**5. Discussion**

An accurate RCF map is crucial in agricultural planning and managemen<sup>t</sup> [19,22,33]. Unlike terrestrial methods, satellite images have high application and high efficiency in preparing these maps due to their extensive spatiotemporal coverage and their low cost [8,29,30,34]. However, the accuracy of different crops' RCF mapping using satellite data is dependent on various factors, including (1) dependent variables used in the modeling process, (2) the quality of calibration and validation data and (3) the algorithms used to construct the appropriate model between the effective variables and the RCF [8,29,33]. In this study, the effect of dependent variables and algorithms used in the RCF modeling process was investigated.

In previous studies, various spectral indices based on satellite images have been provided to prepare this map [29,33]. In a number of these studies, the efficiencies of these spectral indices in RCF modeling were evaluated and compared [8,33]. The results showed a different performance of each of these indicators in different conditions. In this study, it was also shown that the efficiency of a number of indices, such as NDI5, NDI7, NDTI, NDVI, STI, DFI and BAI, was higher than other spectral indices when preparing the RCF map. Even the efficiency of each spectral index in preparing a RCF map for different crops is different. The results showed that for modeling the RCF of corn, wheat and soybean crops, BAI efficiency was higher than other indicators. The BAI is designed to minimize the effect of background soil moisture in the process of RCF estimating. Yue, Tian, Dong and Xu [8] showed that BAI had a high efficiency in estimating the RCF of wheat and maize products by reducing the effect of field soil moisture. In general, the spectral behavior of different crops at different wavelengths and their differences with the background soil and vegetation in the study area can affect the accuracy of the developed spectral indices. NDVI

preparing the receipt map and agricultural products are highly sensitive to vegetation in the study area. As a result, for areas with high vegetation cover, such as forests and pastures, the RCF values are overestimated.

The focus of previous studies has been on the development of optimal indicators and methods in RCF modeling and on the use of reflective bands of satellite images, including Landsat, Sentinel 2, ASTER, etc. [16,30,33,34]. In a limited number of studies, the efficiency of the information obtained from radar images, including RADARSAT, in RCF modeling was evaluated [24]. However, in this study, for the first time, the efficiency of Sentinel 1 VV and VH bands in modeling this parameter was evaluated and compared with the efficiency of spectral indices. The results showed that considering spectral indices and VV and VH radar bands simultaneously as effective variables increases the accuracy of RCF modeling based on machine learning algorithms. Due to the simple and free access to Sentinel 1 images with a high frequency for different agricultural regions around the world, the use of these images in RCF modeling can be of grea<sup>t</sup> practical importance. In general, the accuracy of multivariate RCF modeling is higher than univariate modeling using machine learning algorithms. The results of this study showed that the efficiencies of machine learning algorithms in RCF modeling were different to one another. The efficiency of RFR algorithm in modeling this parameter was higher than ANN, SVR and PLSR. Ding, Zhang, Wang, Xie, Wang, Liu and Hall [33] also showed that RFR was highly efficient in RCF mapping.

Each of these algorithms may have high or low performance under different conditions. Therefore, providing an integrated model based on the results of these algorithms can be useful. In various fields, such as land cover classification, improving the spatial resolution of land surface temperature, etc., the strategy of combining the results of different algorithms called fusion at the decision level has been used to improve the modeling accuracy of target variable. In this study, the results of the RCF estimation obtained from different machine learning algorithms were combined based on the degree of importance of each algorithm to improve the modeling accuracy of this variable. The results showed that by implementing the fusion strategy at the decision level, the accuracy of the RCF map was significantly improved.
