@article{Jeong:2024:10.1016/j.rse.2024.114282,
author = {Jeong, S and Ryu, Y and Gentine, P and Lian, X and Fang, J and Li, X and Dechant, B and Kong, J and Choi, W and Jiang, C and Keenan, TF and Harrison, SP and Prentice, IC},
doi = {10.1016/j.rse.2024.114282},
journal = {Remote Sensing of Environment},
title = {Persistent global greening over the last four decades using novel long-term vegetation index data with enhanced temporal consistency},
url = {http://dx.doi.org/10.1016/j.rse.2024.114282},
volume = {311},
year = {2024}
}

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TY  - JOUR
AB  - Advanced Very High-Resolution Radiometer (AVHRR) satellite observations have provided the longest global daily records from 1980s, but the remaining temporal inconsistency in vegetation index datasets has hindered reliable assessment of vegetation greenness trends. To tackle this, we generated novel global long-term Normalized Difference Vegetation Index (NDVI) and Near-Infrared Reflectance of vegetation (NIRv) datasets derived from AVHRR and Moderate Resolution Imaging Spectroradiometer (MODIS). We addressed residual temporal inconsistency through three-step post processing including cross-sensor calibration among AVHRR sensors, orbital drifting correction for AVHRR sensors, and machine learning-based harmonization between AVHRR and MODIS. After applying each processing step, we confirmed the enhanced temporal consistency in terms of detrended anomaly, trend and interannual variability of NDVI and NIRv at calibration sites. Our refined NDVI and NIRv datasets showed a persistent global greening trend over the last four decades (NDVI: 0.0008 yr−1; NIRv: 0.0003 yr−1), contrasting with those without the three processing steps that showed rapid greening trends before 2000 (NDVI: 0.0017 yr−1; NIRv: 0.0008 yr−1) and weakened greening trends after 2000 (NDVI: 0.0004 yr−1; NIRv: 0.0001 yr−1). These findings highlight the importance of minimizing temporal inconsistency in long-term vegetation index datasets, which can support more reliable trend analysis in global vegetation response to climate changes.
AU  - Jeong,S
AU  - Ryu,Y
AU  - Gentine,P
AU  - Lian,X
AU  - Fang,J
AU  - Li,X
AU  - Dechant,B
AU  - Kong,J
AU  - Choi,W
AU  - Jiang,C
AU  - Keenan,TF
AU  - Harrison,SP
AU  - Prentice,IC
DO  - 10.1016/j.rse.2024.114282
PY  - 2024///
SN  - 0034-4257
TI  - Persistent global greening over the last four decades using novel long-term vegetation index data with enhanced temporal consistency
T2  - Remote Sensing of Environment
UR  - http://dx.doi.org/10.1016/j.rse.2024.114282
VL  - 311
ER  - 

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