IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v259y2022ics0378377421005266.html
   My bibliography  Save this article

Estimation of crop evapotranspiration from MODIS data by combining random forest and trapezoidal models

Author

Listed:
  • Hao, Pengyu
  • Di, Liping
  • Guo, Liying

Abstract

Evapotranspiration (ET) is an important parameter for crop growth monitoring and land surface modeling. This paper proposed a new workflow, namely ESVEP-RF, to calculate ET during the crop growing season using MODIS data by combining the advantages of the trapezoidal model and Random Forest (RF) algorithm. In ESVEP-RF, the endmember-based soil and vegetation energy partitioning (ESVEP) model was first used to calculate a series of parameters from MODIS and meteorological inputs, and then all parameters derived from remote sensing data, meteorological data and ESVEP models were used as inputs to the RF algorithm for latent heat flux (LE) calculation. In-situ data of 12 years (2003–2012, 2018 and 2019) from five flux towers located in Nebraska (NE) and Michigan (MI) were used to test the performance of ESVEP-RF, and results showed that ESVEP-RF had great potential to accurately calculate ET when the number of training samples was sufficient and representative. In 2010 and 2011, R2 of LE were around 0.8 and RMSE were around 70 W/m2, which outperformed original ESVEP model results. This indicated that the RF algorithm could better describe the non-linear correlation between in LST/FVC space endmembers and LE. Among all parameters, LAI, PLEv and R-vw had high contribution with percentage importance of 18.49%, 15.71% and 13.57%, respectively. Furthermore, all samples between 2003 and 2012 collected from the three NE sites were used to train RF models and then calculate LE for both NE and MI sites in 2018 and 2019. In NE sites, RMSE was around 65 W/m2 and R2 was around 0.8. In MI sites, it was noted that no samples from these sites were included in the training data set, and RMSE was around 70 W/m2 and R2 was higher than 0.7. These results showed the potential of ESVEP-RF for providing up-to-date ET information.

