Soil moisture depletion during the growing season can induce plant water stress, thereby driving declines in grassland fuel moisture and accelerating curing. These drying and curing dynamics and their dependencies on soil moisture are inadequately represented in fire danger models. To elucidate these relationships, grassland fuelbed characteristics and soil moisture were monitored in nine patches of tallgrass prairie under patch-burn management in Oklahoma, USA, during two growing seasons. This study period included a severe drought (in 2012), which resulted in a large wildfire outbreak near the study site. Fuel moisture of the mixed live and dead herbaceous fuels (MFM) clearly tracked soil moisture, expressed as fraction of available water capacity (FAW). MFM decreased with decreasing soil moisture below an FAW threshold of 0.59 and fell below 30% only when FAW fell below 0.30. Likewise, the curing rate increased linearly as FAW declined below 0.30, while Normalized Difference Vegetation Index (NDVI) readings failed to adequately respond to rapid drying and curing of the fuelbed. Incorporating soil moisture observations into grassland fuelbed models could result in more accurate fuel moisture and curing estimates, contributing to improved wildfire danger assessments and reduced losses of life and property due to wildfire outbreaks.

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Accurate and site-specific information on tillage practice is vital to understand the impacts of crop management on water quality, soil conservation, and soil carbon sequestration. Remote sensing is a cost-effective technique for surveillance and rapid assessment of tillage practice over large areas. A new empirical approach for accurately predicting tillage class using discriminant analysis (DA) on historical multitemporal Landsat-TM 5 imagery has been developed. Ground truth data were obtained from the USDA-NRCS at 48 locations (20 conventional till [CT] and 28 conservation tillage or no-till [NT]). Classification accuracies were obtained for the DA models using reflectance values of Landsat-5 TM bands and Normalized Difference Tillage Index (NDTI) values. The performance of the DA models was compared with Logistic Regression (LR) models. On the basis of classification accuracy and kappa (κ) value, our results showed that the DA models performed better in tillage classification than the LR models. However, using NDTI values, both the DA and LR models performed similarly in tillage class discrimination. Model performance improved when a subset of locations rather than years was used. The results indicated broad-scale mapping of tillage practices is feasible using historical Landsat-5 TM imagery and DA-based classification.

With increasing forest and grassland wildfire trends strongly correlated to anthropogenic climate change, assessing wildfire danger is vital to reduce catastrophic human, economic, and environmental loss. From this viewpoint, the authors discuss various approaches deployed to evaluate wildfire danger, from in-situ observations to satellite-based fire prediction systems. Lately, the merit of soil moisture in predicting fuel moisture content and the likelihood of wildfire occurrence has been widely realized. Harmonized soil moisture measurement initiatives via state-of-the-art soil moisture networks have facilitated the use of soil moisture information in developing innovative applications for wildfire prediction and risk management applications. Additionally, the increasing availability of remote-sensing data has enabled the monitoring and modeling of wildfires across various terrestrial ecosystems. When coupled with remotely sensed data, field-based soil moisture measurements have been more valuable predictors of assessing wildfire than alone. However, sensors capable of acquiring higher spectral information and radiometry across large spatiotemporal domains are still lacking. The automation aspect of such extensive data from remote-sensing and field data is needed to rapidly assess wildfire and mitigation of wildfire-related damage at operational scales.