From remotely sensed solar‐induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I—Harnessing theory

Global Change Biology, Journal Year: 2023, Volume and Issue: 29(11), P. 2926 - 2952

Published: Feb. 17, 2023

Abstract Solar‐induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability SIF data at increasingly higher spatial and temporal resolutions, sparking applications diverse research sectors (e.g., ecology, agriculture, hydrology, climate, socioeconomics). These must deal with complexities caused by tremendous variations scale impacts interacting superimposing plant physiology three‐dimensional vegetation structure on emission scattering SIF. At present, these not been overcome. To advance future research, companion reviews aim to (1) develop analytical framework for inferring terrestrial structures function that are tied emission, (2) synthesize progress identify challenges via lens multi‐sector applications, (3) map out actionable solutions tackle offer our vision priorities over next 5–10 years based proposed framework. This paper first reviews, theory oriented. It introduces theoretically rigorous yet practically applicable Guided this framework, we theoretical perspectives three overarching questions: forward (mechanism) question —How dynamics affected ecosystem function? inference : What aspects structure, function, service can be reliably inferred from how? innovation innovations needed realize full potential remote sensing real‐world under climate change? elucidates process complexity appreciated observed SIF; serve as diagnosis tool versatile across scales.

Language: Английский

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From remotely‐sensed solar‐induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II—Harnessing data

Global Change Biology, Journal Year: 2023, Volume and Issue: 29(11), P. 2893 - 2925

Published: Feb. 18, 2023

Abstract Although our observing capabilities of solar‐induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency SIF datasets are still in an active stage research development. As a result, there considerable inconsistencies among diverse at all scales widespread applications them led to contradictory findings. The present review is second two companion reviews, data oriented. It aims (1) synthesize variety, scale, uncertainty existing datasets, (2) sector ecology, agriculture, hydrology, climate, socioeconomics, (3) clarify how such inconsistency superimposed with theoretical complexities laid out (Sun et al., 2023) may impact process interpretation various contribute inconsistent We emphasize that accurate functional relationships between other ecological indicators contingent upon complete understanding uncertainty. Biases uncertainties observations can significantly confound their respond environmental variations. Built syntheses, we summarize gaps current observations. Further, offer perspectives on innovations needed help improve informing ecosystem structure, function, service under climate change, including enhancing in‐situ capability especially “data desert” regions, improving cross‐instrument standardization network coordination, advancing by fully harnessing theory data.

Language: Английский

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Proximal remote sensing: an essential tool for bridging the gap between high‐resolution ecosystem monitoring and global ecology

New Phytologist, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

Summary A new proliferation of optical instruments that can be attached to towers over or within ecosystems, ‘proximal’ remote sensing, enables a comprehensive characterization terrestrial ecosystem structure, function, and fluxes energy, water, carbon. Proximal sensing bridge the gap between individual plants, site‐level eddy‐covariance fluxes, airborne spaceborne by providing continuous data at high‐spatiotemporal resolution. Here, we review recent advances in proximal for improving our mechanistic understanding plant processes, model development, validation current upcoming satellite missions. We provide best practices availability metadata sensing: spectral reflectance, solar‐induced fluorescence, thermal infrared radiation, microwave backscatter, LiDAR. Our paper outlines steps necessary making these streams more widespread, accessible, interoperable, information‐rich, enabling us address key ecological questions unanswerable from space‐based observations alone and, ultimately, demonstrate feasibility technologies critical local global ecology.

Language: Английский

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A multi-layer perceptron approach for SIF retrieval in the O2-A absorption band from hyperspectral imagery of the HyPlant airborne sensor system

Remote Sensing of Environment, Journal Year: 2025, Volume and Issue: 318, P. 114596 - 114596

Published: Jan. 15, 2025

Language: Английский

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A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms

Scientific Data, Journal Year: 2022, Volume and Issue: 9(1)

Published: July 20, 2022

Photosynthesis is a key process linking carbon and water cycles, satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in providing high spatial temporal resolution SIF observations, but short coverage of data records has limited its applications long-term studies. This study uses machine learning to reconstruct TROPOMI (RTSIF) over 2001-2020 period clear-sky conditions with spatio-temporal resolutions (0.05° 8-day). Our model achieves accuracies training testing datasets (R

Language: Английский

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Net fluxes of broadband shortwave and photosynthetically active radiation complement NDVI and near infrared reflectance of vegetation to explain gross photosynthesis variability across ecosystems and climate

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 307, P. 114123 - 114123

Published: April 26, 2024

Language: Английский

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The biological basis for using optical signals to track evergreen needleleaf photosynthesis

BioScience, Journal Year: 2024, Volume and Issue: 74(3), P. 130 - 145

Published: Jan. 5, 2024

Abstract Evergreen needleleaf forests (ENFs) play a sizable role in the global carbon cycle, but biological and physical controls on ENF cycle feedback loops are poorly understood difficult to measure. To address this challenge, growing appreciation for stress physiology of photosynthesis has inspired emerging techniques designed detect photosynthetic activity with optical signals. This Overview summarizes how fundamental plant biophysical processes control fate photons from leaf globe, ultimately enabling remote estimates photosynthesis. We demonstrate using data across four sites spanning broad range environmental conditions link leaf- stand-scale observations (i.e., needle biochemistry flux towers) tower- satellite-based sensing. The multidisciplinary nature work can serve as model coordination integration made at multiple scales.

