## 4 Discussions
### Parameterization and Uncertainties  
Several parameters are required to derive the potential of carbon uptake from the optimal LMPs in an urban ecosystem. First of all, the carbon uptake potential is based on the vegetation productivity of historical datasets. To model NPP distribution, two models, namely CASA and 3-PGS, were integrated to map NPP for GGA. Those process-based models are simple to build but a couple of empirical parameters must be decided for model execution. In the CASA model, a constant value of 0.5 for the ratio of effective solar radiation is given for all the locations; other parameters such as FPARmax and FPARmin were fixed to be 0.001 and 0.95 (Equation (3)). Similarly, the 3-PGS model depends on several empirical parameters (for example, \(C_{pp}\) and \(\epsilon_{\max}\); see Equations (4) and (5)) which are likely to vary over space. Though such parameters have been tested and recommended by previous studies, they are often questioned whether they are really constant across different locations and vegetation cover classes. An alternative way to determine the model coefficients is to make them dynamically fit from field collected samples, which is believed to be more acceptable. In the computation of the potential of carbon uptake, the size of the neighborhood within which to search for the optimal LMPs was set to be 20 km and the reference percentage (RP) 90%. Combination of the two parameters may affect the target level of carbon uptake under optimal LMPs and the carbon uptake potential. Our previous study has shown that given a reference percentage (RP) of 90%, a neighborhood with a size of ~20 km is optimal [38]. The result is rather sensitive to the parameter RP. A comparative study with RP setting at different levels in the Inner Mongolia grasslands revealed that the potential of carbon capture differed considerably; for example, with RP set at three levels, i.e., mean (50%), 95%, and max (100%), vegetation could add an extra carbon of 11.8, 58.9, and 74.6 gC m\({}^{-2}\) yr\({}^{-1}\), respectively, to its original level [69]. Higher RP means a stricter standard in defining the optimal LMPs, resulting in fewer candidates to select from and making it harder to implement the LMPs in practice. The decision by setting RP = 90% considers the tradeoff between the carbon uptake potential and practical implementation of the optimal LMPs. Instead of NEP, or net ecosystem productivity which reflects the net carbon gain (source or sink) within an ecosystem, we apply NPP as the proxy to indicate the capacity of vegetation carbon uptake. NPP reflects the net carbon sequestration from vegetation but does not account for the carbon sequestration from soil pool. Conversely, NEP considers the carbon released from heterotrophic respiration (Rh) by soils and is derived from NPP and Rh. Because of the difficulty in obtaining Rh, the current study was unable to use NEP. Nevertheless, previous studies show that the total amount of Rh is largely proportional to NPP; for example, the previous study showed that the ratio of Rh to NPP was 0.71 [15]. Thus, the general pattern in the NEP potential from optimal LMPs is likely identical to the potential of NPP, though in different magnitude, and thus the conclusion that optimal LMPs can improve net carbon sequestration from vegetation should still hold. We acknowledge the uncertainties in our assessed potential of carbon uptake due to various reasons. First, the carbon uptake potential was essentially derived from the historical vegetation productivity data, i.e., NPP, simulated by process-based LUE models. The models were not calibrated. The key driven variables for the models, including NDVI from the sentinel-2 remote sensing images and the meteorological variables may also contain certain noises [70], which would be reflected in the output from the models. Second, the factors driving vegetation carbon capture are complex and quantitative analysis of the impact from LMPs imposes a big challenge.