Ideas and perspectives: Research on ecosystem-atmosphere interactions in Asia: early career researcher opinion DOI Creative Commons
Sung‐Ching Lee, Hojin Lee, Tin W. Satriawan

et al.

Published: Aug. 22, 2024

Abstract. Due to a growing recognition of the need study how ecosystems and atmosphere interact with each other, many regional networks as well global network networks, FLUXNET, were formed. Since 1999, when AsiaFlux was established, scientists in region have been measuring flux densities energy, water vapor, greenhouse gas exchanges better evaluate ecosystem-atmosphere interactions understand their underlying mechanisms. The includes natural managed that span broad climatic ecological gradients, experience diverse management practices disturbances. In this ideas perspectives paper, from view early career researchers (ECRs), we synthesize key research foci recent years, focus on latest conferences, highlight selected discoveries. While achieving significant milestones, ECRs argue community should work together emphasize importance long-term observations, rejuvenate network’s shared open-access database, actively engage stakeholders. With unique ecosystem types Asian region, efforts expertise can provide critical insights into roles climate change, extreme weather events, soil properties, vegetation physiology structure, breathing biosphere. closing, hope paper inspire future generation Asia promote between across different cultures stages.

Language: Английский

Estimation of potato above-ground biomass based on the VGC-AGB model and deep learning DOI
Haikuan Feng,

Yiguang Fan,

Jibo Yue

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 232, P. 110122 - 110122

Published: Feb. 17, 2025

Language: Английский

Citations

3
Enhancing potato leaf protein content, carbon-based constituents, and leaf area index monitoring using radiative transfer model and deep learning DOI
Haikuan Feng,

Yiguang Fan,

Jibo Yue

et al.

European Journal of Agronomy, Journal Year: 2025, Volume and Issue: 166, P. 127580 - 127580

Published: March 2, 2025

Language: Английский

Citations

2
Evaluation of spatial and temporal variability in Sentinel-2 surface reflectance on a rice paddy landscape DOI Creative Commons

WonSeok Choi,

Youngryel Ryu, Juwon Kong

et al.

Agricultural and Forest Meteorology, Journal Year: 2025, Volume and Issue: 363, P. 110401 - 110401

Published: Feb. 8, 2025

Language: Английский

Citations

1
Correcting confounding canopy structure, biochemistry and soil background effects improves leaf area index estimates across diverse ecosystems from Sentinel-2 imagery DOI Creative Commons
Liang Wan, Youngryel Ryu, Benjamin Dechant

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 309, P. 114224 - 114224

Published: May 28, 2024

High-spatiotemporal-resolution leaf area index (LAI) data are essential for sustainable agro-ecosystem management and precise disturbance detection. Previous LAI products were primarily derived from satellite with limited spatiotemporal or spectral resolutions, which could be overcome the use of Sentinel-2. While hybrid methods that integrate PROSAIL simulations machine learning offer advantages in extracting high-spatiotemporal-resolution Sentinel-2, they still face challenges due to confounding factors related canopy structure, biochemistry, soil background. To reduce impacts these confounders, we developed an efficient method Sentinel-2-based retrieval. Our approach consists random forest models trained on simulated datasets generated by PROSAIL-5B two refinements: variable fraction fully senescent leaves (FS) bidirectional reflectance factor (BRF) Brightness-Shape-Moisture (BSM) model. We corrected BRF using near-infrared vegetation (NIRV) cover within mixed pixels (VC). For validation, used ground measurements across different types Copernicus Ground Based Observations Validation (GBOV) Korea flux (KoFlux) sites during 2019–2023. results showed coupling BSM FS improved estimates, reducing RMSE 10.8%–73.8%. Utilizing NIRV VC correct better quantified most types, reduced 15.3%–64.8%. robust agreement validation GBOV (R2 = 0.88, 0.71) KoFlux 0.80, 0.75). Overall, our 0.58–0.93, 0.04–0.83) outperformed both benchmark Sentinel Application Platform 0.11–0.85, 0.28–1.67) data-driven 0.09–0.85, 0.29–0.93) algorithms producing seasonal at finer resolutions. findings underscore potential proposed retrieval diverse ecosystems.

