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

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

Agricultural Water Management, 2020, vol. 241, issue C

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.

Keywords: Irrigation management; Decision-making; Machine learning; Remote sensing; Vegetation indices (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:241:y:2020:i:c:s0378377420303097

DOI: 10.1016/j.agwat.2020.106346

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