Agriculture faces significant challenges with current climate change and a growing global population. Increasing dry periods, even in Sweden, require increased field irrigation to secure harvests while water availability decreases. Research in remote sensing has led to significant technological advancements where soil moisture can now be detected with precision at 100x100 meters. This study aims to evaluate how well satellite analysis of soil moisture aligns with field experiments and its usefulness for irrigation decisions. Through the collection and analysis of data from both field measurements and satellites, the agreement between these has been examined where statistical measures such as NNSE, RMSE, and MAE have been used. A challenge with satellite analysis is its limitation to measuring soil moisture only in the top layer (3-5 cm) of the soil, which does not estimate the water content in the root zone required for irrigation decisions. To address this, the cumulative function matching method (CDF-method) was used to estimate the water content in the root zone based on satellite measurements of the top soil layer. The results show that satellite data aligns with field measurements for various locations in Sweden, with NNSE values between 0.08-0.61 and an accuracy (MAE) of 4.51-6.99 % water content. Furthermore, the results indicate that water content in the root zone can be estimated from surface soil moisture using the CDF matching method with an accuracy of 1.28-6.41 %. However, this method requires cross-validation in the field where the estimation is to be performed, and the use of satellite analysis for irrigation decisions is limited to the field level. To effectively utilize satellite analysis as a global decision support, methods for estimating water content in the root zone need to be developed to be more applicable for the different soil types.