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Soil moisture monitoring is an important part of precision agriculture, and it plays a key role in monitoring agricultural conditions and agricultural production. Ultra-wide band (UWB) radar has been widely used in soil moisture monitoring due to its small size, light weight, strong penetrating power and low power consumption. However, most of soil moisture retrieved with UWB radar is for the case of ideal bare soil conditions. In practical applications, surface vegetation coverage will have a great impact on the results. To solve this problem, support vector machines (SVM) model was used to predict soil moisture under different vegetation coverages in farmland scale by combining UWB radar and multispectral data, so as to eliminate or mitigate the effect of vegetation coverage. The experimental results showed that among different time-domain feature combinations extracted from UWB radar echo data, SVM model with the inputs of nine selected time domain features, including crest factor, kurtosis, root mean square, peak-to-peak value, maximum amplitude, variance, skewness, average and minimum, generated the best prediction results, and the overall accuracy and Kappa coefficient were 95.59% and 0.9492, respectively. After adding the normalized difference vegetation index (NDVI), the model accuracy for different time-domain feature combinations was significantly improved. Among them, the results by combining the nine selected time-domain features and NDVI were the best, and the overall accuracy and Kappa coefficient reached 98.09% and 0.9780, which were raised by 2.50 percentage points and 0.0288 compared with those without considering the influence of vegetation covers.