## 4 Discussion  
### The Added Value of Incorporating Soil Moisture Information  
Under drought conditions, LUE models generally appear to overestimate GPP, with obvious inter-model differences at the same sites. This is mainly associated with the diverse ways among different LUE models in characterizing the response to moisture stresses, which is a main factor affecting GPP during drought periods [4]. For example, the TL-LUE model uses VPD while VPM utilizes LSWI. According to our study, the simulation results improved after incorporating LSWI into the TL-LUE model, which can be explained by a better capacity of LSWI to decipher drought impacts than that of VPD for grass-type ecosystems [44]. However, for forest ecosystems, the TL-LUE model has overall superior performance than VPM, for both including and not including SM, which may associate with the better capacities of either VPD in characterizing drought impact or the two-leaf schemes in characterizing complex radiation regimes among vegetation canopies. For the TL-LUE model, there was a substantial improvement at most grass-type sites after replacing VPD with SM. VPD is sensitive to air temperature and it is challenging to characterize the effect of soil water deficit on vegetation growth during extreme drought. In addition, grass-type ecosystems have relatively short root lengths and face more difficulties in drawing water from deeper soil layers [45], thus, they are largely dependent on surface SM. [PERSON] improved the SCOPE model by integrating soil moisture data, creating SCOPE-SM to better capture water stress effects on photosynthesis and evapotranspiration under drought. Validation at the US-Var Fluxnet site shows significant accuracy improvements, emphasizing the value of soil moisture observations, which aligns with this study's findings on the critical role of soil moisture in enhancing GPP simulations for grass-type ecosystems [46]. Thus, the response of the LUE model to drought can be better improved by using the surface SM measurements at the site scale. At all forest sites, the results of the various improvement schemes were very similar to the original model. It should be noted that even at the US-GLE site, the simulations with the original model were better than those with improvement schemes. In forest ecosystems, the vegetation is taller and has a greater number of biomes, where various types of water exist, including a greater proportion of atmospheric water, and the vegetation roots are longer and can draw water  
Figure 13: Performance of simulation results with different water stress indicators (including LSWI, VPD, SM*LSWI, and SM*VPD) in VPM at forest sites. from deeper soils. Therefore, the incorporation of surface SM might have only a minimal effect on the improvement of GPP simulations. For VPM, the GPP simulations were improved at most grass-type sites. The results also provide further evidence that SM has a critical influence on the vegetation growth of grass-type ecosystems during dry periods. The results for the US-Var site and the US-SRG site indicate that considering the effects of both SM and LSWI in VPM can substantially improve GPP simulations. Moreover, the scheme multiplying LSWI and SM produced the best performance. This may be because LSWI is only able to represent the water content status in leaves, whereas during extreme droughts, vegetation growth is controlled by both leaf water and SM availabilities [47]. [PERSON]'s study also highlights that plant water stress (measured by LSWI, EF, PM, etc.) influences stomatal conductance and regulates evapotranspiration and photosynthesis, which are crucial for accurately modeling GPP. Atmospheric water stress is often included in models like MOD17 and EC-LUE, while soil moisture and plant water stress are key in models such as GLO-PEM and VPM, reinforcing the importance of integrating these proxies, especially soil moisture, into ecosystem flux models [48].