Potential of the existing and novel spectral reflectance indices for estimating the leaf water status and grain yield of spring wheat exposed to different irrigation rates
Author
Abstract
Suggested Citation
DOI: 10.1016/j.agwat.2019.03.006
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Elsayed, Salah & Elhoweity, Mohamed & Ibrahim, Hazem H. & Dewir, Yaser Hassan & Migdadi, Hussein M. & Schmidhalter, Urs, 2017. "Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes," Agricultural Water Management, Elsevier, vol. 189(C), pages 98-110.
- El-Hendawy, Salah E. & Hassan, Wael M. & Al-Suhaibani, Nasser A. & Schmidhalter, Urs, 2017. "Spectral assessment of drought tolerance indices and grain yield in advanced spring wheat lines grown under full and limited water irrigation," Agricultural Water Management, Elsevier, vol. 182(C), pages 1-12.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Shaeden Gokool & Maqsooda Mahomed & Richard Kunz & Alistair Clulow & Mbulisi Sibanda & Vivek Naiken & Kershani Chetty & Tafadzwanashe Mabhaudhi, 2023. "Crop Monitoring in Smallholder Farms Using Unmanned Aerial Vehicles to Facilitate Precision Agriculture Practices: A Scoping Review and Bibliometric Analysis," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
- Adel H. Elmetwalli & Yasser S. A. Mazrou & Andrew N. Tyler & Peter D. Hunter & Osama Elsherbiny & Zaher Mundher Yaseen & Salah Elsayed, 2022. "Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt," Agriculture, MDPI, vol. 12(3), pages 1-21, February.
- Crusiol, Luís Guilherme Teixeira & Nanni, Marcos Rafael & Furlanetto, Renato Herrig & Sibaldelli, Rubson Natal Ribeiro & Sun, Liang & Gonçalves, Sergio Luiz & Foloni, José Salvador Simonetto & Mertz-H, 2023. "Assessing the sensitive spectral bands for soybean water status monitoring and soil moisture prediction using leaf-based hyperspectral reflectance," Agricultural Water Management, Elsevier, vol. 277(C).
- Hong Li & Wunian Yang & Junjie Lei & Jinxing She & Xiangshan Zhou, 2021. "Estimation of leaf water content from hyperspectral data of different plant species by using three new spectral absorption indices," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-16, March.
- Luís Guilherme Teixeira Crusiol & Liang Sun & Zheng Sun & Ruiqing Chen & Yongfeng Wu & Juncheng Ma & Chenxi Song, 2022. "In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
- Elmetwalli, Adel H. & Tyler, Andrew N., 2020. "Estimation of maize properties and differentiating moisture and nitrogen deficiency stress via ground – Based remotely sensed data," Agricultural Water Management, Elsevier, vol. 242(C).
- Peng, Zhigong & Lin, Shaozhe & Zhang, Baozhong & Wei, Zheng & Liu, Lu & Han, Nana & Cai, Jiabing & Chen, He, 2020. "Winter Wheat Canopy Water Content Monitoring Based on Spectral Transforms and “Three-edge” Parameters," Agricultural Water Management, Elsevier, vol. 240(C).
- Song, Xingyang & Zhou, Guangsheng & He, Qijing & Zhou, Huailin, 2020. "Stomatal limitations to photosynthesis and their critical Water conditions in different growth stages of maize under water stress," Agricultural Water Management, Elsevier, vol. 241(C).
- Du, Ruiqi & Xiang, Youzhen & Zhang, Fucang & Chen, Junying & Shi, Hongzhao & Liu, Hao & Yang, Xiaofei & Yang, Ning & Yang, Xizhen & Wang, Tianyang & Wu, Yuxiao, 2024. "Combing transfer learning with the OPtical TRApezoid Model (OPTRAM) to diagnosis small-scale field soil moisture from hyperspectral data," Agricultural Water Management, Elsevier, vol. 298(C).
