Seasonal Differences in Vegetation Susceptibility to Soil Drought During 2001–2021 DOI
Jiwang Tang, Ben Niu, Jinlong Peng

et al.

Journal of Geophysical Research Biogeosciences, Journal Year: 2024, Volume and Issue: 129(12)

Published: Dec. 1, 2024

Abstract Droughts typically exert negative effects on vegetation growth, which largely depend the timing of drought onset. However, huge inconsistencies exist in seasonal response to among diverse regions across globe. Here, using leaf area index (LAI) and solar‐induced chlorophyll fluorescence (SIF), we quantified susceptibility by calculating coincidence rate between suppression extremes soil droughts, further investigated spatiotemporal changes during different seasons from 2001 2021. We found summer dry were most susceptible droughts extra‐tropics tropics, respectively. Temporally, autumn was strengthening drought‐susceptible extra‐tropics, albeit with insignificant change spring, entire growing season. Both wet showed evidently increasing tropical ecosystems, dominated enhanced global regions. Our findings determined spatial pattern globe highlighted risk especially tropics.

Language: Английский

Influence of ecological restoration on regional temperature-vegetation-precipitation dryness index in the middle Yellow River of China DOI
Wei Chen,

Yuxing Guo,

Congjian Sun

et al.

Journal of Mountain Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

Language: Английский

Citations

0
Application of a Random Forest Method to Estimate the Water Use Efficiency on the Qinghai Tibetan Plateau During the 1982–2018 Growing Season DOI Creative Commons

Xuemei Wu,

Tao Zhou, Jingyu Zeng

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 527 - 527

Published: Feb. 4, 2025

Water use efficiency (WUE) reflects the quantitative relationship between vegetation gross primary productivity (GPP) and surface evapotranspiration (ET), serving as a crucial indicator for assessing coupling of carbon water cycles in ecosystems. As sensitive region to climate change, Qinghai Tibetan Plateau’s WUE dynamics are significant scientific interest understanding interactions forecasting future trends. However, due scarcity observational data unique environmental conditions plateau, existing studies show substantial errors GPP simulation accuracy considerable discrepancies ET outputs from different models, leading uncertainties current estimates. This study addresses these gaps by first employing machine learning approach (random forest) integrate observed flux with multi-source information, developing predictive model capable accurately simulating Plateau (QTP). The random forest results, RF_GPP (R2 = 0.611, RMSE 69.162 gC·m−2·month−1), is higher than that multiple linear regression model, regGPP 0.429, 86.578 significantly better GLASS product, GLASS_GPP 0.360, 91.764 gC·m−2·month−1). Subsequently, based on data, we quantitatively evaluate products various models construct integrates products. REG_ET, obtained integrating five using 0.601, 21.04 mm·month−1), product derived through mean processing, MEAN_ET 0.591, 25.641 mm·month−1). Finally, optimized calculate during growing season 1982 2018 analyze its spatiotemporal evolution. In this study, were observation thereby enhancing estimation WUE. On basis, interannual variation was analyzed, providing foundation studying QTP ecosystems supporting formulation policies ecological construction resource management future.

Language: Английский

Citations

0
Refining landsat-based annual NDVImax estimation using shape model fitting and phenological metrics DOI Creative Commons
Lihao Zhang, Miaogen Shen, Licong Liu

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103107 - 103107

Published: March 1, 2025

Language: Английский

Citations

0
Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation (NVDI) with MODIS NDVI DOI Creative Commons
Chad Burton, Sami W. Rifai, Luigi J. Renzullo

et al.

Earth system science data, Journal Year: 2024, Volume and Issue: 16(10), P. 4389 - 4416

Published: Oct. 1, 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 Advanced Very High Resolution Radiometer (AVHRR) normalised-difference index (NDVI) products their suitability long-term monitoring Australia. Comparisons with the MODIS NDVI highlight significant deficiencies, over densely vegetated regions. Moreover, all assessed failed adequately reproduce variability pre-MODIS era as indicated Landsat anomalies. To address these limitations, we propose a new approach calibrating harmonising NOAA's Climate Data Record AVHRR MCD43A4 Australia using gradient-boosting decision tree ensemble method. Two versions developed, one incorporating data predictors (“AusENDVI-clim”: Australian Empirical NDVI-climate) another that independent (“AusENDVI-noclim”). These datasets, spanning 1982–2013 at spatial resolution 0.05° monthly time step, exhibit correlations (r2=0.89–0.94) low mean errors compared (mean absolute error (MAE) = 0.014–0.028, RMSE 0.021–0.046), accurately reproducing seasonal cycles Furthermore, they closely replicate era. A method gap-filling AusENDVI record also developed leverages climate, atmospheric CO2 concentration, woody-cover fraction predictors. The resulting synthetic dataset shows excellent agreement recalibrated series (r2=0.82–0.95, MAE 0.016–0.029, 0.039–0.041). Finally, provide complete 41-year where gap-filled AusENDVI-clim from January 1982 February 2000 joined March December 2022. Analysing 40-year per-pixel trends Australia's annual maximum revealed increasing values, shifts timing, peak across most continent, underscoring dataset's potential crucial questions regarding changing phenology its drivers. can be used studying dynamics downstream impacts on carbon water cycles, it provides foundation further research into drivers change. open access available https://doi.org/10.5281/zenodo.10802703 (Burton et al., 2024).

Language: Английский

Citations

0
NAO Signal in the Increased Interannual Variability of Spring Vegetation in Northeast Asia After the Early 2000s DOI Creative Commons
Ning Xin, Botao Zhou, Haishan Chen

et al.

Journal of Geophysical Research Atmospheres, Journal Year: 2024, Volume and Issue: 129(23)

Published: Nov. 29, 2024

Abstract Based on the leaf area index (LAI) and normalized difference vegetation (NDVI) from 1982 to 2020, this study reveals a significant increase in intensity of interannual variability (IIV) spring (April–May) over Northeast Asia since early 2000s. This change is closely linked notable IIV April surface air temperatures former period (1986–2001) latter (2002–2016). Further analysis also highlights salient impact North Atlantic Oscillation (NAO) strengthened vegetation. During period, there substantial March NAO compared with period. greater allows positive significantly influence net heat fluxes, thereby leading phase tripole (NAT) sea temperature (SST) pattern March. Given persistence SSTs, NAT SST lasts April, subsequently causing height anomalies through wave train that originates propagates downstream. process consequently results an hence local Thus, increased conducive enhancing Asia.

Language: Английский

Citations

0
Seasonal Differences in Vegetation Susceptibility to Soil Drought During 2001–2021 DOI
Jiwang Tang, Ben Niu, Jinlong Peng

et al.

Journal of Geophysical Research Biogeosciences, Journal Year: 2024, Volume and Issue: 129(12)

Published: Dec. 1, 2024

Abstract Droughts typically exert negative effects on vegetation growth, which largely depend the timing of drought onset. However, huge inconsistencies exist in seasonal response to among diverse regions across globe. Here, using leaf area index (LAI) and solar‐induced chlorophyll fluorescence (SIF), we quantified susceptibility by calculating coincidence rate between suppression extremes soil droughts, further investigated spatiotemporal changes during different seasons from 2001 2021. We found summer dry were most susceptible droughts extra‐tropics tropics, respectively. Temporally, autumn was strengthening drought‐susceptible extra‐tropics, albeit with insignificant change spring, entire growing season. Both wet showed evidently increasing tropical ecosystems, dominated enhanced global regions. Our findings determined spatial pattern globe highlighted risk especially tropics.

Language: Английский

Citations

0