## 2 Materials and Methods  
### Data  
Gridded datasets of SIF, SST, specific humidity, air temperature, soil moisture, rainfall and wind components collected during 2001-2018 were used in the study (Table 1). Global Orbiting Carbon Observatory SIF (GOSIF) version 2 data used in this study which was developed by integrating coarser Orbiting Carbon Observatory-2 (OCO-2) SIF retrievals at 757 nm, surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS) and reanalysis meteorological data from the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) using Cubist regression tree model, which is an extension of [PERSON]'s M5 model tree [46]. Approximately 2.6 million data points were used to train the model, which could encompass different climatic conditions and ecosystem types. This model predicted SIF fairly well across the globe (R\({}^{2}\) = 0.79, RMSE = 0.07 W m\({}^{-2}\)\(\mu\)m\({}^{-1}\) s\({}^{-1}\)) with a continuous global coverage over 2000-2021, the longest length of SIF record available so far [47]. The predicted SIF has an improved spatial (0.05\({}^{\circ}\)) and temporal (8-day) resolution compared with other gridded SIF datasets (GOSAT and GOME-2) and a reasonable seasonal cycle which is similar to that of coarser-resolution OCO-2 SIF records [48]. GOSIF estimates are highly correlated with ground-based GPP from 91 eddy covariance flux sites (R\({}^{2}\) = 0.73, \(p\) < 0.001) across the globe [49]. Only a few studies explored the potential of SIF data to monitor the Indian terrestrial ecosystems [50], and it well-represented the vegetation anomalies across India during the monsoon period [51]. SST data were collected from the high-resolution product of the NOAA 0.25\({}^{\circ}\) daily Optimum Interpolation Sea Surface Temperature version 2 (OISST V2) [52]. OISST V2 is one of the most popular L4 datasets constructed by combining satellite and in-situ [53] on a regular global grid, with gaps filled in by the interpolation technique. The bias correction of satellite SSTs using in situ data is applied to compensate for any biases in the satellite data using the empirical orthogonal teleconnections method [54]. To represent the influence of ENSO, the NINO3 index was used in the study. NINO3 index is the SST anomalies averaged over 5\({}^{\circ}\)S-5\({}^{\circ}\)N & 150\({}^{\circ}\)W-90\({}^{\circ}\)W (Nino 3 region). This index was derived by calculating the monthly anomalies of Nino 3 region-averaged SST obtained from the global climate observing system of the Physical Sciences Laboratory (PSL) [55]. The intensity of IOD is measured by dipole mode index (DMI). Dipole mode index (DMI) is the SST anomaly (SSTA) difference between the tropical Western Indian Ocean (10\({}^{\circ}\)S-10\({}^{\circ}\)N & 50\({}^{\circ}\)E-70\({}^{\circ}\)E, IOD west) and the tropical South-eastern Indian Ocean (10\({}^{\circ}\)S-0\({}^{\circ}\)S & 90\({}^{\circ}\)E-110\({}^{\circ}\)E, IOD east) which is computed as DMI = SSTA \({}_{\text{IOD west}}\) - SSTA \({}_{\text{IOD east}}\). The monthly DMI dataset was obtained from PSL [56]. Specific humidity and air temperature data (2 m) were obtained from the MERRA-2 model outputs [57]. The MERRA-2 data products have a horizontal resolution of 0.625\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) and can be retrieved from the Geovanni. Soil moisture data were obtained from the Farime Early Warning Systems Network (FEWS NET) land data assimilation system (FLDAS). FEWS NET offers soil moisture distribution products derived from the FLDAS Noah Land Surface Model L4 at 0.10\({}^{\circ}\) horizontal resolution on a monthly and