**6. Conclusions**

In this study, to improve the accuracy of RCF modeling and mapping, a new strategy based on the fusion of different machine learning algorithms' results at the decision level was developed. The results showed that by considering both spectral indices based on reflective bands and radar bands as dependent variables in machine learning algorithms, the RCF modeling error is reduced by an average of 15%. Among the various machine learning algorithms in RCF modeling, RF accuracy is higher than other algorithms, including ANN, SVR and PLSR. The results of the proposed strategy showed that the integration of the capabilities of different machine learning algorithms increases the accuracy of RCF modeling. With the fusion of the results of different machine learning algorithms at the decision level, the accuracy of RCF modeling for corn, wheat and soybean crops compared to the most optimal algorithm has increased by more than 33, 25 and 34%, respectively. It is suggested that in future studies, the efficiency of deep learning algorithms in RCF modeling be evaluated. It could also be very useful to use the proposed algorithm to prepare a more accurate RCF map in agricultural areas around the world and implement optimal programs to improve the agricultural situation and conserve soil and environmental quality.

**Author Contributions:** Conceptualization, A.B. and S.F.; methodology, S.F., M.K.F. and A.B.; software, S.F. and M.K.F.; validation, S.F. and M.K.F.; formal analysis, S.F. and M.K.F.; data curation, S.F., M.K.F. and A.B.; writing—original draft preparation, S.F. and M.K.F.; writing—review and editing, A.B.; supervision, A.B.; project administration, A.B.; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This project is supported by the Canada First Research Excellence Fund (CFREF)–Food from Thought project at the University of Guelph and Natural Sciences and Engineering Research Council of Canada (NSERC) (RGPIN-2014-4100). Funding for open access charge: is from Natural Sciences and Engineering Research Council of Canada (NSERC) (RGPIN-2014-4100).

**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.
