Shifting vegetation phenology in eddy covariance data: Methodological challenges and new perspectives DOI Creative Commons
Annu Panwar, Mirco Migliavacca, Jacob A. Nelson

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: June 1, 2023

Abstract Numerous studies report shifts in vegetation phenology, however, this regard eddy covariance (EC) data is still not fully exploited despite their continuous high-frequency observations. Moreover, there no general consensus on optimal methodologies for smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing present new prospects to investigate changes Gross Primary Productivity (GPP) from EC measurements. First, a technique of GPP time series through the derivative its smoothed annual cumulative sum. Second, calculate PTDs trends commonly used threshold method that identifies days with fixed percentage maximum GPP. A systematic analysis performed various thresholds ranging 0.1 0.7. Lastly, examine relation across years weekly basis. Results 47 sites long (> 10 years) show advancing start season (SOS) are strongest at lower but end (EOS) higher thresholds. variable different individual types sites, outlining reasonable concerns using single value. Relationship reveal association advanced SOS delayed EOS increase immediate primary productivity, overall seasonal productivity. Drawing these analyses, emphasise abstaining subjective choices investigating relationship trend finer temporal Our study examines methodological challenges presents approaches optimize use identifying carbon uptake.

Language: Английский

Quantifying climate variability and regional anthropogenic influence on vegetation dynamics in northwest India DOI Creative Commons
Abhishek Banerjee, Shichang Kang, Michael E. Meadows

et al.

Environmental Research, Journal Year: 2023, Volume and Issue: 234, P. 116541 - 116541

Published: July 5, 2023

Language: Английский

Citations

44
Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data DOI Creative Commons
Annu Panwar, Mirco Migliavacca, Jacob A. Nelson

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Aug. 24, 2023

Abstract While numerous studies report shifts in vegetation phenology, this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing present new prospects to investigate changes gross primary productivity (GPP) from EC measurements. First, a technique of GPP time series through the derivative smoothed annual cumulative sum. Second, calculate PTDs their trends commonly used threshold method that identifies days with fixed percentage maximum GPP. A systematic analysis performed various thresholds ranging 0.1 0.7. Lastly, examine relation across years weekly basis. Results 47 sites long (> 10 years) show advancing start season (SOS) are strongest at lower but end (EOS) higher thresholds. Moreover, variable different individual types sites, outlining reasonable concerns using single value. Relationship reveal association advanced SOS delayed EOS increase immediate productivity, not overall seasonal productivity. Drawing these analyses, emphasise abstaining subjective choices investigating relationship trend finer temporal Our study examines methodological challenges presents approaches optimize use identifying carbon uptake.

Language: Английский

Citations

5
Varying effects of tree cover on relationships between satellite-observed vegetation greenup date and spring temperature across Eurasian boreal forests DOI
Chao Ding, Yuanyuan Meng, Wenjiang Huang

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 899, P. 165650 - 165650

Published: July 18, 2023

Language: Английский

Citations

4
Study on the Response of Net Primary Productivity to Vegetation Phenology and Its Influencing Factors in Karst Ecologically Fragile Regions DOI Creative Commons
Jiale Wang, Zhongfa Zhou, Meng Zhu

et al.

Atmosphere, Journal Year: 2024, Volume and Issue: 15(12), P. 1464 - 1464

Published: Dec. 7, 2024

Net primary productivity (NPP) is a crucial indicator of ecosystem function and sustainability. Quantifying the response NPP to phenological dynamics essential for understanding impact climate change on processes. In this study, vegetation phenology data Guizhou Province were extracted from MCD12Q2 dataset, was estimated using Normalized Difference Vegetation Index (NDVI) combined with meteorological data. Linear regression, trend analysis, structural equation modeling employed clarify spatiotemporal patterns as basis exploring role climatic factors in NPP’s changes. The results indicate that 72.15% shows an increasing (slope = 5.0981, p 0.002). start growing season (measured SOS) tends advance −0.4004, 0.0528), while end EOS) delay 0.2747, 0.1011), resulting overall extension length (LOS) 0.64549, 0.0065). SOS, EOS, LOS, varied elevation For every 500 m increase altitude, decreased by 25.3 gC/m2, SOS delayed 7.1 days, EOS advanced 1.25 LOS 8.36 days. These findings suggest changes primarily controlled local topographical conditions. Additionally, indirect effects through more significant than direct effects. Climatic play varying roles dynamics, highlighting profound influence regulating mechanisms which responds

Language: Английский

Citations

1
Enhancing Long-Term Vegetation Monitoring in Australia: A New Approach for Harmonising and Gap-Filling AVHRR and MODIS NDVI DOI Creative Commons
Chad Burton, Sami W. Rifai, Luigi J. Renzullo

et al.