Language: Английский

Citations

7
Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches DOI Creative Commons
Benjamin Dechant, Jens Kattge, Ryan Pavlick

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 311, P. 114276 - 114276

Published: June 27, 2024

Foliar traits such as specific leaf area (SLA), nitrogen (N), and phosphorus (P) concentrations play important roles in plant economic strategies ecosystem functioning.Various global maps of these foliar have been generated using statistical upscaling approaches based on in-situ trait observations.Here, we intercompare upscaled at 0.5 • spatial resolution (six for SLA, five N, three P), categorize the used to generate them, evaluate with estimates from a database vegetation plots (sPlotOpen).We disentangled contributions different functional types (PFTs) quantified impacts plot-level metrics evaluation sPlotOpen: community weighted mean (CWM) top-of-canopy (TWM).We found that SLA N differ drastically fall into two groups are almost uncorrelated (for P only one group were available).The primary factor explaining differences between is use PFT information combined remote sensing-derived land cover products while other mostly relied environmental predictors alone.The corresponding exhibit considerable similarities patterns strongly driven by cover.The not PFTs show lower level similarity tend be individual variables.Upscaled both moderately correlated sPlotOpen data aggregated grid-cell (R = 0.2-0.6)when processing way consistent respective approaches, including metric (CWM or TWM) scaling grid cells without accounting fractional impact TWM CWM was relevant, but considerably smaller than information.The better reproduce between-PFT data, performed similarly capturing within-PFT variation.Our findings highlight importance explicitly within-grid-cell variation, which has implications applications existing future efforts.Remote sensing great potential reduce uncertainties related observations regression-based mapping steps involved upscaling.

Language: Английский

Citations

6
Ground-Based Hyperspectral Estimation of Maize Leaf Chlorophyll Content Considering Phenological Characteristics DOI Creative Commons
Yiming Guo, Shiyu Jiang,

Huiling Miao

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(12), P. 2133 - 2133

Published: June 13, 2024

Accurately measuring leaf chlorophyll content (LCC) is crucial for monitoring maize growth. This study aims to rapidly and non-destructively estimate the LCC during four critical growth stages investigate ability of phenological parameters (PPs) LCC. First, spectra were obtained by spectral denoising followed transformation. Next, sensitive bands (Rλ), indices (SIs), PPs extracted from all at each stage. Then, univariate models constructed determine their potential independent estimation. The multivariate regression (LCC-MR) built based on SIs, SIs + Rλ, Rλ after feature variable selection. results indicate that our machine-learning-based LCC-MR demonstrated high overall accuracy. Notably, 83.33% 58.33% these showed improved accuracy when successively introduced SIs. Additionally, model accuracies milk-ripe tasseling outperformed those flare–opening jointing under identical conditions. optimal was created using XGBoost, incorporating SI, PP variables R3 These findings will provide guidance support management.

Language: Английский

Citations

4
Chlorophyll content estimation in radiata pine using hyperspectral imagery: A comparison between empirical models, scaling-up algorithms, and radiative transfer inversions DOI Creative Commons
T. Poblete, Michael S. Watt, Henning Buddenbaum

et al.

Agricultural and Forest Meteorology, Journal Year: 2025, Volume and Issue: 362, P. 110402 - 110402

Published: Jan. 17, 2025

Language: Английский

Citations

0
Linking remotely sensed growth-related canopy attributes to interannual tree-ring width variations: A species-specific study using Sentinel optical and SAR time series DOI Creative Commons
Vahid Nasiri, Paweł Hawryło, Piotr Tompalski

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2025, Volume and Issue: 221, P. 347 - 362

Published: Feb. 20, 2025

Language: Английский

Citations

0
The relationship between the ratio of far-red to red leaf SIF and leaf chlorophyll content: Theoretical derivation and experimental validation DOI
Runfei Zhang, Peiqi Yang, Shan Xu

et al.

Remote Sensing of Environment, Journal Year: 2025, Volume and Issue: 324, P. 114762 - 114762

Published: April 22, 2025

Language: Английский

Citations

0
Evaluating the sensitivity of vegetation indices to leaf area index variability at individual tree level using multispectral drone acquisitions DOI

Xianchao Tian,

Xingyu Jia,

Yizhuo Da

et al.

Agricultural and Forest Meteorology, Journal Year: 2025, Volume and Issue: 364, P. 110441 - 110441

Published: Feb. 17, 2025

Language: Английский

Citations

0