Woodwell Climate Research Center, Falmouth, MA 02540, USA.
Sensors (Basel). 2020 Nov 25;20(23):6729. doi: 10.3390/s20236729.
Recent developments in diffuse reflectance soil spectroscopy have increasingly focused on building and using large soil spectral libraries with the purpose of supporting many activities relevant to monitoring, mapping and managing soil resources. A potential limitation of using a mid-infrared (MIR) spectral library developed by another laboratory is the need to account for inherent differences in the signal strength at each wavelength associated with different instrumental and environmental conditions. Here we apply predictive models built using the USDA National Soil Survey Center-Kellogg Soil Survey Laboratory (NSSC-KSSL) MIR spectral library ( = 56,155) to samples sets of European and US origin scanned on a secondary spectrometer to assess the need for calibration transfer using a piecewise direct standardization (PDS) approach in transforming spectra before predicting carbon cycle relevant soil properties (bulk density, CaCO, organic carbon, clay and pH). The European soil samples were from the land use/cover area frame statistical survey (LUCAS) database available through the European Soil Data Center (ESDAC), while the US soil samples were from the National Ecological Observatory Network (NEON). Additionally, the performance of the predictive models on PDS transfer spectra was tested against the direct calibration models built using samples scanned on the secondary spectrometer. On independent test sets of European and US origin, PDS improved predictions for most but not all soil properties with memory based learning (MBL) models generally outperforming partial least squares regression and Cubist models. Our study suggests that while good-to-excellent results can be obtained without calibration transfer, for most of the cases presented in this study, PDS was necessary for unbiased predictions. The MBL models also outperformed the direct calibration models for most of the soil properties. For laboratories building new spectroscopy capacity utilizing existing spectral libraries, it appears necessary to develop calibration transfer using PDS or other calibration transfer techniques to obtain the least biased and most precise predictions of different soil properties.
近年来,漫反射土壤光谱技术的发展越来越侧重于构建和使用大型土壤光谱库,以支持与监测、制图和管理土壤资源相关的许多活动。使用另一个实验室开发的中红外(MIR)光谱库的一个潜在限制是需要考虑到与不同仪器和环境条件相关的每个波长的信号强度的固有差异。在这里,我们应用基于美国农业部国家土壤调查中心-凯洛格土壤调查实验室(NSSC-KSSL)MIR 光谱库(=56155)建立的预测模型,对在二次光谱仪上扫描的欧洲和美国来源的样本集进行评估,以确定是否需要使用分段直接标准化(PDS)方法在预测与碳循环相关的土壤特性(体密度、CaCO、有机碳、粘土和 pH)之前转换光谱,以进行校准转移。欧洲土壤样本来自欧洲土壤数据中心(ESDAC)提供的土地利用/覆盖面积框架统计调查(LUCAS)数据库,而美国土壤样本来自国家生态观测网(NEON)。此外,我们还测试了基于 PDS 转移光谱的预测模型在直接校准模型上的性能,这些直接校准模型是使用在二次光谱仪上扫描的样本建立的。在独立的欧洲和美国来源的测试集中,PDS 提高了对大多数但不是所有土壤特性的预测,基于记忆的学习(MBL)模型通常优于偏最小二乘回归和 Cubist 模型。我们的研究表明,虽然在没有校准转移的情况下可以获得良好到优秀的结果,但在本研究中提出的大多数情况下,PDS 对于无偏预测是必要的。MBL 模型也优于大多数土壤特性的直接校准模型。对于利用现有光谱库构建新光谱能力的实验室,似乎需要使用 PDS 或其他校准转移技术来开发校准转移,以获得对不同土壤特性的最小偏差和最精确的预测。