## 1 Introduction  
Soil organic carbon (SOC) content is a key parameter that helps assess soil quality. Comprehension of its spatial and temporal dynamics facilitates simultaneously a better assessment of soil fertility and several parameters that are closely associated with it, such as structural stability, nutrient cycling, biological activity, and soil aeration [1, 2, 3]. Its regular monitoring has prompted the research community to investigate the reliability of rapid, non-destructive, and cost-effective assessment methods as alternatives to conventional methods. Soil spectral information is particularly relevant for estimating soil physical-chemical properties, especially organic matter and organic carbon content [4; 5; 6; 7; 8; 9]. Different types of proximal, aerial, or satellite sensors have been used, each with different spectral and spatial resolutions [4; 7; 10; 11]. Despite considerable advances in multispectral and hyperspectral remote sensing imagery, their signal-to-noise ratio depends on a variety of parameters not encountered with laboratory spectroscopy [12]. Laboratory soil spectra are acquired under controlled conditions to avoid interfering factors, while remote sensing spectra are influenced by the non-transparent atmosphere and the subpixel composition [13; 14], as well as by soil surface roughness, which causes bidirectional effects [7; 14; 15], and soil moisture [16; 17; 18; 14; 19]. Given its high spectral resolution, visible and near-infrared (Vis-NIR) spectroscopy is the most accurate method for estimating SOC content, but it provides only point estimates in space [20; 21; 22; 23; 24; 25; 26]. Remotely sensed imagery from optical satellites allows for area-based estimates but with lower prediction accuracy [8; 27; 28; 29; 30; 31; 32; 33; 34; 35]. The performance of the developed models varies according to the spatial scale of the studied area for both laboratory and remotely sensed spectra. To increase the accuracy of models developed using remote sensing data, in particular multispectral satellite and/or airborne imagery, three main approaches have been developed: (1) Inclusion of spectral indices to consider effects of interference factors [30; 33; 36; 37; 38], especially for several dates in a time series [30; 38]; (2) Combination of remotely sensed data with laboratory or field spectra to fuse spectral and spatial soil information [39; 40; 41; 42; 18]; (3) Combination of spectral information from optical images with non-spectral covariates, such as digital elevation models, soil and land use maps, or meteorological and environmental data [43; 44; 45; 46; 47]. Other studies have used both synthetic aperture radar (SAR) data and derive predictors (e.g., radar indices, soil moisture) as covariates [48; 49; 50; 51] or as a way to select relevant Sentinel-2 dates by estimating the state of the soil surface [38; 50]. The increasing availability of spectral data (proximal and remotely sensed), computational power, and interest in data science has favored a transition from linear methods to machine-learning algorithms in soil science [52; 53] and, more recently, to deep-learning techniques [54]. Machine-learning algorithms, such as support vector machine (SVM) regression, random forest (RF), and cubist regression (CR), have shown their ability to analyze non-linear relationships between spectral information and SOC content when compared to multiple linear regression and partial least squares (PLS) regression [55; 56; 57; 58]. Furthermore, studies of neural networks have found them to perform significantly better than other techniques. For example, [PERSON] et al. [58] and [PERSON] et al. [59] found that convolutional neural networks (CNN) outperform CR, SVM, and PLS regression models when using laboratory reflectance spectra. Similarly, artificial neural networks and deep neural networks (DNN) predicted SOC content well using laboratory spectra [60; 61] but, to our knowledge, have rarely been tested using reflectance spectra from satellite images.