The quantification and monitoring of soil organic carbon (SOC) in croplands is crucial in a context of effective and precise agricultural management. Conventional methods to assess SOC changes pose some limitations on time and labor consumption to efficiently assess SOC distributions and dynamics in a spatial and temporal context. Recent developments in Uncrewed Aircraft Systems (UAS), combined with miniaturized visible-near infrared sensors bring opportunities for the rapid and low-cost field-scale SOC mapping. The UAS sensing systems enable the multiple dimensional monitoring (i.e., temporal, spatial and spectral) of soil properties, while it lacks a capability evaluation for different sensor types in the estimation of soil properties. Meanwhile, there is still room for improving the pre-processing approaches against the perturbing factors such as soil roughness and reflectance anisotropy. The overall aim of this thesis was therefore to improve the spatial and spectral accuracy and effectiveness of UAS-based multi- and hyper-spectral measurement for the estimation of SOC content in croplands.

In this thesis, we started with a non-imaging spectrometer mounted on UAS-borne and showed good capability for SOC content prediction. Meanwhile, we developed a transfer function to align UAS-based and laboratory-based measurements to enable the use of an existing model based on a soil spectral library. In a second phase, we focused on imaging sensors to have spatial and structural information of the soil surface. The result demonstrated the robustness and repeatability of photogrammetry using Structure from Motion (SfM) algorithms with precise georeferencing and accurate camera calibration based on post-processed positioning data. Next, we assessed the pre-processing approaches for UAS-based multispectral images to reduce the error caused by soil roughness and anisotropy to optimize the workflow of SOC mapping in bare croplands. Furthermore, we characterized soil surface roughness and illumination geometry using UAS-based imaging spectroscopy, and investigated their influence on soil reflectance measurement and SOC estimation. These studies help to understand the capabilities and limitations of UAS-based spectroscopy in soil mapping and monitoring in croplands, and the potential of improving its accuracy and efficiency in SOC estimation by comprehensively considering perturbing factors and mechanisms behind it.