Spatial Distribution Of Moisture Research Articles

Rapid prediction and visualization of safe moisture content in alfalfa seeds based on multispectral imaging technology

Moisture significantly impacts seed sales, storage, and processing. Traditional moisture testing methods are often slow, labor-intensive, and inadequate for the rapid detection demands of modern agriculture, particularly for non-destructive testing of individual seeds. This study applied multispectral imaging to obtain morphological and spectral data from alfalfa seeds at six moisture levels (4 %, 8 %, 12 %, 16 %, 25 %, and 41 %). By integrating algorithms such as Support Vector Machines (SVM), Random Forests (RF), Linear Discriminant Analysis (LDA), Back Propagation Neural Network (BPNN), and normalized typical discriminant analysis (nCDA) algorithms, classification models were developed to distinguish between safe and unsafe moisture levels. The Results indicated that spectral data alone significantly improved model accuracy and prediction. nCDA visualizations effectively illustrated spatial moisture distribution, highlighting stark color differences between seeds in the safe moisture range (4 %, 8 %, 12 %) and those in the unsafe range (16 %, 25 %, 41 %). BPNN exhibited high model precision, achieving a recognition accuracy rate of 90.1 % for safe and unsafe moisture content. Key wavelengths identified by the Permutation method included 970, 880, 570, and 490 nm. Pearson correlation analysis showed a significant positive correlation between germination indicators and spectral data, which strengthened with longer seed storage. These findings confirm the potential of multispectral imaging for assessing the safe moisture content of alfalfa seeds, supporting the development of detection systems for evaluating moisture content in individual seeds. This advancement enables the rapid removal of high-moisture seeds, preventing deterioration during storage.

Development of FE modeling for hybrid greenhouse dryer for potato chips drying.

In this communication, a combination of heat and mass transfer model was developed using finite element (FE) model to explain the drying performance of the hybrid greenhouse dryer for potato chips. The hybrid greenhouse dryer is integrated with a single-pass solar air heater (SAH). A partial differential equation for a combined set of heat and mass transfer was numerically solved by the FE method. In order to see the spatial moisture distribution within the potato sample, a 3-dimensional FE model was created, and moisture removal takes place from the surface during drying of the products. Lagrange triangle FEs of extremely small size and second-order geometry shape were employed for meshing the geometry of model. Time-dependent study was express the fluctuation in time interval of 0-5h. The developed model showed the maximum crop and ground temperature are 67.1 and 79.1°C, respectively. Moisture ratio in dry basis is reduced from 1 to 0.005 in 03h and remains constant at 0.005. Thus, average moisture ratio in dry basis was found as 0.18902. Drying efficiency for the hybrid greenhouse dryer found to be 20.52%, whereas thermal efficiency for SAH was found 54.53%. Relative humidity inside the drying chamber found to be 26.50% in hybrid greenhouse dryer. The predicted versus the experimental results observed that hybrid greenhouse dryer having moderate inside temperature is suitable for crop drying as well as ith sustaining the environmental balance, hybrid greenhouse proves to be most effective.

Imaging of unsaturated moisture flow inside cracked porous brick using electrical capacitance volume tomography

Concrete structures unavoidably suffer physical and chemical damage during service and hence easily crack. It is well-known that cracks in cement-based materials are preferential paths for the ingress of moisture and corrosive ions. Electrical capacitance volume tomography (ECVT) has previously been shown to be a powerful tool for imaging the spatial moisture distribution within cement-based materials. This paper investigated whether ECVT can be used for monitoring three-dimensional (3D) water penetration into porous materials containing different cracks. ECVT sensor with top electrodes and reconstruction algorithms were developed and evaluated respectively to improve the spatial resolution. The process of moisture ingress into bricks with different cracks was successfully visualized by ECVT, and the reconstructed results are in good agreement with the known properties of unsaturated water transport in porous materials. The ECVT reconstructions were also compared with moisture flow simulations, showing good agreement. Results suggest that ECVT is a viable tool for imaging 3D water penetration around cracks in porous materials, even for quantifying spatial water distribution.

An Investigation into the Spatial Distribution of Moisture in Freeze-Dried Products Using NIR Spectroscopy and Chemical Imaging.

