Evaluating the capability of a UAV‐borne spectrometer for soil organic carbon mapping in bare croplands. (4th August 2021)

• Record Type: Journal Article
• Authors: Zhang, He; Shi, Pu; Crucil, Giacomo; van Wesemael, Bas; Limbourg, Quentin; Van Oost, Kristof
• Is Part Of: Land degradation & development. Volume 32:Number 15(2021)
• Page Start: 4375
• Page End: 4389
• Publication Date: 2021-08-04
• Subjects: hyperspectral sensors -- precision agriculture -- soil organic carbon -- soil spectral library -- UAV -- Vis–NIR spectroscopy; Land degradation -- Periodicals; Soil conservation -- Periodicals; Reclamation of land -- Periodicals; Land use -- Periodicals; Economic development -- Environmental aspects -- Periodicals; 333.7315
• DOI: 10.1002/ldr.4043
• Languages: English
• ISSNs: 1085-3278

Abstract

Abstract: 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.