Surface Soil Texture Research Articles

Effects of topographic and edaphic conditions on alpine plant species distribution along a slope gradient on Mount Norikura, central Japan

AbstractThis study examined the effects of topographic and edaphic conditions on alpine plant species distribution along a slope gradient on Mt. Norikura (3026 m a.s.l.), central Japan. Topographic and edaphic factors investigated at 40 plots were: slope inclination, ground surface texture, soil water content and soil inorganic nitrogen concentration (NO3‐N, NH4‐N). The topographic and edaphic factors changed with slope positions: slope inclination was steeper, soil texture was coarser, and soil water and inorganic nitrogen concentration decreased with increasing slope position. Five vegetation types were located along the slope gradient and related to two factor‐groups: (1) changes in soil water, NH4‐N, slope inclination along the slope gradient, and (2) ground surface texture. A tall herbaceous plant community developed at the low slope position, near tarns, with fine soil surface texture, high soil water and NH4‐N, while Dicentra peregrina dominated on an unstable rubble slope near the ridge top. The distribution of each species was predictable from the two factor‐groups. Although the five vegetation types were related to the two factor‐groups, responses to the two factor‐groups differed among species, even within the same vegetation type. Therefore, this study showed that the topography of the terrain largely regulated alpine plant distribution by affecting edaphic conditions, and that global warming may alter species composition by changing edaphic conditions.

Soil, snow, weather, and sub-surface storage data from a mountain catchment in the rain–snow transition zone

Abstract. A comprehensive hydroclimatic data set is presented for the 2011 water year to improve understanding of hydrologic processes in the rain–snow transition zone. This type of data set is extremely rare in scientific literature because of the quality and quantity of soil depth, soil texture, soil moisture, and soil temperature data. Standard meteorological and snow cover data for the entire 2011 water year are included, which include several rain-on-snow (ROS) events. Surface soil textures and soil depths from 57 points are presented as well as soil texture profiles from 14 points. Meteorological data include continuous hourly shielded, unshielded, and wind-corrected precipitation, wind speed and direction, air temperature, relative humidity, dew point temperature, and incoming solar and thermal radiation data. This data is often viewed as "forcing data", and is gap filled and serially complete. Sub-surface data included are hourly soil moisture data from multiple depths from seven soil profiles within the catchment, and soil temperatures from multiple depths from two soil profiles. Hydrologic response data include hourly stream discharge from the catchment outlet weir, continuous snow depths from one location, intermittent snow depths from 5 locations, and snow depth and density data from ten weekly snow surveys. Snow and hydrologic response data are meant to provide data on the catchment hydrologic response to the weather data. This data is mostly presented "as measured" although snow depths from one sensor and streamflow at the catchment outlet have been gap filled and are serially complete. Though the weather, snow, and hydrologic response data only covers one water year, the presentation of the additional subsurface data (soil depth, texture, moisture, and temperature) makes it one of the most detailed and complete hydro-climatic data sets from the climatically sensitive rain–snow transition zone. The data presented are appropriate for a wide range of modeling (energy balance snow modeling, soil capacitance parametric modeling, etc.) and descriptive studies. Data is available at doi:10.1594/PANGAEA.819837.

Calibration and Validation of the COSMOS Rover for Surface Soil Moisture Measurement

The COsmic‐ray Soil Moisture Observing System (COSMOS) rover may be useful for validating satellite‐based estimates of near‐surface soil moisture, but the accuracy with which the rover can measure 0‐ to 5‐cm soil moisture has not been previously determined. Our objectives were to calibrate and validate a COSMOS rover for mapping 0‐ to 5‐cm soil moisture at spatial scales suitable for evaluating satellite‐based soil moisture estimates. Region‐specific calibrations for the COSMOS rover were developed using field‐average soil moisture measured using impedance probes and the oven‐drying method on volumetric samples. The resulting calibrations were applied to map soil moisture on two dates in a 16‐ by 10‐km region around the Marena, Oklahoma, In Situ Sensor Testbed (MOISST) in north central Oklahoma and one date in a 34‐ by 14‐km region in the Little Washita River watershed in southwestern Oklahoma. The mapped soil moisture patterns were consistent with the regional spatial variability in surface soil texture and with soil wetting by an intervening rainfall. The rover measured field‐average soil moisture with a root mean squared difference (RMSD) of 0.03 cm3 cm−3 relative to impedance probes. Likewise, the regional‐average 0‐ to 5‐cm soil moisture determined by the rover was within ±0.03 cm3 cm−3 of the best available independent estimates for each region. These results demonstrate that a COSMOS rover can be used effectively for 0‐ to 5‐cm soil moisture mapping and for determining the average soil moisture at spatial scales suitable for satellite calibration and validation.

Rangeland hydrology and erosion model (RHEM) enhancements for applications on disturbed rangelands

