Monitoring Of Moisture Content Research Articles

Remote sensing of vegetation and soil moisture content in Atlantic humid mountains with Sentinel-1 and 2 satellite sensor data

The satellite monitoring of vegetation moisture content (VMC) and soil moisture content (SMC) in Southern European Atlantic mountains remains poorly understood but is a fundamental tool to better manage landscape moisture dynamics under climate change. In the Atlantic humid mountains of Portugal, we investigated an empirical model incorporating satellite (Sentinel-1 radar, S1; Sentinel-2 optical, S2) and ancillary predictors (topography and vegetation cover type) to monitor VMC (%) and SMC (%). Predictors derived from the S1 (VV, HH and VV/HH) and S2 (NDVI and NDMI) are compared to field measurements of VMC (n = 48) and SMC (n = 48) obtained during the early, mid and end of summer. Linear regression modelling was applied to uncover the feasibility of a landscape model for VMC and SMC, the role of vegetation type models (i.e. native forest, grasslands and shrubland) to enhance predictive capacity and the seasonal variation in the relationships between satellite predictors and VMC and SMC. Results revealed a significant but weak relationship between VMC and predictors at landscape level (R2 = 0.30, RMSEcv = 69.9 %) with S2_NDMI and vegetation cover type being the only significant predictors. The relationship improves in vegetation type models for grasslands (R2 = 0.35, RMSEcv = 95.0 % with S2_NDVI) and shrublands conditions (R2 = 0.52, RMSEcv = 45.3 %). A model incorporating S2_NDVI and S1_VV explained 52 % of the variation in VMC in shrublands. The relationship between SMC and satellite predictors at the landscape level was also weak, with only the S2_NDMI and vegetation cover type exhibiting a significant relationship (R2 = 0.28, RMSEcv = 18.9 %). Vegetation type models found significant associations with SMC only in shrublands (R2 = 0.31, RMSEcv = 9.03 %) based on the S2_NDMI and S1_VV/VH ratio. The seasonal analysis revealed however that predictors associated to VMC and SMC may vary over the summer. The relationships with VMC were stronger in the early summer (R2 = 0.31, RMSEcv = 90.1 %; based on S2_NDMI) and mid (R2 = 0.37, RMSEcv = 70.8 %; based on S2_NDVI), butnon-significant in the end of summer. Similar pattern was found for SMC, where the link with predictors decreases from the early summer (R2 = 0.33, RMSEcv = 16.0 %; based on S1_VH) and mid summer (R2 = 0.30, RMSEcv = 17.8 %; based on S2_NDMI) to the end of summer (non-significant). Overall, the hypothesis of a universal landscape model for VMC and SMC was not fully supported. Vegetation type models showed promise, particularly for VMC in shrubland conditions. Sentinel optical and radar data were the most significant predictors in all models, despite the inclusion of ancillary predictors. S2_NDVI, S2_NDMI, S1_VV and S1_VV/VH ratio were the most relevant predictors for VMC and, to a lesser extent, SMC. Future research should quantify misregistration effects using plot vs. moving window values for the satellite predictors, consider meteorological control factors, and enhance sampling to overcome a main limitation of our study, small sample size.

Enhancing precision of root-zone soil moisture content prediction in a kiwifruit orchard using UAV multi-spectral image features and ensemble learning

Accurate and real-time monitoring of soil moisture content (SMC) is of utmost importance for effective field irrigation and maximizing crop water productivity. However, a comprehensive investigation into the inversion study for determining suitable combinations of unmanned aerial vehicle (UAV) image features and enhancing the precision of SMC model prediction has yet to be fully validated within a kiwifruit orchard setting. This study addresses this gap by employing a pre-processing method and an optimal band combination algorithm to assess the impact of various combinations of kiwifruit canopy reflectance and fraction vegetation coverage (FVC) features on the sensitivity of root-zone SMC. Furthermore, an optimal ensemble learning (EL) framework was developed to monitor SMC at various root-zone depths (0–10 cm [SMC10], 0–20 cm [SMC20], 0–30 cm [SMC30], 0–40 cm [SMC40], 0–50 cm [SMC50], 0–60 cm [SMC60]). The key findings of this research highlight the successful derivation of 10 wavebands and FVC features, exhibiting a strong correlation with SMC at different root depths. The gradient boosting (GBDT) model demonstrated the exceptional accuracy in estimating SMC10, with an impressive R2 value of 0.963 ± 0.030 and low RMSE values of 0.238 ± 0.111. Similarly, the eXtreme Gradient Boosting (XGBoost) model outperformed in estimating SMC20 to SMC60, with R2 and RMSE values of 0.963 ± 0.024 and 0.117 ± 0.053, respectively. Additionally, the utilization of the optimal EL model allows for digital mapping of SMC at different depths across fruit growth stages, showcasing superior adaptability for SMC30 to SMC60 (with R2 and RMSE of 0.782 ± 0.090 and 0.037 ± 0.011) compared to SMC10 and SMC20 (with R2 and RMSE of 0.765 ± 0.097 and 0.056 ± 0.024). These results underscore the potential of the EL estimation framework in characterizing the spatial distribution of root-zone SMC at the individual kiwifruit plant level.