Published: April 9, 2024

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing AVHRR NDVI products their suitability long-term monitoring Australia. Comparisons with MODIS highlight significant deficiencies, over densely vegetated regions. Moreover, all the assessed failed adequately reproduce inter-annual variability pre-MODIS era as indicated Landsat anomalies. To address these limitations, we propose a new approach calibrating harmonising NOAA’s Climate Data Record MCD43A4 using gradient-boosting decision tree ensemble method. Two versions developed, one incorporating data predictors (‘AusENDVI-clim’: Australian Empirical NDVI-climate) another independent (‘AusENDVI-noclim’). These datasets, spanning 1982–2013 at spatial resolution 0.05°, exhibit correlation low relative errors compared NDVI, accurately reproducing seasonal cycles Furthermore, they closely replicate era. A method gap-filling AusENDVI record also developed that leverages climate, atmospheric CO2 concentration, woody cover fraction predictors. The resulting synthetic dataset shows excellent agreement observations. Finally, provide complete 41-year where gap filled from January 1982 February 2000 seamlessly joined March December 2022. Analysing 40-year per-pixel trends Australia’s annual maximum revealed increasing values across most continent. shifts timing peak identified, underscoring dataset's potential crucial questions regarding changing phenology its drivers. can be used studying Australia's dynamics downstream impacts on carbon water cycles, provides foundation further research into drivers change. open access available https://doi.org/10.5281/zenodo.10802704 (Burton, 2024).

Language: Английский

Citations

0
Comment on essd-2024-89 DOI Creative Commons
Chad Burton, Sami W. Rifai, Luigi J. Renzullo

et al.

Published: April 17, 2024

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing AVHRR NDVI products their suitability long-term monitoring Australia. Comparisons with MODIS highlight significant deficiencies, over densely vegetated regions. Moreover, all the assessed failed adequately reproduce inter-annual variability pre-MODIS era as indicated Landsat anomalies. To address these limitations, we propose a new approach calibrating harmonising NOAA’s Climate Data Record MCD43A4 using gradient-boosting decision tree ensemble method. Two versions developed, one incorporating data predictors (‘AusENDVI-clim’: Australian Empirical NDVI-climate) another independent (‘AusENDVI-noclim’). These datasets, spanning 1982–2013 at spatial resolution 0.05°, exhibit correlation low relative errors compared NDVI, accurately reproducing seasonal cycles Furthermore, they closely replicate era. A method gap-filling AusENDVI record also developed that leverages climate, atmospheric CO2 concentration, woody cover fraction predictors. The resulting synthetic dataset shows excellent agreement observations. Finally, provide complete 41-year where gap filled from January 1982 February 2000 seamlessly joined March December 2022. Analysing 40-year per-pixel trends Australia’s annual maximum revealed increasing values across most continent. shifts timing peak identified, underscoring dataset's potential crucial questions regarding changing phenology its drivers. can be used studying Australia's dynamics downstream impacts on carbon water cycles, provides foundation further research into drivers change. open access available https://doi.org/10.5281/zenodo.10802704 (Burton, 2024).

Language: Английский

Citations

0
Comment on essd-2024-89 DOI Creative Commons
Chad Burton, Sami W. Rifai, Luigi J. Renzullo

et al.

Published: May 5, 2024

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing AVHRR NDVI products their suitability long-term monitoring Australia. Comparisons with MODIS highlight significant deficiencies, over densely vegetated regions. Moreover, all the assessed failed adequately reproduce inter-annual variability pre-MODIS era as indicated Landsat anomalies. To address these limitations, we propose a new approach calibrating harmonising NOAA’s Climate Data Record MCD43A4 using gradient-boosting decision tree ensemble method. Two versions developed, one incorporating data predictors (‘AusENDVI-clim’: Australian Empirical NDVI-climate) another independent (‘AusENDVI-noclim’). These datasets, spanning 1982–2013 at spatial resolution 0.05°, exhibit correlation low relative errors compared NDVI, accurately reproducing seasonal cycles Furthermore, they closely replicate era. A method gap-filling AusENDVI record also developed that leverages climate, atmospheric CO2 concentration, woody cover fraction predictors. The resulting synthetic dataset shows excellent agreement observations. Finally, provide complete 41-year where gap filled from January 1982 February 2000 seamlessly joined March December 2022. Analysing 40-year per-pixel trends Australia’s annual maximum revealed increasing values across most continent. shifts timing peak identified, underscoring dataset's potential crucial questions regarding changing phenology its drivers. can be used studying Australia's dynamics downstream impacts on carbon water cycles, provides foundation further research into drivers change. open access available https://doi.org/10.5281/zenodo.10802704 (Burton, 2024).

