IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v264y2022ics0378377422000531.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377422000531
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2022.107506?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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).

    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. Zhang, Liyuan & Zhang, Huihui & Zhu, Qingzhen & Niu, Yaxiao, 2023. "Further investigating the performance of crop water stress index for maize from baseline fluctuation, effects of environmental factors, and variation of critical value," Agricultural Water Management, Elsevier, vol. 285(C).
    2. 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).
    3. Shao, Guomin & Han, Wenting & Zhang, Huihui & Zhang, Liyuan & Wang, Yi & Zhang, Yu, 2023. "Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods," Agricultural Water Management, Elsevier, vol. 276(C).
    4. Ezenne, G.I. & Jupp, Louise & Mantel, S.K. & Tanner, J.L., 2019. "Current and potential capabilities of UAS for crop water productivity in precision agriculture," Agricultural Water Management, Elsevier, vol. 218(C), pages 158-164.
    5. 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.
    6. Nakabuye, Hope Njuki & Rudnick, Daran & DeJonge, Kendall C. & Lo, Tsz Him & Heeren, Derek & Qiao, Xin & Franz, Trenton E. & Katimbo, Abia & Duan, Jiaming, 2022. "Real-time irrigation scheduling of maize using Degrees Above Non-Stressed (DANS) index in semi-arid environment," Agricultural Water Management, Elsevier, vol. 274(C).
    7. Katimbo, Abia & Rudnick, Daran R. & DeJonge, Kendall C. & Lo, Tsz Him & Qiao, Xin & Franz, Trenton E. & Nakabuye, Hope Njuki & Duan, Jiaming, 2022. "Crop water stress index computation approaches and their sensitivity to soil water dynamics," Agricultural Water Management, Elsevier, vol. 266(C).
    8. Katimbo, Abia & Rudnick, Daran R. & Liang, Wei-zhen & DeJonge, Kendall C. & Lo, Tsz Him & Franz, Trenton E. & Ge, Yufeng & Qiao, Xin & Kabenge, Isa & Nakabuye, Hope Njuki & Duan, Jiaming, 2022. "Two source energy balance maize evapotranspiration estimates using close-canopy mobile infrared sensors and upscaling methods under variable water stress conditions," Agricultural Water Management, Elsevier, vol. 274(C).
    9. Bhatti, Sandeep & Heeren, Derek M. & Evett, Steven R. & O’Shaughnessy, Susan A. & Rudnick, Daran R. & Franz, Trenton E. & Ge, Yufeng & Neale, Christopher M.U., 2022. "Crop response to thermal stress without yield loss in irrigated maize and soybean in Nebraska," Agricultural Water Management, Elsevier, vol. 274(C).
    10. 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).
    11. de Almeida, Ailson Maciel & Coelho, Rubens Duarte & da Silva Barros, Timóteo Herculino & de Oliveira Costa, Jéfferson & Quiloango-Chimarro, Carlos Alberto & Moreno-Pizani, Maria Alejandra & Farias-Ram, 2022. "Water productivity and canopy thermal response of pearl millet subjected to different irrigation levels," Agricultural Water Management, Elsevier, vol. 272(C).
    12. 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.
    13. Singh, Jasreman & Ge, Yufeng & Heeren, Derek M. & Walter-Shea, Elizabeth & Neale, Christopher M.U. & Irmak, Suat & Woldt, Wayne E. & Bai, Geng & Bhatti, Sandeep & Maguire, Mitchell S., 2021. "Inter-relationships between water depletion and temperature differential in row crop canopies in a sub-humid climate," Agricultural Water Management, Elsevier, vol. 256(C).
    14. Ramírez-Cuesta, J.M. & Ortuño, M.F. & Gonzalez-Dugo, V. & Zarco-Tejada, P.J. & Parra, M. & Rubio-Asensio, J.S. & Intrigliolo, D.S., 2022. "Assessment of peach trees water status and leaf gas exchange using on-the-ground versus airborne-based thermal imagery," Agricultural Water Management, Elsevier, vol. 267(C).
    15. Luan, Yajun & Xu, Junzeng & Lv, Yuping & Liu, Xiaoyin & Wang, Haiyu & Liu, Shimeng, 2021. "Improving the performance in crop water deficit diagnosis with canopy temperature spatial distribution information measured by thermal imaging," Agricultural Water Management, Elsevier, vol. 246(C).
    16. Ekinzog, Elmer Kanjo & Schlerf, Martin & Kraft, Martin & Werner, Florian & Riedel, Angela & Rock, Gilles & Mallick, Kaniska, 2022. "Revisiting crop water stress index based on potato field experiments in Northern Germany," Agricultural Water Management, Elsevier, vol. 269(C).
    17. Cheng, Minghan & Sun, Chengming & Nie, Chenwei & Liu, Shuaibing & Yu, Xun & Bai, Yi & Liu, Yadong & Meng, Lin & Jia, Xiao & Liu, Yuan & Zhou, Lili & Nan, Fei & Cui, Tengyu & Jin, Xiuliang, 2023. "Evaluation of UAV-based drought indices for crop water conditions monitoring: A case study of summer maize," Agricultural Water Management, Elsevier, vol. 287(C).
    18. Komlan Koudahe & Aleksey Y. Sheshukov & Jonathan Aguilar & Koffi Djaman, 2021. "Irrigation-Water Management and Productivity of Cotton: A Review," Sustainability, MDPI, vol. 13(18), pages 1-21, September.
    19. Stepanovic, Strahinja & Rudnick, Daran & Kruger, Greg, 2021. "Impact of maize hybrid selection on water productivity under deficit irrigation in semiarid western Nebraska," Agricultural Water Management, Elsevier, vol. 244(C).
    20. Singh, Sukhbir & Angadi, Sangamesh V. & Grover, Kulbhushan K. & Hilaire, Rolston St. & Begna, Sultan, 2016. "Effect of growth stage based irrigation on soil water extraction and water use efficiency of spring safflower cultivars," Agricultural Water Management, Elsevier, vol. 177(C), pages 432-439.

    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:eee:agiwat:v:264:y:2022:i:c:s0378377422000531. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.