Remote Sensing Monitoring of Soil Organic Carbon Content

Abstract

Soil organic carbon is a key component of the terrestrial carbon cycle and plays an important role in regulating the climate, enhancing agricultural productivity, and protecting ecosystems. Efficient large-scale monitoring of SOC is therefore of great significance. Compared with traditional chemical analysis, remote sensing provides a rapid, non-contact, and spatially continuous approach for SOC estimation. This paper reviews the main remote sensing platforms, sensors, and estimation methods used for SOC monitoring, including multispectral and hyperspectral satellites, UAV-based observations, SAR data, and LiDAR-assisted approaches. The advantages and limitations of different data sources are analysed, with particular attention to the use of machine learning, deep learning, and empirical models in SOC inversion. Furthermore, the paper discusses the performance of SOC remote sensing under four representative surface conditions defined by vegetation cover and terrain complexity, namely gentle topography with sparse vegetation, gentle topography with dense vegetation, rugged topography with sparse vegetation, and rugged topography with dense vegetation. The results indicate that remote sensing technology has strong potential for SOC mapping, but vegetation interference, topographic effects, soil moisture, and model uncertainty still constrain its accuracy.