采用无人机载高分辨率光谱仪反演土壤有机碳含量

小型无人机(Unmanned Aerial Vehicle,UAV)平台与土壤高光谱技术的有机结合可作为一种快速、准确获取高分辨率土壤有机碳(Soil Organic Carbon,SOC)空间信息的手段,适用于精准农业管理和土地监测,但目前该方面应用不多。该研究选取中国东北黑土和比利时黄土研究区,通过构建与UAV兼容的土壤高光谱数据获取平台,研究其在暗室和野外自然光条件下快速反演SOC含量的能力;进行多源光谱数据修正,探索暗室SOC模型直接应用到野外条件的可行性。结果表明:1)暗室条件下构建的基于UAV兼容光谱数据(FX)的偏最小二乘回归(Partial Least Squares Regression,PLSR)模型能准确预测2个研究区的SOC含量(相对分析误差大于1.6,R~2≥0.65);2)野外自然光条件下构建的SOC预测模型精度略有下降(R~2=0.58),但SOC含量估算值与实测值的值域相近,说明仍能捕捉SOC含量在其值域的变化;3)利用校准标样对不同光照条件下的FX数据进行修正,将基于实验室光谱数据的PLSR模型应用于野外光谱数据,为实现无需实地采样即可利用无人机载高光谱数据进行SOC快速调查奠定了基础。

Soil organic carbon content retrieved by UAV-borne high resolution spectrometer

Abstract: Soil stores more carbon in the terrestrial ecosystem than the combined vegetation and atmosphere. Soil organic carbon (SOC) as the key component of soil carbon pool is highly sensitive to earth surface evolution and anthropogenic-induced changes in climate and agricultural management practices. The spatiotemporal dynamics can exert important controls over soil productivity and ecosystem services. There is thus an increasing demand to quantify SOC at sufficiently high resolution and accuracy, thereby detecting localized soil degradation as well as ensuring sustainable agricultural management. Field-, airborne and satellite-based multi-platform Visible and Near-Infrared (Vis-NIR) reflectance spectroscopy has increasingly been used as a fast and effective tool to predict SOC, and thereby capture the variability at field to landscape scales. Comparing to the satellite-based remote sensing systems, commercially available portable Unmanned Aerial Vehicle (UAV) equipped with high-resolution Vis-NIR spectrometers can greatly improve the spatial resolution and acquisition efficiency of soil spectral information. It is also more flexible to carry out field surveys thanks to the small size, but applications of UAV-based spectroscopic assessment of SOC so far are still scarce. In this study, a UAV-compatible soil hyperspectral data acquisition platform was tested in two types of soil located in the Northeastern Black Soil Belt of China and the Belgian Loam Belt. The specific objectives were: 1) to test the ability of UAV-compatible Vis-NIR spectrometer for the accurate prediction of SOC content; and 2) to explore a spectral correction approach in a laboratory-based spectral model under field conditions. Soil hyperspectral data was gathered in a dark room and under natural sunlight. Subsequently, spectral-based SOC prediction models were developed using Partial Least Squares Regression (PLSR). Results show that: 1) PLSR models behaved excellent performances for both study sites using UAV-compatible spectral data (FX) from a dark room with the Relative Percent Difference (RPD) higher than 1.6 and R2 higher than 0.65. 2) FX spectral data acquired under natural sunlight also achieved an acceptable PLSR model (RPD=1.48, R2=0.58) suitable for capturing the range of variation in SOC, although the accuracy slightly decreased, compared with the dark room. 3) A standard sand sample from Lucky Bay (Australia) was selected to correct and align the FX spectral data under two light conditions. The PLSR model using the laboratory spectra was directly applied to field spectra for the excellent performance (R2 = 0.53, RMSE= 0.29%, RPD = 1.45, RPIQ = 1.75). The spectral correction approach can offer promising potential in future applications to avoid the large sampling, when using UAV-based spectroscopy to rapidly assess SOC. This finding highlighted the UAV-based hyperspectral remote sensing to predict SOC in a fast, accurate and detailed fashion, providing technical reference in fields, such as digital soil mapping and precision agriculture. Future studies can explore the influence of soil surface roughness and moisture on the quality of soil spectral data acquired from UAV platforms, thereby correcting for the noise caused by external factors.