IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v222y2019icp182-192.html
   My bibliography  Save this article

Evaluation of canopy temperature depression, transpiration, and canopy greenness in relation to yield of soybean at reproductive stage based on remote sensing imagery

Author

Listed:
  • Hou, Mengjie
  • Tian, Fei
  • Zhang, Tong
  • Huang, Mengsi

Abstract

Canopy temperature depression (CTD = canopy temperature (Tc) – air temperature (Ta)), transpiration (T), and canopy greenness (CG) have much to do with crop yields, and they have been widely used to estimate crop yields. However, the issues relating to the best measurement time to predict crop yields have seldom been addressed. Hence, the present study was conducted to identify the best measurement time and provide a new way to rapidly predict soybean yield prediction in a semiarid environment. Tc was measured during the reproductive stage under different water stress conditions using a handheld infrared thermal imager that allowed rapid acquisition of high-quality thermal and visible images. T was estimated using the three-temperature model (3 T model) based on thermography, and CG was estimated by analyzing visible images. The results indicate that yield is positively correlated with CG and T to a certain extent; however, it is negatively correlated with CTD. CTD and T at noon during the soybean reproductive period, especially at the flowering and podding stage, are effective in predicting soybean seed yield. During this period, each 1 °C increase in CTD at noon will on average reduce the yield of soybean by 273–304 kg/ha, and when the average T reaches about 1.1 mm/h, the yield no longer increases significantly. Moreover, there is a high correlation between CG (measured by SPAD, Soil-Plant Analysis Development) and the soybean yield during the reproductive stage, especially during the podding and pod-filling stage (R2 = 0.79), which indicates that chlorophyll-based analysis could be used to estimate soybean yield. Therefore, CTD, T, and CG measurements based on remote sensing can be used as key traits to predict soybean yield and make appropriate adaptions to water stress conditions in semiarid areas.

