Evaluating evapotranspiration using data mining instead of physical-based model in remote sensing

Lamya Neissi,Abd Ali Naseri,Mona Golabi,Mohammad Albaji

doi:10.1007/s00704-021-03822-7

Lamya Neissi, Abd Ali Naseri + Show 2 more

Open Access

https://doi.org/10.1007/s00704-021-03822-7

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Abstract

Precise calculations for determining the water requirements of plants and the extent of evapotranspiration are crucial in determining the volume of water consumed for plant production. In order to estimate evapotranspiration over an extended area, different remote sensing algorithms require numerous climatological variables; however, climatological variable measurements cover only limited areas thus resulting into erroneous calculations over extended areas. The exploiting of both data mining and remote sensing technologies allows for the modeling of the evapotranspiration process. In this research, the physical-based SEBAL evapotranspiration algorithm was remodeled using M5 decision tree equations in GIS. The input variables of the M5 decision tree consisted of Albedo, emissivity, and Normalized Difference Water Index (NDWI) which were defined as absorbed light, transformed light, and plant moisture, respectively. After extracting the best equations in the M5 decision tree model for 8 April 2019, these equations were modeled in GIS using python scripts for 8 April 2019 and 3 April 2020, respectively. The calculated correlation coefficient (R2), mean absolute error (MAE), and root mean squared error (RMSE) for 8 April 2019 were 0.92, 0.54, and 0.42, respectively, and for 3 April 2020 were 0.95, 0.31, and 0.23 in order. Moreover, for the further evaluation of the model, a sensitivity analysis and an uncertainty analysis were carried out. The analysis revealed that evapotranspiration is more sensitive to Albedo than the two other model inputs, and when applying data mining techniques instead of SEBAL, the estimation of evapotranspiration has a lower accuracy.

Highlights

These days precision agriculture has become more important and is applicable in most sections of agriculture, especially in the irrigation section
Instead of a physical-based model evapotranspiration estimation, the M5 decision tree was used for using a data mining model which could increase the spatial resolution of an evapotranspiration map
Input variables of the M5 decision tree consisted of four satellite images including Albedo, Emissivity, Normalized Difference Water Index (NDWI), and estimated evapotranspiration calculated by SEBAL algorithm

Summary

Introduction

These days precision agriculture has become more important and is applicable in most sections of agriculture, especially in the irrigation section. Different hardware methods including of pressurized irrigation systems, drones, and the internet of things for crop monitoring. Software methods, in this case, consist machine learning, deep learning, and data mining especially those regarding the decision trees. Irrigation scheduling of crops can be done by using meteorological data for evapotranspiration calculation. By using satellite images and different algorithms, evapotranspiration can be estimated in an extended area and reach an accurate irrigation scheduling (Jaferian et al, 2019; Song et al, 2018; Goodarzi and Eslamian, 2018; Diarraa et al, 2017; Colaizzi et al, 2017; Anderson et al, 2012; Abdolhosseini et al, 2012)

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