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Evaluating the Impact of Summer Drought on Vegetation Growth Using Space-Based Solar-Induced Chlorophyll Fluorescence Across Extensive Spatial Measures.

Authors
  • Pandiyan, Sanjeevi1
  • Govindjee, Govindjee2
  • Meenatchi, S3
  • Prasanna, S3
  • Gunasekaran, G3
  • Guo, Ya1, 4
  • 1 Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi, China. , (China)
  • 2 Department of Plant Biology, Department of Biochemistry, and Center of Biophysics & Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
  • 3 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India. , (India)
  • 4 Department of Bioengineering, University of Missouri, Columbia, Missouri, USA.
Type
Published Article
Journal
Big data
Publication Date
Jun 01, 2022
Volume
10
Issue
3
Pages
230–245
Identifiers
DOI: 10.1089/big.2020.0350
PMID: 33983846
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Drought is the primary and dominant natural cause of stress on vegetation, and thus, it needs our full attention. Current understanding of drought across extensive spatial measures, around the world, is considerably limited. As case studies to evaluate the feasibility of utilizing space-based solar-induced chlorophyll fluorescence (SIF) across extensive spatial measures, here, we have used data from 2007 to 2017 in Heilongjiang and Jiangsu provinces of China. The onset of the 2015 drought was accompanied by a substantial response of SIF from vegetation in both the provinces; these data were associated with changes in soil moisture, standardized precipitation evapotranspiration index, and emissivity. Our findings suggest that SIF can effectively provide the spatial and temporal progress of drought, as inferred through substantial associations with SIF normalized by absorbed photosynthetically active radiation (related to ΦF) and by photosynthetically active radiation (SIFpar). For the depiction of onset to drought, SIF, ΦF, and SIFpar provide a significant association and a quicker response than the leaf area index and the normalized difference vegetation index. Furthermore, we found that the correlation between gross primary productivity and SIF is highly substantial in both Heilongjiang (R2 = 0.85, p < 0.001) and Jiangsu (R2 = 0.75, p < 0.001) during the drought period. Our results indicate that continuing evaluation from space-based SIF can indeed provide an understanding of the seasonal differences in vegetation for evaluating the impact of drought across extensive spatial measures.

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