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Remote sensing assessment of multi-year drought vulnerability of agriculture in Kangavar, Kermanshah Province, Western Iran

Author

Listed:
  • Farzaneh Imani Buzhani

    (Hamedan Branch, Islamic Azad University)

  • Maryam Kiani Sadr

    (Hamedan Branch, Islamic Azad University)

  • Soheil Sobhanardakani

    (Hamedan Branch, Islamic Azad University)

  • Bahareh Lorestani

    (Hamedan Branch, Islamic Azad University)

  • Mehrdad Cheraghi

    (Hamedan Branch, Islamic Azad University)

Abstract

The current study investigated the spatiotemporal distribution and detection of drought severity using ground indices in R-Studio software and applying indices on the images of Landsat 8 and Sentinel 2 satellites in the growing season and annually. The outcomes showed that typical months had the largest range in terrestrial indices results, and the Normalized Difference Vegetation Index (NDVI) index had the greatest compatibility with the Standard Precipitation Evaporation Index (SPEI). By comparing these two times, during the growing season, plant health was evident in the northwest and annual images in the northeast. Moreover, a negative correlation was found between Land Surface Temperature (LST) and NDVI in elevation, which was more moderate at northern heights. High mean annual temperatures during growing seasons were common in the east and south. The eastern side of the study area had high annual vegetation density, but soil moisture was higher in the southwest. Floods negatively affected plant health; with the 2019 flood, the canola yield was very low. In summary, Sentinel 2 images showed drought better in the growing season and Landsat 8 images showed drought better in the dry season or at the end of plant growth.

Suggested Citation

  • Farzaneh Imani Buzhani & Maryam Kiani Sadr & Soheil Sobhanardakani & Bahareh Lorestani & Mehrdad Cheraghi, 2024. "Remote sensing assessment of multi-year drought vulnerability of agriculture in Kangavar, Kermanshah Province, Western Iran," 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. 120(4), pages 3865-3890, March.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:4:d:10.1007_s11069-023-06354-7
    DOI: 10.1007/s11069-023-06354-7
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    References listed on IDEAS

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    1. Bi, Wuxia & Weng, Baisha & Yan, Denghua & Wang, Mengke & Wang, Hao & Jing, Lanshu & Yan, Siying, 2022. "Soil phosphorus loss increases under drought-flood abrupt alternation in summer maize planting area," Agricultural Water Management, Elsevier, vol. 262(C).
    2. Potopová, Vera & Trnka, Miroslav & Hamouz, Pavel & Soukup, Josef & Castraveț, Tudor, 2020. "Statistical modelling of drought-related yield losses using soil moisture-vegetation remote sensing and multiscalar indices in the south-eastern Europe," Agricultural Water Management, Elsevier, vol. 236(C).
    3. Ma, Chunfeng & Johansen, Kasper & McCabe, Matthew F., 2022. "Combining Sentinel-2 data with an optical-trapezoid approach to infer within-field soil moisture variability and monitor agricultural production stages," Agricultural Water Management, Elsevier, vol. 274(C).
    4. Dai, Meng & Huang, Shengzhi & Huang, Qiang & Leng, Guoyong & Guo, Yi & Wang, Lu & Fang, Wei & Li, Pei & Zheng, Xudong, 2020. "Assessing agricultural drought risk and its dynamic evolution characteristics," Agricultural Water Management, Elsevier, vol. 231(C).
    5. Graham, Scott L. & Laubach, Johannes & Hunt, John E. & Eger, Andre & Carrick, Sam & Whitehead, David, 2019. "Predicting soil water balance for irrigated and non-irrigated lucerne on stony, alluvial soils," Agricultural Water Management, Elsevier, vol. 226(C).
    6. Jorge Castillo-Mateo & Miguel Lafuente & Jesús Asín & Ana C. Cebrián & Alan E. Gelfand & Jesús Abaurrea, 2022. "Spatial Modeling of Day-Within-Year Temperature Time Series: An Examination of Daily Maximum Temperatures in Aragón, Spain," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 487-505, September.
    7. Rahimi-Moghaddam, Sajjad & Eyni-Nargeseh, Hamed & Ahmadi, Seyed Ahmad Kalantar & Azizi, Khosro, 2021. "Towards withholding irrigation regimes and drought-resistant genotypes as strategies to increase canola production in drought-prone environments: A modeling approach," Agricultural Water Management, Elsevier, vol. 243(C).
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