The occurrence of large, high‐intensity wildfires requires plant biomass, or fuel, that is sufficiently dry to burn. This poses the question, what is “sufficiently dry”? Until recently, the ability ...to address this question has been constrained by the spatiotemporal scale of available methods to monitor the moisture contents of both dead and live fuels. Here we take advantage of recent developments in macroscale monitoring of fuel moisture through a combination of remote sensing and climatic modeling. We show there are clear thresholds of fuel moisture content associated with the occurrence of wildfires in forests and woodlands. Furthermore, we show that transformations in fuel moisture conditions across these thresholds can occur rapidly, within a month. Both the approach presented here, and our findings, can be immediately applied and may greatly improve fire risk assessments in forests and woodlands globally.
Key Points
We apply recently developed models for monitoring of live and dead fuel moisture contents
We show that the relationship between fuel moisture and wildfire exhibits threshold behavior
Changes in fuel moisture sufficient to cross critical thresholds can occur over weeks to months
Each equestrian disciplines have unique footing needs, but all disciplines require a stable and consistent footing to train, ride, and compete on. Managing footing properly is key to maintaining this ...surface and water application is a common treatment to control dust as well as increase the shear strength of the footing. The bulk density of the footing mixture is an important material characteristic for determining how a surface will compact, how much water it can hold, and serves as a target moisture content. While bulk density is the target, there is little information regarding how moisture content in arena footing changes over time. The objective of this study was to examine how the change in moisture content over a 5-d period was impacted by different environmental conditions and varying drag schedules. Representative footing models (RFM) were built for 3 different footing types: sand, sand with fiber, and sand with organic material. The environmental conditions were winter (7°C/75%RH), summer, humid (30°C/80%RH) and summer, dry (30°C/40%RH) and the drag schedules were daily, every other day (MWF), and weekly (W). The starting gravimetric moisture content for the each footing was determined by the bulk density: sand with fiber 21%, sand 12%, sand with organic material 14%. All footing types were statistically significant for the environmental conditions (all p-values < 0.01). The average (expressed as least squares means) moisture content decrease over the 5 d for the 3 RFM under each environmental condition are provided. While the drag schedule was notstatistically significant for the change in overall moisture content, there was statistical significance or a trend toward statistical significance in the difference between the moisture contents of the top and bottom layers of the RFMs for the sand (P = 0.066) and sand with organic material (P < 0.001). In addition, the environmental conditions were statistically significant for all 3 footing types (sand with fiber (P = 0.033), sand (P = 0.088), sand with organic material (P = 0.004). Understanding how the moisture content changes in different arena footing allows for better management of the arena surfaces at equine facilities as well as providing information on how resources, particularly water, can be best applied for maximum benefit.
Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing ...moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors' knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used.
•Estimate soil moisture content on the spatial scale of the catchment region.•The synergic application of Sentinel-1 radar backscattering coefficients and Sentinel-2 optical images to monitor soil ...moisture content.•The revision of the water cloud model localization.•Inversion of vegetation moisture content by combining NDVI from Sentinel-2 and on-site samplings.
Remote sensing techniques, which provide timely and accurate soil moisture information to improve food production, are becoming increasingly popular among agricultural production in China and many other regions. It also shows its importance in monitoring agricultural basins where the meteorological and topographic conditions vary greatly. In this case, the reliability of soil moisture content (SMC) monitoring for theses basins could be promised. The main objective of this paper is to develop a synergic method of radar and optical data, as well as an on-site sampling scheme, to accurately estimate SMC in the basin. The reason is that in the winter wheat production area of Dawen River basin in China, there are few conventional SMC testing equipment. Considering the characteristics of different crops and the conditions of crops in different growth periods, the VV polarization mode of Sentinel-1 C-band Synthetic Aperture Radar (SAR) images was used to retrieve SMC. Combined with the Normalized difference vegetation index (NDVI) extracted from Sentinel-2 MSI optical images and the optimized Water Cloud Model (WCM), the surface backscattering coefficient was obtained by eliminating vegetation moisture content (VMC), and the SMC model was established. The results showed that from March to May in 2020, the coefficient of determination (R2) between the estimated VMC and the field observation was 0.655, and the Root Mean Square Error (RMSE) between the two was 0.334. In the inversion algorithm model, the R2 between the retrieved SMC and the field measurement was 0.506, and the RMSE was 3.725.
Projections for near-surface soil moisture content in Europe for the 21st century were derived from simulations performed with 26 CMIP5 global climate models (GCMs). Two Representative Concentration ...Pathways, RCP4.5 and RCP8.5, were considered. Unlike in previous research in general, projections were calculated separately for all four calendar seasons. To make the moisture contents simulated by the various GCMs commensurate, the moisture data were normalized by the corresponding local maxima found in the output of each individual GCM. A majority of the GCMs proved to perform satisfactorily in simulating the geographical distribution of recent soil moisture in the warm season, the spatial correlation with an satellite-derived estimate varying between 0.4 and 0.8. In southern Europe, long-term mean soil moisture is projected to decline substantially in all seasons. In summer and autumn, pronounced soil drying also afflicts western and central Europe. In northern Europe, drying mainly occurs in spring, in correspondence with an earlier melt of snow and soil frost. The spatial pattern of drying is qualitatively similar for both RCP scenarios, but weaker in magnitude under RCP4.5. In general, those GCMs that simulate the largest decreases in precipitation and increases in temperature and solar radiation tend to produce the most severe soil drying. Concurrently with the reduction of time-mean soil moisture, episodes with an anomalously low soil moisture, occurring once in 10 years in the recent past simulations, become far more common. In southern Europe by the late 21st century under RCP8.5, such events would be experienced about every second year.
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