Near-infrared (NIR) spectroscopy (NIRS) is a widely accepted method of measuring moisture in pharmaceutical freeze-dried products, both during the process and in the finished products. Multiple NIR measurement approaches have been introduced to monitor product moisture in freeze-dried vials. However, the spatial moisture gradients within a vial have not been investigated in depth. Like any other point-focused process analytical technology (PAT) tool, a spectrum produced by NIRS represents an average over a given area of the product vial. Implementing a point-focused NIR on any random position without proper understanding of spatial moisture variations within the vial may severely impact the reliability of the results. The present work focuses on understanding the moisture distribution within freeze-dried vials. We performed an investigation using NIR tools, NIR chemical imaging (NIR-CI), and NIRS to understand the spatial variations in moisture on the outer surface (i.e., periphery) of the freeze-dried vials. To achieve this, the moisture distribution within individual vials was mapped using NIR images. Then, NIRS was used to determine the necessity of using multiple measurement points to produce robust models quantifying the moisture inside freeze-dried products. Overall, the results show a simplified version of the phenomenon in which non-homogenous distribution of moisture, as well as the non-uniform drying front, occur within the vials. The findings from the NIRS-based partial least squares (PLS) models indicate that to achieve reliable product/process information, measurements must be drawn from multiple measurement points on the surface of the freeze-dried products.

Spatial variability of soil moisture in a mining subsidence area of northwest China

The current study investigated the impact of coal mining on deep soil moisture from the perspective of the absolute value of soil moisture. A combined classical statistics and multi-dimensional geo-statistics approach was employed to analyze the temporal and spatial changes in soil moisture from 0 to 10 m in the mining face of the Nalin River No. 2 Mine in Northwest China from the perspective of spatial variability. The results of the study show that compared with the control area, the average value of soil moisture in 1- and 2-year subsidence areas decreased by 1.18% and 0.96%, respectively, whereas the coefficient of variation increased by 17.92% and 3.63%, respectively. Interpolation of soil moisture spatial distribution results showed that the spatial variability of soil moisture in the control area was less than that in the subsidence areas, and the spatial variability of soil moisture in the 2-year subsidence area was less than that in the 1-year subsidence area, indicating that mining increases the spatial variability of soil moisture and that the degree of spatial variability of soil moisture decreases as the subsidence enters the stable period. These results provide evidence for the mechanism by which coal mining subsidence affects soil moisture. Preferential flow caused by surface cracks, soil texture, the soil pore microstructure, and other factors in the coal mining subsidence area are the primary drivers of the increase in spatial variability of soil moisture.

An accurate and effective single-seed moisture selection method based on nuclear magnetic resonance (NMR) in maize

Kernel moisture content (KMC) is important for maize grain development, harvest, and storage. Breeders always consider KMC as a necessary indicator when conducting breeding programs. The fact that a large number of samples are required for the typical breeding process necessitates the development of accurate, high-throughput, and non-destructive detection techniques for KMC. Here, we proposed an effective method based on nuclear magnetic resonance (NMR) spectroscopy to track moisture variation in maize kernels and select offspring with low KMC at harvest. Both moisture mass and content could be accurately measured via regression analysis (R2 = 0.9924 and 0.9139, respectively). Magnetic resonance imaging technology was also applied to monitor the spatial distribution of moisture in individual kernels and ears. On the other hand, with two generations of KMC selection experiments, we demonstrated the effectiveness of the proposed method. KMC was measured for self-crossing kernels of two elite hybrids, namely Zhengdan958 and Xianyu335, and partial kernels were classified into a high-kernel-moisture group (HMG) or low-kernel-moisture group (LMG). The progenies were then selected for kernels having a high KMC in HMG and low KMC in LMG. After selection, KMCs in LMG were significantly lower than those in the HMG in both hybrids at generations F2 and F3. Finally, phenotypic response surveys revealed that KMC selection significantly affected the flowering time. Our results demonstrate the effectiveness of the NMR-based platform for optimizing KMC trait, with potential applications in maize breeding programs.

