Evaluation of photosynthesis estimation from machine learning-based solar-induced chlorophyll fluorescence downscaling from canopy to leaf level

• Published in: Ecological indicators Vol. 166; p. 112439
• Main Authors: Li, Hui, Zhang, Hongyan, Wang, Yeqiao, Zhao, Jianjun, Feng, Zhiqiang, Chen, Hongbing, Guo, Xiaoyi, Xiong, Tao, Xiao, Jingfeng, Li, Xing
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2024; Elsevier
• Subjects: Escape ratio; Gross primary productivity; Leaf level; SCOPE; Solar-induced chlorophyll fluorescence; TROPOMI; TROPOMI; Solar-induced chlorophyll fluorescence; Leaf level; SCOPE; Gross primary productivity; Escape ratio
• ISSN: 1470-160X
• DOI: 10.1016/j.ecolind.2024.112439

•A more appropriate downscaling method is constructed for satellite-observed SIF.•fesc estimation by using SIF as an input parameter to ML has a good performance.•Different downscaling methods are applied to TROPOMI SIF.•SIFtotal can better reflect photosynthetic differences across vegetation types.•Our downscaling method has a better GPP estimation effect in sparse plant areas. Solar-induced chlorophyll fluorescence (SIF) is strongly correlated with gross primary productivity (GPP). Satellite-observed canopy SIF (SIFobs) captures only a part of the total leaf-emitted SIF (SIFtotal); therefore, SIFobs may hinder the interpretation of the physiological mechanism for GPP estimation. Furthermore, there are still significant discrepancies in the estimated SIFobs escape ratio (fesc) from the canopy to the leaf level with current methods. Here, we selected several vegetation canopy variables and downscaled SIFobs based on the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model from the canopy to the leaf level using machine learning (ML) algorithms and then applied our method to the TROPOspheric Monitoring Instrument (TROPOMI) near-infrared (NIR) SIFobs. The results showed that simulating the fesc with SIFobs, TROPOMI NIR reflectance, and the fraction of photosynthetically active radiation (FPAR) avoided the effects of different sun-sensor geometry conditions introduced by different sensors and was more suitable for satellite-observed SIFobs downscaling. Our downscaled SIFtotal also correlated well with the flux site GPP in areas with sparse vegetation types. SIFtotal better reflected the photosynthetic differences among vegetation types and showed an enhanced relationship with absorbed photosynthetically active radiation (APAR) compared with SIFobs. We provide an efficient canopy-to-leaf SIFobs downscaling method improved SIFtotal and GPP estimation, and our results also demonstrated the potential for using SIFobs as vegetation information in sparse coverage areas.