Species-specific prediction model of wood moisture content based on electromagnetic wave propagation time

Laboratory and field experiments were performed to examine the feasibility of using Time Domain Reflectometry (TDR) to monitor moisture content (MC) of wood and standing trees. The TDR was used to detect the electromagnetic wave propagation time of four tree species (Betula platyphylla, Tilia tuan, Picea asperata, and Fraxinus mandshurica) at different MCs. During the TDR test, effects of probe insertion depths on MC predictive accuracy were considered. The best results were obtained at an insertion depth of 8 cm. At the selective 8 cm insertion depth, a species-specific MC prediction model (0.94 ≤ R2 ≤ 0.98), a generalized model for the four species (R2 = 0.65), and a hybrid model for the species with similar densities (0.80 ≤ R2 ≤ 0.96) were constructed, respectively. Overall, the species-specific MC prediction model showed good predictive ability for both tree and wood disc samples, including that TDR can be used to detect wood and standing tree MC. If possible, the hybrid model can be used for species with similar density.

Study on Acoustic–Electric Response Characteristics of Unsaturated Loess under Different Moisture Content

In order to study the characteristics of P-wave velocity and resistivity of loess with different moisture contents, low-field nuclear magnetic resonance, resistivity, and P-wave velocity tests were carried out on loess samples with 11 different moisture contents. The test results show that under the condition of the same dry density, the water in loess exists in two forms: bound water and free water. With the increase in moisture content, the water porosity of loess increases, the proportion of free water increases, and the resistivity gradually decreases and then tends to be stable, showing a power function relationship with moisture content. When the moisture content is less than 20%, the P-wave velocity decreases with the increase in the moisture content. In comparison, when the moisture content is greater than 20%, the wave velocity increases with the increase in the moisture content. A modified relation between wave velocity and moisture content and saturation is put forward, and the relationship expression between wave velocity and resistivity of loess is established. Finally, the reliability is verified by experimental data. The research results have a certain guiding significance for real-time monitoring of loess moisture content and engineering stability analysis in the loess area.

Model moisture transport in oil‐paper insulation of transformer: Theory and experiment

AbstractThe insulation performance of oil paper insulation is significantly affected by moisture such that monitoring moisture content is important. However, it is difficult to obtain the moisture content accurately due to its dynamic change under multi physical fields. Studying the moisture transportation in oil paper insulation under multi physical fields becomes a vital method in solving the problem. A multi physical model describing moisture migration was proposed, which considered the effects of temperature on moisture in different existing states in oil‐immersed paper (OIP). The temperature distribution formed a vapour pressure gradient to drive moisture migration and affected migration speed. Then, experiments and simulations of moisture migration were performed, which showed that the experiments were in good agreement with simulation. The results revealed that the temperature gradient caused uneven moisture distribution and the increased temperature reduced OIP moisture content. The established model could fully characterise moisture migration under temperature gradient, which provided a theoretical reference for predicting the risk of partial dampness and evaluating insulation performance.

Moisture Determination in CO2 Gas Cylinders for Application in Annulus Gas Monitoring Systems of Pressurized Heavy Water Reactors (PHWRs)

Background:: Critical monitoring of moisture content in annulus gas is required due to its detrimental effect on the pressure tube, leading to surface corrosion and finally the rupture of the pressure tube. Methods and Results:: The present work describes a simple and reliable thermo-analytical method for the quantitative determination of moisture in CO2 gas cylinders, using anhydrous barium chloride as the novel adsorbent material and simultaneous. Thermogravimetry - Differential Thermal Analysis - Evolved Gas Analysis (Fourier Transformed InfraRed Spectroscopy) [TG-DTA-EGA(FTIR)] as determination techniques. Conclusion:: Conclusion: Differential Scanning Calorimetry (DSC) coupled with Microscopy has been used to see the surface morphological variations during the heat treatment. The results of these studies are being discussed here.

