Modelling the influence of incident radiation on the SIF-based GPP estimation for maize

Liu, Xinjie; Liu, Zhunqiao; Liu, Liangyun; Lü, Xiaoliang; Chen, Jidai; Du, Shanshan; Zou, Chu

doi:10.1016/j.agrformet.2021.108522

Cited by 39 publications

(16 citation statements)

References 51 publications

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“…Furthermore, the improvements at the maize site are very limited since the effect of CI on LUE for C 4 plant is much smaller than that on LUE for C 3 plants. This result is supported by a previous study (Liu et al., 2021) who demonstrated that PAR dominates the variation in LUE rather than CI at the maize site. Due to the weaker sensitivity of C 4 plants to CI than C 3 plants, the weak improvement of integrating CI into the SIF‐based GPP estimation model for maize is reasonable.…”

Section: Discussionsupporting

confidence: 88%

How Do Sky Conditions Affect the Relationships Between Ground‐Based Solar‐Induced Chlorophyll Fluorescence and Gross Primary Productivity Across Different Plant Types?

Zhang

et al. 2022

JGR Biogeosciences

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Solar‐induced chlorophyll fluorescence (SIF) has been used as a proxy for gross primary productivity (GPP) estimations. However, knowledge on how links between SIF and GPP across different plant types vary in response to sky conditions remain unclear. Here, we investigated the effects of sky conditions on the GPP‐SIF relationship based on continuous measurements of SIF and flux across four different plant types. Our analysis shows that the GPP‐SIF links are affected by sky conditions and these linking patterns respond differently across plant types. We propose that the inconsistent responses of SIF and GPP to sky conditions are primarily driven by variations in light use efficiency (LUE = GPP/absorbed photosynthetic active radiation (APAR)). Furthermore, we explore a quantitative variation in LUE and SIFyield (SIF/APAR) separately via a decoupling of clearness index (CI) and photosynthetic active radiation under different sky conditions. LUE is more sensitive to sky conditions for the C3 plants (Forest, Wheat and Rice) than the C4 plant (Maize), and SIFyield shows more sensitivity to sky conditions for the forest than croplands. Due to the tight link between CI and other environmental factors, the incorporation of CI into the SIF‐based GPP model improves GPP estimates for all C3 plants at both instantaneous and daily scales. Our study implies that a consideration of sky conditions into the SIF‐based GPP model can significantly advance the GPP modeling under all sky conditions.

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Section: Discussionsupporting

confidence: 88%

How Do Sky Conditions Affect the Relationships Between Ground‐Based Solar‐Induced Chlorophyll Fluorescence and Gross Primary Productivity Across Different Plant Types?

Zhang

et al. 2022

JGR Biogeosciences

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“…This method can extract a more accurate and reliable SIF data set from long-term field observations for studying the relationship between SIF and vegetation photosynthesis [73]. SIF can better estimate yield after PAR normalization [74,75].…”

