## 2 Materials and Methods
### Remote Sensing and Drought Data  
Among the existing remote sensing vegetation indices, SIF signals most closely reflect the actual photosynthesis of vegetation and can more effectively indicate vegetation growth status. The global dataset of solar-induced chlorophyll fluorescence dataset (GOSIF) is proven to be one of the most effective SIF datasets developed so far [37; 42]. The GOSIF dataset was developed by [PERSON] and [PERSON] with a resolution of 0.05\({}^{\circ}\)and an eight-day interval [43]. This dataset is generated using a Cubist regression tree model based on discrete OCO-2 SIF measurements, MODIS EVI, MODIS land cover type, and MERRA-2 meteorological variables.  
To ensure the representativeness of the data at each site, we extracted SIF values within a 25 km radius of each flux tower, with the underlying surface being rubber plantations (Figure 1). The GOSIF data extraction period ranges from 2001 to 2020.  
The NDVI, EVI, and NIRv vegetation indices were calculated using MCD43A4 Version 6.1 [44] based on the following formulas. The reflectance data in the MCD43A4 dataset represent the adjusted surface normalized bidirectional reflectance distribution function (NBAR). NBAR represents the surface reflectance corrected for atmospheric scattering and absorption, reflecting the intrinsic optical properties of the surface. Adjusting the reflectance data eliminates atmospheric effects, providing more accurate surface reflectance information. When extracting MCD43A4 band values, we averaged all high-quality data pixels within a 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) window centered on each flux tower. Then, we used the maximum value composite method to obtain vegetation indices with an eight-day temporal resolution.  
\[\mathrm{NDVI}=\frac{\rho_{\mathrm{NIR}}-\rho_{\mathrm{RED}}}{\rho_{\mathrm{ NIR}}+\rho_{\mathrm{RED}}} \tag{1}\]  
\[\mathrm{EVI}=2.5\times\frac{\rho_{\mathrm{NIR}}-\rho_{\mathrm{RED}}}{\rho_{ \mathrm{NIR}}+6\times\rho_{\mathrm{RED}}-7.5\times\rho_{\mathrm{BLUE}}+1} \tag{2}\]  
\[\mathrm{NIRv}=\rho_{\mathrm{NIR}}\times(\mathrm{NDVI}-0.08) \tag{3}\]  
PAR data were extracted from MCD18C2 Version 6.1. This dataset is a level 3 gridded product of PAR, combined from MODIS Terra and Aqua, produced daily at a 0.05-degree resolution (5600 m at the equator) and generated every 3 h [45]. Then, the eight-day PAR value corresponding to GOSIF time interval is calculated by summation.  
The drought index used is the standardized precipitation evapotranspiration index (SPEI) dataset version 2.9 [46]. The SPEI is calculated using precipitation and temperature data, represents wetness and dryness conditions, and can be computed over various time scales (1, 3, 12, or 24 months, etc.). Higher SPEI values indicate wetter conditions, while lower values indicate drier conditions, making it widely used in drought research. It is an advancement of the standardized precipitation index (SPI), incorporating the effects of evapotranspiration, making it more suitable for regions with significant temperature trends and particularly effective for long-term studies. Its spatial resolution is 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\), with a monthly temporal resolution.