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Monitoring responses of vegetation phenology and productivity to extreme climatic conditions using remote sensing across different sub-regions of China

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Abstract

Drought is a major natural disaster that significantly impacts the susceptibility and flexibility of the ecosystem by changing vegetation phenology and productivity. This study aimed to investigate the impact of extreme climatic variation on vegetation phenology and productivity over the four sub-regions of China from 2000 to 2017. Daily rain gauge precipitation and air temperature datasets were used to estimate the trends, and to compute the standardized precipitation-evapotranspiration index (SPEI). Remote sensing–based Enhanced Vegetation Index (EVI) data from a moderate resolution imaging spectroradiometer (MODIS) was used to characterize vegetation phenology. The results revealed that (1) air temperature had significant increasing trends (P < 0.05) in all sub-regions. Precipitation showed a non-significant increasing trend in Northwest China (NWC) and insignificant decreasing trends in North China (NC), Qinghai Tibet area (QTA), and South China (SC). (2) Integrated enhanced vegetation index (iEVI) and SPEI variations depicted that 2011 and 2016 were the extremely driest and wettest years during 2000–2017. (3) Rapid changes were observed in the vegetation phenology and productivity between 2011 and 2016. In 2011, changes in the vegetation phenology with the length of the growing season (ΔLGS) = was − 14 ± 36 days. In 2016, the overall net effect changed at the onset and end of the growing season with ΔLGS of 34 ± 71 days. The change in iEVI per SPEI increased rapidly with a changing rate of 0.16 from arid (NWC, and QTA) to semi-arid (NWC, QTA and NC) and declined with a rate of − 0.04 from semi-humid (QTA, NC, and SC) to humid (SC) region. A higher association was observed between iEVI and SPEI as compared to iEVI and precipitation. Our finding exposed that north China is more sensitive to climatic variation.

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Data availability

The daily precipitation and temperature datasets were provided by the China meteorological bureau (http://data.cma.cn/). The global land cover data was provided by the national mapping organization and European space agency (ESA) climate change initiative (CCI) (http://globalmaps.github.io/glcnmo.html and http://maps.elie.ucl.ac.be/CCI/viewer/index.php). The Vegetation Indices data was provided by NASA land processes distributed active archive center (https://lpdaac.usgs.gov). The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This study received financial support from the Foreign Expert Introduction Project (G20190027163), and the China 111 project (B12007).

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Authors and Affiliations

  1. College of Water Resources and Architecture Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, People’s Republic of China

    Tehseen Javed, Yi Li, Kai Feng, Xinguo Chen & Sovannaka Suon

  2. Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling, 712100, Shaanxi, People’s Republic of China

    Tehseen Javed, Yi Li & Shakeel Ahmad

  3. Department of Water Resources Management and Agricultural-Meteorology, Federal University of Agriculture, Abeokuta, PMB 2240, Nigeria

    Olusola O. Ayantobo

  4. College of Agronomy, Northwest Agriculture & Forestry University/Key Laboratory of Physio-ecology, and Tillage in Loess Plateau, Ministry of Agriculture, Yangling, 712100, China

    Shakeel Ahmad

  5. Department of Physics, Islamia College University Peshawar, Peshawar, Khyber Pakhtunkhwa, 25120, Pakistan

    Sadaf Rashid

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  1. Tehseen Javed
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  2. Yi Li
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  3. Kai Feng
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  4. Olusola O. Ayantobo
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Contributions

Conceptualization, T.J., and L.Y.; data curation, S.S., S.R., and O.O.A; formal analysis, T.J; funding acquisition, L.Y.; methodology, T.J.; project administration, L.Y.; software, T.J., F.K., and X.C.; supervision, L.Y.; writing—original draft, T.J. and L.Y.; writing—review and editing, S.A., and S.R.

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Correspondence to Yi Li.

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The study examined the drought effect on vegetation phenology; this type of study is non-human subject research and waived the need for informed consent.

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Javed, T., Li, Y., Feng, K. et al. Monitoring responses of vegetation phenology and productivity to extreme climatic conditions using remote sensing across different sub-regions of China. Environ Sci Pollut Res 28, 3644–3659 (2021). https://doi.org/10.1007/s11356-020-10769-1

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