The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.

There is a huge potential for nutrient recovery from organic waste materials for soil fertility restoration as well as negative environmental emission mitigation. Previous research has found vermicomposting the optimal choice for converting organic waste into beneficial organic fertilizer while reducing reactive N loss. However, a great deal of the processes of greenhouse gases (GHG) and ammonia volatilization during vermicomposting are not well-documented. A field vermicomposting experiment was conducted by deploying earthworms (Eisenia fetida) with three types of agricultural by-products—namely, cow manure (VCM), pig manure (VPM), and biochar (VBC)—and crop (maize) residues compared with traditional composting (COM) without earthworms in the Sichuan Basin, China. Results showed that vermicomposting caused a decrease in electrical conductivity (EC) and total organic carbon (TOC) while increasing total nitrogen (TN). The greatest TN increase was found with VCM. The cumulative NH₃ volatilization in COM, VCM, VPM, and VBC during experimental duration was 9.00, 8.02, 15.16, and 8.91 kg N ha⁻¹, respectively. The cumulative CO₂ emissions in COM, VCM, VPM, and VBC were 2369, 2814, 3435, and 2984 (g·C·m⁻²), while for CH₄, they were 0.36, 0.28, 4.07, and 0.19 (g·C·m⁻²) and, for N₂O, they were 0.12, 0.06, 0.76, and 0.04 (g·N m⁻²), respectively. Lower emissions of N₂O, CH₄, and NH₃ were observed in VBC. We concluded that earthworms, as ecological engineers, enhanced reactive nutrients and reduced ammonia volatilization during vermicomposting in our test system. Overall, vermicomposting is proposed as an eco-friendly, sustainable technique that helps to reduce environmental impacts and associated health risks.