High-resolution, field-scale soil organic carbon (SOC) mapping in croplands is crucial for effective and precise agricultural management. Recent developments in unmanned aerial vehicles (UAVs) combined with miniaturized visible–near infrared spectrometers have enabled the rapid and low-cost field-scale SOC mapping. However, a field-specific spectrotransfer model is often needed for such UAV-based hyperspectral measurements, implying local sampling and model development are still required, and this hampers the widespread application of UAV-based methods. In this study, we aim to test to what extent SOC prediction models derived from an existing regional soil spectral library (SSL) can be applied to UAV-based hyperspectral data, without the need for additional field sampling. To this end, an UAV survey was conducted over a bare cropland within the Belgian Loam Belt for field-scale SOC mapping. We evaluated two calibration approaches, one based on local sampling and model development, and one where we capitalized on an existing (laboratory-based) regional SSL. For the local calibration approach, we obtained a good prediction performance with RMSE of 0.57 g kg⁻¹ and RPIQ of 2.35. For the regional model, a spectral alignment procedure was needed to resolve the discrepancy between UAV- and laboratory-based measurements. This resulted in a fair SOC prediction accuracy with RMSE of 0.93 g kg⁻¹ and RPIQ of 1.45. The comparison of SOC maps derived from the two approaches, along with an external validation showed a high consistency, indicating that UAV-based spectral measurements, in combination with SSLs have the potential to improve the efficiency of high-resolution SOC mapping.

中文翻译：

---

评估无人机载光谱仪对裸露农田土壤有机碳作图的能力

农田中高分辨率的田间土壤有机碳 (SOC) 绘图对于有效和精确的农业管理至关重要。无人机 (UAV) 的最新发展与小型化可见-近红外光谱仪相结合，实现了快速、低成本的现场规模 SOC 映射。然而，这种基于无人机的高光谱测量通常需要特定领域的光谱传输模型，这意味着仍然需要局部采样和模型开发，这阻碍了基于无人机的方法的广泛应用。在这项研究中，我们旨在测试从现有区域土壤光谱库 (SSL) 得出的 SOC 预测模型在多大程度上可以应用于基于无人机的高光谱数据，而无需额外的现场采样。为此，无人机调查是在比利时壤土带内的裸露农田上进行的，用于田间规模的 SOC 制图。我们评估了两种校准方法，一种基于本地采样和模型开发，另一种利用现有（基于实验室的）区域 SSL。对于局部校准方法，我们获得了良好的预测性能，RMSE 为 0.57 g kg⁻¹和 RPIQ 为 2.35。对于区域模型，需要一个光谱校准程序来解决 UAV 和实验室测量之间的差异。这导致具有 0.93 g kg ^{-1 的}RMSE和 1.45 RPIQ 的公平 SOC 预测准确度。来自两种方法的 SOC 地图的比较以及外部验证显示出高度的一致性，表明基于无人机的光谱测量与 SSL 相结合有可能提高高分辨率 SOC 地图的效率。