IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v210y2018icp59-69.html
   My bibliography  Save this article

Accessible remote sensing data based reference evapotranspiration estimation modelling

Author

Listed:
  • Zhang, Zixiong
  • Gong, Yicheng
  • Wang, Zhongjing

Abstract

Estimating reference evapotranspiration (ET0) is a fundamental requirement of agricultural water management. The FAO Penman–Monteith (FAO-PM) equation has been used as the standard for ET0 estimation. However, the lack of necessary meteorological data makes it difficult to estimate spatially distributed ET0 using the FAO-PM method in the wider ungauged areas. In this study, the aim is to explore the methodology for estimating reference evapotranspiration based on remote sensing data. In this method, remote sensing data are combined with machine learning algorithms to establish a model for spatially distributed ET0 estimation. Three machine learning algorithms were tested, including support vector machine (SVM), back-propagation neural network (BP), and adaptive neuro fuzzy inference system (ANFIS). Results showed this method had good ability in estimating ET0. Application of the method in Northwest China indicated that the land surface temperature (LST) can be used to accurately estimate ET0 with high correlation coefficients (r2 of 0.897–0.915). The surface reflectance has potential for estimating ET0 with LST and can slightly improve model accuracy based on LST. Evaluation showed LST was more essential than surface reflectance and the model only based on LST had satisfactory performance. This method could be applicability in worldwide with available remote sensing and meteorological data due to the relationship between LST and ET0.

Suggested Citation

  • Zhang, Zixiong & Gong, Yicheng & Wang, Zhongjing, 2018. "Accessible remote sensing data based reference evapotranspiration estimation modelling," Agricultural Water Management, Elsevier, vol. 210(C), pages 59-69.
    • Handle: RePEc:eee:agiwat:v:210:y:2018:i:c:p:59-69
    DOI: 10.1016/j.agwat.2018.07.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377418311314
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2018.07.039?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. Falamarzi, Yashar & Palizdan, Narges & Huang, Yuk Feng & Lee, Teang Shui, 2014. "Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs)," Agricultural Water Management, Elsevier, vol. 140(C), pages 26-36.
    2. Sentelhas, Paulo C. & Gillespie, Terry J. & Santos, Eduardo A., 2010. "Evaluation of FAO Penman-Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada," Agricultural Water Management, Elsevier, vol. 97(5), pages 635-644, May.
    3. Liu, Yujie & Luo, Yi, 2010. "A consolidated evaluation of the FAO-56 dual crop coefficient approach using the lysimeter data in the North China Plain," Agricultural Water Management, Elsevier, vol. 97(1), pages 31-40, January.
    4. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Erratum to: Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4113-4113, September.
    5. Bagher Shirmohammadi & Mehdi Vafakhah & Vahid Moosavi & Alireza Moghaddamnia, 2013. "Application of Several Data-Driven Techniques for Predicting Groundwater Level," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 419-432, January.
    6. Du, Taisheng & Kang, Shaozhong & Sun, Jingsheng & Zhang, Xiying & Zhang, Jianhua, 2010. "An improved water use efficiency of cereals under temporal and spatial deficit irrigation in north China," Agricultural Water Management, Elsevier, vol. 97(1), pages 66-74, January.
    7. Kang, Shaozhong & Zhang, Lu & Liang, Yinli & Hu, Xiaotao & Cai, Huanjie & Gu, Binjie, 2002. "Effects of limited irrigation on yield and water use efficiency of winter wheat in the Loess Plateau of China," Agricultural Water Management, Elsevier, vol. 55(3), pages 203-216, June.
    8. Gavilan, P. & Lorite, I.J. & Tornero, S. & Berengena, J., 2006. "Regional calibration of Hargreaves equation for estimating reference ET in a semiarid environment," Agricultural Water Management, Elsevier, vol. 81(3), pages 257-281, March.
    9. Nariman Valizadeh & Ahmed El-Shafie, 2013. "Forecasting the Level of Reservoirs Using Multiple Input Fuzzification in ANFIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3319-3331, July.
    10. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
    11. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3803-3823, August.
    12. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    13. Jabloun, M. & Sahli, A., 2008. "Evaluation of FAO-56 methodology for estimating reference evapotranspiration using limited climatic data: Application to Tunisia," Agricultural Water Management, Elsevier, vol. 95(6), pages 707-715, June.
    14. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    15. Tomas-Burguera, Miquel & Vicente-Serrano, Sergio M. & Grimalt, Miquel & Beguería, Santiago, 2017. "Accuracy of reference evapotranspiration (ETo) estimates under data scarcity scenarios in the Iberian Peninsula," Agricultural Water Management, Elsevier, vol. 182(C), pages 103-116.
    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. Granata, Francesco, 2019. "Evapotranspiration evaluation models based on machine learning algorithms—A comparative study," Agricultural Water Management, Elsevier, vol. 217(C), pages 303-315.
    2. Wu, Lifeng & Peng, Youwen & Fan, Junliang & Wang, Yicheng & Huang, Guomin, 2021. "A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation," Agricultural Water Management, Elsevier, vol. 245(C).
    3. Long Zhao & Liwen Xing & Yuhang Wang & Ningbo Cui & Hanmi Zhou & Yi Shi & Sudan Chen & Xinbo Zhao & Zhe Li, 2023. "Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation Algorithm with Limited Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1207-1222, February.

