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The Factors Affecting the Quality of the Temperature Vegetation Dryness Index (TVDI) and the Spatial–Temporal Variations in Drought from 2011 to 2020 in Regions Affected by Climate Change

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  • Yuchen Guo

    (School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China)

  • Liusheng Han

    (School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China
    Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China)

  • Dafu Zhang

    (School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China)

  • Guangwei Sun

    (School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China)

  • Junfu Fan

    (School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China)

  • Xiaoyu Ren

    (School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China)

Abstract

The temperature vegetation dryness index (TVDI) is widely used for the monitoring of global or regional drought because of its strong drought-monitoring capabilities and ease of implementation. However, the temporal errors in the land surface temperature (LST) and normalized difference vegetation index (NDVI) can affect warm and cold edges, thus determining the quality of the TVDI, especially in regions affected by climate change, such as Shandong Province. This paper explores this issue in the region in 2011, using daily MODIS MOD09GA and MOD11A1 data products. For each image acquisition time, the warm and cold edges of the NDVI–LST were extracted based on the NDVI, derived from red and near-infrared reflectance data, and the LST, derived from the MOD11A1 dataset. Then, the variations in the warm and cold edges with the LST and NDVI were analyzed. Subsequently, the influence of warm and cold edges, based on the daily values of the temperature, NDVI and precipitation during the observed period, was assessed using a linear regression. The soil moisture (SM) data obtained from the Global Land Data Assimilation System (GLDAS) datasets and the crop water stress index (CWSI) obtained from the MOD16A2 products were used for the assessment. The spatial and temporal variations in drought in Shandong Province from 2011 to 2020 were measured based on Theil–Sen median trend analysis and the Mann–Kendall test. The results show that apparently random variations were evident in the temporal evolution of the slope of the warm edge, indicating that daily data were appropriate to determine the boundary of the warm edge. Daily data were also appropriate to determine the boundary of the cold edge in a similar way. Additionally, the temperature, NDVI and precipitation in this region affected by climate change had a negative correlation with the slope and a positive correlation with the intercept. The validation results show that there was a significant negative correlation between the observed TVDI and GLDAS soil moisture values (R 2 > 0.62) in 12 scatter plots. Therefore, we deduced that the monthly or yearly TVDI product produced by the daily MODIS data has a higher precision than that produced by 8-day or monthly data in regions affected by climate change. The spatial and temporal variations show that the trend of slight and moderate droughts first increased and then decreased, and, in particular, some areas presented severe drought from 2011 to 2015. The results obtained in this study are important for the scheduling of irrigation and drought warnings.

Suggested Citation

  • Yuchen Guo & Liusheng Han & Dafu Zhang & Guangwei Sun & Junfu Fan & Xiaoyu Ren, 2023. "The Factors Affecting the Quality of the Temperature Vegetation Dryness Index (TVDI) and the Spatial–Temporal Variations in Drought from 2011 to 2020 in Regions Affected by Climate Change," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11350-:d:1199175
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    References listed on IDEAS

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    1. Ji, Zhonglin & Pan, Yaozhong & Li, Nan, 2021. "Integrating the temperature vegetation dryness index and meteorology parameters to dynamically predict crop yield with fixed date intervals using an integral regression model," Ecological Modelling, Elsevier, vol. 455(C).
    2. Meiyi Jiang & Xiaoping Xue & Lijuan Zhang & Yuying Chen & Cheng Zhao & Haiyan Song & Nan Wang, 2022. "Peanut Drought Risk Zoning in Shandong Province, China," Sustainability, MDPI, vol. 14(6), pages 1-21, March.
    3. Jeewanthi Sirisena & Denie Augustijn & Aftab Nazeer & Janaka Bamunawala, 2022. "Use of Remote-Sensing-Based Global Products for Agricultural Drought Assessment in the Narmada Basin, India," Sustainability, MDPI, vol. 14(20), pages 1-21, October.
    4. Wan, Wei & Han, Yiwen & Wu, Hanqing & Liu, Fan & Liu, Zhong, 2021. "Application of the source–sink landscape method in the evaluation of agricultural non-point source pollution: First estimation of an orchard-dominated area in China," Agricultural Water Management, Elsevier, vol. 252(C).
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