# A Machine Learning Method to Estimate Reference Evapotranspiration Using Soil Moisture Sensors
## 4 Conclusions  
One of the main applications of remote image sensing systems in agriculture is the computation of the optimal irrigation requirements of the crops. For this purpose, estimating the crop evapotranspiration (ETc) is an essential and preliminary step, since it models the water consumption of the crops. In this paper, we presented a new methodology to create precise models that are able to estimate the reference evapotranspiration (ETo) of crops using moisture sensors located in the ground. This estimation of ETo is then integrated with other existing techniques to calculate ETc using remote images and ETo, such as that in Reference [38]. Through the analysis of the aerial images of the crops, the percentage of ground cover is firstly computed, then the crop coefficient is deduced from it, and finally ETc is calculated using ETo and the crop coefficient.  
The obtained results indicate that it is possible to obtain a very accurate approximation of ETo using only daily temperature, rainfall, watering and moisture data, and generic machine learning methods. This can be compared with the standard Penman-Monteith method for ETo estimation [20], which requires more climatic data, such as solar radiation, air temperature, humidity, wind speed, and atmospheric pressure, in addition to elevation above sea level, Julian day, and latitude degree of the study site. The high correlation coefficient found between both ETo estimations, over 0.993, and the small mean relative error of 6.5% indicate that this method could be used as an effective substitution of other more expensive solutions, such as lysimeters and Bowen stations, offering a cost-efficient alternative.  
As discussed in the paper, this proposal opens new opportunities to experiment with these excellent results under different conditions, for example, in diverse environmental settings, with different climates, varieties of crops, ground types, etc. The proposed methodology consists of obtaining the datasets aggregated at 24- or 6-h intervals, applying different machine learning regression algorithms, and selecting the optimal model and time interval. This procedure would be the same for different conditions, although the best model and the optimal time interval could be dissimilar under those conditions. Another interesting future line is to perform a direct estimation of the ETc of the crop of interest using the moisture sensors, agroclimatic data, and remote image sensing. This would eliminate the need to use a reference crop as a previous step in the process.  
Conceptualization, [PERSON]-S., [PERSON]-L., G.G.-M., and A.R.-C.; methodology, J.M.M.-M., M.F.-V.-G., and A.R.-C.; software, [PERSON]-S.; validation, [PERSON]-S., [PERSON]-L., [PERSON]-M., and A.R.-C.; formal analysis, [PERSON]-S. and A.F.-L.; investigation, [PERSON]-S., [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]; resources, [PERSON], [PERSON], and [PERSON]-; data curation, [PERSON] and [PERSON]; writing\(-\)original draft preparation, [PERSON], [PERSON], and [PERSON]; writing\(-\)review and editing, [PERSON]; visualization, [PERSON] and [PERSON]; supervision, [PERSON], [PERSON], and [PERSON]; project administration, [PERSON], [PERSON], and [PERSON]; funding acquisition, [PERSON], [PERSON], [PERSON], and [PERSON]. All authors read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Spanish MICINN, as well as the European Commission FEDER funds, under grant KTI2018-098156-B-C53. It was also funded by the FEDER through the Multi-Regional Operational Program of Spain 2014-2020, under grant IDI-20190146, in collaboration with the company Agrosolmen, S.L.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.