scholarly journals Long-Term Detection of Global Vegetation Phenology from Satellite Instruments

10.5772/39197 ◽  
2012 ◽  
Author(s):  
Xiaoyang Zhang ◽  
Mark A. ◽  
Bin Tan ◽  
Mitchell D. ◽  
Yunyue Yu
Keyword(s):  
Vegetation Phenology ◽  
Global Vegetation ◽  
Satellite Instruments

scholarly journals Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics

2018 ◽  
Vol 373 (1760) ◽  
pp. 20170315 ◽  
Author(s):  
Cleiton B. Eller ◽  
Lucy Rowland ◽  
Rafael S. Oliveira ◽  
Paulo R. L. Bittencourt ◽  
Fernanda V. Barros ◽  
...  
Keyword(s):  
Tropical Forest ◽  
El Niño ◽  
Optimization Model ◽  
El Nino ◽  
Soil Drought ◽  
Forest Sites ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

The current generation of dynamic global vegetation models (DGVMs) lacks a mechanistic representation of vegetation responses to soil drought, impairing their ability to accurately predict Earth system responses to future climate scenarios and climatic anomalies, such as El Niño events. We propose a simple numerical approach to model plant responses to drought coupling stomatal optimality theory and plant hydraulics that can be used in dynamic global vegetation models (DGVMs). The model is validated against stand-scale forest transpiration ( E ) observations from a long-term soil drought experiment and used to predict the response of three Amazonian forest sites to climatic anomalies during the twentieth century. We show that our stomatal optimization model produces realistic stomatal responses to environmental conditions and can accurately simulate how tropical forest E responds to seasonal, and even long-term soil drought. Our model predicts a stronger cumulative effect of climatic anomalies in Amazon forest sites exposed to soil drought during El Niño years than can be captured by alternative empirical drought representation schemes. The contrasting responses between our model and empirical drought factors highlight the utility of hydraulically-based stomatal optimization models to represent vegetation responses to drought and climatic anomalies in DGVMs. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

scholarly journals The MODIS Global Vegetation Fractional Cover Product 2001–2018: Characteristics of Vegetation Fractional Cover in Grasslands and Savanna Woodlands

2020 ◽  
Vol 12 (3) ◽  
pp. 406 ◽  
Author(s):  
Michael J. Hill ◽  
Juan P. Guerschman
Keyword(s):  
Land Use ◽  
Production Systems ◽  
Land Use Changes ◽  
Bare Soil ◽  
Additional Information ◽  
Fractional Cover ◽  
Grassland Type ◽  
Global Vegetation ◽  
Global Indicator

Vegetation Fractional Cover (VFC) is an important global indicator of land cover change, land use practice and landscape, and ecosystem function. In this study, we present the Global Vegetation Fractional Cover Product (GVFCP) and explore the levels and trends in VFC across World Grassland Type (WGT) Ecoregions considering variation associated with Global Livestock Production Systems (GLPS). Long-term average levels and trends in fractional cover of photosynthetic vegetation (FPV), non-photosynthetic vegetation (FNPV), and bare soil (FBS) are mapped, and variation among GLPS types within WGT Divisions and Ecoregions is explored. Analysis also focused on the savanna-woodland WGT Formations. Many WGT Divisions showed wide variation in long-term average VFC and trends in VFC across GLPS types. Results showed large areas of many ecoregions experiencing significant positive and negative trends in VFC. East Africa, Patagonia, and the Mitchell Grasslands of Australia exhibited large areas of negative trends in FNPV and positive trends FBS. These trends may reflect interactions between extended drought, heavy livestock utilization, expanded agriculture, and other land use changes. Compared to previous studies, explicit measurement of FNPV revealed interesting additional information about vegetation cover and trends in many ecoregions. The Australian and Global products are available via the GEOGLAM RAPP (Group on Earth Observations Global Agricultural Monitoring Rangeland and Pasture Productivity) website, and the scientific community is encouraged to utilize the data and contribute to improved validation.

scholarly journals Hydroclimatic Controls on the Means and Variability of Vegetation Phenology and Carbon Uptake

