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Effects of image spatial resolution and statistical scale on water stress estimation performance of MGDEXG: A new crop water stress indicator derived from RGB images

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  • Zhang, Liyuan
  • Zhang, Huihui
  • Han, Wenting
  • Niu, Yaxiao
  • Chávez, José L.
  • Ma, Weitong

Abstract

To further explore the performance of the newly proposed crop water stress indicator - the Mean Value of Gaussian Distribution of Excess Green index (MGDEXG) for maize canopy in RGB imagery, studies were conducted in two maize fields with different irrigation levels during the 2015 and 2019 growing seasons. Specifically, the effects of spatial resolution of RGB images collected by both ground and UAV platforms, and segmentation scale of UAV RGB orthophoto on the performance of MGDEXG were investigated, and MGDEXG maps were derived based on UAV RGB orthophoto to monitor maize water status and its inter-field variability. The results show that when the spatial resolution of ground RGB images (2.4 mm) was resized by bilinear interpolation algorithm to 4.8, 9.6, 19.2, 38.4, and 76.8 mm, similar water estimation performances of MGDEXG were observed when compared to the crop water stress index (CWSI), with R2 values ranging 0.80–0.83. However, the processing time per RGB image with 2.4 mm spatial resolution was greatly reduced from 232.26 s to 0.32 s when the resolution was reduced by 32 times, providing a better opportunity to obtain MGDEXG in real-time. When UAV RGB images with two spatial resolutions of 2.7 mm and 14.7 mm were adopted, a poor water stress estimation performance was observed for the lower resolution with R2 of 0.62 and RMSE of 0.12 mol·m-2·s-1 for the maize stomatal conductance. The possible reason could be the errors introduced during the mosaicking process. When segmentation scales of 2 m x 2 m, 4 m x 4 m, 6 m x 6 m, 8 m x 8 m, 10 m x 10 m, and 12 m x 12 m were adopted to crop UAV RGB orthophoto, similar results were also observed. Finally, MGDEXG maps were derived from UAV RGB orthophoto. Overall, this study demonstrated that the water stress estimation performance of MGDEXG index was not affected by image spatial resolution and statistical scale, and MGDEXG maps could be successfully acquired by a UAV RGB remote sensing platform with the advantages of low cost and easy to be adopted by users.

Suggested Citation

  • Zhang, Liyuan & Zhang, Huihui & Han, Wenting & Niu, Yaxiao & Chávez, José L. & Ma, Weitong, 2022. "Effects of image spatial resolution and statistical scale on water stress estimation performance of MGDEXG: A new crop water stress indicator derived from RGB images," Agricultural Water Management, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:agiwat:v:264:y:2022:i:c:s0378377422000531
    DOI: 10.1016/j.agwat.2022.107506
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    1. García-Tejero, I.F. & Rubio, A.E. & Viñuela, I. & Hernández, A & Gutiérrez-Gordillo, S & Rodríguez-Pleguezuelo, C.R. & Durán-Zuazo, V.H., 2018. "Thermal imaging at plant level to assess the crop-water status in almond trees (cv. Guara) under deficit irrigation strategies," Agricultural Water Management, Elsevier, vol. 208(C), pages 176-186.
    2. Poirier-Pocovi, Magalie & Volder, Astrid & Bailey, Brian N., 2020. "Modeling of reference temperatures for calculating crop water stress indices from infrared thermography," Agricultural Water Management, Elsevier, vol. 233(C).
    3. Han, Ming & Zhang, Huihui & DeJonge, Kendall C. & Comas, Louise H. & Gleason, Sean, 2018. "Comparison of three crop water stress index models with sap flow measurements in maize," Agricultural Water Management, Elsevier, vol. 203(C), pages 366-375.
    4. Han, Ming & Zhang, Huihui & DeJonge, Kendall C. & Comas, Louise H. & Trout, Thomas J., 2016. "Estimating maize water stress by standard deviation of canopy temperature in thermal imagery," Agricultural Water Management, Elsevier, vol. 177(C), pages 400-409.
    5. Geerts, Sam & Raes, Dirk, 2009. "Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas," Agricultural Water Management, Elsevier, vol. 96(9), pages 1275-1284, September.
    6. Egea, Gregorio & Padilla-Díaz, Carmen M. & Martinez-Guanter, Jorge & Fernández, José E. & Pérez-Ruiz, Manuel, 2017. "Assessing a crop water stress index derived from aerial thermal imaging and infrared thermometry in super-high density olive orchards," Agricultural Water Management, Elsevier, vol. 187(C), pages 210-221.
    7. Zhang, Liyuan & Zhang, Huihui & Han, Wenting & Niu, Yaxiao & Chávez, José L. & Ma, Weitong, 2021. "The mean value of gaussian distribution of excess green index: A new crop water stress indicator," Agricultural Water Management, Elsevier, vol. 251(C).
    8. DeJonge, Kendall C. & Taghvaeian, Saleh & Trout, Thomas J. & Comas, Louise H., 2015. "Comparison of canopy temperature-based water stress indices for maize," Agricultural Water Management, Elsevier, vol. 156(C), pages 51-62.
    9. Khorsand, Afshin & Rezaverdinejad, Vahid & Asgarzadeh, Hossein & Majnooni-Heris, Abolfazl & Rahimi, Amir & Besharat, Sina, 2019. "Irrigation scheduling of maize based on plant and soil indices with surface drip irrigation subjected to different irrigation regimes," Agricultural Water Management, Elsevier, vol. 224(C), pages 1-1.
    10. Venturin, Afonso Zucolotto & Guimarães, Claudinei Martins & Sousa, Elias Fernandes de & Machado Filho, José Altino & Rodrigues, Weverton Pereira & Serrazine, Ícaro de Araujo & Bressan-Smith, Ricardo &, 2020. "Using a crop water stress index based on a sap flow method to estimate water status in conilon coffee plants," Agricultural Water Management, Elsevier, vol. 241(C).
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    1. Zhang, Yu & Han, Wenting & Zhang, Huihui & Niu, Xiaotao & Shao, Guomin, 2023. "Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach," Agricultural Water Management, Elsevier, vol. 275(C).

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