The impact of indicator selection on assessment of global greening
Creators: name: Qiu, Bingwen
name: Yan, Xiongfei
name: Chen, Chongcheng
name: Tang, Zhenghong
name: Wu, Wenbin
name: Xu, Weiming
name: Zhao, Zhiyuan
name: Yan, Chao
name: Berry, Joe
name: Huang, Wenqing
name: Chen, Fangxin
Publisher: name: Taylor & Francis
Year: 2021
Subjects: subject: Environmental Sciences not elsewhere classified
subject: Ecology
subject: FOS: Biological sciences
subject: Sociology
subject: FOS: Sociology
subject: Biological Sciences not elsewhere classified
subject: Science Policy
subject: Plant Biology

description: Greening represents a significant increase in vegetation activity. Accurate quantification of global greening in peak growth is vital for quantifying changes in terrestrial productivity. Normalized Difference Vegetation Index (NDVI) is the remote sensing indicator most commonly applied for this purpose. However, there is limited knowledge about how the applications of other improved or newly available products can impact global greening assessments. This study reports the first systematic investigation of the impact of vegetation indicator selection on global greening in peak growth. It examines the period from 2003 to 2017 using six indicators with a spatial resolution of 500 m: Near Infrared Reflectance of terrestrial vegetation (NIRv) and the coupled diagnostic biophysical modeled Gross Primary Production (PML-GPP), together with NDVI, Enhanced Vegetation Index with two bands (EVI2), Leaf Area Index (LAI) and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP (MOD-GPP) calculated based on MODIS images. Vegetation trends were estimated using the Mann–Kendall test, the unanimous trends by six vegetation indicators were derived, and the concordance ratio in estimated trends by each pair of indicators was investigated among different biomes and their sensitivity to changes in climate were investigated. We found that the estimated greening and browning varied between 12–23% and 2–13% of the vegetated areas, respectively. There was more net greening estimated from NDVI and MOD-GPP (around 19%) compared to the other indicators (8–11%). The concordance ratio between EVI2 and NIRv was much higher (&gt;94%) than other combinations (61–76%) at the global scale. The concordance ratio between one specific indicator and the other indicators gradually increased with its change magnitude. Unanimous results from these six indicators were exhibited in less than two-fifths (38.71%) of vegetated areas. Apart from the EVI2 and NIRv, the concordance ratio varied among different biomes: high in cropland (67–81%), but low in deciduous needle-leaf forest (51–62%) and very low in evergreen broadleaf forest between PML-GPP and others (37–49%). The diverse sensitivity to changes in climate contributed to the discrepancies: greening was more pronounced during warming conditions for the GPP products when compared with the greenness indices. At the global scale, the concordance ratio was higher during cooling conditions among all the indicators except between PML-GPP. Datasets supporing these findings are available online at https://github.com/FuzhouSIRC/Greening_6indicators.2