###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and a promising indicator of drought monitoring, but the ability of high-resolution satellite-derived _SIF_ for drought monitoring has not been widely investigated due to a lack of data. The lack of high spatiotemporal resolution satellite _SIF_ hinders the resolution enhancement of _SIF_ derived by downscaling or reconstruction algorithms. The TROPOspheric Monitoring Instrument (TROPOMI) _SIF_ provides an alternative with finer spatiotemporal resolution. We present an operational downscaling method to generate 500 m 16-day _SIF_ (TSIF) using Neural Networks over a local spatiotemporal window. The results showed that our method is very robust against overfitting, and TSIF has a strong spatiotemporal consistency with TROPOMI _SIF_ (TROPOOSIF) with R\({}^{2}\)= 0.956 and RMSE = 0.054 mWm\({}^{-2}\)sr\({}^{-1}\)nm\({}^{-1}\). Comparison with another _SIF_ product (CASIF) showed a spatiotemporal consistency with TSIF. Comparison with tower gross primary productivity (GPP) from AmeriFlux in California showed a strong correlation with R\({}^{2}\) for multiple ecosystems ranging from 0.58 to 0.88. We explored the capacity of TSIF for monitoring a drought event in Henan, China, showing that TSIF is more sensitive to drought and precipitation compared to the Enhanced Vegetation Index. Our TSIF is a very promising indicator for regional drought monitoring. 16 February 2022  
1 2022  
An Operational Downscaling Method of Solar-Induced Chlorophyll Fluorescence (SIF) for Regional Drought Monitoring  
[PERSON]  
1 School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China; [EMAIL_ADDRESS] (Z.H. ); [EMAIL_ADDRESS] (C.C. ); [EMAIL_ADDRESS] (C.T. ); [PERSON]DRESS] (W.Z. ); [EMAIL_ADDRESS] (L.L. )1  
Footnote 1: Received: 16 February 2022  
Accepted: 9 April 2022  
Published: 12 April 2022  
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**Copyright** © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/))). **Copyright** © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons. (OCO-2) [8], Chinese Carbon Dioxide Observation Satellite Mission (TanSat) [9], and TROPOspheric Monitoring Instrument (TROPOMI) [10]. In the case of _SIF_ retrievals from GOSAT, GOME-2 SCIAMACHY, their coarse spatiotemporal resolutions (10.5 km circle, 40 km \(\times\) 40 km, and 30 km \(\times\) 60 km, respectively) seriously hinders applications for fine-scale drought monitoring. OCO-2 and TanSat have high spatial resolution _SIF_ products at 1.3 km \(\times\) 2.25 km and 1 km \(\times\) 2 km, respectively, but their sparse sampling strategy and long revisit cycles seriously reduce their usability as they have to be aggregated to coarse spatiotemporal resolutions (e.g., monthly and \(1^{\circ}\times 1^{\circ}\)) for applications. To enhance the spatial resolution of satellite _SIF_ or reconstruct high-resolution and spatially continuous _SIF_ products, significant effort has been put into the generation of downscaled or reconstructed _SIF_ using other satellite data, such as _SIF_LUE derived from GOME-2 _SIF_ by [PERSON] [11], GOSIF from OCO-2 _SIF_ by [PERSON] [12], CSF from OCO-2 by [PERSON] [13], \(\overline{SIF}_{\text{coo2},005}\) from OCO-2 by [PERSON] [14], \(\overline{SIF}_{\text{o05}}\) from SCIAMACHY, and GOME-2 by [PERSON] [15].