## 4 Discussion  
We presented an operational downscaling method that can generate a 500 m 16-day _SIF_ product using TROPOSIF and MODIS data over a local spatiotemporal window. Similar methods have been widely used for LST downscaling [27; 28; 29] and the same idea has also been used in _SIF_ downscaling in previously published studies [14; 15]. For example, [PERSON] et al. [14] used a machine learning method stratified by biomes and times for training and predictions to generate a 0.05\({}^{\circ}\) 16-day OCO-2-derived reconstructed _SIF_ product [14]. [PERSON] et al. used a machine learning method imposed with regionalization constraints to account for relationships between explanatory variables and _SIF_ in space and time to generate a long time series of a _SIF_ product at 0.05\({}^{\circ}\) and monthly resolution [8]. However, they only provide coarse resolution reconstructed _SIF_ at 0.05\({}^{\circ}\). Due to the sparse sampling strategy and a long revisit period of OCO-2 and TanSat, training a local model stratified by space and time is impossible for small regions. For other very coarse satellite-observed SIF, the very coarse resolution hinders further resolution enhancement. While _SIF_ applications for drought monitoring have been explored, to the best of our knowledge, no high-resolution _SIF_ has been used. We selected six bands of reflectance data from MCD43A4 and fPAR and LAI from MCD15A2 as our explanatory variables because many studies have shown the BRDF reflectance is a valid explanatory variable and widely used to generate _SIF_ products. We did not select band Band6, because we found that it makes a small contribution to the improvement of the model performance and has too many data gaps in our study area. We selected fPAR because it is one of the components of _SIF_ according to [22]. LAI is related to the _SIF_ escape factor [30]. We did not use ET and LST due to too many data gaps, which would reduce the usability of the downscaled SIF. During the aggregation of TROPOSIF, we calculated the average of all pixels falling within the aggregated grid cells, which is an approach used in many studies [11; 12; 14]. This approach did not allow for _SIF_ footprint geometry and vegetation fraction [15; 24]. In [PERSON]'s work, _SIF_ was aggregated, allowing for vegetation fraction as a weighting factor which assumes that _SIF_ from TROPOMI originates from more vegetated regions. In the future, we will test the differences between different aggregation methods. In addition, as the residuals between TROPOSIF and TSIF show a strong seasonal cycle, it suggests there are still some variations in TROPOSIF that cannot be learned fully due to the 16-day time step, but in general, TSIF reproduces TROPOSIF very well. Our results also show that TSIF has a better temporal consistency with TROPOSIF compared to USIF. The universal model only learns features of samples by the value distribution and learns the temporal features indirectly from the value changes of the samples. So, some studies used the local models to generate downscaled _SIF_ instead of the universal model [14; 15; 31]. During the training process, we found that our method is robust against overfitting. The most reasonable reason is that training data and validation data have common distribution and temporal features which reduce the uncertainties caused by variations in data distribution and seasonality. The data used for fine resolution _SIF_ generation also have similar data distribution and temporal features because the coarse-resolution aggregated MODIS data are simply the average of the fine resolution pixels. The use of _SIF_ for drought monitoring has been widely explored. [PERSON] et al.