    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. Paredes, P. & Pereira, L.S. & Almorox, J. & Darouich, H., 2020. "Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables," Agricultural Water Management, Elsevier, vol. 240(C).
    2. Paredes, Paula & Martins, Diogo S. & Pereira, Luis Santos & Cadima, Jorge & Pires, Carlos, 2018. "Accuracy of daily estimation of grass reference evapotranspiration using ERA-Interim reanalysis products with assessment of alternative bias correction schemes," Agricultural Water Management, Elsevier, vol. 210(C), pages 340-353.
    3. Dilip Kumar Roy & Sujit Kumar Biswas & Kowshik Kumar Saha & Khandakar Faisal Ibn Murad, 2021. "Groundwater Level Forecast Via a Discrete Space-State Modelling Approach as a Surrogate to Complex Groundwater Simulation Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1653-1672, April.
    4. Paredes, Paula & Trigo, Isabel & de Bruin, Henk & Simões, Nuno & Pereira, Luis S., 2021. "Daily grass reference evapotranspiration with Meteosat Second Generation shortwave radiation and reference ET products," Agricultural Water Management, Elsevier, vol. 248(C).
    5. Aouissi, Jalel & Benabdallah, Sihem & Lili Chabaâne, Zohra & Cudennec, Christophe, 2016. "Evaluation of potential evapotranspiration assessment methods for hydrological modelling with SWAT—Application in data-scarce rural Tunisia," Agricultural Water Management, Elsevier, vol. 174(C), pages 39-51.
    6. Wen-Ping Tsai & Yen-Ming Chiang & Jun-Lin Huang & Fi-John Chang, 2016. "Exploring the Mechanism of Surface and Ground Water through Data-Driven Techniques with Sensitivity Analysis for Water Resources Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4789-4806, October.
    7. Berti, Antonio & Tardivo, Gianmarco & Chiaudani, Alessandro & Rech, Francesco & Borin, Maurizio, 2014. "Assessing reference evapotranspiration by the Hargreaves method in north-eastern Italy," Agricultural Water Management, Elsevier, vol. 140(C), pages 20-25.
    8. Cruz-Blanco, M. & Lorite, I.J. & Santos, C., 2014. "An innovative remote sensing based reference evapotranspiration method to support irrigation water management under semi-arid conditions," Agricultural Water Management, Elsevier, vol. 131(C), pages 135-145.
    9. Hsi-Ting Fang & Bing-Chen Jhong & Yih-Chi Tan & Kai-Yuan Ke & Mo-Hsiung Chuang, 2019. "A Two-Stage Approach Integrating SOM- and MOGA-SVM-Based Algorithms to Forecast Spatial-temporal Groundwater Level with Meteorological Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 797-818, January.
    10. Ahmed El-Shafie & Amr El-Shafie & Muhammad Mukhlisin, 2014. "New Approach: Integrated Risk-Stochastic Dynamic Model for Dam and Reservoir Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2093-2107, June.
    11. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    12. Vásquez, Cristina & Célleri, Rolando & Córdova, Mario & Carrillo-Rojas, Galo, 2022. "Improving reference evapotranspiration (ETo) calculation under limited data conditions in the high Tropical Andes," Agricultural Water Management, Elsevier, vol. 262(C).
    13. Raúl Parada & Jordi Font & Jordi Casas-Roma, 2019. "Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs," Energies, MDPI, vol. 12(10), pages 1-21, May.
    14. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    15. Kang, Shaozhong & Hao, Xinmei & Du, Taisheng & Tong, Ling & Su, Xiaoling & Lu, Hongna & Li, Xiaolin & Huo, Zailin & Li, Sien & Ding, Risheng, 2017. "Improving agricultural water productivity to ensure food security in China under changing environment: From research to practice," Agricultural Water Management, Elsevier, vol. 179(C), pages 5-17.
    16. Ali Nouh Mabdeh & A’kif Al-Fugara & Khaled Mohamed Khedher & Muhammed Mabdeh & Abdel Rahman Al-Shabeeb & Rida Al-Adamat, 2022. "Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms," Sustainability, MDPI, vol. 14(15), pages 1-26, August.
    17. Saeideh Samani & Meysam Vadiati & Farahnaz Azizi & Efat Zamani & Ozgur Kisi, 2022. "Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3627-3647, August.
    18. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2014. "Two-Stage Pumping Control Model for Flood Mitigation in Inundated Urban Drainage Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 425-444, January.
    19. Mirzaei, Mohsen & Jafari, Ali & Gholamalifard, Mehdi & Azadi, Hossein & Shooshtari, Sharif Joorabian & Moghaddam, Saghi Movahhed & Gebrehiwot, Kindeya & Witlox, Frank, 2020. "Mitigating environmental risks: Modeling the interaction of water quality parameters and land use cover," Land Use Policy, Elsevier, vol. 95(C).
    20. Ma, Shou-Chen & Duan, Ai-Wang & Wang, Rui & Guan, Zhong-Mei & Yang, Shen-Jiao & Ma, Shou-Tian & Shao, Yun, 2015. "Root-sourced signal and photosynthetic traits, dry matter accumulation and remobilization, and yield stability in winter wheat as affected by regulated deficit irrigation," Agricultural Water Management, Elsevier, vol. 148(C), pages 123-129.

    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:210:y:2018:i:c:p:59-69. 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.