Methodology
Agriculture is an important sector to provide in our needs. Since 2000, the arable land per farmer increased with 66%. To help farmers managing their increasing amount of arable land, it is important to develop new techniques in example based on remote sensing data. One of the key parameters for agricultural management is the water availability in the area. The water availability can be clarified with help of two different soil water properties, soil water holding capacity and soil moisture content. The aim of this study is to estimate soil water properties at a scale of 30 meters based on bare soil surface reflectance and thermal infrared image analysis. In this study, two newly developed models are proposed to determine the spatial patterns of soil wetness. The first model is based on bare soil surface reflectance, named as SoilGrids30m, a spatial estimation model for clay content and organic matter content in the study area. These estimates are then used as input of pedotransfer functions to obtain spatial estimates of soil water holding capacity in the study area. The second model is based on the relation between the normalized difference vegetation index and the crop temperature, named as the soil wetness indicator. The soil wetness indicator is a representative of soil moisture content in the root zone of crops. Both models are evaluated against the soil moisture content estimates obtained from SEBAL. Soil water holding capacity is not directly related to soil moisture content. The hypothesis is therefore, soils with a high soil water holding capacity will tend to have higher soil moisture content estimates and vice versa during a long period of drought. The year 2018 has been used as reference year because of the extreme drought conditions in the months May until July.

Conclusions
The SoilGrids30m model improved the estimation of clay content and organic matter content in the study area compared to the existing coarser SoilGrids250m and SoilGrids1000m models. Especially, the organic matter content estimates significantly improves with help of the SoilGrids30m model. However, the influence of adding bare soil surface reflectance to the model was relatively low for both clay content (37%) and organic matter content (13%). The influence of bare soil surface reflectance only improved the clay content estimates compared to the SoilGrids1000m model. In all other cases, the target variable estimates did not improve by adding bare soil surface reflectance data. It can therefore be concluded that the improvement of the SoilGrids30m model is due to the use of a larger set of observation data from the study area and not because of the input of bare soil surface reflectance data.
The obtained soil water holding capacity estimates with help of pedotransfer functions did not show reliable results compared to the soil moisture estimates of SEBAL. The highly empirical pedotransfer functions are the main cause of the unreliable results but also seepage and irrigation are important factors that could have played a role. In future work it would therefore be recommended to avoid using pedotransfer functions calibrated in other regions than the study area.
The results of the soil wetness indicator showed a clear correlation with the soil moisture content estimates from SEBAL. The soil wetness indicator is therefore a simple and reliable tool to recognize spatial patterns of wetness. In example for precision agriculture, the soil wetness indicator could be used for irrigation management. The relative representation of the soil wetness indicator could determine the distribution of irrigation within a field.