Online monitoring system of material moisture content based on the Kalman filter fusion algorithm in air-impingement dryer

A Kalman filter fusion algorithm was proposed, and an online monitoring system was developed for real-time monitoring of the moisture content of materials in an air-impingement dryer. The Kalman filter algorithm was used to estimate the optimal state of the original detection values of the weighting sensor and air velocity sensor. A backpropagation (BP) neural network fusion model was established, where the weight detection value, elastic substrate temperature, air velocity, and impingement distance were considered inputs and the real weight of the material was the output. The optimal topology of the BP neural network was selected, and the initial weights and thresholds of the BP neural network were optimized using a genetic algorithm. The coefficient of determination (R2) and root mean square error (RMSE) of the optimized BP neural network fusion model were 0.9995 and 4.9, respectively. The Kalman filter fusion algorithm, which can realize online monitoring of moisture content, was established using the Kalman filter algorithm and fusion model. Moreover, an online monitoring system for material moisture content was developed, validation experiments were carried out, and the R2 and RMSE of the nine sets of validation experiments were 0.9963 and 0.78, respectively. The monitoring system satisfied the requirements of material moisture content detection accuracy in the drying process. The developed monitoring system is greatly important for improving the automation level of the drying equipment for fruits and vegetables. The proposed Kalman filter fusion algorithm also provides a reference for other multifactor fusion detection.

More Precise Monitoring of Soil Moisture Content in Agricultural Fields by Upscaling Conversion of Multispectral Image Data From Unmanned Aerial Vehicles

More Precise Monitoring of Soil Moisture Content in Agricultural Fields by Upscaling Conversion of Multispectral Image Data From Unmanned Aerial Vehicles

RESULTS OF USING SPECTRORADIOMETERS FOR IN SOIL MOISTURE OF MONGOLIAN STEPPE ECOSYSTEM

Abstract. Determining soil moisture remains an important issue in environmental, agricultural, and crop fields. The use of remote sensing data in the study of soil surface moisture has many benefits, such as: saving time, resources, and money, as well as monitoring soil condition (surface soil moisture). Based on point data from field measurements, can be used to obtain results of soil moisture. The importance of this study is highlighted by the useof spectral measurements and laboratory-determined moisture content of a variety of soils with different geographical conditions spread over three different natural zones, combined with satellite data. The purpose of this research is to determine and analyse surface soil moisture using Sentinel-2 satellite data and Spectro radiometric measurements. For this research area, four areas representative of forest steppe, steppe, and Gobi eco-regions of Mongolia were selected, field research was conducted, data were collected, and Sentinel-2 satellite data was processed and analysed. According to the results of the spectral reflectance measured during the field research, the high reflectance of the wet soil spectrum in the forest field, and the low reflectance of the spectrum representing the Gobi region, respectively, this create the possibility of studying soil moisture by spectral measurement and analysis. The relationship between the spectral reflectance of the red (665 nm) and near-violet (842 nm) light regions shows that the soil of the forest-steppe region is relatively moist compared to the soil of the Gobi region and that the Gobi soil is drier, and the field soil is drier, and these parameters are confirmed by laboratory analysis. A model developed from a linear relationship between field-measured spectrometer reflectance and laboratory-determined moisture provides a formula that can be used for regular monitoring of moisture content using the Normalized Difference Water Index (NDWI) estimated from satellite data. Surface soil moisture (%) = −44.133*NDWI − 11.553. The NDWI calculated from the spectroradiometer compared to the humidity determined in the laboratory had a linear correlation of R2=0.74 or 74%, and when comparing the humidity calculated from Sentinel-2, the agreement was high. In terms of space, the results calculated from satellite data show that the forest and steppe areas have relatively high humidity compared to the Gobi region, and the total area representing the Gobi region has a lower moisture content. Based on these results, it is considered that it is possible to continuously determine the amount of moisture from satellite data using NDWI (surface soil moisture) in a certain space-time range and use it for soil control and monitoring.

