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Spatial distributions of soil total nitrogen (STN) can greatly contribute to the precision fertilization and crop yield in black soil area. Many efforts have been devoted to the accurate algorithms for the estimation of STN contents. This study aims to firstly propose the applicable integrated machine learning algorithms (e.g., Random Forest (RF), Adaptive boosting (AdaBoost) and Gradient boosting categorical features (CatBoost)) and Supervised learning algorithms (e.g., Simple linear regression (SLR), Support vector regression (SVR) and Back propagation neural network (BPNN)). The spectral indexes and environmental variables were then integrated using Multispectral Imager (MSI) product, in order to seamlessly retrieve the spatial distributions of STN. A large number of soil samples were collected in Suihua City, and the synchronous reflectance that embedded in better quality of Sentinel-2 Level-2A images. Likewise, two scenarios were considered, e.g., band 1-12 reflectance or combining them with spectral indexes and environmental variables (digital elevation model, temperature, precipitation and soil types). The results showed that the average STN of in situ measured samples was 1 904.06 mg/kg, with a coefficient of variation of 17.93%. The coefficients of determination (R²) were smaller than 0.6 between the measured and derived values from the developed STN algorithms, when the band 1-12 reflectance as the input variables. The performances of six STN algorithms for the validated dataset were ranked in the descending order of RF, CatBoost, AdaBoost, BPNN, SLR and SVR, whereas, the importance were ranked in the order of RF, SVR, BPNN, AdaBoost, CatBoost, and SLR. Once the band 1- 12 reflectance, spectral indexes, and environmental variables were as the input variables, the performance of STN algorithms was improved significantly in the validated dataset, of which the R² increased by 0.22 and root mean square error (RMSE) decreased by 35.30 mg/kg. In total, the accuracies of STN algorithms were in the descending order of RF, CatBoost, AdaBoost, BPNN, MLSR, and SVR. Hence, the RF can be expected to simulate the nonlinear relationships between reflectance and STN, and then obtain a better degree of measured- and derived- fitting, indicating powerful nonlinear ability. Furthermore, the STN content was mapped using Sentinel-2 level2A imagery and RF algorithm, in order to examine the spatial variation. The spatial distribution of STN content was higher in the northeast, whereas lower in southwest-decreasing gradually from north to southand slightly higher in middle of Suihua City. This was attributed to the large number of environmental variables. Anyway, much more attention can be paid for the decision-making on the protection of ‘Black soil’ and natural ecosystems. The finding can provide the technical assistances on dynamically monitoring STN contents, in order to evaluate the soil fertility for the sustainable agricultural development in black soil area of Northeast China. [ABSTRACT FROM AUTHOR]