using the ancillary datasets, we attempted to explore the potential relationship the DMS-based lead fractions and sea ice dynamic and thermodynamic variables in the ancillary datasets. Assuming that (1) these variables will be the outcomes in the large-scale atmosphere and ocean circulation and (two) the combination of those variables somehow affects the formation of leads, we normalized all explanatory variables and constructed a series of multiple-variables linear regression models, as shown in Equation (7). SILF =k =a xnk k(7)exactly where xk is one of the normalized dynamics-thermodynamic variables, and ak are corresponding coefficients.Remote Sens. 2021, 13, 4177 PEER Assessment Remote Sens. 2021, 13, x FOR15 14 of 18 ofFigure 9. (a) DMS-based lead fraction and nearby ice types; (b) ERA5 air temperature; (c) ERA5 wind velocity; (d) sea ice Figure 9. (a) DMS-based lead fraction and nearby ice kinds; (b) ERA5 air temperature; (c) ERA5 wind velocity; (d) sea ice motion for each year. motion for every year.Remote Sens. 2021, 13,15 ofThe lead fraction variable is the imply of all DMS image-based lead fractions within a 25 km block. However, all dynamic-thermodynamic variables, which includes 4 kinetic moments in the NSIDC sea ice motion data, ERA5 air temperature, and wind velocity data, had been averaged by 1, two,.