Integrating chlorophyll fAPAR and nadir photochemical reflectance index from EO-1/Hyperion to predict cornfield daily gross primary production

Zhang, Qingyuan; Middleton, Elizabeth M.; Cheng, Yi‐Feng; Huemmrich, K. F.; Cook, Bruce D.; Corp, Lawrence A.; Kustas, William P.; Russ, Andrew L.; Prueger, John H.; Yao, Tian

doi:10.1016/j.rse.2016.08.026

Cited by 24 publications

(16 citation statements)

References 101 publications

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“…In this study we have addressed the change in SIF properties from juvenile to mature stands, however future research, considering the natural lifespan of the loblolly trees of 100+ years of age (Burns & Honkala, ), is needed to confirm our findings. Moreover, accurate determination of true SIF yield should require an estimation of PAR absorbed by green leaves (e.g., Gitelson & Gamon, ; Zhang et al., ) and this could be another key point that should be considered for future investigations.…”

Section: Discussionmentioning

confidence: 99%

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Variability of sun‐induced chlorophyll fluorescence according to stand age‐related processes in a managed loblolly pine forest

Colombo

Celesti

Bianchi

et al. 2018

Global Change Biology

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Leaf fluorescence can be used to track plant development and stress, and is considered the most direct measurement of photosynthetic activity available from remote sensing techniques. Red and far-red sun-induced chlorophyll fluorescence (SIF) maps were generated from high spatial resolution images collected with the HyPlant airborne spectrometer over even-aged loblolly pine plantations in North Carolina (United States). Canopy fluorescence yield (i.e., the fluorescence flux normalized by the light absorbed) in the red and far-red peaks was computed. This quantifies the fluorescence emission efficiencies that are more directly linked to canopy function compared to SIF radiances. Fluorescence fluxes and yields were investigated in relation to tree age to infer new insights on the potential of those measurements in better describing ecosystem processes. The results showed that red fluorescence yield varies with stand age. Young stands exhibited a nearly twofold higher red fluorescence yield than mature forest plantations, while the far-red fluorescence yield remained constant. We interpreted this finding in a context of photosynthetic stomatal limitation in aging loblolly pine stands. Current and future satellite missions provide global datasets of SIF at coarse spatial resolution, resulting in intrapixel mixture effects, which could be a confounding factor for fluorescence signal interpretation. To mitigate this effect, we propose a surrogate of the fluorescence yield, namely the Canopy Cover Fluorescence Index (CCFI) that accounts for the spatial variability in canopy structure by exploiting the vegetation fractional cover. It was found that spatial aggregation tended to mask the effective relationships, while the CCFI was still able to maintain this link. This study is a first attempt in interpreting the fluorescence variability in aging forest stands and it may open new perspectives in understanding long-term forest dynamics in response to future climatic conditions from remote sensing of SIF.

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Section: Discussionmentioning

confidence: 99%

“…Moreover, accurate determination of true SIF yield should require an estimation of PAR absorbed by green leaves (e.g., Gitelson & Gamon, 2015;Zhang et al, 2016) and this could be another key point that should be considered for future investigations.…”

Section: Sun-induced Canopy Fluorescence and Agerelated Processesmentioning

confidence: 99%

Variability of sun‐induced chlorophyll fluorescence according to stand age‐related processes in a managed loblolly pine forest

Colombo

Celesti

Bianchi

et al. 2018

Global Change Biology

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“…The pixel size of TOROPOMI is 7 km by 7 km. For satellite monitoring of PRI, one of the possible options is using EO‐1's Hyperion (Asner, Nepstad, Cardinot, & Ray, ; Zhang et al, ). Hyperion can measure at a relatively high spatial resolution (30 m) for user‐selected sites, but it was deactivated on March 2017.…”

Section: Discussionmentioning

confidence: 99%

Modeling leaf CO₂ assimilation and Photosystem II photochemistry from chlorophyll fluorescence and the photochemical reflectance index

