Abstract: Soil fertility of a cultivated land is an important indicator of cultivated land productivity. It is necessary to obtain the rapid and accurate inversion of cultivated land fertility via remote sensing for the better utilization and management of land resource. In this study, a new inversion model was constructed and optimized using the classical statistical analysis (SLR, CR, and MLSR), and machine learning (BPNN and ELM). An effective way was also proposed for rapid quantitative remote sensing inversion of cultivated land fertility in hilly areas. Dongping County and Tengzhou City were selected as two representative counties and cities in the hilly area of center southern Shandong Province, China. In Dongping County, the TM image during the turning green and jointing stage was used to construct and screen spectral indexes of cultivated land fertility. Tengzhou City was selected to verify the spatial universality of inversion model for the soil fertility of a cultivated land. Furthermore, the remote sensing inversion model was used to quantitatively monitor the spatial-temporal dynamic status of cultivated land fertility in Tengzhou City in 2007, 2011, and 2016. The prediction accuracy of inversion models was compared in different periods. The results showed that there were significant correlations between the five kinds of spectral indexes in a remote sensing and the Integrated Fertility Index (IFI), among which the correlation coefficients of improved spectral index were greater than 0.684, indicating better reflecting the status of cultivated land fertility. The best inversion model was the IIG-MLSR model (Rv2=0.684, RMSE=5.674) in the classical statistical analysis, while, the IIG-BPNN model (Rv2=0.746, RMSE=5.089) in the machine learning. The obtained model demonstrated excellent universal applicability in hilly areas, where there were similar spatial distribution characteristics between the inversion and evaluation on the cultivated land fertility, and the similar proportion of cultivated land and high spatial compatibility. In the two best models, the difference in the area ratio of the high, middle, and low levels of cultivated land fertility inversion and cultivated land fertility evaluation was generally less than 5.55 percentage point, where the spatial fit was 84.50% and 88.76%, respectively. The dynamic inversion analysis showed that the cultivated land fertility of Tengzhou City increased continuously in recent 10 years (from 2007 to 2016). The area proportion of high-level land increased from 67.30% to 80.72%, whereas, that of middle-level and low-level land decreased. The multi-temporal remote sensing inversion of cultivated land fertility was feasible, compared with the remote sensing inversion models in different time periods. The optimal time phase to invert the cultivated land fertility was the turning green and jointing stage of winter wheat in April, followed by bare soil in October, and the worst in summer maize in August. The remote sensing inversion index and model can be used to effectively increase the evaluation efficiency of cultivated land fertility. At the same time, this finding can provide a positive reference for the related research of cultivated land quality.