Multistatic Uniform Diffraction Tomography Derived Structural-Prior in Bayesian Inversion Framework for Microwave Tomography

In this work, a quantitative Bayesian inversion framework for microwave tomography (MWT) is coupled with a multistatic uniform diffraction tomography (MUDT) method to improve the imaging quality. The method is applied for an industrial use-case of MWT in which we estimate the 2-D spatial distribution of moisture (in terms of dielectric constant) in a polymer foam. In essence, we modify the prior information in the single-frequency Bayesian inversion framework using high-resolution complementary structural information of the imaging domain from a qualitative approach MUDT utilizing broadband frequency-domain data. This way of obtaining structural prior information is effective as it utilizes the data from the same microwave sensor setup in contrast to the frequency-hopping approach, priors derived for other imaging modalities or radar-based techniques with the co-located sensor using, for example, uniform diffraction tomography (UDT) inversion framework. Proposed algorithm performance is tested for different moisture scenarios in the polymer foam with 3D numerical and experimental data from our developed MWT system. It is shown that the proposed approach significantly improves the reconstruction accuracy for the considered cases over just using the Bayesian inversion approach.

Quantifying the effect of surface heterogeneity on soil moisture across regions and surface characteristic

Modeling soil moisture (SM) spatial distribution is important for hydrological forecasting, runoff estimation and catchment management and other applications. The objective of this study was to quantify the effect of surface heterogeneity including vegetation, soil, topography etc. on SM at the regional and surface characteristics classes (SCC) scales. Landsat 8 images, ASTER digital elevation model, land cover map, climatic data, and SM measured at 148 locations in the Balikhli-Chay catchment of Iran were used. Greenness, brightness, wetness, and land surface temperature are the surface biophysical characteristics and the topographical characteristics including elevation, slope, aspect, Topographic Wetness Index (TWI) and solar local incident angle calculated using Tasseled Cap Transformation, Single Channel algorithm used in this study. The Triangular method and the bootstrapping model were used to model SM and mitigate prediction uncertainty. Correlation coefficient (r) and Root Mean Square Error (RMSE) between the modeled and measured SM values were used to evaluate the modeled SM. The mean r and RMSE between the modeled and measured SM for different months were 0.83 and 1.84 (volumetric percentage), respectively. The degree of heterogeneity of the spatial distribution of SM in different classes of surface biophysical and topographical characteristics was different. The impact of surface heterogeneity on SM varied across regional and SCC scales. The mean r values between SM and surface biophysical and topographical characteristics in the regional and SCC strategies for different months were 0.41 and 0.54, respectively. Using the SCC scale instead of the regional scale heterogeneity information can increase the accuracy of SM modeling.

Effect of mulching on spatial moisture distribution pattern in Brinjal

Effect of mulching on spatial moisture distribution pattern in Brinjal

Effects of combined pulsed electric field and blanching pretreatment on the physiochemical properties of French fries

The aim of this work was to explore the effect of combining pulsed electric field (PEF) with blanching pretreatment on the physiochemical properties of French fries. The effects of PEF plus blanching pretreatment on the color parameters, texture, content and spatial distribution of moisture and oil, microstructure, colony count and acrylamide content of French fries were investigated. Results showed that the pretreated French fries were brighter (L value was 60.44) and golden yellow, with lower hardness (312.27 g), higher springiness (0.81) and higher chewiness (119.71). In addition, moisture and oil contents of the fries were reduced, with more uniform distributions. The cross-section microstructure of these French fries also showed near-complete gelatinization of starch particles, and the internal structure of fries was fluffy. Finally, French fries obtained by the combined pretreatment had an improved food safety profile, with a lower colony count (13 CFU/g) and acrylamide content (1533.75 ng/g). Industrial relevanceBlanching is a traditional thermal processing technology widely used in the field of fried potatoes. However, blanching has some disadvantages such as slow heat conduction, long processing time, substantial loss of soluble nutrients, and huge water wastage. PEF is an emerging technology in the 21st century that can maintain the original flavor, color, taste, and nutritional value of foods treated by it. The French fries obtained by combining PEF and blanching pretreatment were brighter and golden yellow, and were also softer, and more springy and chewy – an overall net improvement in aesthetic appeal and organoleptic profile. The above findings may provide useful information to guide effective strategies for the practical application of PEF combined with blanching in the preparation of fried potato foods.

Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China

Temporal and spatial variability of soil moisture has an important impact on hydrological processes in mountainous areas. Understanding such variability requires soil moisture datasets at multiple temporal and spatial scales. Remote sensing is a very effective method to obtain surface (~5 cm depth) soil moisture at the regional scale but cannot directly measure soil moisture at deep soil layers (>5 cm depth) currently. This study chose the upstream of the Heihe River Watershed in the Qilian Mountain Ranges in Northwest China as the study area to estimate the profile soil moisture (0–70 cm depth) at the regional scale using satellite Vegetation Index (NDVI) and Land Surface Temperature (LST) products. The study area was divided into 31 zones according to the combination of altitude, vegetation and soil type. Long-term in situ soil moisture observation stations were set up at each of the zones. Soil moisture probe, ECH2O, was used to collect soil moisture at five layers (0–10, 10–20, 20–30, 30–50 and 50–70 cm) continuously. Multiple linear regression equations of time series MODIS (Moderate-resolution Imaging Spectroradiometer) NDVI, LST and soil moisture were developed for each of the five soil layers at the 31 zones to estimate the soil moisture (0–70 cm) on a regional scale with a spatial resolution of 1 km2 and a temporal resolution of 16-d from October, 2013 to September, 2016. The correlation coefficient R of the regression equations was between 0.47 and 0.94, the RMSE was 0.03, indicating that the estimation method based on the MODIS NDVI and LST data was suitable and could be applied to alpine mountainous areas with complex topography, soil and vegetation types. The overall pattern of soil moisture spatial distribution indicated that soil moisture was higher in the eastern region than in the western region, and the soil moisture content in the whole study area was 14.5%. The algorithm and results provide novel applications of remote sensing to support soil moisture data acquisition and hydrological research in mountainous areas.

Hyperspectral NIR time series imaging used as a new method for estimating the moisture content dynamics of thermally modified Scots pine

ABSTRACT The purpose of this research is to develop a method for estimating the spatially and temporally resolved moisture content of thermally modified Scots pine (Pinus sylvestris) using remote sensing. Hyperspectral time series imaging in the NIR wavelength region (953–2516 nm) was used to gather information about the absorbance of eight thermally modified pine samples each minute as they dried during a period of approximately 20 h. After preprocessing the collected spectral data and identifying an appropriate wavelength selection, partial least squares regression (PLS) was used to map the absorbance data of each pine sample to a distribution of moisture contents within the samples at different time steps during the drying process. To enable separate studying and comparison of the drying dynamics taking place within the early- and latewood regions of the pine samples, the collected images were spatially segmented to separate between early- and latewood pixels. The results of the study indicate that the 1966–2244 nm region of a NIR spectrum, when preprocessed with extended multiplicative scatter correction and first order derivation, can be used to model the average moisture content of thermally modified pine using PLS. The methods presented in this paper allows for estimation and visualization of the intrasample spatial distribution of moisture in thermally modified pine wood.

Comparison of environmental controls on soil moisture spatial patterns at mesoscales: Observational evidence from two regions in China

Soil moisture plays a vital role in terrestrial water cycles, which requires the understanding of the spatial characteristics of soil moisture patterns and their influencing factors across different scales; however, inconsistent findings have been reported in the past with regards to the relative importance of meteorological (e.g., precipitation and evapotranspiration) and local (e.g., soil texture) factors in controlling soil moisture spatial variability at mesoscales. To further elucidate this issue, long-term soil moisture data from East and Northwest China were examined in this study, using two statistical methods of temporal stability analysis (TSA) and empirical orthogonal function (EOF) analysis. The TSA results showed that the spatial distributions of mean relative difference (MRD; an indicator of the relative wetness condition at a location as compared to the areal average wetness condition) of soil moisture had statistically significant correlations with mean annual precipitation (P-) and soil texture in both study regions; however, the spatial correlation between MRD and P- was much stronger in the Northwest region, suggesting that precipitation played a more important role in determining the spatial distribution of soil moisture in Northwest China. Moreover, the EOF analysis revealed that the primary soil moisture spatial structure was mostly related with soil texture in the East region while with P- in the Northwest region, which was also consistent with the TSA results. The stronger impact of precipitation on the soil moisture spatial distribution in the Northwest region could be largely attributed to the greater spatial variability in P- that varied by ~4.6 times from 171.2 to 791.0 mm/year across the region. By comparison, in the East region characterized by lower spatial variability in P- that varied by ~2.6 times from 484.2 to 1236.0 mm/year, soil texture exerted a stronger impact on the primary soil moisture spatial structure. Therefore, the observational evidence from this study demonstrated that the relative importance of meteorological and local factors in controlling mesoscale soil moisture spatial variability might vary considerably across regions, primarily depending on the spatial variability in those influencing factors across the region under consideration.

