# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 1 Introduction  
Soil represents a complex mixture of organic and inorganic constituents with different physical and chemical properties, which vary significantly between locations and even within a single field [1]. It is a key component of terrestrial ecosystems, as it facilitates the circulation of energy and materials between the atmosphere and the biosphere [2]. Soil health can be defined as the ability of the soil to function effectively as a component in a thriving ecosystem [3]. In order to ensure effective monitoring and enable adequate assessment of the condition of the soil, one needs to select appropriate indicators of its condition. The indicators should meet certain criteria: they should be accepted by expertsas valid; their measurement should be carried out routinely and on a large scale; they need to be understood and accepted by the general population in order to achieve a global impact [4]. Soil organic carbon (SOC) content is a widely accepted indicator of soil quality, as SOC plays a central role in various soil functions [5]. SOC measurement is a common component of soil property analysis. Furthermore, carbon as an element is well known and recognized by the global population [6]. All of this makes SOC a valuable indicator for assessing and monitoring changes in soil health. The amount and quality of SOC are closely related to key soil functions, including nutrient mineralization, aggregate stability, air and water permeability, water retention, and flood control ability [5]. These soil functions are, in turn, related to a wide range of ecosystem attributes. For example, high SOC levels in mineral soils tend to correlate with high plant productivity, which has a positive effect on wildlife habitat, distribution, and population size [7]. Through the protection and increase of stored SOC, one can protect or increase soil fertility, reduce soil erosion, and reduce habitat conversions [8]. In addition to its importance for the soil, SOC has the potential to help neutralize the negative effects of increasing concentrations of CO\({}_{2}\) in the atmosphere (which significantly contribute to global warming and climate change [9]) and help ensure food security around the wold [10]. While SOC plays a key role in mitigating climate change by acting as a carbon sink, the historical loss of carbon from this pool [11] has been significant, and the potential for future accelerated loss under warming scenarios is a serious threat [12;13]. As a natural solution to fight climate change, strategies that involve conserving existing SOC stocks (avoidance of losses) and replenishing stocks in carbon-depleted soils [14] can be used as a means of achieving the United Nations Sustainable Development Goals (UNSDG), the goals of the United Nations Framework Convention on Climate Change (UNFCCC), and the United Nations Convention on Combating Desertification (UNCCD) [15]. Despite the scientific consensus about the potential and myriad benefits that can be brought about by the development and application of soil organic carbon storage and sequestration techniques, they remain limited in practice. A fundamental issue affecting the adoption of such methodologies is the lack of accurate and cost-effective ways of measuring SOC content in the top layer of the soil (as this is most affected by land use, agricultural practices, etc.). When it comes to measuring global SOC stocks, many estimates have been published over the past decades, and most studies report a global SOC estimate of approximately 1500 Pg of carbon (Pg C), but there is considerable variation among estimates (ranging from 504 to 3000 Pg C) [16]. The large variation in the estimates of global SOC stocks arises from differences in the sampling period, the intensity and spatial resolution of soil profile databases, as well as from differences in approaches to calculating the estimates themselves [17]. The uneven distribution of georeferenced soil profiles around the world is another reason for such a large variation in the estimates [18].