Widespread and complex drought effects on vegetation physiology inferred from space

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Aug. 15, 2023

Abstract The response of vegetation physiology to drought at large spatial scales is poorly understood due a lack direct observations. Here, we study responses related photosynthesis, evaporation, and water content using remotely sensed data, isolate physiological machine learning technique. We find that functional decreases are largely driven by the downregulation such as stomatal conductance light use efficiency, with strongest in water-limited regions. Vegetation wet regions also result discrepancy between structural changes under severe drought. similar patterns simulations from soil–plant–atmosphere continuum model coupled radiative transfer model. Observation-derived across space mainly controlled aridity additionally modulated abnormal hydro-meteorological conditions types. Hence, isolating quantifying enables better understanding ecosystem biogeochemical biophysical feedback modulating climate change.

Language: Английский

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Deriving photosystem-level red chlorophyll fluorescence emission by combining leaf chlorophyll content and canopy far-red solar-induced fluorescence: Possibilities and challenges

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 304, P. 114043 - 114043

Published: Feb. 10, 2024

Language: Английский

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Remote sensing of terrestrial gross primary productivity: a review of advances in theoretical foundation, key parameters and methods

GIScience & Remote Sensing, Journal Year: 2024, Volume and Issue: 61(1)

Published: Feb. 20, 2024

Accurately estimating gross primary productivity (GPP), the largest carbon flux in terrestrial ecosystems, is crucial for advancing our understanding of global cycle and predicting climate feedbacks. The advancements remote sensing (RS) have facilitated development GPP estimation models at regional scales recent decades. This article systemically reviews RS-based three main aspects: theoretical foundation, key parameters methods. Regarding RS generally excels representing characteristics during light transmission process photosynthesis. However, it exhibits a relatively weaker ability to describe reaction process, severely limiting in-depth mechanisms estimation. Concerning parameters, definition traditional such as leaf area index (LAI), photosynthetically active radiation (PAR), fraction absorbing PAR, has been detailed (e.g. LAI divided into sunlit shaded LAI). their accuracy still needs improvement. Additionally, researchers developed effective photochemical reflectance index, sun-induced chlorophyll fluorescence, maximum carboxylation rate) that possess increased capability represent interpret methods, although four categories (statistical model, use efficiency model machine learning-based model) made significant progress parameter optimization, mechanism innovation remain less than satisfactory. Finally, we summarize current issues related performance accuracy, capabilities, well scale connotation mismatch. Integrating more adequate situ comprehensive observations would enhance interpretability models, providing reliable insights future studies. contributes photosynthetic estimation, potentially aiding optimization (improving existing developing new ones) design (introducing exploring mechanistic models).

Language: Английский

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A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations

Biogeosciences, Journal Year: 2023, Volume and Issue: 20(13), P. 2671 - 2692

Published: July 6, 2023

Abstract. Photosynthesis plays an important role in carbon, nitrogen, and water cycles. Ecosystem models for photosynthesis are characterized by many parameters that obtained from limited situ measurements applied to the same plant types. Previous site-by-site calibration approaches could not leverage big data faced issues like overfitting or parameter non-uniqueness. Here we developed end-to-end programmatically differentiable (meaning gradients of outputs variables used model can be efficiently accurately) version process representation within Functionally Assembled Terrestrial Simulator (FATES) model. As a genre physics-informed machine learning (ML), couple physics-based formulations neural networks (NNs) learn parameterizations (and potentially processes) observations, here rates. We first demonstrated framework was able correctly recover multiple assumed values concurrently using synthetic training data. Then, real-world dataset consisting different functional types (PFTs), learned performed substantially better greatly reduced biases compared literature values. Further, allowed us gain insights at large scale. Our results showed carboxylation rate 25 ∘C (Vc,max25) more impactful than factor representing limitation, although tuning both helpful addressing with default This enable substantial improvement our capability reduce ecosystem modeling scales.