Suggested Citation

  • Hou, Mengjie & Tian, Fei & Zhang, Tong & Huang, Mengsi, 2019. "Evaluation of canopy temperature depression, transpiration, and canopy greenness in relation to yield of soybean at reproductive stage based on remote sensing imagery," Agricultural Water Management, Elsevier, vol. 222(C), pages 182-192.
    • Handle: RePEc:eee:agiwat:v:222:y:2019:i:c:p:182-192
    DOI: 10.1016/j.agwat.2019.06.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377419300939
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2019.06.005?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. Ran, Hui & Kang, Shaozhong & Li, Fusheng & Du, Taisheng & Tong, Ling & Li, Sien & Ding, Risheng & Zhang, Xiaotao, 2018. "Parameterization of the AquaCrop model for full and deficit irrigated maize for seed production in arid Northwest China," Agricultural Water Management, Elsevier, vol. 203(C), pages 438-450.
    2. Li, Sien & Kang, Shaozhong & Zhang, Lu & Du, Taisheng & Tong, Ling & Ding, Risheng & Guo, Weihua & Zhao, Peng & Chen, Xia & Xiao, Huan, 2015. "Ecosystem water use efficiency for a sparse vineyard in arid northwest China," Agricultural Water Management, Elsevier, vol. 148(C), pages 24-33.
    3. O'Shaughnessy, Susan A. & Evett, Steven R. & Colaizzi, Paul D. & Howell, Terry A., 2012. "A crop water stress index and time threshold for automatic irrigation scheduling of grain sorghum," Agricultural Water Management, Elsevier, vol. 107(C), pages 122-132.
    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. Zhao, Yin & Mao, Xiaomin & Shukla, Manoj K. & Tian, Fei & Hou, Mengjie & Zhang, Tong & Li, Sien, 2021. "How does film mulching modify available energy, evapotranspiration, and crop coefficient during the seed–maize growing season in northwest China?," Agricultural Water Management, Elsevier, vol. 245(C).
    2. Kang, Jian & Hao, Xinmei & Zhou, Huiping & Ding, Risheng, 2021. "An integrated strategy for improving water use efficiency by understanding physiological mechanisms of crops responding to water deficit: Present and prospect," Agricultural Water Management, Elsevier, vol. 255(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. Yunfeng Li & Quanqing Feng & Dongwei Li & Mingfa Li & Huifeng Ning & Qisheng Han & Abdoul Kader Mounkaila Hamani & Yang Gao & Jingsheng Sun, 2022. "Water-Salt Thresholds of Cotton ( Gossypium hirsutum L.) under Film Drip Irrigation in Arid Saline-Alkali Area," Agriculture, MDPI, vol. 12(11), pages 1-21, October.
    2. Wang, Haidong & Cheng, Minghui & Liao, Zhenqi & Guo, Jinjin & Zhang, Fucang & Fan, Junliang & Feng, Hao & Yang, Qiliang & Wu, Lifeng & Wang, Xiukang, 2023. "Performance evaluation of AquaCrop and DSSAT-SUBSTOR-Potato models in simulating potato growth, yield and water productivity under various drip fertigation regimes," Agricultural Water Management, Elsevier, vol. 276(C).
    3. Ran, Hui & Kang, Shaozhong & Li, Fusheng & Du, Taisheng & Tong, Ling & Li, Sien & Ding, Risheng & Zhang, Xiaotao, 2018. "Parameterization of the AquaCrop model for full and deficit irrigated maize for seed production in arid Northwest China," Agricultural Water Management, Elsevier, vol. 203(C), pages 438-450.
    4. Silva, Marcos Dornelas Freitas Machado e & Calijuri, Maria Lúcia & Sales, Francisco José Ferreira de & Souza, Mauro Henrique Batalha de & Lopes, Lucas Sampaio, 2014. "Integration of technologies and alternative sources of water and energy to promote the sustainability of urban landscapes," Resources, Conservation & Recycling, Elsevier, vol. 91(C), pages 71-81.
    5. Laouisset, M. B. & Dellal, A., 2016. "Estimation of Barley (Hordeum Vulgare L.) Crop Water Requirements Using Cropwat Software in Ksar-Chellala Region, Algeria," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 8(3), pages 1-12, September.
    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. Gao, Lei & Zhao, Peng & Kang, Shaozhong & Li, Sien & Tong, Ling & Ding, Risheng & Lu, Hongna, 2019. "Surface soil water content dominates the difference between ecosystem and canopy water use efficiency in a sparse vineyard," Agricultural Water Management, Elsevier, vol. 226(C).
    8. Zhu, Xiufang & Xu, Kun & Liu, Ying & Guo, Rui & Chen, Lingyi, 2021. "Assessing the vulnerability and risk of maize to drought in China based on the AquaCrop model," Agricultural Systems, Elsevier, vol. 189(C).
    9. Chen, Rui & Chang, Hongda & Wang, Zhenhua & Lin, Haixia, 2023. "Determining organic-inorganic fertilizer application threshold to maximize the yield and quality of drip-irrigated grapes in an extremely arid area of Xinjiang, China," Agricultural Water Management, Elsevier, vol. 276(C).
    10. Wu, Hui & Yue, Qiong & Guo, Ping & Xu, Xiaoyu & Huang, Xi, 2022. "Improving the AquaCrop model to achieve direct simulation of evapotranspiration under nitrogen stress and joint simulation-optimization of irrigation and fertilizer schedules," Agricultural Water Management, Elsevier, vol. 266(C).
    11. 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).
    12. Delorit, Justin D. & Parker, Dominic P. & Block, Paul J., 2019. "An agro-economic approach to framing perennial farm-scale water resources demand management for water rights markets," Agricultural Water Management, Elsevier, vol. 218(C), pages 68-81.
    13. 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.
    14. Ihuoma, Samuel O. & Madramootoo, Chandra A., 2019. "Crop reflectance indices for mapping water stress in greenhouse grown bell pepper," Agricultural Water Management, Elsevier, vol. 219(C), pages 49-58.
    15. Guo, Hui & Li, Sien & Kang, Shaozhong & Du, Taisheng & Liu, Wenfeng & Tong, Ling & Hao, Xinmei & Ding, Risheng, 2022. "The controlling factors of ecosystem water use efficiency in maize fields under drip and border irrigation systems in Northwest China," Agricultural Water Management, Elsevier, vol. 272(C).
    16. Gong, Daozhi & Mei, Xurong & Hao, Weiping & Wang, Hanbo & Caylor, Kelly K., 2017. "Comparison of multi-level water use efficiency between plastic film partially mulched and non-mulched croplands at eastern Loess Plateau of China," Agricultural Water Management, Elsevier, vol. 179(C), pages 215-226.
    17. Zhao, Peng & Kang, Shaozhong & Li, Sien & Ding, Risheng & Tong, Ling & Du, Taisheng, 2018. "Seasonal variations in vineyard ET partitioning and dual crop coefficients correlate with canopy development and surface soil moisture," Agricultural Water Management, Elsevier, vol. 197(C), pages 19-33.
    18. Wu, Yinshan & Jiang, Jie & Zhang, Xiufeng & Zhang, Jiayi & Cao, Qiang & Tian, Yongchao & Zhu, Yan & Cao, Weixing & Liu, Xiaojun, 2023. "Combining machine learning algorithm and multi-temporal temperature indices to estimate the water status of rice," Agricultural Water Management, Elsevier, vol. 289(C).
    19. Guo, Hui & Li, Sien & Kang, Shaozhong & Du, Taisheng & Liu, Wenfeng & Tong, Ling & Hao, Xinmei & Ding, Risheng, 2022. "Comparison of several models for estimating gross primary production of drip-irrigated maize in arid regions," Ecological Modelling, Elsevier, vol. 468(C).
    20. Guo, Hui & Zhou, Xiao & Dong, Yi & Wang, Yahui & Li, Sien, 2023. "On the use of machine learning methods to improve the estimation of gross primary productivity of maize field with drip irrigation," Ecological Modelling, Elsevier, vol. 476(C).

    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:222:y:2019:i:c:p:182-192. 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.