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Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data

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  • Filgueiras, Roberto
  • Almeida, Thomé Simpliciano
  • Mantovani, Everardo Chartuni
  • Dias, Santos Henrique Brant
  • Fernandes-Filho, Elpídio Inácio
  • da Cunha, Fernando França
  • Venancio, Luan Peroni

Abstract

The application of technology and the development of data analysis, such as remote sensing and regression algorithms, are an easy and inexpensive way to estimate parameters related to water management, such as actual evapotranspiration (ETa) and soil water content (SWC). Therefore, the objective of this study was to predict the water management parameters with vegetation indices (VIs) and regression algorithms to enable irrigation management in a totally remote manner. The study was carried out in commercial maize areas irrigated by central pivots in the western part of the state of Bahia, Brazil. The MOD09GQ product was used to generate input data for the training models and to understand the phenology variations in the crops. The prediction of the dependent variables was tested using six regression algorithms, and the best algorithm was selected based on five statistical metrics. Among the regression models tested, the three that best fit the ETa and SWC data were RF (random forest), cubist (cubist regression), and GBM (gradient boosting machine), with slight superiority of cubist for the ETa and RF for the SWC. The fitted models for ETa and SWC showed the potential of VIs in providing information for irrigated agriculture and reinforcing the ability of regression algorithms in modelling the SWC and ETa variables. The findings make it possible to monitor irrigation efficiently with only the red and near infrared wavelengths, a fact that is considered the main contribution of this research to the practical and scientific communities.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:241:y:2020:i:c:s0378377420303097
    DOI: 10.1016/j.agwat.2020.106346
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    References listed on IDEAS

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    1. 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).
    2. Xinqin Gu & Li Yao & Lifeng Wu, 2023. "Prediction of Water Carbon Fluxes and Emission Causes in Rice Paddies Using Two Tree-Based Ensemble Algorithms," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
    3. 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).
    4. Dexi Zhan & Yongqi Mu & Wenxu Duan & Mingzhu Ye & Yingqiang Song & Zhenqi Song & Kaizhong Yao & Dengkuo Sun & Ziqi Ding, 2023. "Spatial Prediction and Mapping of Soil Water Content by TPE-GBDT Model in Chinese Coastal Delta Farmland with Sentinel-2 Remote Sensing Data," Agriculture, MDPI, vol. 13(5), pages 1-19, May.
    5. 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).
    6. Zhang, Fan & Cai, Yanpeng & Tan, Qian & Wang, Xuan, 2021. "Spatial water footprint optimization of crop planting: A fuzzy multiobjective optimal approach based on MOD16 evapotranspiration products," Agricultural Water Management, Elsevier, vol. 256(C).

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