D thick ice. Though these observations of one particular day per year
D thick ice. Though these observations of a single day per year for seven years can not represent the general continuous Cy5-DBCO Protocol spatiotemporal variations of lead fraction, this basic spatial pattern agrees with that of earlier lead research [5,18,19,39]. Figure 5b portrays the averaged area of person leads for the 25 km track segment, and Figure 5c portrays the ratio of your variety of lead-included images to the total quantity of photos for the 25 km segment. The lead fraction (Figure 5a) was determined by the individual lead region (Figure 5b) as well as the frequency of leads (Figure 5c). By way of example, despite the fact that large leads had been observed in 2013 for 000 km (Figure 5b), lead frequency for this element was low (Figure 5c) resulting from the compact variety of big leads. Consequently, the averaged lead fraction for this Ba 39089 custom synthesis segment was not higher since with the low lead frequency. Moreover, the lead frequency in 2018 for 1000500 km was comparatively high, but the averaged lead fraction was not so high because of the big number of compact leads.Remote Sens. 2021, 13,to the total quantity of photos for the 25 km segment. The lead fraction (Figure 5a) was determined by the person lead area (Figure 5b) and also the frequency of leads (Figure 5c). For instance, although massive leads have been observed in 2013 for 000 km (Figure 5b), lead frequency for this portion was low (Figure 5c) as a result of the small number of massive leads. As a result, the averaged lead fraction for this segment was not high since on the low lead frequency. In addi11 of 18 tion, the lead frequency in 2018 for 1000500 km was relatively high, but the averaged lead fraction was not so higher because of the substantial variety of little leads.Figure five. (a) Averaged lead fraction for each 25 km; (b) averaged location of individual leads for each and every 25 km; (c) frequency Figure 5. (a) Averaged lead fraction for each 25 km; (b) averaged area of individual leads for each and every 25 km; (c) frequency of lead-included photos for each 25 km. Gray components indicate missing/invalid information. of lead-included pictures for every single 25 km. Gray parts indicate missing/invalid data.four.two.two. Retrieval of Freeboard 4.2.two. Retrieval of Freeboard Depending on the DMS lead detection result, we calculated the 400 m imply sea ice freeboard Determined by the DMS lead detection outcome, we calculated the 400 m imply sea ice freeboard fromthe ATM surface height data (Figure six). The MYI region (close to centralcentralOcean) at track in the ATM surface height data (Figure 6). The MYI area (close to Arctic Arctic Ocean) at track 1200 km showed higher a higher (i.e., thicker ice) in comparison to that of to FYI distance distance 1200 kmashowedfreeboard freeboard (i.e., thicker ice) comparedthe that on the FYI region (near the Beaufort Sea using a track distance beyond 1200 km). As shown in Table 7, the FYI area always showed a reduced freeboard than the MYI region. Furthermore, the freeboard retrieved from our lead detection shows a very good correlation using the ATM freeboard item offered by NSIDC [32]–correlation coefficient (R) was 0.832, and root imply square difference (RMSD) was 0.105 m (Table eight). It is also noted that 2015, 2016, and 2017 showed relatively reduced R and larger root mean square error (RMSE) than the other years (Table eight and Figure 7), which could be on account of the lower classification accuracy of these years (Table six). Some misclassified leads can make substantial variations in estimation of sea surface height, ultimately major towards the variations amongst our freeboard estimation plus the NSIDC freeboard solution.