2014 ◽  
Vol 27 (14) ◽  
pp. 5632-5652 ◽  
Author(s):  
R. D. Koster ◽  
G. K. Walker ◽  
G. J. Collatz ◽  
P. E. Thornton
Keyword(s):  
Spatial Variation ◽  
Primary Productivity ◽  
Vegetation Type ◽  
Temporal Variations ◽  
Carbon Uptake ◽  
Type Ii ◽  
Vegetation Phenology ◽  
Spatial And Temporal Distributions ◽  
Phenology Model

Abstract Long-term, global offline (land only) simulations with a dynamic vegetation phenology model are used to examine the control of hydroclimate over vegetation-related quantities. First, with a control simulation, the model is shown to capture successfully (though with some bias) key observed relationships between hydroclimate and the spatial and temporal variations of phenological expression. In subsequent simulations, the model shows that (i) the global spatial variation of seasonal phenological maxima is controlled mostly by hydroclimate, irrespective of distributions in vegetation type; (ii) the occurrence of high interannual moisture-related phenological variability in grassland areas is determined by hydroclimate rather than by the specific properties of grassland; and (iii) hydroclimatic means and variability have a corresponding impact on the spatial and temporal distributions of gross primary productivity (GPP).

scholarly journals Climate change and plant reproduction: trends and drivers of mast seeding change

2021 ◽  
Vol 376 (1839) ◽  
Author(s):  
Andrew Hacket-Pain ◽  
Michał Bogdziewicz
Keyword(s):  
Climate Change ◽  
Seed Production ◽  
Mast Seeding ◽  
Ecosystem Impacts ◽  
Proximate Mechanisms ◽  
Proximate Factors ◽  
Global Vegetation ◽  
Term Response ◽  
Impacts Of Climate Change

Climate change is reshaping global vegetation through its impacts on plant mortality, but recruitment creates the next generation of plants and will determine the structure and composition of future communities. Recruitment depends on mean seed production, but also on the interannual variability and among-plant synchrony in seed production, the phenomenon known as mast seeding. Thus, predicting the long-term response of global vegetation dynamics to climate change requires understanding the response of masting to changing climate. Recently, data and methods have become available allowing the first assessments of long-term changes in masting. Reviewing the literature, we evaluate evidence for a fingerprint of climate change on mast seeding and discuss the drivers and impacts of these changes. We divide our discussion into the main characteristics of mast seeding: interannual variation, synchrony, temporal autocorrelation and mast frequency. Data indicate that masting patterns are changing but the direction of that change varies, likely reflecting the diversity of proximate factors underlying masting across taxa. Experiments to understand the proximate mechanisms underlying masting, in combination with the analysis of long-term datasets, will enable us to understand this observed variability in the response of masting. This will allow us to predict future shifts in masting patterns, and consequently ecosystem impacts of climate change via its impacts on masting. This article is part of the theme issue ‘The ecology and evolution of synchronized seed production in plants’.

scholarly journals ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model

2021 ◽  
Vol 13 (12) ◽  
pp. 5711-5729
Author(s):  
Sandip S. Dhomse ◽  
Carlo Arosio ◽  
Wuhu Feng ◽  
Alexei Rozanov ◽  
Mark Weber ◽  
...  
Keyword(s):  
Transport Model ◽  
Stratospheric Ozone ◽  
Chemical Transport ◽  
Data Sets ◽  
Chemical Transport Model ◽  
Profile Data ◽  
Data Set ◽  
Ozone Profile ◽  
Satellite Instruments