Language: Английский

Citations

0
Comment on essd-2024-89 DOI Creative Commons
Chad Burton, Sami W. Rifai, Luigi J. Renzullo

et al.

Published: May 7, 2024

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing AVHRR NDVI products their suitability long-term monitoring Australia. Comparisons with MODIS highlight significant deficiencies, over densely vegetated regions. Moreover, all the assessed failed adequately reproduce inter-annual variability pre-MODIS era as indicated Landsat anomalies. To address these limitations, we propose a new approach calibrating harmonising NOAA’s Climate Data Record MCD43A4 using gradient-boosting decision tree ensemble method. Two versions developed, one incorporating data predictors (‘AusENDVI-clim’: Australian Empirical NDVI-climate) another independent (‘AusENDVI-noclim’). These datasets, spanning 1982–2013 at spatial resolution 0.05°, exhibit correlation low relative errors compared NDVI, accurately reproducing seasonal cycles Furthermore, they closely replicate era. A method gap-filling AusENDVI record also developed that leverages climate, atmospheric CO2 concentration, woody cover fraction predictors. The resulting synthetic dataset shows excellent agreement observations. Finally, provide complete 41-year where gap filled from January 1982 February 2000 seamlessly joined March December 2022. Analysing 40-year per-pixel trends Australia’s annual maximum revealed increasing values across most continent. shifts timing peak identified, underscoring dataset's potential crucial questions regarding changing phenology its drivers. can be used studying Australia's dynamics downstream impacts on carbon water cycles, provides foundation further research into drivers change. open access available https://doi.org/10.5281/zenodo.10802704 (Burton, 2024).

Language: Английский

Citations

0
Reply on RC2 DOI Creative Commons
Chad Burton

Published: May 17, 2024

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing AVHRR NDVI products their suitability long-term monitoring Australia. Comparisons with MODIS highlight significant deficiencies, over densely vegetated regions. Moreover, all the assessed failed adequately reproduce inter-annual variability pre-MODIS era as indicated Landsat anomalies. To address these limitations, we propose a new approach calibrating harmonising NOAA’s Climate Data Record MCD43A4 using gradient-boosting decision tree ensemble method. Two versions developed, one incorporating data predictors (‘AusENDVI-clim’: Australian Empirical NDVI-climate) another independent (‘AusENDVI-noclim’). These datasets, spanning 1982–2013 at spatial resolution 0.05°, exhibit correlation low relative errors compared NDVI, accurately reproducing seasonal cycles Furthermore, they closely replicate era. A method gap-filling AusENDVI record also developed that leverages climate, atmospheric CO2 concentration, woody cover fraction predictors. The resulting synthetic dataset shows excellent agreement observations. Finally, provide complete 41-year where gap filled from January 1982 February 2000 seamlessly joined March December 2022. Analysing 40-year per-pixel trends Australia’s annual maximum revealed increasing values across most continent. shifts timing peak identified, underscoring dataset's potential crucial questions regarding changing phenology its drivers. can be used studying Australia's dynamics downstream impacts on carbon water cycles, provides foundation further research into drivers change. open access available https://doi.org/10.5281/zenodo.10802704 (Burton, 2024).

Language: Английский

Citations

0
Reply on RC3 DOI Creative Commons
Chad Burton

Published: May 17, 2024

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing AVHRR NDVI products their suitability long-term monitoring Australia. Comparisons with MODIS highlight significant deficiencies, over densely vegetated regions. Moreover, all the assessed failed adequately reproduce inter-annual variability pre-MODIS era as indicated Landsat anomalies. To address these limitations, we propose a new approach calibrating harmonising NOAA’s Climate Data Record MCD43A4 using gradient-boosting decision tree ensemble method. Two versions developed, one incorporating data predictors (‘AusENDVI-clim’: Australian Empirical NDVI-climate) another independent (‘AusENDVI-noclim’). These datasets, spanning 1982–2013 at spatial resolution 0.05°, exhibit correlation low relative errors compared NDVI, accurately reproducing seasonal cycles Furthermore, they closely replicate era. A method gap-filling AusENDVI record also developed that leverages climate, atmospheric CO2 concentration, woody cover fraction predictors. The resulting synthetic dataset shows excellent agreement observations. Finally, provide complete 41-year where gap filled from January 1982 February 2000 seamlessly joined March December 2022. Analysing 40-year per-pixel trends Australia’s annual maximum revealed increasing values across most continent. shifts timing peak identified, underscoring dataset's potential crucial questions regarding changing phenology its drivers. can be used studying Australia's dynamics downstream impacts on carbon water cycles, provides foundation further research into drivers change. open access available https://doi.org/10.5281/zenodo.10802704 (Burton, 2024).

Language: Английский

Citations

0