Correcting Clear‐Sky Bias in Gross Primary Production Modeling From Satellite Solar‐Induced Chlorophyll Fluorescence Data. Issue 9 (15th September 2020)

• Record Type: Journal Article
• Authors: Zhang, Zhaoying; Zhang, Yongguang; Zhang, Yao; Chen, Jing M.
• Is Part Of: Journal of geophysical research. Volume 125:Issue 9(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-15
• Subjects: clear‐sky bias -- gross primary production -- solar‐induced chlorophyll fluorescence -- terrestrial photosynthesis -- TROPOMI; Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1029/2020JG005822
• Languages: English
• ISSNs: 2169-8953

Abstract

Key Points: A practical approach is proposed to correct clear‐sky bias in GPP estimation from satellite clear‐sky SIF data The reduction in GPP due to the decreasing PAR is partly compensated by enhancement of light use efficiency under cloudy conditions Global terrestrial GPP is obtained from total canopy SIF emission after correcting clear‐sky bias Abstract: Satellite solar‐induced chlorophyll fluorescence (SIF) has been demonstrated the potential to monitor photosynthesis, quantified as gross primary production (GPP), for large areas. However, satellite SIF retrievals are only reliable for clear‐sky conditions, creating overestimation in estimating GPP for all‐sky conditions, called clear‐sky bias. Clouds can reduce the total radiation and increase the diffuse radiation, which can have counteracting effects on photosynthesis since plants are more efficient in using diffuse radiation. To mitigate this uncertainty in estimating global GPP from satellite SIF, we propose a clear‐sky bias correction approach that considers both the increase in light use efficiency (LUE) and the decrease in photosynthetically active radiation (PAR) under cloudy conditions. An enhancement factor ( α ) of LUE under cloudy conditions is first parameterized from FLUXNET data set, which is found to converge to 1.21 among all vegetation types. Then, a correction factor of clear‐sky bias is developed to account for the variations of LUE under cloudy conditions, which is used to estimate global terrestrial GPP in 2019 with spaceborne SIF retrievals from TROPOspheric Monitoring Instrument (TROPOMI). We estimate global annual terrestrial GPP of 135.47 ± 3.36 and 142.03 ± 3.53 PgC/year, respectively, with and without correcting clear‐sky bias. This approach is also able to identify regions with large uncertainties in GPP estimation from satellite SIF data due to the effects of clear‐sky bias, such as eastern America, western Europe, and southern China. Our results highlight the importance of considering clear‐sky bias in estimating terrestrial photosynthesis from satellite SIF data. Plain Language Summary: Photosynthesis is an important process by which plants remove the atmosphere of carbon dioxide (CO2 ) and fuel the life on Earth. Satellite solar‐induced chlorophyll fluorescence (SIF) shows great potential in monitoring terrestrial photosynthesis. However, satellite SIF retrievals are only reliable for clear‐sky days, creating biases in estimating photosynthesis for all‐sky condition. Cloud can reduce the total radiation and increase the diffused radiation, which can have counteracting effects on photosynthesis since plants are more efficient in using diffused radiation. Under slightly cloudy conditions, the decrease and increase from these two effects are approximately equal. However, under extremely cloudy conditions, the decreased radiation outweighs the efficiency enhancement, leading to an overall negative effect on photosynthesis. In this study, we propose a practical approach to mitigate this uncertainty in estimating global photosynthesis from SIF. Using the TROPOspheric Monitoring Instrument (TROPOMI) SIF during the year of 2019, we estimate a global photosynthesis for all sky conditions to be 135.47 ± 3.36 PgC/year. We also recognize three regions including eastern America, western Europe, and southern China, where clear‐sky bias is clearly corrected with our approach. Mitigating clear‐sky bias could help to better estimate the terrestrial photosynthesis using satellite SIF data.