- Xingyang Song & Guangsheng Zhou & Qijin He, 2021. "Critical Leaf Water Content for Maize Photosynthesis under Drought Stress and Its Response to Rewatering," Sustainability, MDPI, vol. 13(13), pages 1-14, June.
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.- Adel H. Elmetwalli & Yasser S. A. Mazrou & Andrew N. Tyler & Peter D. Hunter & Osama Elsherbiny & Zaher Mundher Yaseen & Salah Elsayed, 2022. "Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt," Agriculture, MDPI, vol. 12(3), pages 1-21, February.
- Salah Elsayed & Mohamed Gad & Mohamed Farouk & Ali H. Saleh & Hend Hussein & Adel H. Elmetwalli & Osama Elsherbiny & Farahat S. Moghanm & Moustapha E. Moustapha & Mostafa A. Taher & Ebrahem M. Eid & M, 2021. "Using Optimized Two and Three-Band Spectral Indices and Multivariate Models to Assess Some Water Quality Indicators of Qaroun Lake in Egypt," Sustainability, MDPI, vol. 13(18), pages 1-23, September.
- Mohammed Mohi-Ud-Din & Md. Alamgir Hossain & Md. Motiar Rohman & Md. Nesar Uddin & Md. Sabibul Haque & Eldessoky S. Dessoky & Mohammed Alqurashi & Salman Aloufi, 2022. "Assessment of Genetic Diversity of Bread Wheat Genotypes for Drought Tolerance Using Canopy Reflectance-Based Phenotyping and SSR Marker-Based Genotyping," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
- Peng, Zhigong & Lin, Shaozhe & Zhang, Baozhong & Wei, Zheng & Liu, Lu & Han, Nana & Cai, Jiabing & Chen, He, 2020. "Winter Wheat Canopy Water Content Monitoring Based on Spectral Transforms and “Three-edge” Parameters," Agricultural Water Management, Elsevier, vol. 240(C).
- Wenfeng Li & Kun Pan & Wenrong Liu & Weihua Xiao & Shijian Ni & Peng Shi & Xiuyue Chen & Tong Li, 2024. "Monitoring Maize Canopy Chlorophyll Content throughout the Growth Stages Based on UAV MS and RGB Feature Fusion," Agriculture, MDPI, vol. 14(8), pages 1-22, August.
- Zhang, Minne & Zhao, Weixia & Zhu, Changxin & Li, Jiusheng, 2024. "Influence of the sampling time interval of canopy temperature on the dynamic zoning of variable rate irrigation," Agricultural Water Management, Elsevier, vol. 295(C).
- 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).
- 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).
- Melo, Leonardo Leite de & Melo, Verônica Gaspar Martins Leite de & Marques, Patrícia Angélica Alves & Frizzone, Jose Antônio & Coelho, Rubens Duarte & Romero, Roseli Aparecida Francelin & Barros, Timó, 2022. "Deep learning for identification of water deficits in sugarcane based on thermal images," Agricultural Water Management, Elsevier, vol. 272(C).
- Fan Ding & Changchun Li & Weiguang Zhai & Shuaipeng Fei & Qian Cheng & Zhen Chen, 2022. "Estimation of Nitrogen Content in Winter Wheat Based on Multi-Source Data Fusion and Machine Learning," Agriculture, MDPI, vol. 12(11), pages 1-16, October.
- 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).
- Mohamed E. Abowaly & Abdel-Aziz A. Belal & Enas E. Abd Elkhalek & Salah Elsayed & Rasha M. Abou Samra & Abdullah S. Alshammari & Farahat S. Moghanm & Kamal H. Shaltout & Saad A. M. Alamri & Ebrahem M., 2021. "Assessment of Soil Pollution Levels in North Nile Delta, by Integrating Contamination Indices, GIS, and Multivariate Modeling," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
More about this item
Keywords
Equivalent water thickness; Estimated evapotranspiration; Hyperspectral reflectance; Leaf water potential; Phenotyping; Wavelength selection;All these keywords.
Statistics
Access and download statisticsCorrections
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:217:y:2019:i:c:p:356-373. 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.