## 3 Results  
### Spatial-Temporal Variations of GPP, GOSIF, and VIs during 2000-2020  
To investigate the effects of drought on vegetation GPP, firstly, we verify the applicability of MODIS GPP product data in the MLRYR by adopting the monthly observed GPP datasets from the Qianyanzhou flux tower during 2003-2005. As shown in Figure 2a, the monthly observed GPP and MODIS GPP exhibited a consistent variation trend, and the MODIS GPP was generally underestimated, especially in 2004. In addition, the effect of scattering fitting between the observed GPP and MODIS GPP is satisfactory, with the R\({}^{2}\) value of 0.854 and the \(p\)-value less than 0.05 (Figure 2b). The above phenomena manifested that the overall accuracy of MODIS GPP product data is high and can effectively reflect the GPP variation in this region.  
To explore the correlation between GPP and GOSIF, NDVI, EVI, and NIRv, the spatial characteristics of these variables during 2000-2020 are displayed in Figure 3. As shown in Figure 3, the annual mean GPP exhibited a gradually decreasing trend from south to north. The spatial difference is evident, with the high values mainly distributed in forests and grasslands. Although GOSIF, NDVI, EVI, and NIRv showed similar spatial distribution characteristics to GPP, the spatial differences between these factors were still apparent. For example, the values of EVI and NIRv were high in the northern parts of Jiangsu and Anhui provinces; the spatial difference of NDVI in the central and southern regions was not noticeable, and there was no gradual change. Moreover, the linear fitting R\({}^{2}\) between GPP and SIF, NDVI, EVI, and NIRv at pixel scale is 0.681, 0.676, 0.590, and 0.548, respectively, with all \(p\)-values less than 0.05; therefore, the spatial distribution characteristics of GOSIF and GPP are most similar by comparison.  
Figure 2: Comparison between the monthly observed GPP in Qianyanzhou flux tower and the monthly MODIS GPP (**a**) and their linear fitting (**b**) during 2003–2005.  
Moreover, as shown in Figure 4a, the GPP, GOSIF, NDVI, EVI, and NIRv all exhibited significant increasing trends during the past 21 years, with the change rate of 8.60 gC\(\cdot\)m\({}^{-2}\).a\({}^{-2}\), 0.002 W\(\cdot\)m\({}^{-2}\).\(\cdot\)um\({}^{-1}\).sr\({}^{-1}\).a\({}^{-1}\), 0.004 a\({}^{-1}\), 0.003 a\({}^{-1}\), and 0.002 a\({}^{-1}\), respectively. In terms of the changing trend, the GOSIF was also closest to the GPP. To reveal the correlation between GPP and other VIs, the annual scatter fittings between GPP and GOSIF, NDVI, EVI, and NIRv are displayed in Figure 4b-e. The relationship between GPP and each vegetation index exhibited linear correlation; the corresponding R\({}^{2}\) values were 0.87, 0.83, 0.79, and 0.80, respectively, with all \(p\)-values less than 0.05, which verified that the GOSIF is most correlated with GPP on an annual basis time scale.  
Figure 3: Spatial patterns of the annual mean values during 2000–2020: (**a**) MODIS GPP; (**b**) GOSIF; (**c**) NDVI; (**d**) EVI; (**e**) NIRv.  
The monthly GPP, GOSIF, NDVI, EVI, and NIRv exhibited evident interdecadal variation characteristics (Figure 5a). In addition, the correlation between monthly GPP and GOSIF, NDVI, EVI, and NIRv during 2000-2020 presented polynomial distribution (Figure 5b-e). The fitting R2 are 0.976, 0.891, 0.948, and 0.945, respectively, which further verified that the GOSIF is also most correlated with GPP on the monthly time scale.