The validation of satellite retrieved soil moisture over a tropical region (Bago, Myanmar) has been conducted in this research. The downscaled soil moisture products of VanderSat using the Land Parameter Retrieval Model, were compared with an in-situ soil moisture network that operated between 2017 and 2018 and a newly placed network installed during a fieldwork trip early 2020. Soil moisture is a key variable in the hydro- logical cycle, but the understanding of soil moisture content in tropical areas like Myanmar is substandard. This study provides new insights, being one of the first of its kind in the tropics. The reliability of both in-situ soil moisture networks was analysed and indicated that the 2017 network could be used for the validation, and the 2020 network for a visual comparison, with a focus on the soil temperature as a source of error. The results of the validation showed high correlation with Pearson’s R ranging between 0.70 and 0.89. The highest correlations were found for the L-band, between 0.78 and 0.89 and for the location Hpayargyi. The root mean square difference (RMSD), unbiased RMSD and bias indicated that the accuracy and precision need improve- ment. For the ubRMSD values between 0.059 m3/m3 and 0.129 m3/m3 are found. The best result is found for the L-band at Hpayargyi, which is the only location/band combination that meets the target accuracy of 0.06 m3/m3 that is often set for SMAP satellite retrieval. The visual comparison of the 2020 network showed good agreement for the soil moisture measurement. When the in-situ soil temperature measurements were compared with the satellite retrieved land surface temperature measurement, however, this resulted in a large bias of approximately 15 degrees Celsius. To evaluate possible improvements for the validation result, several sources of errors were explored. Two sources concerning the in-situ network indicated the challenges of an in-situ network in a remote tropical area. The first showed that re-calibration of the sensor is necessary to improve representation of the ground truth. The second discussed the spatial mismatch between the point scale in-situ measurement and the downscaled satellite products, with a pixel size of 100x100m. The sparse in-situ network with four stations cannot represent the entire area and can therefore result in errors. The other sources of errors that were discussed originated from the retrieval algorithm of the Land Parameter Re- trieval Model. A parameterisation was performed to assess the sensitivity of the ubRMSD to the polarisation mixing factor (Q), the single scattering albedo (!) and the roughness parameters (h1 and h2). The parame- ters were optimised to achieve the lowest ubRMSD, the result indicated that an improvement of the ubRMSD up to 40% can be achieved. However the evidence that linked the optimised parameters to the tropics was not found and it was concluded that the optimising could be covering the large temperature bias, that is a direct input in the Land Parameter Retrieval Model. The large bias in the temperature was evaluated as a source of error. The bias can partially be explained by the masking of water bodies, where the dynamic ef- fect of the tropical monsoon on the water bodies is not taken into account. Another explanation for the bias could potentially be found in the emissivity that is determined by optimising the ¢T , which can cause im- perfections in the prediction of the temperature. It was concluded that the satellite retrieved soil moisture products show good correlation but need improvement in accuracy and precision. This can be accomplished by updating the in-situ soil moisture network, creating new water masks, solving the retrieval algorithm for the temperature bias and then optimising tropical specific location parameters.