Approaches for evaluation of relief morphometric characteristics influence on spatial distribution of moisture in the soils of steppe part of Crimea

Data on soil moisture reserves are the basis for decision-making in the agricultural boghara system, because it determines the development of agricultural crops potential, terms of top-dressing and additional fertilizing, and makes it possible to predict yield of agricultural crops. In this article the influence of relief morphometric characteristics on the distribution of precipitation over the territory was studied. The research area is the land of the eastern part of Klepininsky rural settlement of Krasnogvardeysky district, the central part of Crimean Peninsula. The article considers approaches, divided into 2 main categories (according to the type of data used), based on the use of GIS capabilities and remote sensing data, to analyze the soil water content (SWC) using the example of research area and relationship of this parameter to the terrain relief. It was established that the morphometric characteristics of relief affect the amount of soil moisture.

The Space–Time Distribution of Soil Water and Temperature of a Desert Ecosystem Using Spatio-Temporal Kriging and PCA Analysis

Straw checkerboard and vegetation restoration are used for soil and water conservation on this area. This study presents soil moisture and temperature spatial distribution in different seasons from the restoration sites using the geostatistical analysis expansion module of ArcGIS. The results suggested that the soil temperature shows an overall decreasing trend accompanied with the increasing of the depth in June and August; while, there was no obvious temperature differences among all the depths of September. In addition, the humidity in August and September increased with the increase of depth. However, the mean soil moisture of June did not follow any rules. The spatial distributions of soil temperature were high in the south of the survey area. Compared with the temperature, the spatial distribution of moisture shows a much larger variation. Principal component analysis was also used in this study. The results showed the high similarity of temperatures with different depths, while the soil moisture showed an obvious spatial differences between deep and top layers. Taking a shifting sand dune as the control area, we examined the effects of ecological restoration project on soil temperature and moisture of the study area. The data indicated a remarkable environmental improvement in the restoration area.

Pore-Structure-Based Determination of Unsaturated Hygric Properties of Porous Materials

A reliable and practical method of hygric property characterisation is a major determinant in properly analysing hygric performance of built constructions. The current empirical approach, however, yields data that are still incomplete, not fully representative, and not entirely reliable. In this study, hygric properties are therefore determined directly from pore structure information by applying stationary unsaturated pore-scale physics to model moisture storage and transport. The pore space is captured by visualisation techniques and further extracted into a discrete network of variably shaped pore bodies interconnected by pore throats. In this network, corner and surface adsorption and capillary condensation are simulated to define the moisture storage. Air entrapment is furthermore considered to determine the capillary moisture content. The resulting spatial moisture distribution in the pore network allows moisture to flow, in wet elements in the liquid phase and in dry elements in the vapour phase. The adsorbed corner and surface films moreover enable additional liquid flow. These various simultaneous flows aggregately define the transport property. A comparison to measured data validates the presented hygric property model.

Characterization of Soil Moisture Distribution and Movement Under the Influence of Watering-dewatering Using AHFO and BOTDA Technologies

ABSTRACT The infiltration and distribution of water through unsaturated soil determine its mechanical and hydrological properties. However, there are few methods that can accurately capture the spatial distribution of moisture inside soil. This study aims to demonstrate the use of actively heated fiber optic (AHFO) and Brillouin optical time domain analysis (BOTDA) technologies for monitoring soil moisture distribution as well as strain distribution. In addition to a laboratory model test, finite element analyses were conducted to interpret the measurements. During the experiment, the fine particle migration was also measured to understand its influence on soil hydraulic conductivity. The results of the experiment indicate that (i) for a soil that has never experienced a watering-dewatering cycle, water infiltration can be accurately calculated using the Richards’ equation; (ii) migration of fine soil particles caused by the watering-dewatering cycle significantly increases the hydraulic conductivity; and (iii) two critical zones (drainage and erosion) play significant roles in determining the overall hydraulic conductivity of the entire soil. This study provides a new method for monitoring the changes in soil moisture, soil strain, and hydraulic conductivity. The observations suggest that the effect of fine particles migration should be considered while evaluating soil moisture distribution and water movement.

