Soil moisture conditions are represented in fire danger rating systems mainly through simple drought indices based on meteorological variables, even though better sources of soil moisture information are increasingly available. This review summarises a growing body of evidence indicating that greater use of in situ, remotely sensed, and modelled soil moisture information in fire danger rating systems could lead to better estimates of dynamic live and dead herbaceous fuel loads, more accurate live and dead fuel moisture predictions, earlier warning of wildfire danger, and better forecasts of wildfire occurrence and size. Potential uses of soil moisture information in existing wildfire danger rating systems include (1) as a supplement or replacement for drought indices, (2) for live and (3) dead fuel moisture modelling, (4) for estimating herbaceous fuel curing, and (5) for estimating fuel loads. We identify key remaining research questions and note the logistical challenge of convincing wildfire professionals of the importance of soil moisture compared with more familiar wildfire danger metrics. While obstacles remain, the path forward is clear. Soil moisture information can and should be used to improve fire danger rating systems and contribute to more effective fire management for the protection of communities and ecosystems worldwide.

Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r² = 0.68 and 0.20, respectively, p < 2.2 × 10⁻¹⁶ for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian’s Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies.

Cover crops can improve soil health by increasing soil organic matter, soil porosity, permeability, and crop yield. Yet, land planted to cover crops are often limited by economic constraints. Perennial living mulch (LM) cover crops may provide better benefits to soil health because they are actively growing throughout the year and self‐regenerate without reseeding. The objective of this study was to compare the impact of a white clover (Trifolium repens L.) LM vs. annual cover crops on soil health traits. Treatments were established on a Cecil sandy loam soil in the fall of 2014 and annual cover crop treatments re‐established each fall of the following 3 yr. White clover re‐established in the LM without reseeding. Corn (Zea mays L.) was planted into the treatments in the spring of each year. Soils were sampled at the V4/V5, V12, and R5 stages of corn development and analyzed for chemical traits. Surface soil characteristics were measured after corn harvest in 2018. Soils in the LM system had lower lime buffering capacity and greater pH, base saturation, cation exchange capacity (CEC), Ca, K₂O, Mg, P₂O₅, and total organic C concentrations than other treatments. Soil NH₃ and NO₃ had seasonal fluctuations associated with mineral N fertilizer and were lower in the LM treatment. After 3 yr, the soil bulk density was lower and porosity, water infiltration, and labile C were greater in surface soils from the LM treatment than in the surface soils of the other treatments. Use of a perennial LM cover crops expedited soil health regeneration compared to other treatments.

Soil properties have important effects on fire occurrence and spread, but soils are often overlooked in fire prediction models. Quantifying soil−fire linkages is limited by information in conventional soil maps, but digital soil mapping products (e.g., detailed soil property maps) could improve both wildfire prediction models and post-fire management decisions. Of our estimated 3.7 Mkm2 of rangeland in the continental US and Alaska, an average of 38 000 km2 burned per year between 2008 and 2017. To highlight the role of soils in fire ecology, we present 1) a conceptual framework explaining why soil information can be useful for fire models, 2) a comprehensive suite of literature examples that used soil property information in traditional soil survey for predicting wildfire, and 3) specific examples of how more detailed soil information can be applied for pre- and post-fire decisions. Digital soil mapping can improve fire prediction models and inform post-fire management decisions.