Suggested Citation

  • Hao, Pengyu & Di, Liping & Guo, Liying, 2022. "Estimation of crop evapotranspiration from MODIS data by combining random forest and trapezoidal models," Agricultural Water Management, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:agiwat:v:259:y:2022:i:c:s0378377421005266
    DOI: 10.1016/j.agwat.2021.107249
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377421005266
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2021.107249?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. Knipper, K.R. & Kustas, W.P. & Anderson, M.C. & Nieto, H. & Alfieri, J.G. & Prueger, J.H. & Hain, C.R. & Gao, F. & McKee, L.G. & Alsina, M. Mar & Sanchez, L., 2020. "Using high-spatiotemporal thermal satellite ET retrievals to monitor water use over California vineyards of different climate, vine variety and trellis design," Agricultural Water Management, Elsevier, vol. 241(C).
    2. Pereira, L.S. & Paredes, P. & Melton, F. & Johnson, L. & Wang, T. & López-Urrea, R. & Cancela, J.J. & Allen, R.G., 2020. "Prediction of crop coefficients from fraction of ground cover and height. Background and validation using ground and remote sensing data," Agricultural Water Management, Elsevier, vol. 241(C).
    3. Xue, Jingyuan & Bali, Khaled M. & Light, Sarah & Hessels, Tim & Kisekka, Isaya, 2020. "Evaluation of remote sensing-based evapotranspiration models against surface renewal in almonds, tomatoes and maize," Agricultural Water Management, Elsevier, vol. 238(C).
    4. Filgueiras, Roberto & Almeida, Thomé Simpliciano & Mantovani, Everardo Chartuni & Dias, Santos Henrique Brant & Fernandes-Filho, Elpídio Inácio & da Cunha, Fernando França & Venancio, Luan Peroni, 2020. "Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data," Agricultural Water Management, Elsevier, vol. 241(C).
    5. Md Shahinoor Rahman & Liping Di, 2020. "A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment," Agriculture, MDPI, vol. 10(4), pages 1-30, April.
    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. Hu, Xinyu & Zhao, Jinfeng & Sun, Shikun & Jia, Chengru & Zhang, Fuyao & Ma, Yizhe & Wang, Kaixuan & Wang, Yubao, 2023. "Evaluation of the temporal reconstruction methods for MODIS-based continuous daily actual evapotranspiration estimation," 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. Williams, Larry E. & Levin, Alexander D. & Fidelibus, Matthew W., 2022. "Crop coefficients (Kc) developed from canopy shaded area in California vineyards," Agricultural Water Management, Elsevier, vol. 271(C).
    2. Yan, Haofang & Li, Mi & Zhang, Chuan & Zhang, Jianyun & Wang, Guoqing & Yu, Jianjun & Ma, Jiamin & Zhao, Shuang, 2022. "Comparison of evapotranspiration upscaling methods from instantaneous to daytime scale for tea and wheat in southeast China," Agricultural Water Management, Elsevier, vol. 264(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. Darouich, Hanaa & Karfoul, Razan & Ramos, Tiago B. & Moustafa, Ali & Shaheen, Baraa & Pereira, Luis S., 2021. "Crop water requirements and crop coefficients for jute mallow (Corchorus olitorius L.) using the SIMDualKc model and assessing irrigation strategies for the Syrian Akkar region," Agricultural Water Management, Elsevier, vol. 255(C).
    5. Peddinti, Srinivasa Rao & Kisekka, Isaya, 2022. "Estimation of turbulent fluxes over almond orchards using high-resolution aerial imagery with one and two-source energy balance models," Agricultural Water Management, Elsevier, vol. 269(C).
    6. Jovanovic, N. & Pereira, L.S. & Paredes, P. & Pôças, I. & Cantore, V. & Todorovic, M., 2020. "A review of strategies, methods and technologies to reduce non-beneficial consumptive water use on farms considering the FAO56 methods," Agricultural Water Management, Elsevier, vol. 239(C).
    7. Fuentes, Sigfredo & Ortega-Farías, Samuel & Carrasco-Benavides, Marcos & Tongson, Eden & Gonzalez Viejo, Claudia, 2024. "Actual evapotranspiration and energy balance estimation from vineyards using micro-meteorological data and machine learning modeling," Agricultural Water Management, Elsevier, vol. 297(C).
    8. Wang, Tianxin & Melton, Forrest S. & Pôças, Isabel & Johnson, Lee F. & Thao, Touyee & Post, Kirk & Cassel-Sharma, Florence, 2021. "Evaluation of crop coefficient and evapotranspiration data for sugar beets from landsat surface reflectances using micrometeorological measurements and weighing lysimetry," Agricultural Water Management, Elsevier, vol. 244(C).
    9. Pereira, L.S. & Paredes, P. & Melton, F. & Johnson, L. & Mota, M. & Wang, T., 2021. "Prediction of crop coefficients from fraction of ground cover and height: Practical application to vegetable, field and fruit crops with focus on parameterization," Agricultural Water Management, Elsevier, vol. 252(C).
    10. Pei Wang & Jingjing Ma & Juanjuan Ma & Haitao Sun & Qi Chen, 2021. "A Novel Approach for the Simulation of Reference Evapotranspiration and Its Partitioning," Agriculture, MDPI, vol. 11(5), pages 1-12, April.
    11. Liu, Meihan & Paredes, Paula & Shi, Haibin & Ramos, Tiago B. & Dou, Xu & Dai, Liping & Pereira, Luis S., 2022. "Impacts of a shallow saline water table on maize evapotranspiration and groundwater contribution using static water table lysimeters and the dual Kc water balance model SIMDualKc," Agricultural Water Management, Elsevier, vol. 273(C).
    12. Xue, Jingyuan & Fulton, Allan & Kisekka, Isaya, 2021. "Evaluating the role of remote sensing-based energy balance models in improving site-specific irrigation management for young walnut orchards," Agricultural Water Management, Elsevier, vol. 256(C).
    13. Pereira, L.S. & Paredes, P. & Hunsaker, D.J. & López-Urrea, R. & Mohammadi Shad, Z., 2021. "Standard single and basal crop coefficients for field crops. Updates and advances to the FAO56 crop water requirements method," Agricultural Water Management, Elsevier, vol. 243(C).
    14. Ramos, Tiago B. & Oliveira, Ana R. & Darouich, Hanaa & Gonçalves, Maria C. & Martínez-Moreno, Francisco J. & Rodríguez, Mario Ramos & Vanderlinden, Karl & Farzamian, Mohammad, 2023. "Field-scale assessment of soil water dynamics using distributed modeling and electromagnetic conductivity imaging," Agricultural Water Management, Elsevier, vol. 288(C).
    15. Xi Wang & Zhanyan Liu & Huili Chen, 2022. "Investigating Flood Impact on Crop Production under a Comprehensive and Spatially Explicit Risk Evaluation Framework," Agriculture, MDPI, vol. 12(4), pages 1-23, March.
    16. Rouault, Pierre & Courault, Dominique & Flamain, Fabrice & Pouget, Guillaume & Doussan, Claude & Lopez-Lozano, Raul & McCabe, Matthew & Debolini, Marta, 2024. "High-resolution satellite imagery to assess orchard characteristics impacting water use," Agricultural Water Management, Elsevier, vol. 295(C).
    17. Knipper, Kyle & Yang, Yun & Anderson, Martha & Bambach, Nicolas & Kustas, William & McElrone, Andrew & Gao, Feng & Alsina, Maria Mar, 2023. "Decreased latency in landsat-derived land surface temperature products: A case for near-real-time evapotranspiration estimation in California," Agricultural Water Management, Elsevier, vol. 283(C).
    18. Ramírez-Cuesta, J.M. & Intrigliolo, D.S. & Lorite, I.J. & Moreno, M.A. & Vanella, D. & Ballesteros, R. & Hernández-López, D. & Buesa, I., 2023. "Determining grapevine water use under different sustainable agronomic practices using METRIC-UAV surface energy balance model," Agricultural Water Management, Elsevier, vol. 281(C).
    19. French, Andrew N. & Sanchez, Charles A. & Wirth, Troy & Scott, Andrew & Shields, John W. & Bautista, Eduardo & Saber, Mazin N. & Wisniewski, Elzbieta & Gohardoust, Mohammadreza R., 2023. "Remote sensing of evapotranspiration for irrigated crops at Yuma, Arizona, USA," Agricultural Water Management, Elsevier, vol. 290(C).
    20. Bretreger, David & Yeo, In-Young & Hancock, Greg, 2022. "Quantifying irrigation water use with remote sensing: Soil water deficit modelling with uncertain soil parameters," Agricultural Water Management, Elsevier, vol. 260(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:259:y:2022:i:c:s0378377421005266. 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.