Loss-on-Drying Prediction for a Vibrated Fluidised Bed Dryer by Means of Mass and Energy Balances

PurposeContinuous wet granulation and drying require an adequate process control strategy to ensure the product quality. The most important critical quality attributes of dried granules are the granule size distribution and moisture content. Process analytical technologies (PATs) are available for real-time monitoring of moisture content by, e.g., near-infrared spectroscopy (NIRS), which requires additional installation and complex multivariate validation. Thus, a mass and energy balance (MEB) was derived for a vibrated fluidised bed dryer, which is part of the QbCon® 1 intended for continuous wet granulation and drying.MethodProcess parameters that are frequently logged were used for the derivation of a MEB. The predicted MEB was compared with the measured loss-on-drying (LOD) for two different formulations.ResultsThe model-derived data were in good agreement with the observed LOD, leading to RMSE values of 0.12–0.45.ConclusionThe implemented MEB can predict the LOD over time and thus might be suitable as a soft sensor without the installation of additional sensors. The obtained energy flux gives insight into the heat transfer, and the derived energy balance might be used to determine the required energy under certain drying conditions.

Inversion method for soil moisture content based on a distributed fiber optic acoustic sensing system.

The traditional measurement method can't achieve real-time monitoring of soil moisture content (SMC) within a two-dimensional area. To solve the above problems, we propose a rapid SMC monitoring method for two-dimensional area based on distributed acoustic sensing (DAS). DAS demodulates the backward Rayleigh scattering signal containing seismic wave sound velocity information from the active seismic source. The folding ruler approximation is employed to calculate the sound velocity of the soil, which is then inverted to determine the soil moisture content. The experiment measured the soil within a two-dimensional area formed by the seismic source and the acoustic sensing optical cable. The sensing optical cable and the active seismic source are organized into a two-dimensional area and the measurement range is 3 × 10 m with 33 points. The SMC ranges from 15% to 40%. The experiment shows that the absolute error between the measured values obtained by DAS and the water cut meter is 7%. This experiment verifies the feasibility of using the Rayleigh scattering properties to invert SMC and provides a new method for real-time monitoring of SMC in a large area.

Investigation of Dielectric Measurement Model for Coconut Fiber Water Content and the Associated Factors

Coconut fiber has excellent permeability and water retention properties, making it a popular choice as an organic substrate for cultivation in the field of facility agriculture. To investigate the dielectric properties of coconut fiber and develop an accurate measurement model for dielectric water content, a vector network analyzer was used to measure the relative dielectric permittivity of coconut fiber and study the dielectric properties of coconut fiber under different volumetric water contents, temperatures, and salinities. The results indicated that the relative dielectric permittivity of the coconut fiber increased with increasing volumetric water content. The optimal frequency range for measuring the water content of the coconut fiber was 200 MHz–3 GHz, with an optimal frequency of 210 MHz. The dielectric measurement model for the water content in the coconut fiber established at this frequency demonstrated good applicability, with an R2 of 0.9704 and RMSE of 0.0453 cm3/cm3. At the same moisture level (θV of approximately 30% and 50%), the error in the real part of the dielectric permittivity was less than 1.35 between 15 and 35 °C, and the error in the moisture content was less than 0.0339 cm3/cm3. The error due to the salinity was 0.0750 cm3/cm3. The experimental results showed that the modeling of the water content measurement of coconut fiber at 210 MHz can be applied to general greenhouse environmental conditions. This study serves as a valuable reference for calibrating the moisture monitoring sensors used in greenhouses for coir cultivation substrates, emphasizing the need for non-destructive, real-time monitoring of moisture content.

Performance evaluation of NIR spectrophotometer simulating in-line acquisition for moisture content prediction of woodchips and comparison with hand-held NIR spectrophotometer

The European Green Deal, together with the current political and economic background, aims at improving the use of renewable energy resources rather than fossil fuel dependence. Woodchip could be considered as a valid and widely available alternative, but the high heterogeneity of its properties must be evaluated according to the important influence in each step of the supply chain to combustion process. Moisture content is the main parameter involved in the quality characterization because it influences the energetic (it reduces the biomass calorific value in combustion), the management (during harvesting, handling, and storage steps), and the economic (price based on the load, affected by bulk density) aspects of woodchip utilization. Continuous monitoring of moisture content results essential, but laboratory analyses are time-consuming, destructive, and need economic efforts in terms of instruments and skilled personnel. Near infrared Spectroscopy (NIRS) is a fast, friendly-use, and alternative method to qualitative characterization, based on non-destructive interaction between wavelength and chemical components. So, the aim of this project is to evaluate the prediction performance of moisture content using a portable NIRS adapted to in-line utilization. Then, the results have been compared with the already implemented prediction models of a different portable NIRS, applying statistics to assess the industrial woodchip quality, but also the Tukey’s Test method to evaluate the replicates effect. Considering the general consistency of industrial woodchip quality and the use of three different replicates approach, results show good performance ranging from 0.64 to 0.86 as determination coefficient, with errors of prediction from 4.44 to 2.88%, confirmed by a prediction bias below the 2.5% for more than 50% of the samples. Tukey’s Test shows that replicates adequately described samples variability thanks to constant and uniform analysis that dilutes the error. Furthermore, accepting a minimum error threshold, no investigation on replicates feasibility is needed, reducing time and economic efforts. In conclusion, the crucial demand of moisture control meets the several advantages of NIRS technique, with the potential in-line application to obtain real-time quality tracing and high-performance energy production.

