Generation of a Global Spatially Continuous TanSat Solar-Induced Chlorophyll Fluorescence Product by Considering the Impact of the Solar Radiation Intensity

A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms

Photosynthesis is a key process linking carbon and water cycles, and satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in providing high spatial and temporal resolution SIF observations, but the short temporal coverage of the data records has limited its applications in long-term studies. This study uses machine learning to reconstruct TROPOMI SIF (RTSIF) over the 2001-2020 period in clear-sky conditions with high spatio-temporal resolutions (0.05° 8-day). Our machine learning model achieves high accuracies on the training and testing datasets (R2 = 0.907, regression slope = 1.001). The RTSIF dataset is validated against TROPOMI SIF and tower-based SIF, and compared with other satellite-derived SIF (GOME-2 SIF and OCO-2 SIF). Comparing RTSIF with Gross Primary Production (GPP) illustrates the potential of RTSIF for estimating gross carbon fluxes. We anticipate that this new dataset will be valuable in assessing long-term terrestrial photosynthesis and constraining the global carbon budget and associated water fluxes.

A Reconstructed Global Daily Seamless SIF Product at 0.05 Degree Resolution Based on TROPOMI, MODIS and ERA5 Data

Satellite-derived solar-induced chlorophyll fluorescence (SIF) has been proven to be a valuable tool for monitoring vegetation’s photosynthetic activity at regional or global scales. However, the coarse spatiotemporal resolution or discrete space coverage of most satellite SIF datasets hinders their full potential for studying carbon cycle and ecological processes at finer scales. Although the recent TROPOspheric Monitoring Instrument (TROPOMI) partially addresses this issue, the SIF still has drawbacks in spatial insufficiency and spatiotemporal discontinuities when gridded at high spatiotemporal resolutions (e.g., 0.05°, 1-day or 2-day) due to its nonuniform sampling sizes, swath gaps, and clouds contaminations. Here, we generated a new global SIF product with Seamless spatiotemporal coverage at Daily and 0.05° resolutions (SDSIF) during 2018–2020, using the random forest (RF) approach together with TROPOMI SIF, MODIS reflectance and meteorological datasets. We investigated how the model accuracy was affected by selection of explanatory variables and model constraints. Eventually, models were trained and applied for specific continents and months given the similar response of SIF to environmental variables within closer space and time. This strategy achieved better accuracy (R2 = 0.928, RMSE = 0.0597 mW/m2/nm/sr) than one universal model (R2 = 0.913, RMSE = 0.0653 mW/m2/nm/sr) for testing samples. The SDSIF product can well preserve the temporal and spatial characteristics in original TROPOMI SIF with high temporal correlations (mean R2 around 0.750) and low spatial residuals (less than ±0.081 mW/m2/nm/sr) between them two at most regions (80% of global pixels). Compared with the original SIF at five flux sites, SDSIF filled the temporal gaps and was better consistent with tower-based SIF at the daily scale (the mean R2 increased from 0.467 to 0.744. Consequently, it provided more reliable 4-day SIF averages than the original ones from sparse daily observations (e.g., the R2 at Daman site was raised from 0.614 to 0.837), which resulted in a better correlation with 4-day tower-based GPP. Additionally, the global coverage ratio and local spatial details had also been improved by the reconstructed seamless SIF. Our product has advantages in spatiotemporal continuities and details over the original TROPOMI SIF, which will benefit the application of satellite SIF for understanding carbon cycle and ecological processes at finer spatial and temporal scales.

Can Vegetation Indices Serve as Proxies for Potential Sun-Induced Fluorescence (SIF)? A Fuzzy Simulation Approach on Airborne Imaging Spectroscopy Data

In this study, we are testing a proxy for red and far-red Sun-induced fluorescence (SIF) using an integrated fuzzy logic modelling approach, termed as SIFfuzzy and SIFfuzzy-APAR. The SIF emitted from the core of the photosynthesis and observed at the top-of-canopy is regulated by three major controlling factors: (1) light interception and absorption by canopy plant cover; (2) escape fraction of SIF photons (fesc); (3) light use efficiency and non-photochemical quenching (NPQ) processes. In our study, we proposed and validated a fuzzy logic modelling approach that uses different combinations of spectral vegetation indices (SVIs) reflecting such controlling factors to approximate the potential SIF signals at 760 nm and 687 nm. The HyPlant derived and field validated SVIs (i.e., SR, NDVI, EVI, NDVIre, PRI) have been processed through the membership transformation in the first stage, and in the next stage the membership transformed maps have been processed through the Fuzzy Gamma simulation to calculate the SIFfuzzy. To test whether the inclusion of absorbed photosynthetic active radiation (APAR) increases the accuracy of the model, the SIFfuzzy was multiplied by APAR (SIFfuzzy-APAR). The agreement between the modelled SIFfuzzy and actual SIF airborne retrievals expressed by R2 ranged from 0.38 to 0.69 for SIF760 and from 0.85 to 0.92 for SIF687. The inclusion of APAR improved the R2 value between SIFfuzzy-APAR and actual SIF. This study showed, for the first time, that a diverse set of SVIs considered as proxies of different vegetation traits, such as biochemical, structural, and functional, can be successfully combined to work as a first-order proxy of SIF. The previous studies mainly included the far-red SIF whereas, in this study, we have also focused on red SIF along with far-red SIF. The analysis carried out at 1 m spatial resolution permits to better infer SIF behaviour at an ecosystem-relevant scale.

Modeling Transpiration with Sun-Induced Chlorophyll Fluorescence Observations via Carbon-Water Coupling Methods

Successfully applied in the carbon research area, sun-induced chlorophyll fluorescence (SIF) has raised the interest of researchers from the water research domain. However, current works focused on the empirical relationship between SIF and plant transpiration (T), while the mechanistic linkage between them has not been fully explored. Two mechanism methods were developed to estimate T via SIF, namely the water-use efficiency (WUE) method and conductance method based on the carbon–water coupling framework. The T estimated by these two methods was compared with T partitioned from eddy covariance instrument measured evapotranspiration at four different sites. Both methods showed good performance at the hourly (R2 = 0.57 for the WUE method and 0.67 for the conductance method) and daily scales (R2 = 0.67 for the WUE method and 0.78 for the conductance method). The developed mechanism methods provide theoretical support and have a great potential basis for deriving ecosystem T by satellite SIF observations.