## 5 Discussion  
The parameters \(d\) and \(z_{\rm 0m}\) have been used in ET models that include MOS theory, such as the SEBS model. Although our sensitivity analysis indicated that errors within the SEBS led to estimated \(H\) values with deviations (\(\pm\) 25%) of \(z_{\rm 0m}\) that were smaller than those for LST and air temperature, a 50% deviation in the parameterization of \(z_{\rm 0m}\), as well as forest height, can generate errors of more than 40% for the \(H\) estimation. The result is consistent with those presented by [PERSON] _et al._[50], [PERSON] _et al._[75], and [PERSON] _et al._[79]. In particular, according to the study of [PERSON] _et al._[71], the annual maximum effect of the roughness length dynamic on sensible heat flux was 33.80% and 18.11% for the Qianyanzhou (with a 11-12 meter high artificial needle forest) and Changbai Mountains (with a 26 meter high natural mixed forest) experimental stations, respectively. If canopy height information is not available for \(z_{\rm 0m}\) parameterization within the SEBS, it can be retrieved using the empirical equation that involves NDVI values (NDVIs of fully vegetated and bare soil), as well as maximum and minimum canopy heights. Some sensitivity analyses [38, 49] for maximum height have indicated the importance of this parameter through replacement with measured values. This type of empirical method may be suitable for low canopies with short height ranges. However, due to the saturation of spectral signals, the NDVI in forests cannot represent canopy height variations, so canopy heights, especially for long-term heterogeneous forest parameterization, will generally be underestimated. Additional studies [43, 44, 47] have indicated that vegetation vertical parameters (_i.e._, the height and FAI) are critical for estimates of \(d\) and \(z_{\rm 0m}\) obtained via remote sensing-based roughness models [42, 43, 44]. However, few studies have focused on the long term parameterization scheme of \(d\) and \(z_{\rm 0m}\) while considering the variation of vertical vegetation information over time. Vegetation height is commonly derived from vegetation indexes (_i.e._, NDVI and LAI) using optical remote sensing data that may be suitable for low vegetation (for example, pastures, crops, shrubs, _etc._) [80, 81] or that can be derived from active remote sensing (_i.e._, Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR)) data sensitive to forest vertical structure [82, 83]. Nevertheless, these methods are not possible for growing forests. On the one hand, vegetation indexes in the QMs represent a mixture of the top canopy and the understory in forest gaps. On the other hand, the local extremely complicated terrain condition hinders SAR data applicability, and expensive single-pass airborne LiDAR data obtained by WATER can only derive vegetation height over a certain period and for a certain area. Therefore, at present, remote sensing data cannot provide reliable forest vertical dynamic information for this study area. In an earlier study [47], we reported that the SD00 model outperformed other aerodynamic roughness models. Here, dynamic forest vertical information for height and the FAI were obtained from field calibrated regression models and interannual forest AGB variations in order to drive the SD model. Instead of the arithmetical mean height, the Lorey's height was derived due to the fact that it is a better expression for forest stand status, as well as for \(d\) and \(z_{\rm 0m}\)[44, 47]. Although the \(R^{2}\) values (0.52 and 0.36) of both fittings were not high, the RMSEs (2.35 m and 0.35 were relatively low. Based on the high heterogeneity of local forests under various stand conditions (topography, tree density, height, FBH, age, AGB level, overlapping canopies, _etc._), regression relationships (forest Lorey'sheight _versus_ forest AGB and FAI _versus_ forest AGB) established on the basis of forest measurements were reasonable.