## 1 Introduction  
As a major component of the terrestrial nitrogen (N) pool, soil total nitrogen (STN) not only provides essential nutrients for plant growth but also affects soil function and the concentration of greenhouse gases in the atmosphere. Low STN values limit plant growth while excessive STN may result in loss of nitrogen from the soil, causing soil fertility degradation and water pollution [1]. Soil degradation associated with soil nitrogen loss reduces soil security and greatly contributes to climate change. In total, 21% of the total annual emissions of nitric oxide (NO) are related to soil degassing [2]. On a global scale, NO emissions from soil and from fossil fuel combustion are in the same range [3;4]. In addition, as a key indicator of soil fertility and quality, STN content is closely related to agricultural productivity and food security. Therefore, reliable predictions of STN content are critical to sustaining sustainable agricultural development and understanding the regional N cycle. Up to date STN maps are important to identify spatial variation and control factors of STN, which can help maintain food security and soil security and provide a reference for managing climate change. To address environmental challenges, such as climate change and land degradation, an accurate and efficient method is needed to predict the spatial distribution of STN and improve its prediction accuracy. Digital soil mapping provides a low-cost and efficient method for predicting the spatial distribution of soil nutrients. Most digital soil mapping methods are based on soil landscape models, which establish mathematical or statistical relationships between soil properties and related environmental variables [5;6]. Many digital soil mapping methods have been used to predict soil properties, including generalized linear model (GLM), random forest (RF), artificial neural networks (ANNs), boosted regression trees (BRTs), support vector machine (SVM), and regression kriging (RK). Although these models have achieved satisfactory results in different fields, choosing the best prediction method for a given landscape has always been a challenge. [PERSON] et al. [7] used RF and SVM models to conduct STN mapping studies in the Soyang lake watershed in South Korea and found that the RF model performed better while [PERSON] et al. [8] found that the latter performed better than the former in Kenya. [PERSON] et al. [9] reported that the RF model was superior to BRT in the prediction of soil properties in Nigeria while [PERSON] et al. [10] found that the latter had better predictive performance in an alpine ecosystem. Because these studies obtained inconsistent results for different environments, the predictive models should be compared and evaluated for a given landscape. STN prediction using digital soil mapping techniques requires sufficient environmental information. Commonly used environmental data are on land use/land cover (LULC), the climate, the topography, as well as remote sensing-derived variables. Remote sensing has received recent attention to improve digital soil mapping due to the extensive area it can cover and the rich spatial information it provides. Optical satellite imagery is one of the most commonly used remote sensing data sources for digital soil mapping and has contributed significantly to research on the prediction of STN. Recent studies have also noted the potential of synthetic aperture radar (SAR) for predicting soil chemical properties. For example, [PERSON] et al. [11] explored the feasibility of C-band ENVISAT ASAR data in soil organic carbon (SOC) mapping and found that the SOC in the study area can be quantified by SAR images. [PERSON] et al. [12] used optical and L-band ALOS PALSAR data to conduct SOC prediction studies in central Amazon, and found that the backscattering coefficient of SAR data can improve the prediction accuracy. A similar study was also reported by [PERSON] et al. [13], who used SAR (Sentinel-1) and optical (Landsat 7) images to map soil properties in eastern China.