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The Soil Moisture Active Passive (SMAP) mission with high-precision soil moisture (SM) retrieval products provides global daily composites of SM at 3, 9, and 36 km earth grids measured by L-band active and passive microwave sensors. The capability of passive microwave remote sensing has been recognized for the estimation of SM variations. The purpose of this work was to establish an interaction between the highly variable SM spatial distribution on the ground and the SMAP’s coarse resolution radiometer-based SM retrievals. In this work, SMAP Level 3 (L3) and Level 4 (L4) SM products are validated with in situ datasets observed from the different locations of the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin over the period of January 2018 to December 2019. The values of the unbiased root mean square error (ubRMSE) for L3 (SPL3SMP_E) SM retrievals are close to the standard SMAP mission SM accuracy requirement of 0.04 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">m</mi><mn>3</mn></msup><mo>/</mo><msup><mi mathvariant="normal">m</mi><mn>3</mn></msup></mrow></semantics></math></inline-formula> at the 9-km scale, with an averaged ubRMSE value of 0.041 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">m</mi><mn>3</mn></msup><mo>/</mo><msup><mi mathvariant="normal">m</mi><mn>3</mn></msup></mrow></semantics></math></inline-formula> (0.050 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">m</mi><mn>3</mn></msup><mo>/</mo><msup><mi mathvariant="normal">m</mi><mn>3</mn></msup></mrow></semantics></math></inline-formula>) for descending (ascending) SM with the correlation (R) values of 0.62 (0.42) against the sparse network sites. The L4 (SPL4SMGP) Surface and Root-zone SM (RZSM) estimates show less error (ubRMSE < 0.04) and high correlation (R > 0.60) values, and are consistent with the previous SMAP-based SM estimations. The SMAP L4 SM products (SPL4SMGP) performed well compared to the L3 SM retrieval products (SPL3SMP_E). In vegetated land, the variability and compatibility of the SMAP SM estimates with the evaluation metrics for both products (L3 and L4) showed a good performance in the grassland, then in the farmland, and worst in the woodlands. Finally, SMAP algorithm parameters sensitivity analysis of the satellite products was conducted to produce time-series and highly precise SM datasets in China.