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Mapping salt marsh soil properties using imaging spectroscopy
摘要： Tidal salt marshes sequester and store blue carbon at both short and long time scales. Marsh soils shape and maintain the ecosystem by supporting complex biogeochemical reactions, deposition of sediment, and accumulation of organic matter. In this study, we examined the potential of imaging spectroscopy techniques to indirectly quantify and map tidal marsh soil properties at a National Estuarine Research Reserve in Georgia, USA. A framework was developed to combine modern digital image processing techniques for marsh soil mapping, including object-based image analysis (OBIA), machine learning modeling, and ensemble analysis. We also evaluated the efficacy of airborne hyperspectral sensors in estimating marsh soil properties compared to spaceborne multispectral sensors, WorldView-2 and QuickBird. The pros and cons of object-based modeling and mapping were assessed and compared with traditional pixel-based mapping methods. The results showed that the designed framework was effective in quantifying and mapping three marsh soil properties using the composite reflectance from salt marsh environment: soil salinity, soil water content, and soil organic matter content. Multispectral sensors were successful in quantifying soil salinity and soil water content but failed to model soil organic matter. The study also demonstrated the value of minimum noise fraction transformation and ensemble analysis techniques for marsh soil mapping. The results suggest that imaging spectroscopy based modeling is a promising tool to quantify and map marsh soil properties at a local scale, and is a potential alternative to traditional soil data acquisition to support carbon cycle research and the conservation and restoration of tidal marshes.
关键词： Salt marsh,Object-based modeling,Soil properties,Imaging spectroscopy,Machine learning
更新于2025-09-23 15:22:29
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Optimization of measuring procedure of farmland soils using laser-induced breakdown spectroscopy
摘要： Laser-induced breakdown spectroscopy (LIBS) is an emerging multi-elemental analytical technique offering fast and simultaneous quantification of soil properties with minimal sample preparation and effective cost. Due to soil heterogeneity, spectral variation however limits the quantitative robustness. In this study, 348 soil samples were collected and prepared for acquisition of LIBS spectra. Influences of shot layer and number on LIBS quality were evaluated by spectral intensity and relative standard deviation (RSD). Effects of shot layer and number and five normalization procedures on LIBS ability to measure soil organic matter (SOM), total nitrogen (TN), and total soluble salt content (TSC), were evaluated using partial least squares regression (PLSR). Increasing shot number reduced LIBS spectral variance, thereby improving the quantitative accuracy of selected soil properties. Deep shot layers (4th or 5th shot layers) reduced the intensities of soil spectra and thereby decreased the quantitative accuracy for TSC. However, deep shot layers improved the SOM and TN prediction performances. Among the normalization approaches, the method based on the correction of Si line (DS) showed superior performance for improving quantitation of SOM and TN. The arithmetic average method (AA) was best for TSC prediction. Optimization of shot layer, number and normalization procedures of LIBS spectra resulted in fair prediction of SOM (residual prediction deviation of validation set, RPDV = 1.608), good prediction of TN (RPDV = 1.836), and very good quantitative analysis of TSC (RPDV = 2.456). Therefore, our findings illustrate very good potential for improving the quantitative accuracy of the LIBS soil spectra.
关键词： quantitative analysis,shot layer,soil properties,shot number,normalization methods,Laser-induced breakdown spectroscopy
更新于2025-09-23 15:19:57