# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 2 Background and Related Work
The RF approach performed the best and achieved an R\({}_{2}\) of 0.47 on their dataset. Several studies tried to evaluate the effectiveness of hyperspectral data obtained from airborne sensors and extended their findings to evaluate the expected performance of sensors expected to be deployed in the future [34; 35]. While we focus on multispectral data in the study presented here, it is worth noting that, albeit relying on a very limited set of soil samples (81) obtained for a 7 km\({}^{2}\) area in Luxembourg, 40% of which were used as a test set, [PERSON] et al. achieved a relatively high R\({}_{2}\) (0.74) and an RMSE of 0.22% for SOC using autoPSLR applied to hyperspectral data from an airborne sensor [35]. Once sufficient hyperspectral data are available, the methodology we propose can easily be adapted to that domain, leading to even better performance. Over the last decade, deep learning has revolutionized the area of machine learning and artificial intelligence and has become the dominant paradigm in the domain. The crucial advance over previously used methods is that the approach relies on end-to-end learning, which allows the ML models to learn the features on which to make their decisions and estimated directly from the raw input data, instead of relying on human-engineered features [36]. [PERSON] et al. provided an overview of the applications of both classical neural networks and DL models to the monitoring of environmental parameters using remote sensing data [37]. They showed that DL outperformed traditional ML models and has led to significant improvements in many applications, including land cover mapping, vegetation parameter, soil moisture, evapotranspiration, agricultural yield prediction, etc. The authors correctly highlighted the limitation of the DL approaches, which is related to the relatively limited amounts of training data available, as well as the potential to apply transfer learning to circumvent this problem. They mentioned two types of transfer learning: region-based and data-based. The first relates to pretraining on a geographical region for which ample data are available and adjusting the model to a different region with limited data available. In the ML community, this is usually referred to as fine-tuning. The latter is more in line with what the meaning of transfer learning is in the ML domain and relates to transferring the models trained on data obtained from a sensor or a group of sensors to other sensors. In the study presented here, we use a third kind of transfer learning, common in the computer vision community [38], where the initial model is trained on the same type of input data (Sentinel-2), but for a different visual task (land cover classification), and is used as a feature extractor for the final model (which performs SOC estimation in our case). While the first application that [PERSON] et al. discussed was land cover, no approaches to estimating SOC were mentioned in this study. In addition, while approaches based on different DNN architectures were discussed (most relying on convolutional neural networks), none were identified in the study that use the U-Net model. [PERSON] et al., however, successfully applied U-Net with Lovasz softmax loss for land cover classification using RGB data made available as part of the DeepGlobe Challenge [39]. [PERSON] et al. used a CNN to try to infer SOC for a central location based on input data that covered the surrounding region [40]. The input of their model was environmental variables combined with MODIS MCD12Q2 phenology variables. They trained and evaluated their approach on a limited set of 733 samples, collected in Anhui Province of China. This limited the complexity of the CNN they could use, since no transfer learning was used in the study, but the CNN fared better than a random forest model, achieving a modest R\({}_{2}\) of 0.26. [PERSON] et al. [41] focused on Northern Iran and used a large number of input features (105).