Retrieval of Total Precipitable Water from Himawari-8 AHI Data: A Comparison of Random Forest, Extreme Gradient Boosting, and Deep Neural Network

Total precipitable water (TPW), a column of water vapor content in the atmosphere, provides information on the spatial distribution of moisture. The high-resolution TPW, together with atmospheric stability indices such as convective available potential energy (CAPE), is an effective indicator of severe weather phenomena in the pre-convective atmospheric condition. With the advent of high performing imaging instrument onboard geostationary satellites such as Advanced Himawari Imager (AHI) onboard Himawari-8 of Japan and Advanced Meteorological Imager (AMI) onboard GeoKompsat-2A of Korea, it is expected that unprecedented spatiotemporal resolution data (e.g., AMI plans to provide 2 km resolution data at every 2 min over the northeast part of East Asia) will be provided. To derive TPW from such high-resolution data in a timely fashion, an efficient algorithm is highly required. Here, machine learning approaches—random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN)—are assessed for the TPW retrieved from AHI over the clear sky in Northeast Asia area. For the training dataset, the nine infrared brightness temperatures (BT) of AHI (BT8 to 16 centered at 6.2, 6.9, 7.3, 8.6, 9.6, 10.4, 11.2, 12.4, and 13.3 μ m , respectively), six dual channel differences and observation conditions such as time, latitude, longitude, and satellite zenith angle for two years (September 2016 to August 2018) are used. The corresponding TPW is prepared by integrating the water vapor profiles from InterimEuropean Centre for Medium-Range Weather Forecasts Re-Analysis data (ERA-Interim). The algorithm performances are assessed using the ERA-Interim and radiosonde observations (RAOB) as the reference data. The results show that the DNN model performs better than RF and XGB with a correlation coefficient of 0.96, a mean bias of 0.90 mm, and a root mean square error (RMSE) of 4.65 mm when compared to the ERA-Interim. Similarly, DNN results in a correlation coefficient of 0.95, a mean bias of 1.25 mm, and an RMSE of 5.03 mm when compared to RAOB. Contributing variables to retrieve the TPW in each model and the spatial and temporal analysis of the retrieved TPW are carefully examined and discussed.

Micro-irrigation strategies to improve water-use efficiency of cherry trees in Northern China

Extensive irrigation strategies are commonly and widely used for orchards in Northern China, leading to low water-use efficiency (WUE) and potential fertilizer pollution of underground water. Effective micro-irrigation strategies for orchards remain unclear and require further investigation to assist in developing orchard water-saving irrigation systems. In this study, compared with local traditional furrow irrigation, six micro-irrigation strategies (with or without mulch film) were applied to a 6-year-old cherry (Prunus avium L.) field for three years (2015–2017). The moisture spatial distribution of soil profile, water consumption, yield, and fruit quality were measured to evaluate the effectiveness of different irrigation strategies on growth and WUE of cherry trees. The results showed that the strongest variability of moisture in soil profile was in the 0–60 cm soil layer, indicating the water uptake of cherry trees is closely related to the soil moisture content in this region. Compared with furrow irrigation, micro-irrigation strategies can significantly (P < 0.05) reduce the water consumption of cherry trees without reducing yield, and treatments with mulch film will further enhance this effect. Ring drip irrigation with mulch film and parallel drip irrigation with mulch film significantly (P < 0.05) improved WUE. Furthermore, parallel drip irrigation with mulch film at a designed wetting depth of 60 cm was suggested irrigation strategy for a 6-year-old cherry field in terms of WUE, yield, fruit quality, and maintenance cost. The findings of this study provide a typical reference for further research on water-saving irrigation systems of perennial fruit trees.

Effect of dryer fabric structure on the performance of contact paper drying

Existing measurement techniques have prevented extensive investigations of the effect of dryer fabric structure on contact drying of paper. Using a novel optical measurement method, the moisture content (MC) of paper can be accurately quantified at high spatial and temporal resolution while it is sandwiched between the heater surface and the dryer fabric. To study the paper drying process, an experimental setup is designed to simulate realistic conditions of a typical paper dryer while providing optical access for the measurement system. Ten commercially available fabrics manufactured by weaving synthetic filaments are used in the investigations. The 3D structure of the fabrics is characterized using optical coherence tomography (OCT). The fabrics are used in the experiments to investigate the effects of the filament structure and paper/fabric contact on the drying process. It is shown the fabric structure affects the drying rate and the drying time. Fabrics that have a relatively large drying rate at high paper MC may have a relatively small drying rate at low levels of MC. The contact area and 3D arrangement of the filaments have the greatest impact on the drying process. Adjacent filaments result in larger blocked regions of the paper surface, which reduces the drying rate. The spatial distribution of moisture as a function of time reveals that frequent rewetted spots appear during the drying. These rewetting spots are caused by reabsorption of water condensed on the fabric filaments.