Monitoring of carbon dioxide and equilibrium moisture content for early detection of physicochemical and morphological changes in soybeans stored in vertical silos

In the context of grain storage, impurities and soybeans defects in soybeans can significantly impact the equilibrium moisture content. This, cause moisture migration and heating of the stored product, leading to increased respiratory activity. Furthermore, temperature measurements within stored grain mass do not provide sufficient information for effective grain quality monitoring, primarily due to the grains excellent thermal insulating properties. To address this issue, we propose a different approach: monitoring the equilibrium moisture content and CO2 concentration as indicators of soybean respiration within the intergranular spaces of the stored grain mass. This study propose monitoring the CO2 concentration in the intergranular air along with environmental variables for early detection of physicochemical and morphological changes in soybeans stored in vertical silos using near infrared spectroscopy, X-ray diffraction and scanning electron microscopy. Thermogravimetry and spectrometry analyses revealed that the interrelationships among variables had a direct impact on soybean quality attributes. Specifically, the presence of soybeans with 5.2 % impurities led to an increased in respiration rates, resulting in a CO2 concentration of up to 5000 ppm and the consumption of up to 3.6 % of dry matter. Consequently, there were changes in the percentage of ash, proteins, fibers, and oils compositions. These findings highlight the potential for indirect assessments, enabling the prediction of physicochemical quality and contamination of soybeans stored in vertical silos through continuous monitoring of CO2 concentration and equilibrium moisture content.

Substrate moisture variations of extensive green roofs with different structural configurations in a semi-arid region: Observational data and dynamic simulation

Substrate moisture condition is crucial for hydrological performance, water management and plant survival of green roofs. To date, the behaviour of long-term substrate moisture of green roofs in various climate regions has not been well understood. In this study, 24 months continuous field monitoring of substrate moisture content within nine extensive green roof modules with varies structural configurations was conducted to investigate the impacts of structural configurations and meteorological factors on substrate moisture content, and identify the key parameters for simulating the long-term substrate moisture dynamics. Results showed that green roofs planted with SedumSpectabilehave the highest mean volumetricwater content (VWC) (0.12–0.15 m3/m3) compared to other plant species considered. Green roofs with 5-cm substrate depth had lower mean VWC (0.07–0.08 m3/m3) than other substrate depths. The average substrate moisture loss rate of green roofs in summer and autumn seasons was 1.35 and 1.22 mm/day, respectively, which was higher than the spring (0.73 mm/day) and winter (0.64 mm/day) seasons for 2021. There were significant differences in mean VWC of green roofs among substrate material types. The mean VWC of green roofs was gradually enhanced from 0.12 to 0.14 m3/m3 as the substrate depth increased from 5 cm to 15 cm. The differences of mean VWC among vegetation types were all extremely significant (p < 0.001). The relative humidity was the most significant influencing factor on the average VWC level (correlation coefficient r = 0.42), with antecedent dry weather period (ADWP) had less significant effects on substrate VWC (r = 0.33), and followed by precipitation depth (r = 0.27). However, there was no significant relationship between air temperature and average VWC level of green roofs as well as total solar radiation. Nash-Sutcliffe efficient (NSE) values of the model simulations ranged from 0.70 to 0.81, and the mean absolute relative error (MAE) values ranged from 1.25 to 2.36, which indicated the model can adequately simulate the long-term substrate moisture content dynamics of green roofs. The crop coefficient and maximum water holding capacity of the substrate were the key parameters affecting long-term moisture content simulation of green roofs. These results could provide scientific suggestions for selecting suitable structural configuration to enhance water holding capacity of extensive green roofs in arid climates.