Section: Discussionmentioning

confidence: 99%

Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence

Wang,

Ding,

Yao

et al. 2024

Agronomy

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Cotton yield estimation is of great practical significance to producers, allowing them to make rational management decisions. At present, crop yield estimation methods mainly comprise traditional agricultural yield estimation methods, which have many shortcomings. As an ideal “probe” for detecting crop photosynthesis, sun-induced chlorophyll fluorescence (SIF) can directly reflect the dynamics of actual crop photosynthesis and has the potential to predict crop yield, in order to realize cotton yield estimation based on canopy SIF. In this study, we set up field trials with different nitrogen fertilizer gradients. The changes of canopy SIF and the physiological parameters of cotton in different growth periods were analyzed. To investigate the effects of LAI and AGB on canopy SIF estimation of cotton yield, four algorithms, Ada Boost (Adaptive Boosting), Bagging (Bootstrap Aggregating), RF (Random Forest), and BPNN (Backpropagation Neural Network), were used to construct cotton yield estimation models based on the SIF and SIFy (the normalization of SIF by incident photosynthetically active radiation) for different time and growth periods. The results include the following: (1) The effects of the leaf area index (LAI) and aboveground biomass (AGB) on cotton canopy SIF and cotton yield were similar. The correlation coefficients of LAI and AGB with cotton yield and SIF were significantly positively correlated with each other starting from the budding period, reaching the maximum at the flowering and boll period, and decreasing at the boll period; (2) In different monitoring time periods, the R2 of the cotton yield estimation model established based on SIF and SIFy showed a gradual increase from 10:00 to 14:00 and a gradual decrease from 15:00 to 19:00, while the optimal observation time was from 14:00 to 15:00. The R2 increased with the progression of growth from the budding period to the flowering and boll period and decreased at the boll period, while the optimum growth period was the flowering and boll period; (3) Compared to SIF, SIFy has a superior estimation of yield. The best yield estimation model based on the RF algorithm (R2 = 0.9612, RMSE = 66.27 kg·ha−1, RPD = 4.264) was found in the canopy SIFy of the flowering and boll period at 14:00–15:00, followed by the model utilizing the Bagging algorithm (R2 = 0.8898) and Ada Boost algorithm (R2 = 0.8796). In summary, SIFy eliminates the effect of PAR (photosynthetically active radiation) on SIF and can further improve the estimation of SIF production. This study provides empirical support for SIF estimation of cotton yield and methodological and modeling support for the accurate estimation of cotton yield.

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“…QE Pro has a spectral range of 645-805 nm with a spectral resolution of ~0.3 nm and peak SNR of greater than 1000. QE Pro is widely used for ground or tower-based SIF measurements [34][35][36][37][38] because of its high-performance specifications.…”

Section: Methodsmentioning

confidence: 99%

Prospects for Solar-Induced Chlorophyll Fluorescence Remote Sensing from the SIFIS Payload Onboard the TECIS-1 Satellite

Liu

Chen

et al. 2022

J Remote Sens

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The importance of solar-induced chlorophyll fluorescence (SIF) to monitoring vegetation photosynthesis has attracted much attention from the ecological and remote sensing research communities. Space-borne SIF products have been obtained owing to the rapid development of atmospheric satellites in recent years. The SIF Imaging Spectrometer (SIFIS) is a payload onboard the upcoming Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1) that is specifically designed for SIF monitoring. We conducted an in situ experiment to evaluate the performance of SIFIS on spectral measurement and SIF retrieval through comparison to the commercial spectrometer QE Pro. Disregarding the spatiotemporal mismatch between the collected measurements of the two spectrometers, the radiance spectra obtained synchronously by SIFIS and QE Pro showed a high level of consistency. The SIF retrieval, normalized difference vegetation index (NDVI), and near-infrared radiance of vegetation (NIRvR) results for a push-broom image shows consistent spatial distributions over both vegetated and nonvegetated surfaces. A quantitative comparison was conducted by strictly filtering matching pixels. For the far-red band, a high correlation was obtained between the SIF retrieval performances of SIFIS and QE Pro with R2=0.70 and RMSE=0.30 mW m−2 sr−−1 nm−1. However, a relatively poor correlation was observed for the red band with an R2 value of 0.23 and an RMSE of 0.26 mWm−2sr-−1nm−1. Despite the large uncertainties associated with this experiment, the results indicate that TECIS-1 should offer a reliable SIF monitoring performance after its launch.

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Modelling the influence of incident radiation on the SIF-based GPP estimation for maize

Cited by 39 publications

References 51 publications

How Do Sky Conditions Affect the Relationships Between Ground‐Based Solar‐Induced Chlorophyll Fluorescence and Gross Primary Productivity Across Different Plant Types?

How Do Sky Conditions Affect the Relationships Between Ground‐Based Solar‐Induced Chlorophyll Fluorescence and Gross Primary Productivity Across Different Plant Types?

Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence

Prospects for Solar-Induced Chlorophyll Fluorescence Remote Sensing from the SIFIS Payload Onboard the TECIS-1 Satellite

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