# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 2 Background and Related Work  
In recent years, remote sensing has emerged as a particularly effective method for tracking agricultural and environmental changes [23; 24; 25; 26]. The technology relies on diverse sensors and platforms, such as satellite constellations and Unmanned Aerial Systems (UAS) to gather data, which are then typically processed using advanced algorithms, often in the realm of machine learning (ML) and deep learning (DL) [27]. Deep learning represents a specialized subset of machine learning that excels at learning from large, unstructured datasets using complex, layered neural networks. While traditional ML algorithms work well with smaller, structured datasets and often require manual feature selection, DL algorithms automatically extract features and patterns, especially from data like images and speech. This makes deep learning more powerful for certain applications, but it requires more computational resources and is often less interpretable than conventional machine learning techniques. The ongoing advancements in remote sensing represent a promising alternative to traditional SOC monitoring. [PERSON] and [PERSON] provide a fairly recent review of different remote sensing platforms and sensors available today [28]. In the study presented here, the focus is on inferring SOC content from satellite data only. Most studies focused on determining SOC, however, rely on data (spectrograms) collected from hand-held sensors. While the accuracy achieved in this way is typically higher than using satellite imagery, such approaches can hardly be scaled to enable continuous monitoring of carbon stocks on a global level. [PERSON] et al. [29] presented an early, albeit limited study (based on just 146 soil samples), which compared the results that can be achieved applying ML methods to in-the-field Vis-NIR measurements vs. applying them to hyperspectral satellite imagery. The images were obtained from the Hyperion sensor on the EO-1 satellite, which is, unfortunately, no longer functional, and there is no longer an active hyperspectral satellite that captures images in the VNIR-SWIR region, making it hard to replicate their work. In addition to trying to model the whole dataset used in the study, the authors tried focusing on specific land cover classes (cropping soils, pasture soils) and opted for a partial least-squares regression as their SOC predictor. [PERSON] et al. observed that the SOC in their cropping soils ranged between 0.54% and 1% and was lower than in the pastures, where SOC was in the 1.08% to 5.1% range. They evaluated their methodology based on R\({}_{2}\) and the Root-Mean-Squared Error (RMSE). The models based on satellite imagery did not perform well for cropping soils (R\({}_{2}\) of 0.04 and RMSE of 0.11) and lagged significantly behind the hand-held-sensor-based models in terms of R\({}_{2}\) (R\({}_{2}\) of 0.16 and RMSE of 0.1). However, when evaluated on pastures and the whole dataset, the two approaches achieved comparable and much better performance. The approach based solely on satellite data at their native resolution achieved an R\({}_{2}\) of 0.51, but the RMSE was quite high (0.73% SOC). Thus, the study showed that land cover is very important, when it comes to modeling and estimating SOC remotely. More recently, [PERSON] et al. [30] tried to use ML techniques to estimate SOC stock in the semi-arid rangelands of eastern Australia through the application of different machine learning techniques, with a focus on evaluating the impact of considering seasonal fractional cover on model performance. These features were used to extend other hand-crafted features derived from satellite imagery, as well as other remotely sensed climate features such as rainfall and temperature and data about lithology. They trained and evaluated their models using a limited amount of soil samples (705). They used random forests (RF) [31], Boosted Regression Trees (BRT) [32], and support vector machines (SVM) [33] to model their data.