IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v281y2023ics0378377423001257.html
   My bibliography  Save this article

Assessment of water demands for irrigation using energy balance and satellite data fusion models in cloud computing: A study in the Brazilian semiarid region

Author

Listed:
  • Ferreira, Thomás R.
  • Maguire, Mitchell S.
  • da Silva, Bernardo B.
  • Neale, Christopher M.U.
  • Serrão, Edivaldo A.O.
  • Ferreira, Jéssica D.
  • de Moura, Magna S.B.
  • dos Santos, Carlos A.C.
  • Silva, Madson T.
  • Rodrigues, Lineu N.
  • Carvalho, Herica F.S.

Abstract

Assessment of irrigation in arid and semiarid regions is imperative to ensure the sustainable use of limited water resources and guarantee food production. Therefore, this study aimed to assess actual evapotranspiration – ETa derived from the Surface Energy Balance Algorithm for Land – SEBAL model with and without satellite image fusion as input of a soil water balance in a pilot area of sugarcane in the semiarid region of Brazil. A fusion of Landsat sensors’ and Moderate Resolution Imaging Spectroradiometer – MODIS’ images was completed through a Spatial and Temporal Adaptive Reflectance Fusion Model – STARFM script developed using cloud computing, and its performance in estimating key variables for the radiation balance was evaluated. ETa and irrigation were daily estimated between June, 2015 and May, 2016 by combining STARFM with SEBAL and evaluated according to the Bowen ratio technique and irrigation data. In addition, one-minute surface meteorological elements at the satellite overpass times were used. STARFM performed well with RMSE of 17.00 W m−2, 2.28 K, 0.07, and 0.01 for Rn, Ts, NDVI, and albedo, respectively. The metrics employed to evaluate ETa estimates indicated that the SEBAL+STARFM has low mean errors (PBIAS = −2.75% and RMSE = 0.97 mm d−1 and 16.66 mm month−1) and high coefficient of determination (0.87 for daily ETa–ET24, and 0.91 for monthly ETa), in comparison with SEBAL using Landsat-only images (PBIAS = −5.25%, RMSE = 0.97 mm d−1 and 17.66 mm month−1, r² = 0.92). Adding fused images resulted in a better fit of the estimated cumulative ET24 curve to the measured ET24. The water balance indicated that the cultivated cane suffered water stress, which was better represented by estimates using the ET24 curve with the addition of fused images than Landsat images alone. Although this increase in temporal resolution of the estimated ET24 data indicated a greater water consumption, it informs a quantity that would be sufficient to meet the water demand of the crops.

