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Abstract This literature review synthesizes the role of soil moisture in regulating carbon sequestration and greenhouse gas emissions (CS-GHG). Soil moisture directly affects photosynthesis, respiration, microbial activity, and soil organic matter dynamics, with optimal levels enhancing carbon storage while extremes, such as drought and flooding, disrupt these processes. A quantitative analysis is provided on the effects of soil moisture on CS-GHG across various ecosystems and climatic conditions, highlighting a “Peak and Decline” pattern for CO₂ emissions at 40% water-filled pore space (WFPS), while CH₄ and N₂O emissions peak at higher levels (60–80% and around 80% WFPS, respectively). The review also examines ecosystem models, discussing how soil moisture dynamics are incorporated to simulate photosynthesis, microbial activity, and nutrient cycling. Sustainable soil moisture management practices, including conservation agriculture, agroforestry, and optimized water management, prove effective in enhancing carbon sequestration and mitigating GHG emissions by maintaining ideal soil moisture levels. The review further emphasizes the importance of advancing multiscale observations and feedback modeling through high-resolution remote sensing and ground-based data integration, as well as hybrid modeling frameworks. The interactive model-experiment framework emerges as a promising approach for linking experimental data with model refinement, enabling continuous improvement of CS-GHG predictions. From a policy perspective, shifting focus from short-term agricultural productivity to long-term carbon sequestration is crucial. Achieving this shift will require financial incentives, robust monitoring systems, and collaboration among stakeholders to ensure sustainable practices effectively contribute to climate mitigation goals.

Abstract We characterized the bi‐directional spectro‐polarimetry of olivine sands of varying grain size distributions for a comprehensive set of measurement and illumination angles over a wavelength range of 350–2,500 nm. Our laboratory instrumentation included a hyperspectral goniometer, a broadband linear polarizer, and a tungsten‐halogen illumination source. Three distinct grain size distributions of olivine sand samples were used in our experiments. As a function of azimuth, we measured a significant degree of anisotropic scattering, that depends directly on polarization angles, resulting in a distribution that cannot be accurately described solely using phase angle. For media of uniform or similar composition, we observed robust separability of grain size distributions using spectro‐polarimetry. We compared Hapke's polarimetric model for semi‐infinite granular media with a new empirical polarimetric model that we developed. This empirical model more accurately replicates the scattering of unpolarized incident light as a function of all view azimuth, view zenith, and polarization angles for all incident zenith angles. Parameters of our empirical polarimetric model that determine the magnitude of polarization correlate linearly with the inverse diffuse reflectances of the olivine sand samples, exhibiting phenomenology that is most likely due to the Umov effect. Because of the linearity of the correlations, our results show that polarimetry can be used to retrieve medium parameters, such as grain size distributions. We provide our data online and freely available in a Zenodo/GitHub repository.

Polarization provides useful quantitative information about scattering surfaces. In hyperspectral remote sensing of natural surfaces composed of granular materials, there are relatively few studies of polarization. Most earlier remote sensing studies of polarization have been based on multi-spectral data, and the majority focused on the negative branch of polarization, which typically appears at phase angles less than 20 degrees, using models with limited accuracy. Models of the positive branch have also shown limitations, particularly at longer phase angles. We review these earlier studies by Hapke and Shkuratov and present the results of our laboratory study using hyperspectral polarization imagery of particulate surfaces. Although the linear polarization ratio is typically a nonlinear function of phase angle, our results show that in an approximately linear region of the polarization curve, there is a correlation between the slope of the linear polarization ratio and the average grain size.