Minerva J. Dorantes

Resource‐efficient techniques for accurate soil property estimation are necessary to satisfy the increasing demand for soil data to support environmental monitoring, precision agriculture, and spatial modeling. Over the last 30 yr, infrared soil spectroscopy has developed into a rapid, robust, and cost‐effective technique for soil carbon analysis. Ongoing global efforts to make soil spectroscopy operational require the development of soil spectral libraries, which are the main source of data for the construction of calibration models. Understanding calibration optimization is important to ensure the efficient use of soil spectral libraries for the accurate estimation of soil carbon. Moreover, spectral library transfer can benefit new data collection, soil monitoring, and modeling efforts. This review presents techniques for optimization of calibration models and library transfer. Selection of calibration set size and subsetting are presented as current calibration optimization techniques. Moreover, spiking is discussed as an effective technique for spectral library transfer. Overall, studies have suggested that an increase in calibration size improves model performance and this continues until an optimal size is reached. Additionally, subsetting can improve model performance if the resulting subsets reduce the variability of spectrally active components. Studies have also suggested that spiking is effective when used in conjunction with subsetting techniques. These findings denote the current applicability and potential of optimization and library transfer techniques for the accurate estimation of soil carbon with soil spectroscopy. Future efforts should focus on refining optimization techniques to further expand the operability of soil spectroscopy for soil carbon estimation.

Mid-infrared spectroscopy is an efficient technique for soil carbon analysis. Efforts to measure and monitor carbon through mid-infrared spectroscopy require the development of soil spectral libraries. These libraries are used for the construction of calibration models which relate analyte values to spectra. The optimization of these models is an important process for the accurate and resource-efficient estimation of soil carbon. This study demonstrates the effect on model performance of subsetting a soil spectral library for soil organic carbon estimation. Various subsetting criteria were tested across different landscapes in the United States, and results are presented in the context of the development of new soil spectral libraries.

Spatial stratification of landscapes allows for the development of efficient sampling surveys, the inclusion of domain knowledge in data-driven modeling frameworks, and the production of information relating the spatial variability of response phenomena to that of landscape processes. Thiswork presents the rassta R package as a collection of algorithms dedicated to the spatial stratification of landscapes, the calculation of landscape correspondence metrics across geographic space, and the application of these metrics for spatial sampling and modeling of environmental phenomena.The theoretical background of rassta is presented through references to several studies which have benefited from landscape stratification routines. The functionality of rassta is presented through code examples which are complemented with the geographic visualization of their outputs.