Abstract. High-quality stratospheric ozone profile data sets are a key requirement for accurate quantification and attribution of long-term ozone changes. Satellite instruments provide stratospheric ozone profile measurements over typical mission durations of 5–15 years. Various methodologies have then been applied to merge and homogenise the different satellite data in order to create long-term observation-based ozone profile data sets with minimal data gaps. However, individual satellite instruments use different measurement methods, sampling patterns and retrieval algorithms which complicate the merging of these different data sets. In contrast, atmospheric chemical models can produce chemically consistent long-term ozone simulations based on specified changes in external forcings, but they are subject to the deficiencies associated with incomplete understanding of complex atmospheric processes and uncertain photochemical parameters. Here, we use chemically self-consistent output from the TOMCAT 3-D chemical transport model (CTM) and a random-forest (RF) ensemble learning method to create a merged 42-year (1979–2020) stratospheric ozone profile data set (ML-TOMCAT V1.0). The underlying CTM simulation was forced by meteorological reanalyses, specified trends in long-lived source gases, solar flux and aerosol variations. The RF is trained using the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) data set over the time periods of the Microwave Limb Sounder (MLS) from the Upper Atmosphere Research Satellite (UARS) (1991–1998) and Aura (2005–2016) missions. We find that ML-TOMCAT shows excellent agreement with available independent satellite-based data sets which use pressure as a vertical coordinate (e.g. GOZCARDS, SWOOSH for non-MLS periods) but weaker agreement with the data sets which are altitude-based (e.g. SAGE-CCI-OMPS, SCIAMACHY-OMPS). We find that at almost all stratospheric levels ML-TOMCAT ozone concentrations are well within uncertainties of the observational data sets. The ML-TOMCAT (V1.0) data set is ideally suited for the evaluation of chemical model ozone profiles from the tropopause to 0.1 hPa and is freely available via https://doi.org/10.5281/zenodo.5651194 (Dhomse et al., 2021).

scholarly journals Shifts in leaf senescence across the Northern Hemisphere in response to seasonal warming

2021 ◽  
Author(s):  
Lei Chen ◽  
Sergio Rossi ◽  
Nicholas G. Smith ◽  
Jianquan Liu
Keyword(s):  
Northern Hemisphere ◽  
Leaf Senescence ◽  
Carbon Assimilation ◽  
Growth Stages ◽  
Physiological Processes ◽  
Phenological Metrics ◽  
Global Vegetation ◽  
Photosynthetic Carbon Assimilation ◽  
Different Growth Stages

Shifts in plant phenology under ongoing warming affect global vegetation dynamics and carbon assimilation of the biomes. The response of leaf senescence to climate is crucial for predicting changes in the physiological processes of trees at ecosystem scale. We used long-term ground observations, phenological metrics derived from PhenoCam, and satellite imagery of the Northern Hemisphere to show that the timings of leaf senescence can advance or delay in case of warming occurring at the beginning (before June) or during (after June) the main growing season, respectively. Flux data demonstrated that net photosynthetic carbon assimilation converted from positive to negative at the end of June. These findings suggest that leaf senescence is driven by carbon assimilation and nutrient resorption at different growth stages of leaves. Our results provide new insights into understanding and modelling autumn phenology and carbon cycling under warming scenarios.

scholarly journals Long-Term Variation of Global GEOV2 and MODIS Leaf Area Index (LAI) and Their Uncertainties: An Insight into the Product Stabilities

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hongliang Fang ◽  
Yao Wang ◽  
Yinghui Zhang ◽  
Sijia Li
Keyword(s):  
Leaf Area Index ◽  
Product Quality ◽  
Leaf Area ◽  
Vegetation Change ◽  
Temporal Variations ◽  
Area Index ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Global Vegetation

Leaf area index (LAI) is an essential climate variable that is crucial to understand the global vegetation change. Long-term satellite LAI products have been applied in many global vegetation change studies. However, these LAI products contain various uncertainties that are not been fully considered in current studies. The objective of this study is to explore the uncertainties in the global LAI products and the uncertainty variations. Two global LAI datasets—the European Geoland2 Version 2 (GEOV2) and Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2019)—were investigated. The qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) embedded in the product quality layers were analyzed to identify the temporal anomalies in the quality profile. The results show that the global GEOV2 (0.042/10a) and MODIS (0.034/10a) LAI values have steadly increased from 2003 to 2019. The global LAI uncertainty (0.016/10a) and relative uncertainty (0.3%/10a) from GEOV2 have also increased gradually, especially during the growing season from April to October. The uncertainty increase is larger for woody biomes than for herbaceous types. Contrastingly, the MODIS LAI product uncertainty remained stable over the study period. The uncertainty increase indicated by GEOV2 is partly attributed to the sensor shift in the product series. Further algorithm enhancement is necessary to improve the cross-sensor performance. This study highlights the importance of studying the LAI uncertainty and the uncertainty variation. Temporal variations in the LAI products and the product quality revealed herein have significant implications on global vegetation change studies.

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