## 4 Discussion
For the US-Bi2 site, the relationship between GPPMODIS-GPP, GPPEC-LUE and GPPEC was moderate, and GPPEC-LUE obviously underestimated the cropland GPP (RMSE = 7.70 gC/m\({}^{2}\)/d, bias = \(-\)4.53 gC/m\({}^{2}\)/d and RMSE = 7.80 gC/m\({}^{2}\)/d, Bias = \(-\)4.15 gC/m\({}^{2}\)/d, respectively). This finding is aligned with previous research [50; 87]. In crop ecosystems, different crops have different \(\varepsilon_{\text{max}}\) and growth cycles, so a single \(\varepsilon_{\text{max}}\) cannot represent well all types of crops [88; 89]. Additionally, the rotation period varies for different crop types. Therefore, if the same input variables are used to estimate GPP for different crops, this can lead to a large deviation in GPP estimation [90]. The performance of GPPGR and GPPV was diverse for different biome types. The correlation between GPPGR, GPPV, and GPPEC was strongest at the US-WCr site, with R\({}^{2}\) = 0.84 and 0.81, respectively. Compared to the US-WCr site, the correlation between GPPGR, GPPVI and GPPEC was relatively weak at the US-GLE site. This result was consistent with previous research, where OTCI was highly correlated with tower GPP across deciduous forests and weakly correlated with tower GPP across evergreen forests [49; 50]. The reason for this phenomenon could be that stress causes a decrease of the photosynthetic efficiency [91], the conical canopy structure, and the density of evergreen needleleaf trees. The consequent shadowing effect leads to the failure of OTCI for detecting subtle changes within the seasonal chlorophyll content of evergreen needleleaf forests [92; 93]. Additionally, GPPGR and GPPVI showed moderate correlations with GPPEC at the US-PFa site. The factors that caused this can be considered from two aspects. Firstly, the vegetation structure of mixed forests is complex, and different vegetation types will affect the estimation of GPP. Secondly, mixed forest productivity will drop sharply when the temperature drops in a short period. However, the vegetation index cannot change significantly in this case, leading to its failure in capturing the impact of temperature change on GPP [47; 94; 95]. A strong correlation could also be observed between GPPGR, GPPVI, and GPPEC at the US-Bi2 site. Previous studies have found a similar relationship between OTCI or other chlorophyll indices and GPP [49; 78; 96]. Notably, GPP\({}_{\text{GR}}\) and GPP\({}_{\text{VI}}\) had the highest RMSE (4.21 gC/m\({}^{2}\)/d and 4.29 gC/m\({}^{2}\)/d, respectively) at the US-Bi2 site. The reason for this is that compared with GPP\({}_{\text{EC}}\), GPP\({}_{\text{GR}}\) and GPP\({}_{\text{VI}}\) overestimated GPP in the nongrowing season and underestimated it in the growing season (Figure 7h). A previous study demonstrated that OTCI started to increase earlier than the time of corn sowing [49]. The high GPP\({}_{\text{GR}}\) and GPP\({}_{\text{VI}}\) obtained in the nongrowing season may represent fallow land, which is gradually colonized by weed species or affected by humidity changes, and not represent actual crop growth [49]. At the US-Var site, the correlation between GPP\({}_{\text{VI}}\) and GPP\({}_{\text{EC}}\) (R\({}^{2}\) = 0.66) was better than that between GPP\({}_{\text{GR}}\) and GPP\({}_{\text{EC}}\) (R\({}^{2}\) = 0.50). This difference may be related to the vertical and horizontal heterogeneity of grasslands [49]. C3 annual grasses dominated the US-Var site. Previous studies [49; 97] showed that, compared with grasslands dominated by C4 species, C3 grasslands exhibited lower OTCI values during the peak growth period, explaining why GPP\({}_{\text{VI}}\) had a higher accuracy than GPP\({}_{\text{GR}}\). However, GPP\({}_{\text{GR}}\) and GPP\({}_{\text{VI}}\) performed poorly in woody savannas and two shrubland sites. The probable reason for this was that the influence of the soil background on the low vegetation coverage at these sites led to the failure of OTCI in tracking GPP [50].