The near- through mid-infrared offers a unique and, as this thesis aims to show, essential view of extragalactic stellar populations both nearby, at intermediate redshifts and at very high redshift. In chapter 2, I demonstrate that rest-frame near-IR photometry obtained by the Spitzer Space Telescope provides more robust stellar mass estimates for a spectroscopic sample of ~100 galaxies in the redshift desert (0.5<z<2), and is crucial for modeling galaxies with young star-forming populations. From this analysis, a surprising result emerges in the data. Although the rest-frame light short of 2 micron improves stellar mass estimates, the models and observations disagree beyond 2 micron and emission from non-stellar sources becomes significant. At wavelengths from 1-30 micron, stellar and non-stellar emission contribute equally to a galaxy's global spectral energy distribution. This is unlike visible wavelengths where stellar emission dominates or the far-IR where dust emission provides the bulk of a galaxy's luminosity. Using the sample of high-z galaxies, in chapter 3, I quantify the statistical significance of the excess emission at 2-5 micron and find the emission to correlate with the OII luminosity, suggesting a link between the excess emission and star formation. The origin of the excess emission is not clear, although I explore a number of non-stellar candidates in this chapter. Nearby resolved observations provide a clearer picture of the excess by spatially resolving 68 nearby galaxies. By analyzing the pixel-by-pixel near-IR colours within each galaxy at ~1-5 micron, increasingly red near-IR colors are mapped to spatial regions in chapter 4. For regions with red NIR colors and high star formation rates, I find the broad near- through mid-IR spectrum is constant, varying only in amplitude as a function of the intensity of star formation, suggesting the infrared emission of a young, dusty stellar populations can be added to stellar population synthesis models as an additional component tied to the star formation rate. In closing the thesis, the focus is moved to the detection of stellar populations in the earliest star-forming galaxies. By z>6, all visible wavelength emission is redshifted into near-IR wavelengths. In chapter 5, I show how a tunable near-IR filter I have helped develop holds promise for finding bright Lyman alpha emitting galaxies at redshifts of 8<z<11.

The near- through mid-infrared offers a unique and, as this thesis aims to show, essential view of extragalactic stellar populations both nearby, at intermediate redshifts and at very high redshift. In chapter 2, I demonstrate that rest-frame near-IR photometry obtained by the Spitzer Space Telescope provides more robust stellar mass estimates for a spectroscopic sample of ~100 galaxies in the redshift desert (0.5<z<2), and is crucial for modeling galaxies with young star-forming populations. From this analysis, a surprising result emerges in the data. Although the rest-frame light short of 2 micron improves stellar mass estimates, the models and observations disagree beyond 2 micron and emission from non-stellar sources becomes significant. At wavelengths from 1-30 micron, stellar and non-stellar emission contribute equally to a galaxy's global spectral energy distribution. This is unlike visible wavelengths where stellar emission dominates or the far-IR where dust emission provides the bulk of a galaxy's luminosity. Using the sample of high-z galaxies, in chapter 3, I quantify the statistical significance of the excess emission at 2-5 micron and find the emission to correlate with the OII luminosity, suggesting a link between the excess emission and star formation. The origin of the excess emission is not clear, although I explore a number of non-stellar candidates in this chapter. Nearby resolved observations provide a clearer picture of the excess by spatially resolving 68 nearby galaxies. By analyzing the pixel-by-pixel near-IR colours within each galaxy at ~1-5 micron, increasingly red near-IR colors are mapped to spatial regions in chapter 4. For regions with red NIR colors and high star formation rates, I find the broad near- through mid-IR spectrum is constant, varying only in amplitude as a function of the intensity of star formation, suggesting the infrared emission of a young, dusty stellar populations can be added to stellar population synthesis models as an additional component tied to the star formation rate. In closing the thesis, the focus is moved to the detection of stellar populations in the earliest star-forming galaxies. By z>6, all visible wavelength emission is redshifted into near-IR wavelengths. In chapter 5, I show how a tunable near-IR filter I have helped develop holds promise for finding bright Lyman alpha emitting galaxies at redshifts of 8<z<11.

Soil spectroscopy in the visible-to-near infrared (VNIR) and mid-infrared (MIR) is a cost-effective method to determine the soil organic carbon content (SOC) based on predictive spectral models calibrated to analytical-determined SOC reference data. The degree to which uncertainty in reference data and spectral measurements contributes to the estimated accuracy of VNIR and MIR predictions, however, is rarely addressed and remains unclear, in particular for current handheld MIR spectrometers. We thus evaluated the reproducibility of both the spectral reflectance measurements with portable VNIR and MIR spectrometers and the analytical dry combustion SOC reference method, with the aim to assess how varying spectral inputs and reference values impact the calibration and validation of predictive VNIR and MIR models. Soil reflectance spectra and SOC were measured in triplicate, the latter by different laboratories, for a set of 75 finely ground soil samples covering a wide range of parent materials and SOC contents. Predictive partial least-squares regression (PLSR) models were evaluated in a repeated, nested cross-validation approach with systematically varied spectral inputs and reference data, respectively. We found that SOC predictions from both VNIR and MIR spectra were equally highly reproducible on average and similar to the dry combustion method, but MIR spectra were more robust to calibration sample variation. The contributions of spectral variation (ΔRMSE < 0.4 g·kg−1) and reference SOC uncertainty (ΔRMSE < 0.3 g·kg−1) to spectral modeling errors were small compared to the difference between the VNIR and MIR spectral ranges (ΔRMSE ~1.4 g·kg−1 in favor of MIR). For reference SOC, uncertainty was limited to the case of biased reference data appearing in either the calibration or validation. Given better predictive accuracy, comparable spectral reproducibility and greater robustness against calibration sample selection, the portable MIR spectrometer was considered overall superior to the VNIR instrument for SOC analysis. Our results further indicate that random errors in SOC reference values are effectively compensated for during model calibration, while biased SOC calibration data propagates errors into model predictions. Reference data uncertainty is thus more likely to negatively impact the estimated validation accuracy in soil spectroscopy studies where archived data, e.g., from soil spectral libraries, are used for model building, but it should be negligible otherwise.