Effects of biochar on soil evaporation and moisture content and the associated mechanisms

High soil evaporation levels are a major contributor to loss of soil moisture in arid and semiarid regions globally. Therefore, it is important to use effective measures to slow the evaporation from farmland soils. We applied various amounts of straw biochar (BC) in a soil column experiment and a field experiment to study the influence of BC on soil evaporation and moisture content, respectively, to improve the water use efficiency of cultivated soil in arid areas. The addition of BC reduced soil evaporation and delayed water loss from the soil by evaporation. In the field experiment, cumulative evaporation in the treatments declined by 9.58% (Bo-10), 10.95% (Bo-30), and 4.2% (Bo-50) compared with that in the control group, demonstrating that 30 t/hm2 BC is the most effective at suppressing soil evaporation. BC also delayed the time required for the soil moisture content to drop to field capacity and increased the upward transport of water from the deeper soil layers at night. Data from continuous monitoring of moisture content for 3 days during each growth period revealed that the increases in moisture replenishment were 18.52–79.62% at the seedling stage, 55.81–202.38% at the jointing stage, 270.83–587.5% at the tassel stage, and 6.66–61.64% at the maturation stage; hence, BC was shown to work best at the tassel stage.

Stability of loess high-fill slope based on monitored soil moisture changes

This paper aims to assess the influence of moisture content changes during rainfall on the stability of loess high-fill slopes by taking a loess high-fill slope in Lanzhou City as an example. First, according to the moisture content monitoring data collected from a slope online monitoring system, direct shear tests were performed on soil samples of different moisture content to determine the relationship between the shear strength parameters and moisture content. Next, a coupled hydro-mechanical model with soil shear strength related to moisture content was established and used to analyze 16 working conditions with various rainfall intensities and durations for two cases: shear strength parameters from the site exploration report and those from this study. Finally, the results from the two cases were compared regarding the changes in stresses and displacements after rainfall infiltration to analyze the influence of moisture content on the stability of loess high-fill slopes. The conclusions are as follows: (1) Segmental relationship equations of cohesion and angle of internal friction were established for loess with various moisture content. (2) Under the conditions of different rainfall intensities, significant differences were observed in the trends of slope stress and displacement changes. (3) The slope displacement occurred in the shallow soil layer within about 12.5 m from the slope top, and the plastic strain concentrated in the soil layer within about 6 m from the slope top. (4) The results of slope stability analyses based on moisture content monitoring data are more in line with the observed.

Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management

Monitoring the fuel moisture content (FMC) of 10 h dead vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations, numerical weather prediction (NWP) models, and satellite retrievals has facilitated the development of machine learning (ML) models to estimate 10 h dead FMC retrievals over the contiguous US (CONUS). In this study, ML models were trained using variables from the National Water Model, the High-Resolution Rapid Refresh (HRRR) NWP model, and static surface properties, along with surface reflectances and land surface temperature (LST) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the Suomi-NPP satellite system. Extensive hyper-parameter optimization resulted in skillful FMC models compared to a daily climatography RMSE (+44%) and an hourly climatography RMSE (+24%). Notably, VIIRS retrievals played a significant role as predictors for estimating 10 h dead FMC, demonstrating their importance as a group due to their high band correlation. Conversely, individual predictors within the HRRR group exhibited relatively high importance according to explainability techniques. Removing both HRRR and VIIRS retrievals as model inputs led to a significant decline in performance, particularly with worse RMSE values when excluding VIIRS retrievals. The importance of the VIIRS predictor group reinforces the dynamic relationship between 10 h dead fuel, the atmosphere, and soil moisture. These findings underscore the significance of selecting appropriate data sources when utilizing ML models for FMC prediction. VIIRS retrievals, in combination with selected HRRR variables, emerge as critical components in achieving skillful FMC estimates.

Early detection of quality alterations in corn grains stored in vertical prototype silos using real-time monitoring of carbon dioxide and equilibrium moisture content

Early detection of the deterioration of grain stored in vertical silos would benefit food safety in these systems. Real-time monitoring of the intergranular variables - temperature, relative humidity, carbon dioxide and moisture content – was used to determine equilibrium moisture content and dry matter loss in corn stored in vertical prototype silos. Quantitative relationships were established for changes in quality variables – germination rate, apparent specific mass, starch content, protein solubility, pasting metrics, lipid activity and mycotoxin contamination. The results demonstrate that integrated monitoring of the equilibrium moisture content and carbon dioxide concentration in the stored corn mass can inform decision making for ideal storage time, loss reduction and food safety.

Moisture content monitoring of cigar leaves during drying based on a Convolutional Neural Network

Moisture content monitoring of cigar leaves during drying based on a Convolutional Neural Network