IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i4p523-d1375889.html
   My bibliography  Save this article

Seasonal Response of the NDVI to the SPEI at Different Time Scales in Yinshanbeilu, Inner Mongolia, China

Author

Listed:
  • Sinan Wang

    (Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Xigang Xing

    (General Institute of Water Resources and Hydropower Planning and Design, Ministry of Water Resources, Beijing 100120, China)

  • Yingjie Wu

    (Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Jianying Guo

    (Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Mingyang Li

    (Water Resources Research Institute of Shandong Province, Shandong Provincial Key Laboratory of Water Resources and Environment, Jinan 250014, China)

  • Bin Fu

    (School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

Abstract

Recently, the frequent occurrence of droughts has caused a serious impact on vegetation growth and progression. This research is based upon the normalized difference vegetation index (NDVI) from 2001 to 2020. The correlation between the NDVI and standardized precipitation evapotranspiration index (SPEI) at disparate time scales was used to assess the response of vegetation growth to drought in the Yinshanbeilu region. The drought levels of SPEI1, SPEI3, SPEI6, and SPEI12 increased prominently in the eastern region of the country, while the NDVI decreased significantly from east to west in spring, summer, and autumn but was reversed in the winter. The area with an upward trend (33.86%) was slightly lower than that with a downward trend (66.14%). The correlation coefficients between the NDVI and SPEI over the entire year increased with the SPEI timescale. The elevated values were concentrated in the southeastern and western regions of the survey region. Additionally, the best correlation timescales were SPEI6 and SPEI12. Grassland was the most sensitive vegetation type to the SPEI response in the NDVI. The correlation coefficients of NDVI and SPEI1–12 were 0.313, 0.459, 0.422, and 0.406. Both spring and summer were more responsive to SPEI12, whereas autumn and winter were more responsive to SPEI3. The correlation of disparate time scales exhibited complex soil texture features with respect to different seasonal scales, and the soil texture showed a strong response to vegetation in both summer and autumn. Loam, sandy loam, and silty loam all exhibited the highest response to SPEI12, with coefficients of 0.509, 0.474, and 0.403, respectively.

Suggested Citation

  • Sinan Wang & Xigang Xing & Yingjie Wu & Jianying Guo & Mingyang Li & Bin Fu, 2024. "Seasonal Response of the NDVI to the SPEI at Different Time Scales in Yinshanbeilu, Inner Mongolia, China," Land, MDPI, vol. 13(4), pages 1-17, April.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:4:p:523-:d:1375889
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/4/523/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/4/523/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Geer Cheng & Tiejun Liu & Sinan Wang & Yingjie Wu & Cunhou Zhang, 2023. "Responses to the Impact of Drought on Carbon and Water Use Efficiency in Inner Mongolia," Land, MDPI, vol. 12(3), pages 1-14, February.
    2. Zhang, Yu & Liu, Xiaohong & Jiao, Wenzhe & Zhao, Liangju & Zeng, Xiaomin & Xing, Xiaoyu & Zhang, Lingnan & Hong, Yixue & Lu, Qiangqiang, 2022. "A new multi-variable integrated framework for identifying flash drought in the Loess Plateau and Qinling Mountains regions of China," Agricultural Water Management, Elsevier, vol. 265(C).
    3. Anurag Malik & Anil Kumar & Rajesh P. Singh, 2019. "Application of Heuristic Approaches for Prediction of Hydrological Drought Using Multi-scalar Streamflow Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3985-4006, September.
    4. Yang Li & Wen Zhang & Christopher R. Schwalm & Pierre Gentine & William K. Smith & Philippe Ciais & John S. Kimball & Antonio Gazol & Steven A. Kannenberg & Anping Chen & Shilong Piao & Hongyan Liu & , 2023. "Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems," Nature Climate Change, Nature, vol. 13(2), pages 182-188, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Manish Kumar & Anuradha Kumari & Daniel Prakash Kushwaha & Pravendra Kumar & Anurag Malik & Rawshan Ali & Alban Kuriqi, 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India," Sustainability, MDPI, vol. 12(19), pages 1-21, September.
    2. Kusum Pandey & Shiv Kumar & Anurag Malik & Alban Kuriqi, 2020. "Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India," Sustainability, MDPI, vol. 12(21), pages 1-24, October.
    3. Yuzhong Shi & Linlin Zhao & Xueyan Zhao & Haixia Lan & Hezhi Teng, 2022. "The Integrated Impact of Drought on Crop Yield and Farmers’ Livelihood in Semi-Arid Rural Areas in China," Land, MDPI, vol. 11(12), pages 1-13, December.
    4. Seyed Mohammad Ehsan Azimi & Seyed Javad Sadatinejad & Arash Malekian & Mohammad Hossein Jahangir, 2023. "Application of artificial intelligence hybrid models for meteorological drought prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(2), pages 2565-2589, March.
    5. Okan Mert Katipoğlu, 2023. "Prediction of Streamflow Drought Index for Short-Term Hydrological Drought in the Semi-Arid Yesilirmak Basin Using Wavelet Transform and Artificial Intelligence Techniques," Sustainability, MDPI, vol. 15(2), pages 1-24, January.
    6. Farshad Ahmadi & Saeid Mehdizadeh & Babak Mohammadi, 2021. "Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4127-4147, September.
    7. Sedigheh Mohamadi & Saad Sh. Sammen & Fatemeh Panahi & Mohammad Ehteram & Ozgur Kisi & Amir Mosavi & Ali Najah Ahmed & Ahmed El-Shafie & Nadhir Al-Ansari, 2020. "Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 537-579, October.
    8. Soyeon Lim & Seungyub Lee & Donghwi Jung, 2021. "Identifying the Drought Impact Factors and Developing Drought Scenarios Using the DSD Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 4809-4823, November.
    9. Farhang Rahmani & Mohammad Hadi Fattahi, 2021. "A multifractal cross-correlation investigation into sensitivity and dependence of meteorological and hydrological droughts on precipitation and temperature," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(3), pages 2197-2219, December.
    10. Saeed Azimi & Erfan Hassannayebi & Morteza Boroun & Mohammad Tahmoures, 2020. "Probabilistic Analysis of Long-Term Climate Drought Using Steady-State Markov Chain Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4703-4724, December.
    11. Saeid Mehdizadeh, 2020. "Using AR, MA, and ARMA Time Series Models to Improve the Performance of MARS and KNN Approaches in Monthly Precipitation Modeling under Limited Climatic Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 263-282, January.
    12. Karbasi, Masoud & Jamei, Mehdi & Malik, Anurag & Kisi, Ozgur & Yaseen, Zaher Mundher, 2023. "Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model," Agricultural Water Management, Elsevier, vol. 281(C).
    13. Fatemeh Barzegari Banadkooki & Vijay P. Singh & Mohammad Ehteram, 2021. "Multi-timescale drought prediction using new hybrid artificial neural network models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(3), pages 2461-2478, April.
    14. Elnaz Ghabelnezam & Raoof Mostafazadeh & Zeinab Hazbavi & Guangwei Huang, 2023. "Hydrological Drought Severity in Different Return Periods in Rivers of Ardabil Province, Iran," Sustainability, MDPI, vol. 15(3), pages 1-16, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:13:y:2024:i:4:p:523-:d:1375889. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.