Suggested Citation

  • Ferreira, Thomás R. & Maguire, Mitchell S. & da Silva, Bernardo B. & Neale, Christopher M.U. & Serrão, Edivaldo A.O. & Ferreira, Jéssica D. & de Moura, Magna S.B. & dos Santos, Carlos A.C. & Silva, Ma, 2023. "Assessment of water demands for irrigation using energy balance and satellite data fusion models in cloud computing: A study in the Brazilian semiarid region," Agricultural Water Management, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:agiwat:v:281:y:2023:i:c:s0378377423001257
    DOI: 10.1016/j.agwat.2023.108260
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377423001257
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2023.108260?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. 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).
    3. Jose A. Marengo & Ana Paula M. A. Cunha & Carlos A. Nobre & Germano G. Ribeiro Neto & Antonio R. Magalhaes & Roger R. Torres & Gilvan Sampaio & Felipe Alexandre & Lincoln M. Alves & Luz A. Cuartas & K, 2020. "Assessing drought in the drylands of northeast Brazil under regional warming exceeding 4 °C," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(2), pages 2589-2611, September.
    4. Bai, Liangliang & Cai, Jiabing & Liu, Yu & Chen, He & Zhang, Baozhong & Huang, Lingxu, 2017. "Responses of field evapotranspiration to the changes of cropping pattern and groundwater depth in large irrigation district of Yellow River basin," Agricultural Water Management, Elsevier, vol. 188(C), pages 1-11.
    5. Mhawej, Mario & Elias, Georgie & Nasrallah, Ali & Faour, Ghaleb, 2020. "Dynamic calibration for better SEBALI ET estimations: Validations and recommendations," Agricultural Water Management, Elsevier, vol. 230(C).
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. 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).
    3. Franklin Paredes-Trejo & Humberto Alves Barbosa & Gabriel Antunes Daldegan & Ingrid Teich & César Luis García & T. V. Lakshmi Kumar & Catarina de Oliveira Buriti, 2023. "Impact of Drought on Land Productivity and Degradation in the Brazilian Semiarid Region," Land, MDPI, vol. 12(5), pages 1-19, April.
    4. Mhawej, Mario & Nasrallah, Ali & Abunnasr, Yaser & Fadel, Ali & Faour, Ghaleb, 2021. "Better irrigation management using the satellite-based adjusted single crop coefficient (aKc) for over sixty crop types in California, USA," Agricultural Water Management, Elsevier, vol. 256(C).
    5. 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).
    6. Wei, Jun & Cui, Yuanlai & Zhou, Sihang & Luo, Yufeng, 2022. "Regional water-saving potential calculation method for paddy rice based on remote sensing," Agricultural Water Management, Elsevier, vol. 267(C).
    7. Sabzchi-Dehkharghani, Hamed & Nazemi, Amir Hossein & Sadraddini, Ali Ashraf & Majnooni-Heris, Abolfazl & Biswas, Asim, 2021. "Recognition of different yield potentials among rain-fed wheat fields before harvest using remote sensing," Agricultural Water Management, Elsevier, vol. 245(C).
    8. Eduilson Carneiro & Wilza Lopes & Giovana Espindola, 2021. "Linking Urban Sprawl and Surface Urban Heat Island in the Teresina–Timon Conurbation Area in Brazil," Land, MDPI, vol. 10(5), pages 1-16, May.
    9. Liu, Meihan & Shi, Haibin & Paredes, Paula & Ramos, Tiago B. & Dai, Liping & Feng, Zhuangzhuang & Pereira, Luis S., 2022. "Estimating and partitioning maize evapotranspiration as affected by salinity using weighing lysimeters and the SIMDualKc model," Agricultural Water Management, Elsevier, vol. 261(C).
    10. Lima, Carlos Eduardo Santos de & Costa, Valéria Sandra de Oliveira & Galvíncio, Josiclêda Domiciano & Silva, Richarde Marques da & Santos, Celso Augusto Guimarães, 2021. "Assessment of automated evapotranspiration estimates obtained using the GP-SEBAL algorithm for dry forest vegetation (Caatinga) and agricultural areas in the Brazilian semiarid region," Agricultural Water Management, Elsevier, vol. 250(C).
    11. Zhao, Tianxing & Zhu, Yan & Ye, Ming & Yang, Jinzhong & Jia, Biao & Mao, Wei & Wu, Jingwei, 2022. "A new approach for estimating spatial-temporal phreatic evapotranspiration at a regional scale using NDVI and water table depth measurements," Agricultural Water Management, Elsevier, vol. 264(C).
    12. Teixeira, Antônio & Leivas, Janice & Struiving, Tiago & Reis, João & Simão, Fúlvio, 2021. "Energy balance and irrigation performance assessments in lemon orchards by applying the SAFER algorithm to Landsat 8 images," Agricultural Water Management, Elsevier, vol. 247(C).
    13. 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).
    14. Vanella, Daniela & Peddinti, Srinivasa Rao & Kisekka, Isaya, 2022. "Unravelling soil water dynamics in almond orchards characterized by soil-heterogeneity using electrical resistivity tomography," Agricultural Water Management, Elsevier, vol. 269(C).
    15. 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).
    16. Israel R. Orimoloye & Adeyemi O. Olusola & Johanes A. Belle & Chaitanya B. Pande & Olusola O. Ololade, 2022. "Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(2), pages 1085-1106, June.
    17. Xiong, Lvyang & Jiang, Yao & Li, Xinyi & Ren, Dongyang & Huang, Guanhua, 2023. "Long-term regional groundwater responses and their ecological impacts under agricultural water saving in an arid irrigation district, upper Yellow River basin," Agricultural Water Management, Elsevier, vol. 288(C).
    18. Tiago B. Ramos & Meihan Liu & Haibin Shi & Paula Paredes & Luis S. Pereira, 2024. "Leaching Efficiency During Autumn Irrigation in China’s Arid Hetao Plain as Influenced by the Depth of Shallow Saline Groundwater and Irrigation Depth, Using Data from Static Water-Table Lysimeters an," Land, MDPI, vol. 13(11), pages 1-11, October.
    19. Xue, Jingyuan & Huo, Zailin & Kisekka, Isaya, 2021. "Assessing impacts of climate variability and changing cropping patterns on regional evapotranspiration, yield and water productivity in California’s San Joaquin watershed," Agricultural Water Management, Elsevier, vol. 250(C).
    20. Allam, Mona & Mhawej, Mario & Meng, Qingyan & Faour, Ghaleb & Abunnasr, Yaser & Fadel, Ali & Xinli, Hu, 2021. "Monthly 10-m evapotranspiration rates retrieved by SEBALI with Sentinel-2 and MODIS LST data," Agricultural Water Management, Elsevier, vol. 243(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:281:y:2023:i:c:s0378377423001257. 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.