Remote Sensing and Modeling Soil Organic Carbon Sequestration: A Regime in Global Climate Change

Soil is a complex entity of organic and inorganic materials produced from rock protolith, plays a prime role in the carbon cycle, and is surveyed to be the largest carbon reservoir apart from the oceans. SOC is a prime measure of soil fertility and is biologically, physically, and chemically associated with soil as it serves as a dominant root of nutrients for plants, enhances soil structure, improves water-holding capacity, and is also a notable part of the global carbon cycle. A decline in the amount of SOC is frequently seen as an imminent threat to the soil’s longevity since it affects the processes of multiple agrochemical procedures in the soil. Conventional methods of soil monitoring and interpretation of SOC hotspots, such as peatlands, grasslands, and black soils, take a long time, require much labor, and are expensive because they are difficult to access. They also don’t provide enough data to support landscape decision-making or the monitoring of carbon storage or loss over time. With the evolution of GIS and remote sensing (RS) technologies, predictive soil mapping techniques and models provide a cost-effective, reliable, and efficient approach to gathering regionally distributed data on SOC as well as monitoring that can mitigate Climate Change in the near future. RS techniques in the Visible-Near Infrared-Shortwave Infrared (VNIR-SWIR, 400–2500 nm) range potentially help in a more advanced, efficient, and timely way to simulate key indicators for soil processes. While it has been noted that accuracy errors vary with the satellite bands and sensors, improvements in machine-learning approaches may help even more in the development and elevation of calibration models. Additionally, there are still some drawbacks with vegetation cover, soil moisture, and sharpness to be resolved, as well as some problems with geometric, radiometric, and atmospheric corrections. The objective of this chapter is to outline the methodology and conclusion of each technique that was conducted to estimate SOC using RS satellites.