## 2 Related Work
The Soil Wetness Deficit Index (SWetDI), Soil Water Deficit Index (SWDI), Soil Moisture Deficit Index (SMDI), and the Soil Moisture Agricultural Drought Index (SMADI) were compared to the crop moisture index (CMI) and atmospheric water deficit (AWD) to determine the correlation and similarity of the studied indexes. The obtained results indicated that the SMADI and SWDI were the best options for drought monitoring, with correlation coefficients ranging between 0.5 and 0.8. [PERSON] et al. studied soil moisture monitoring from the use of the microwavesatellite sensors named Advanced Microwave Scattering Radiometer--2 (AMSR-2) and Soil Moisture Active Passive (SMAP) [16]. The study was performed for rice and wheat crops, and the soil wetness index was estimated for depths of 10 and 40 cm. The coefficient of determination was calculated, obtaining a result of 0.9 for the SMAP sensor and 0.65 for the AMSR-2 sensor. Furthermore, a higher time delay was observed for the ascending pass. Moreover, [PERSON] et al. performed a comparison of results from soil moisture sensors and results from the DISaggregation based on Physical and Theoretical scale Change (DISPATCH) algorithm, which SMOS and MODIS satellite data as well as NDVI and land surface temperature (LST) to estimate soil moisture [17]. The authors concluded that the DISPATCH algorithm performed appropriate estimations in the case of rainfall but not for sprinkler irrigation, where the water is distributed in a heterogeneous manner. Both [15] and [17] used MODIS, characterized by a low spatial resolution, which impedes the application of their results in our scenario. The same problems apply to the results of [16]. Lastly, [PERSON] et al. presented a framework based on the Remote Sensing-based Soil Water Balance (RS-SWB) from satellite images and the dual crop coefficient from the FAO56 paper to calculate a Remote Sensing-based Irrigation Water Accounting (RS-IWA) metric [18]. For the plot scale, a 12% root square mean error (RMSE) was obtained, comparing the results to those of the farmers. For the case of the water user association management, the obtained RMSE was 15%. Therefore, the authors concluded that their proposed RS-IWA is appropriate to perform soil water balance estimations. Nevertheless, their proposal is specific for certain crops (wheat, maize, and barley). Therefore, its application in other vegetation such as grasses of urban lawns or golf courses or even for natural vegetation cannot be ensured. Most studies on soil moisture that use satellite data are focused on crops such as wheat. However, this functionality is of interest for other types of vegetation, such as the grass in golf courses. In this paper, satellite data is analyzed to estimate soil moisture for a golf course composed of different types of grasses and areas with natural vegetation (which is not irrigated). Therefore, our results will be applicable for multiple scenarios. Moreover, we will include validation of proposed index or indexes by including data of other periods. The main contributions of this paper are the following ones:  
* The combination of sensors and remote sensing generates a tailored index (or indexes) for estimating the soil moisture variations of the studied zone. * The use of remote sensing imagery is characterized by higher spatial resolution (10 m and 20 m) than the most used imagery, characterized by lower spatial resolution such as MODIS. * The data verification and clear presentation of indexes, R, and errors in both training and validation datasets. * The heterogeneity of employed datasets is characterized by different vegetation and different soil types.