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Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra

机译：通过多光谱图像与实验室光谱相结合的区域尺度土壤有机碳建模

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There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l’Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the ‘upland model’ was able to more accurately predict SOC compared with the ‘upland & wetland model’. However, the separately calibrated ‘upland and wetland model’ did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).
机译：将土壤近端光谱和遥感光谱结合起来以提高土壤有机碳（SOC）模型的准确性是一个巨大的挑战。这主要是因为混合来自不同来源和技术的光谱数据以改善土壤模型仍处于起步阶段。这项研究的第一个目标是将实验室中可见近红外反射率（Vis-NIR）光谱中的SOC信息与遥感（RS）图像进行整合，以改善丹麦Skjern河流域表土SOC的预测。第二个目标是通过分别模拟高地和湿地来改善SOC预测结果。总共收集了328个表层土样品并进行了SOC分析。使用Cubist回归和贝叶斯克里金法，对卫星地面观测卫星（SPOT5），Landsat数据连续性任务（Landsat 8）图像，Vis-NIR实验室以及其他辅助环境数据（包括地形参数和土壤图）进行了汇编，以预测表层土壤SOC。结果表明，与开发的模型相比，根据RS数据，辅助环境数据和实验室光谱数据开发的模型具有更低的均方根误差（RMSE）（2.8％）和更高的R 2 （0.59）仅来自RS数据和辅助环境数据（RMSE：3.6％，R 2 ：0.46）。植物可用水（PAW）是所有模型中最重要的预测指标，因为它与土壤有机质含量密切相关。而且，植被指数，例如归一化植被指数（NDVI）和增强植被指数（EVI），在SOC空间模型中是非常重要的预测指标。此外，与“高地和湿地模型”相比，“高地模型”能够更准确地预测SOC。但是，单独校准的“高地和湿地模型”并不能提高湿地站点的预测准确性，因为不可能充分区分RS夏季图像中的植被。我们得出的结论是，与仅使用RS数据相比，实验室的Vis-NIR光谱法增加了关键信息，可显着提高SOC的预测准确性。我们建议将实验室光谱与RS数据和其他环境数据结合起来，以改善土壤空间建模和数字土壤测绘（DSM）。

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