## 2 Related Work  
In this section, we outline the existing options for monitoring soil moisture with remote sensing resources. First, we describe the existing solutions, and then we analyze the main gap of each one. Finally, we detail the contributions of our paper based on the gaps of the existing proposals. The use of satellite data in agriculture has expanded to include numerous functionalities. [PERSON] et al. reviewed the current remote sensing applications for agriculture [10]. Regarding monitoring soil moisture based on satellites, the authors comment on the use of L-band microwave radiometry such as the Soil Moisture Active Passive (SMAP) satellites from the NASA or the Soil Moisture Ocean Salinity (SMOS) sensor from ESA. There are, however, some limitations to this technique, such as the spatial resolution and the 5 cm depth of retrieval. Therefore, it is impossible to use this type of solution due to its low spatial resolution, which precludes its application in heterogenic scenarios such as golf courses, urban laws, or other similar areas. Another review of agricultural applications based on remote sensing is performed by [PERSON] et al. [11]. The authors also comment on the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) and SMAP satellites. Furthermore, these satellites can also be used for other purposes, such as quantifying irrigation water use. Furthermore, the new applications and features of the Sentinel-2 satellites were discussed by [PERSON] et al. [12]. The authors also compared the performance of other satellites to the Sentinel-2 and determined that the latter provides more capabilities for crop management and agricultural monitoring. Furthermore, the results show that the combination of other remote sensing techniques as well as field data is still necessary. Particularly, regarding soil moisture, determination coefficients between 65% and 83% were obtained from the combination of the NDVI obtained from Sentinel-2 and soil moisture models. The use of correlations between NDVI and soil moisture is not valid for our purpose. The main reason is that in the selected scenario is that the soil moisture changes faster than the NDVI. Therefore, when the change in NDVI is detected, the stress of the plats might be too high. An earlier detection method is required. One of the most interesting functionalities is the detection of soil moisture and crop water content to determine irrigation needs without the need of deploying sensing devices. [PERSON] et al. performed data fusion to MODIS and Landsat images to determine the crop water content of soybean and corn crops [13]. A spatial resolution of 30 m and a revisit cycle of 16 days were the parameters for the Landsat image acquisition, and a spatial resolution of 500 m was used in the case of MODIS. Normalized Difference Water Index (NDWI) was obtained after performing the data fusion. Furthermore, measures were taken during the Soil Moisture Active Passive Validation Experiment to obtain the plan and canopy Vegetation Water Content (VWC). The results showed higher correlations for the soybean plants than that of the corn plants. As for [10], this solution cannot be applied in our scenario, given the spatial resolution of the remote sensing source selected. [PERSON] et al. used machine learning to estimate soil moisture from satellite pictures from Landsat 8 with 30 m resolution [14]. The utilized techniques were Artificial Neural Networks (ANN), Random Forest (RF), Elastic Net Regression (EN), and Support Vector Machine (SVM). The results of the near-surface soil moisture showed that a Nash-Sutcliffe efficiency value of 0.73 was obtained for the RF technique, which was the highest value. Again, this proposal has a limitation regarding the spatial resolution of selected images (spatial resolution of 30 m) compared with the used satellite (spatial resolution of 10 m and 20 m). A study on different soil moisture indexes from SMOS and MODIS satellite data was presented by [PERSON] et al. [15].