Improving the potential of red SIF for estimating GPP by downscaling from the canopy level to the photosystem level

Liu, Xinjie; Liu, Liangyun; Hu, Jian; Guo, Junming; Du, Shanshan

doi:10.1016/j.agrformet.2019.107846

Cited by 60 publications

(37 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the far-red band, ε, the canopy escape probability of the SIF, is dominated by the canopy bi-directional reflectance (BRF) [25,[49][50][51]. α gm , the FAPAR for green leaves, has been widely estimated using combinations of BRFs [52][53][54].…”

Section: Tower-based Datasetsmentioning

confidence: 99%

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

Liu

Chen

et al. 2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

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°.

show abstract

Section: Tower-based Datasetsmentioning

confidence: 99%

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

Liu

Chen

et al. 2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The characteristics of these sites are summarized in Table 1. The SIF measurements of the DM and HL sites (760 nm) were measured by a tower-based automatic measurement system named "SIFSpec" and retrieved using the 3FLD method [18]. The SIF data (745 to 758 nm) were observed from a scanning spectrometer (PhotoSpec) at the NR site [19].…”

Section: Methodsmentioning

confidence: 99%

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

Feng

Liu

et al. 2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Meanwhile, most studies in this field have been based on space-borne and airborne technologies, which ignore the influence of canopy structure on the relationship between SIF and GPP. This makes it difficult to explain clearly with a simple SIF-GPP linear relationship [57][58][59][60]. Furthermore, it is difficult to obtain high-quality SIF data with appropriate spatial and temporal resolutions, and it is difficult to quantitatively assess the factors that influence the relationship between SIF and GPP.…”

Section: Introductionmentioning

confidence: 99%

Estimating Global Gross Primary Production from Sun-Induced Chlorophyll Fluorescence Data and Auxiliary Information Using Machine Learning Methods

2021

View full text Add to dashboard Cite

The gross primary production (GPP) is important for regulating the global carbon cycle and climate change. Recent studies have shown that sun-induced chlorophyll fluorescence (SIF) is highly advantageous regarding GPP monitoring. However, using SIF to estimate GPP on a global scale is limited by the lack of a stable SIF-GPP relationship. Here, we estimated global monthly GPP at 0.05° spatial resolution for the period 2001–2017, using the global OCO-2-based SIF product (GOSIF) and other auxiliary data. Large amounts of flux tower data are not available to the public and the available data is not evenly distributed globally and has a smaller measured footprint than the GOSIF data. This makes it difficult to use the flux tower GPP directly as an input to the model. Our strategy is to scale in situ measurements using two moderate-resolution satellite GPP products (MODIS and GLASS). Specifically, these two satellite GPP products were calibrated and eventually integrated by in situ measurements (FLUXNET2015 dataset, 83 sites), which was then used to train a machine learning model (GBRT) that performed the best among five evaluated models. The GPP estimates from GOSIF were highly accurate coefficient of determination (R2) = 0.58, root mean square error (RMSE) = 2.74 g C·m−2, bias = –0.34 g C·m−2) as validated by in situ measurements, and exhibited reasonable spatial and seasonal variations on a global scale. Our method requires fewer input variables and has higher computational efficiency than other satellite GPP estimation methods. Satellite-based SIF data provide a unique opportunity for more accurate, near real-time GPP mapping in the future.

show abstract

Improving the potential of red SIF for estimating GPP by downscaling from the canopy level to the photosystem level

Cited by 60 publications

References 61 publications

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

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

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

Estimating Global Gross Primary Production from Sun-Induced Chlorophyll Fluorescence Data and Auxiliary Information Using Machine Learning Methods

Contact Info

Product

Resources

About