Language: Английский

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Monitoring and Modeling the Soil‐Plant System Toward Understanding Soil Health

Reviews of Geophysics, Journal Year: 2025, Volume and Issue: 63(1)

Published: Jan. 25, 2025

Abstract The soil health assessment has evolved from focusing primarily on agricultural productivity to an integrated evaluation of biota and biotic processes that impact properties. Consequently, shifted a predominantly physicochemical approach incorporating ecological, biological molecular microbiology indicators. This shift enables comprehensive exploration microbial community properties their responses environmental changes arising climate change anthropogenic disturbances. Despite the increasing availability indicators (physical, chemical, biological) data, holistic mechanistic linkage not yet been fully established between functions across multiple spatiotemporal scales. article reviews state‐of‐the‐art monitoring, understanding how soil‐microbiome‐plant contribute feedback mechanisms causes in properties, as well these have functions. Furthermore, we survey opportunities afforded by soil‐plant digital twin approach, integrative framework amalgamates process‐based models, Earth Observation data assimilation, physics‐informed machine learning, achieve nuanced comprehension health. review delineates prospective trajectory for monitoring embracing systematically observe model system. We further identify gaps opportunities, provide perspectives future research enhanced intricate interplay hydrological processes, hydraulics, microbiome, landscape genomics.

Language: Английский

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A practical SIF-based crop model for predicting crop yields by quantifying the fraction of open PSII reaction centers (qL)

Remote Sensing of Environment, Journal Year: 2025, Volume and Issue: 320, P. 114658 - 114658

Published: Feb. 18, 2025

Language: Английский

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Inferring global terrestrial carbon fluxes from the synergy of Sentinel 3 & 5P with Gaussian process hybrid models

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 305, P. 114072 - 114072

Published: March 11, 2024

The ongoing monitoring of terrestrial carbon fluxes (TCF) goes hand in with progress technical capacities, such as the next-generation Earth observation missions Copernicus initiative and advanced machine learning algorithms. Proceeding along this line, we present a physically-based data-driven workflow for quantifying gross primary productivity (GPP) net (NPP) at global scale from synergy Copernicus' Sentinel-3 (S3) Ocean Land Color Instrument (OLCI) TROPOspheric Monitoring (TROPOMI) onboard Sentinel-5 Precursor (S5P), meteorological variables ERA5-Land. Specifically, created generic hybrid Gaussian process regression (GPR) retrieval models combining S3-OLCI-derived vegetation products TROPOMI solar-induced fluorescence (SIF) product to capture GPP NPP. First, GPR algorithms were trained on theoretical simulations through Soil-Canopy-Observation Photosynthesis Energy (SCOPE) model, final termed SCOPE-GPR-TCF. Second, SCOPE-GPR-TCF integrated Google Engine (GEE) fed satellite data (coming Sentinel 3 & 5P ERA5-Land), producing regional (Iberian Peninsula) maps spatial resolutions 5 km 300 m during year 2019. Moderate relative uncertainties range between 10%–40% NPP estimates achieved by models. Analysis driving revealed that S3-OLCI products, i.e., leaf area index (LAI), fraction absorbed photosynthetically active radiation (FAPAR), SIF provided highest prediction strengths. Validation temporal against partitioned 113 flux towers located America Europe highlighted good overall consistency local scale, performances varying depending site type. scores emerged stations croplands, grasslands, deciduous broad-leaf evergreen needle-leaf forests top R2 rmse values above 0.8 below 2 μmolm−2s−1 respectively. Further, benchmarking spatiotemporal analysis strong intra-annual correlation reference same 2019: (i) Cross-comparison LPJ-GUESS resulted modal R = 1.93 GPP. (ii) MOD17A2H estimations cross-correlated 0.94 0.92 1.26 1.05 μmolm−2s−1, We conclude into GEE cloud-computing platform facilitate streamlining mapping TCF efficient processing costs. This is particularly promising preparation upcoming Fluorescence Explorer (FLEX) mission, where are foreseen be customized resolution FLEX streams high-resolution monitoring.

Language: Английский

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