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Combining UAV-Based Multispectral and Thermal Images to Diagnosing Dryness Under Different Crop Areas on the Loess Plateau

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  • Juan Zhang

    (Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Road 320, Lanzhou 730000, China
    University of the Chinese Academy of Sciences, Yanqihu East Road 1, Beijing 100049, China)

  • Yuan Qi

    (Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Road 320, Lanzhou 730000, China)

  • Qian Li

    (College of Agriculture and Forestry, Longdong University, Lanzhou Road 45, Lanzhou 745000, China)

  • Jinlong Zhang

    (Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Road 320, Lanzhou 730000, China
    University of the Chinese Academy of Sciences, Yanqihu East Road 1, Beijing 100049, China)

  • Rui Yang

    (Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Road 320, Lanzhou 730000, China)

  • Hongwei Wang

    (Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Road 320, Lanzhou 730000, China)

  • Xiangfeng Li

    (College of Mathematics and Information Engineering, Longdong University, Lanzhou Road 45, Lanzhou 745000, China)

Abstract

Dryness is a critical limiting factor for achieving high agricultural productivity on China’s Loess Plateau (LP). High-precision, field-scale dryness monitoring is essential for the implementation of precision agriculture. However, obtaining dryness information with adequate spatial and temporal resolution remains a significant challenge. Unmanned aerial vehicle (UAV) systems can capture high-resolution remote sensing images on demand, but the effectiveness of UAV-based dryness indices in mapping the high-resolution spatial heterogeneity of dryness across different crop areas at the agricultural field scale on the LP has yet to be fully explored. Here, we conducted UAV–ground synchronized experiments on three typical croplands in the eastern Gansu province of the Loess Plateau (LP). Multispectral and thermal infrared sensors mounted on the UAV were used to collect high-resolution multispectral and thermal images. The temperature vegetation dryness index (TVDI) and the temperature–vegetation–soil moisture dryness index (TVMDI) were calculated based on UAV imagery. A total of 14 vegetation indices (VIs) were employed to construct various VI-based TVDIs, and the optimal VI was selected. Correlation analysis and Gradient Structure Similarity (GSSIM) were applied to evaluate the suitability and spatial differences between the TVDI and TVMDI for dryness monitoring. The results indicate that TVDIs constructed using the normalized difference vegetation index (NDVI) and the visible atmospherically resistant index (VARI) were more consistent with the characteristics of crop responses to dryness stress. Furthermore, the TVDI demonstrated higher sensitivity in dryness monitoring compared with the TVMDI, making it more suitable for assessing dryness variations in rain-fed agriculture in arid regions.

Suggested Citation

  • Juan Zhang & Yuan Qi & Qian Li & Jinlong Zhang & Rui Yang & Hongwei Wang & Xiangfeng Li, 2025. "Combining UAV-Based Multispectral and Thermal Images to Diagnosing Dryness Under Different Crop Areas on the Loess Plateau," Agriculture, MDPI, vol. 15(2), pages 1-19, January.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:2:p:126-:d:1562533
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    References listed on IDEAS

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    1. Han, Zhiming & Huang, Qiang & Huang, Shengzhi & Leng, Guoyong & Bai, Qingjun & Liang, Hao & Wang, Lu & Zhao, Jing & Fang, Wei, 2021. "Spatial-temporal dynamics of agricultural drought in the Loess Plateau under a changing environment: Characteristics and potential influencing factors," Agricultural Water Management, Elsevier, vol. 244(C).
    2. Cheng, Minghan & Jiao, Xiyun & Liu, Yadong & Shao, Mingchao & Yu, Xun & Bai, Yi & Wang, Zixu & Wang, Siyu & Tuohuti, Nuremanguli & Liu, Shuaibing & Shi, Lei & Yin, Dameng & Huang, Xiao & Nie, Chenwei , 2022. "Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning," Agricultural Water Management, Elsevier, vol. 264(C).
    3. Atiqotun Fitriyah & Alvin Fatikhunnada & Fumi Okura & Bayu Dwi Apri Nugroho & Tasuku Kato, 2019. "Analysis of the Drought Mitigated Mechanism in Terraced Paddy Fields Using CWSI and TVDI Indices and Hydrological Monitoring," Sustainability, MDPI, vol. 11(24), pages 1-18, December.
    4. Jia, Qiong & Shi, Haibin & Li, Ruiping & Miao, Qingfeng & Feng, Yayang & Wang, Ning & Li, Jingwei, 2021. "Evaporation of maize crop under mulch film and soil covered drip irrigation: field assessment and modelling on West Liaohe Plain, China," Agricultural Water Management, Elsevier, vol. 253(C).
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