Hikosaka

Noda

2018

Plant Cell & Environment

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We present a simple model to assess the quantum yield of photochemistry (ΦP) and CO2 assimilation rate from two parameters that are detectable by remote sensing: chlorophyll (chl) fluorescence and the photochemical reflectance index (PRI). ΦP is expressed as a simple function of the chl fluorescence yield (ΦF) and nonphotochemical quenching (NPQ): ΦP = 1–bΦF(1 + NPQ). Because NPQ is known to be related with PRI, ΦP can be remotely assessed from solar‐induced fluorescence and the PRI. The CO2 assimilation rate can be assessed from the estimated ΦP value with either the maximum carboxylation rate (Vcmax), the intercellular CO2 concentration (Ci), or parameters of the stomatal conductance model. The model was applied to experimental data obtained for Chenopodium album leaves under various environmental conditions and was able to successfully predict ΦF values and the CO2 assimilation rate. The present model will improve the accuracy of assessments of gas exchange rates and primary productivity by remote sensing.

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“…APARchl is considered to be the first 30 order approximation of SIF as it exhibits high correlation with SIF at the canopy scale (Du et al, 2017;Rossini et al, 2016;Verrelst et al, 2015;Zhang et al, 2018c). Previous studies have shown that fPARchl can be inversely estimated using the surface reflectances and radiative transfer models (Zhang et al, 2005(Zhang et al, , 2016a. The canopy structure information that affects the SIF reabsorption within canopy is also embedded in the near infrared reflectance (Badgley et al, 2017;Knyazikhin et al, Biogeosciences Discuss., https://doi.org /10.5194/bg-2018-255 Manuscript under review for journal Biogeosciences Discussion started: 22 June 2018 c Author(s) 2018.…”

Section: Introductionmentioning

confidence: 99%

A global spatially Continuous Solar Induced Fluorescence (CSIF) dataset using neural networks

Zhang¹,

Joiner²,

Alemohammad³

et al. 2018

Preprint

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Abstract. Satellite-retrieved Solar Induced Chlorophyll Fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of 10 the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurements footprints also hinder the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially continuous SIF (CSIF) datasets at moderate spatio-temporal resolutions (0.05 degree 4-day) 15 during 2001-2016, one for clear-sky conditions and the other one in all-sky conditions. The clear-sky instantaneous CSIF (CSIFclear-inst) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIFall-daily) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39%) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite 20 observed SIF and CSIF is mostly caused by the environmental down-regulation on SIFyield, the ratio between OCO-2 SIF and CSIFclear-inst can be an effective indicator of drought stress that is more sensitive than normalized difference vegetation index and enhanced vegetation index. By comparing CSIFall-daily with gross primary production (GPP) estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of GPP-SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two 25 continuous SIF datasets and the derived GPP-SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.

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Integrating chlorophyll fAPAR and nadir photochemical reflectance index from EO-1/Hyperion to predict cornfield daily gross primary production

Cited by 24 publications

References 101 publications

Variability of sun‐induced chlorophyll fluorescence according to stand age‐related processes in a managed loblolly pine forest

Variability of sun‐induced chlorophyll fluorescence according to stand age‐related processes in a managed loblolly pine forest

Modeling leaf CO₂ assimilation and Photosystem II photochemistry from chlorophyll fluorescence and the photochemical reflectance index

A global spatially Continuous Solar Induced Fluorescence (CSIF) dataset using neural networks

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Integrating chlorophyll fAPAR and nadir photochemical reflectance index from EO-1/Hyperion to predict cornfield daily gross primary production

Cited by 24 publications

References 101 publications

Variability of sun‐induced chlorophyll fluorescence according to stand age‐related processes in a managed loblolly pine forest

Variability of sun‐induced chlorophyll fluorescence according to stand age‐related processes in a managed loblolly pine forest

Modeling leaf CO2 assimilation and Photosystem II photochemistry from chlorophyll fluorescence and the photochemical reflectance index

A global spatially Continuous Solar Induced Fluorescence (CSIF) dataset using neural networks

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Modeling leaf CO₂ assimilation and Photosystem II photochemistry from chlorophyll fluorescence and the photochemical reflectance index