Remote sensing of soil organic carbon: A critical review of techniques and challenges

Abstract

Spectral reflectance of soil, vegetation, and litter residues on the soil surface is affected primarily by soil organic carbon (SOC) and moisture content, C and N content in vegetation, multiplicative interaction of leaf area index (LAI) and SOC content, tillage and residue management systems, and climate. Because of the relationships between SOC content and agriculture management practices and their effects on crop productivity, SOC is an important in-soil variable. As in previous research, SOC has a positive visible and negative near-infrared (NIR) reflectance response. Good separation was made of soil and plant residues on the soil surface using the spectral bands of 490, 680, 1270 and 1630 nm. Since there is no direct or indirect SOC spectral band response in NIR, SOC cannot be estimated alone using statistical methods such as linear regression. Soil moisture level studies on SOC estimation suggest that surface moisture can increase the predictability of SOC, but dry conditions are preferable. SOC predictions at the compacted soil surface in suppressed field conditions using the Monte Carlo sunlight model during herbaceous vegetation have not been found. Therefore, future microroughness will be added to the model, allowing for better results. Although there are some limitations, the remote sensing technique discussed here is an alternative source of SOC for precise agriculture farming systems. This study aims to investigate important findings of remote sensing SOC research and to develop more optimized research models.