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

Abstract

<jats:p>Solar-induced chlorophyll fluorescence (SIF) provides a new and direct way of monitoring photosynthetic activity. However, current SIF products are limited by low spatial resolution or sparse sampling. In this paper, we present a data-driven method of generating a global, spatially continuous TanSat SIF product. Firstly, the key explanatory variables for modelling canopy SIF were investigated using in-situ and satellite observations. According to theoretical and experimental analysis, the solar radiation intensity was found to be a dominant driving environmental variable for the SIF yield at both the canopy and global scales; this has, however, been neglected in previous research. The cosine value of the solar zenith angle at noon (cos (SZA0)), a proxy for solar radiation intensity, was found to be a dominant abiotic factor for the SIF yield. Next, a Random Forest (RF) approach was employed for SIF prediction based on Moderate Resolution Imaging Spectroradiometer (MODIS) visible-to-NIR reflectance data, the normalized difference vegetation (NDVI), cos (SZA0), and air temperature. The machine learning model performed well at predicting SIF, giving R2 values of 0.73, an RMSE of 0.30 mW m−2 nm−1 sr−1 and a bias of 0.22 mW m−2 nm−1 sr−1 for 2018. If cos (SZA0) was not included, the accuracy of the RF model decreased: the R2 value was then 0.65, the RMSE 0.34 mW m−2 nm−1 sr−1 and an bias of 0.26 mW m−2 nm−1 sr−1, further verifying the importance of cos (SZA0). Finally, the globally continuous TanSat SIF product was developed and compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIF data. The results showed that the globally continuous TanSat SIF product agreed well with the TROPOMI SIF data, with an R2 value of 0.73. Thus, this paper presents an improved approach to modelling satellite SIF that has a better accuracy, and the study also generated a global, spatially continuous TanSat SIF product with a spatial resolution of 0.05°.</jats:p>

Modeling transpiration with sun-induced chlorophyll fluorescence via water use efficiency and stomatal conductance

“…Recently, SIF products with higher temporal-spatial resolution from different satellites, different bands [Du et al, 2018;Köhler et al, 2018;Köhler et al, 2020], and derivative products based on machine learning [Li and Xiao, 2019;Ma et al, 2020;Yu et al, 2019;Zhang et al, 2018] became available. WUE model and conductance model can be easily combined with remote sensing ET/T model like TSEB [Norman et al, 1995;Song et al, 2016] and PML ( [Zhang et al, 2019] or used for assimilating SIF into a land surface model.…”

“…[Pagán et al, 2019] used radiation corrected GOME-2 SIF observations to diagnose transpiration efficiency understood as the ratio between transpiration and potential evaporation worldwide. [Maes et al, 2020] investigated the empirical link between SIF and T using satellite SIF (GOME-2 and OCO-2) and SCOPE model at sites from FLUXNET. However, the studies mentioned above relate T with SIF empirically, not mechanistically.…”

“…WUE is defined as the amount of carbon assimilated relative to water use [Leakey et al, 2019]. [Maes et al, 2020] reported WUE and related variables show a most important impact on the SIF-T relation. Plants take in carbon dioxide and breath out water through stomata simultaneously.…”

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

“…With more and more in situ observations from different ecosystems, understanding of the underlying mechanism between SIF and T will be deepened. Recently, SIF products with higher temporal-spatial resolution from different satellites, different bands [69,70], and derivative products based on machine learning [71][72][73][74] have become available. T can be estimated at larger scales with the WUE method and conductance method by combining remote-sensing SIF data with remote-sensing-based ET/T models like TSEB [75,76] and PML [77] or assimilating SIF into land surface models in future studies.…”

Exploring the Potential of Spatially Downscaled Solar-Induced Chlorophyll Fluorescence to Monitor Drought Effects on Gross Primary Production in Winter Wheat

“…Thus, the spatialtemporal resolution and time series length limit the applicability of satellite-based SIF products in agricultural drought detection, especially for the specific crops with short growth season and sparse spatial distribution. In recent years, there are many spatially downscaled SIF products that offer finer resolution gridded SIF of a longer time series [19][20][21][22][23][24][25]. They provide new data sources for more accurate observation of vegetation growth conditions and productivity.…”