Language: Английский

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Global photosynthetic capacity of C3 biomes retrieved from solar-induced chlorophyll fluorescence and leaf chlorophyll content

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 287, P. 113457 - 113457

Published: Jan. 19, 2023

Language: Английский

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Optimizing seasonally variable photosynthetic parameters based on joint carbon and water flux constraints

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 4, 2024

Terrestrial biosphere models (TBMs) often adopt the Farquhar biochemical model coupled with Ball-Berry stomatal conductance (\({g}_{s}\)) to simulate ecosystem carbon and water fluxes. The parameters \(m\), representing sensitivity of \({g}_{s}\) photosynthetic rate, \({V}_{cmax}^{25}\), leaf capacity, are two pivotal but main sources uncertainties in TBM simulations. spatial temporal variations \(m\) TBMs still elusive, due lack direct observations. It also remains unclear how accurate estimates \({V}_{cmax}^{25}\) can improve simulations In this study, we used a Bayesian parameter optimization approach infer seasonally varying from eddy covariance observations mixed forest stand at Borden Forest Research Station located southern Ontario, Canada, in-situ for validation. Three strategies were tested optimizing including carbon, water, carbon-water coupling scenarios. optimized constraints shows best correlations measured (R² = 0.70) 0.70). By incorporating \({V}_{cmax}^{25}\)with seasonal variations, found considerable improvements estimated gross primary productivity (GPP) evapotranspiration (ET) compared constant R² increasing 0.78 0.85 GPP, 0.65 0.71 ET RMSE reducing 2.579 g C m^{− 2} d^{− 1} 2.038 1.151 mm 0.137 ET. This study proposes an effective retrieve demonstrates efficacy variable GPP simulations, which supports quantifications land-atmosphere exchanges.

Language: Английский

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Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations

Earth system science data, Journal Year: 2024, Volume and Issue: 16(3), P. 1283 - 1300

Published: March 13, 2024

Abstract. Diagnostic terrestrial biosphere models (TBMs) forced by remote sensing observations have been a principal tool for providing benchmarks on global gross primary productivity (GPP) and evapotranspiration (ET). However, these often estimate GPP ET at coarse daily or monthly steps, hindering analysis of ecosystem dynamics the diurnal (hourly) scales, prescribe some essential parameters (i.e., Ball–Berry slope (m) maximum carboxylation rate 25 °C (Vcmax25)) as constant, inducing uncertainties in estimates ET. In this study, we present hourly estimations datasets 0.25° resolution from 2001 to 2020 simulated with widely used diagnostic TBM – Biosphere–atmosphere Exchange Process Simulator (BEPS). We employed eddy covariance machine learning approaches derive upscale seasonally varied m Vcmax25 carbon water fluxes. The estimated are validated against flux observations, sensing, learning-based across multiple spatial temporal scales. correlation coefficients (R2) slopes between tower-measured modeled fluxes R2=0.83, regression =0.92 GPP, R2=0.72, =1.04 At scale, mean 137.78±3.22 Pg C yr−1 (mean ± 1 SD) positive trend 0.53 yr−2 (p<0.001), an 89.03±0.82×103 km3 slight 0.10×103 (p<0.001) 2020. pattern our agrees well other products, R2=0.77–0.85 R2=0.74–0.90 ET, respectively. Overall, new dataset serves “handshake” among process-based models, network, reliable long-term patterns facilitating studies related functional properties, carbon, cycles. available https://doi.org/10.57760/sciencedb.ecodb.00163 (Leng et al., 2023a) accumulated https://doi.org/10.57760/sciencedb.ecodb.00165 2023b).

Language: Английский

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Estimation of global transpiration from remotely sensed solar-induced chlorophyll fluorescence

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 303, P. 113998 - 113998

Published: Jan. 18, 2024

Language: Английский

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Innovative approach for estimating evapotranspiration and gross primary productivity by integrating land data assimilation, machine learning, and multi-source observations

Agricultural and Forest Meteorology, Journal Year: 2024, Volume and Issue: 355, P. 110136 - 110136

Published: June 27, 2024

Language: Английский

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Principles for satellite monitoring of vegetation carbon uptake

Nature Reviews Earth & Environment, Journal Year: 2024, Volume and Issue: 5(11), P. 818 - 832

Published: Oct. 29, 2024

Language: Английский

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