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
Citations
5From Roots to Leaves: Tree Growth Phenology in Forest Ecosystems
Current Forestry Reports, Journal Year: 2025, Volume and Issue: 11(1)
Published: Jan. 29, 2025
Language: Английский
Citations
1Comparison of change-based and shape-based data fusion methods in fine-resolution land surface phenology monitoring with Landsat and Sentinel-2 data
The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 927, P. 172014 - 172014
Published: March 26, 2024
Fine-resolution land surface phenology (LSP) is urgently required for applications on agriculture management and vegetation-climate interaction, especially over heterogeneous areas, such as agricultural lands fragmented forests. The critical challenge of fine-resolution LSP monitoring how to reconstruct the spatiotemporal continuous vegetation index time series. To solve this problem, various data fusion methods have been devised; however, comprehensive inter-comparison lacking across different spatial heterogeneity, quality, types. We divide these into two main categories: change-based fusing satellite observations with resolutions, shape-based prior knowledge shape models observations. selected four rebuilt two-band enhanced (EVI2) series based harmonized Landsat Sentinel-2 (HLS) data, including methods, namely Spatial temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta (FSDAF), Multiple-year Weighting Shape-Matching (MWSM), (SSMM). Four phenological transition dates were extracted, evaluated PhenoCam 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) product. 30 show more details reveal apparent intra-class inter-class variation compared SSMM FSDAF (R
Language: Английский
Citations
4PhenoCam Guidelines for Phenological Measurement and Analysis in an Agricultural Cropping Environment: A Case Study of Soybean
Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 724 - 724
Published: Feb. 19, 2025
A PhenoCam is a near-surface remote sensing system traditionally used for monitoring phenological changes in diverse landscapes. Although initially developed forest landscapes, these systems are increasingly being adopted agricultural settings, with deployment expanding from 106 sites 2020 to 839 by February 2025. However, applications present unique challenges because of rapid crop development and the need precise monitoring. Despite increasing number sites, clear guidelines missing on (i) analysis images, (ii) selection suitable color vegetation index (CVI), (iii) extraction growth stages. This knowledge gap limits full potential PhenoCams applications. Therefore, study was conducted two soybean (Glycine max L.) fields formulate image images. Weekly visual assessments stages were compared total 15 CVIs tested their ability reproduce seasonal variation RGB, HSB, Lab spaces. The effects acquisition time groups (10:00 h–14:00 h) object position (ROI locations: far, middle, near) selected statistically analyzed. Excess green minus excess red (EXGR), (CIVE), leaf (GLI), normalized difference (NGRDI) based least deviation loess-smoothed curve at each time. For four CVIs, did not have significant effect CVI values, while had reproductive phase. Among GLI EXGR exhibited within groups. Overall, we recommend employing consistent ensure sufficient light, capture largest possible ROI middle region field, apply any order GLI, EXGR, NGRDI, CIVE. These results provide standardized methodology serve as cropping environments. can be incorporated into standard protocol network.
Language: Английский
Citations
0Predicting end-of-season timing across diverse North American grasslands
Oecologia, Journal Year: 2025, Volume and Issue: 207(3)
Published: March 1, 2025
Language: Английский
Citations
0Tundra Vegetation Community Type, Not Microclimate, Controls Asynchrony of Above‐ and Below‐Ground Phenology
Global Change Biology, Journal Year: 2025, Volume and Issue: 31(4)
Published: April 1, 2025
The below-ground growing season often extends beyond the above-ground in tundra ecosystems and as climate warms, shifts seasons are expected. However, we do not yet know to what extent, when where asynchrony above- phenology occurs whether variation is driven by local vegetation communities or spatial microclimate. Here, combined plant metrics compare relative timings magnitudes of leaf fine-root growth senescence across microclimates at five sites Arctic alpine biome. We observed asynchronous between tissue, with extending up 74% (~56 days) onset senescence. Plant community type, rather than microclimate, was a key factor controlling timing, productivity, rates fine roots, graminoid roots exhibiting distinct 'pulse' later into shrub roots. Our findings indicate potential change influence carbon storage warms remain active unfrozen soils for longer. Taken together, our increased root that thawed season, combination ongoing including abundance, productivity altered cycling
Language: Английский
Citations
0Advancing Acer phenology monitoring: fine-grained identification and analysis by deep learning RESformer
Journal of Forestry Research, Journal Year: 2025, Volume and Issue: 36(1)
Published: April 10, 2025
Language: Английский
Citations
0Advanced vegetation green-up onset in regions with cooling air temperatures in the Northern Hemisphere: Drivers and impacts on productivity
Global and Planetary Change, Journal Year: 2025, Volume and Issue: unknown, P. 104891 - 104891
Published: May 1, 2025
Language: Английский
Citations
0Near surface camera informed agricultural land monitoring for climate smart agriculture
Climate smart agriculture., Journal Year: 2024, Volume and Issue: 1(1), P. 100008 - 100008
Published: July 8, 2024
Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology responding to climatic anthropogenic changes. However, the widely used optical satellite remote sensing limited by revisit cycles weather conditions, leading gaps in monitoring. To address these limitations, we designed deployed a Near Surface Camera (NSCam) Network across China, explored its application land achieving climate-smart agriculture (CSA). By analyzing image data captured NSCam Network, can accurately assess long-term or abrupt According preliminary results, integrating with imagery greatly enhances temporal details accuracy monitoring, aiding managers making informed decisions. The impacts abnormal conditions human activities on land, which are not imagery, be complemented incorporating our Network. successful implementation this method underscores potential broader CSA, promoting resilient sustainable practices.
Language: Английский
Citations
3Internal physiological drivers of leaf development in trees: Understanding the relationship between non‐structural carbohydrates and leaf phenology
Functional Ecology, Journal Year: 2024, Volume and Issue: unknown
Published: Nov. 11, 2024
Abstract Plant phenology is crucial for understanding plant growth and climate feedback. It affects canopy structure, surface albedo, carbon water fluxes. While the influence of environmental factors on well‐documented, role intrinsic factors, particularly internal physiological processes their interaction with external conditions, has received less attention. Non‐structural carbohydrates (NSC), which include sugars starch essential growth, metabolism osmotic regulation, serve as indicators availability in plants. NSC levels reflect balance between photosynthesis (source activity) demands respiration (sink activity), making them key traits that potentially during critical periods such spring leaf‐out autumn leaf senescence. However, connections concentrations various organs phenological events are poorly understood. This review synthesizes current research relationship dynamics. We qualitatively delineate seasonal variations deciduous evergreen trees propose testable hypotheses about how may interact stages bud break also discuss levels, align existing conceptual models allocation. Accurate characterization simulation dynamics should be incorporated into allocation models. By comparing reviewing development models, we highlight shortcomings methodologies recommend directions to address these gaps future research. Understanding NSC, source–sink relationships, poses challenges due difficulty characterizing high temporal resolution. advocate a multi‐scale approach combines methods, deepening our mechanistic through manipulative experiments, integrating sink source data from multiple observational networks better characterize dynamics, quantifying spatial pattern trends NSC‐phenology using remote sensing modelling. will enhance comprehension impact across different scales environments. Read free Plain Language Summary this article Journal blog.
Language: Английский
Citations
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