An Operational Downscaling Method of Solar-Induced Chlorophyll Fluorescence (SIF) for Regional Drought Monitoring

Zhiming Hong, Yijie Hu, Changlu Cui, Xining Yang, Chongxin Tao, Weiran Luo, Wen Zhang, Linyi Li and Lingkui Meng
Additional contact information
Zhiming Hong: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Yijie Hu: National Disaster Reduction Center of China, Beijing 100124, China
Changlu Cui: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Xining Yang: Department of Geography and Geology, Eastern Michigan University, Ypsilanti, MI 48197, USA
Chongxin Tao: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Weiran Luo: School of Water Conservancy Sciences and Engineering, Zhengzhou University, Zhengzhou 450001, China
Wen Zhang: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Linyi Li: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Lingkui Meng: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Agriculture, 2022, vol. 12, issue 4, 1-21

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 (TROPOSIF) 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.

Keywords: drought monitoring; SIF; downscaling; TROPOMI (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/4/547/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/4/547/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:4:p:547-:d:791738

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().