Abstract

Due to global warming, drought events have become more frequent, which resulted in aggravated crop failures, food shortage, larger and more energetic wildfires, and have seriously affected socio-economic development and agricultural production. In this study, a global long-term (1981–2021), high-resolution (4 km) improved vegetation health index (VHI) dataset integrating climate, vegetation and soil moisture was developed. Based on drought records from the Emergency Event Database, we compared the detection efficiency of the VHI before and after its improvement in the occurrence and scope of observed drought events. The global drought detection efficiency of the improved high-resolution VHI dataset reached values as high as 85%, which is 14% higher than the original VHI dataset. The improved VHI dataset was also more sensitive to mild droughts and more accurate regarding the extent of droughts. This improved dataset can play an important role in long-term drought monitoring but also has the potential to assess the impact of drought on the agricultural, forestry, ecological and environmental sectors.

Details

Title
An improved global vegetation health index dataset in detecting vegetation drought
Author
Zeng, Jingyu 1   VIAFID ORCID Logo  ; Zhou, Tao 2 ; Qu, Yanping 3 ; Bento, Virgílio A. 4 ; Qi, Junyu 5 ; Xu, Yixin 2 ; Li, Ying 6 ; Wang, Qianfeng 7   VIAFID ORCID Logo 

1  Beijing Normal University, Key Laboratory of Environmental Change and Natural Disasters of Ministry of Education, Beijing, China (GRID:grid.20513.35) (ISNI:0000 0004 1789 9964); Fuzhou University, College of Environment & Safety Engineering, Fuzhou, China (GRID:grid.411604.6) (ISNI:0000 0001 0130 6528) 
2  Beijing Normal University, Key Laboratory of Environmental Change and Natural Disasters of Ministry of Education, Beijing, China (GRID:grid.20513.35) (ISNI:0000 0004 1789 9964) 
3  China Institute of Water Resources and Hydropower Research, Research Center on Flood and Drought Disaster Reduction, Beijing, China (GRID:grid.453304.5) (ISNI:0000 0001 0722 2552) 
4  Universidade de Lisboa, Faculdade de Ciências, Instituto Dom Luiz, Lisboa, Portugal (GRID:grid.9983.b) (ISNI:0000 0001 2181 4263) 
5  University of Maryland, 5825 University Research Ct, Earth System Science Interdisciplinary Center, College Park, USA (GRID:grid.164295.d) (ISNI:0000 0001 0941 7177) 
6  Beijing Normal University, Key Laboratory of Environmental Change and Natural Disasters of Ministry of Education, Beijing, China (GRID:grid.20513.35) (ISNI:0000 0004 1789 9964); Zhejiang Institute of Meteorological Sciences, Hangzhou, China (GRID:grid.469631.f) (ISNI:0000 0004 9341 7437) 
7  Fuzhou University, College of Environment & Safety Engineering, Fuzhou, China (GRID:grid.411604.6) (ISNI:0000 0001 0130 6528) 
Pages
338
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41597-023-02255-3
ProQuest document ID
2821255671
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.