AbstractThe rangeland hydrology and erosion model (RHEM) is a new process‐based model developed by the USDA Agricultural Research Service. RHEM was initially developed for functionally intact rangelands where concentrated flow erosion is minimal and most soil loss occurs by rain splash and sheet flow erosion processes. Disturbance such as fire or woody plant encroachment can amplify overland flow erosion by increasing the likelihood of concentrated flow formation. In this study, we enhanced RHEM applications on disturbed rangelands by using a new approach for the prediction and parameterization of concentrated flow erosion. The new approach was conceptualized based on observations and results of experimental studies on rangelands disturbed by fire and/or by tree encroachment. The sediment detachment rate for concentrated flow was calculated using soil erodibility and hydraulic (flow width and stream power) parameters. Concentrated flow width was calculated based on flow discharge and slope using an equation developed specifically for disturbed rangelands. Soil detachment was assumed to begin with concentrated flow initiation. A dynamic erodibility concept was applied where concentrated flow erodibility was set to decrease exponentially during a run‐off event because of declining sediment availability. Erodibility was estimated using an empirical parameterization equation as a function of vegetation cover and surface soil texture. A dynamic partial differential sediment continuity equation was used to model the total detachment rate of concentrated flow and rain splash and sheet flow. The enhanced version of the model was evaluated against rainfall simulation data for three different sites that exhibit some degree of disturbance by fire and/or by tree encroachment. The coefficient of determination (R2) and Nash–Sutcliffe efficiency were 0.78 and 0.71, respectively, which indicates the capability of the model using the new approach for predicting soil loss on disturbed rangeland. By using the new concentrated flow modelling approach, the model was enhanced to be a practical tool that utilizes readily available vegetation and soil data for quantifying erosion and assessing erosion risk following rangeland disturbance. Copyright © 2014 John Wiley & Sons, Ltd.

Digital Mapping of Soil Texture Using RADARSAT-2 Polarimetric Synthetic Aperture Radar Data

This study aimed to assess the contribution of RADARSAT-2 polarimetric synthetic aperture radar (SAR) data to digital mapping of soil surface texture of Rouville County, near Montreal, Canada. First, compositional data transformation using isometric log ratio (ilr) was applied to soil texture data to transpose the simplex into multidimensional real space, which is better suited to multivariate analysis. Thereafter, two assumptions on the relationships between RADARSAT-2 data and the ilr components were evaluated: (i) as linearly dependent by applying cokriging (CK) and regression kriging (RK); and (ii) as nonlinearly dependent by applying the e-insensitive and nonlinear support vector regression (SVR). The results were compared with ordinary kriging (OK). The environmental variables used to define the covariates were monopolarization SAR channels, the parameters extracted from entropy/anisotropy/ mean α polarimetric, Freeman and Durden, and Touzi decompositions. Using 283 soil samples for training and 89 for validation, the results showed that the root mean square error (RMSE) of prediction obtained by OK was 8% for silt, 13% for clay, and 13% for sand. The SVR produced the best prediction accuracy compared with the geostatistical interpolation techniques. Compared with OK, the improvement of the digital mapping accuracies (in terms of RMSE reduction) with SVR was significant: RMSE was reduced by 18% for sand, 17% for silt, and 35% for clay. It was followed by RK, with RMSE reduction ranging from 6 to 13%, and then CK (3–5%). Using SAR polarimetric data extracted from a RADARSAT-2 image as covariates was found to be very useful for digital mapping of soil surface texture.

Evaluation of Landsat TM5 Multispectral Data for Automated Mapping of Surface Soil Texture and Organic Matter in GIS

Mapping fine scale spatial variations of soil properties is important for site specific agriculture. The current study explores the potentials of remote sensing (RS) and geographical information system (GIS) techniques in studying the spatial variability of surface soil attributes. Around 170 surface (0–30 cm) soil samples collected from the soils of Shorkot Tehsil, Punjab, Pakistan were analyzed for surface soil texture and organic matter (O.M.). A multivariate linear regression (MLR) analysis technique was employed to relate surface soil variables with the spectral data from Landsat TM5 satellite. The MLR analysis showed significant (p<0.05) relationship of band 4 and band 6 with silt% (R2 = 0.724) and clay% (R2 = 0.509) while soil O.M. was best modeled using data from band 1, 6 and 7 (R2 = 0.545). The resulting MLR equations were then used for the spatial modeling of these attributes for the entire study area. For developing surface soil texture map, the USDA textural triangle limits for clay% and silt% were used to develop a code in Visual Basic Language in ArcGIS environment. The results showed that ‘sandy clay loam’ was the most abundant textural class in the area followed by ‘sandy loam’ and ‘clay loam’ classes. Moreover, the status of O.M. in the entire study area soils was very poor (<1%). The results indicate that RS and GIS techniques could be successfully used for fine scale mapping of soil texture and O.M. of a larger area.

Spatial assessment of soil quality indicators under different agricultural land uses

The physical, chemical and biological properties of a soil define soil quality which is fundamental for soil fertility. The objective of this study was to assess the spatial variability of these soil properties under four different agricultural land uses. The experiment was conducted at the Institute of Agricultural Research and Training,(IAR&T), Nigeria.The land uses evaluated were mono cropping system with cocoa, grazed land, fallow land and a mixed cropping system (horticulture). Samples were taken randomly at each location consisting of 25 georeferenced points each, and bulked to form 5 composite samples for each location.The result showed that the surface soil textures are loamy sand except for the cocoa plantation soils which was sand, all the soils showed alkalinity, ranging from a pHof 7.43 to 8.09. Total Nitrogen ranged from 1.16-1.81kg/ha, Available P ranged from 0.51-1.40 kg/ha while potassium ranged from 0.15-0.25 Cmol/kg. Average microbial biomass C was from 129.18 to 200.04μgCg -1 while the average microbial biomass N was from 12.74 to 19.84 μgNg- 1 . Soil surface maps generated indicate that the soils are deficient in characteristics required for optimal

Mapping of Airborne Particulate Matter Collected Using Two Sensors along US-Mexico Border

Particulate matter (PM) emissions from various sources can have significant effect on human health and environmental quality especially in the Chihuahuan Desert region along US-Mexico border. The objective of the study was to determine the effect of road dust texture and vehicular speed on airborne PM concentrations from different unpaved rural roads using two sampling techniques (DustTrak R , and Sticky-tape). The surface soil textures of unpaved roads varied from silty clay to loam with less than 4% soil moisture content at the time of PM measurement. Sticky tape method in seven experiment sites showed that PM ranged from 0.529 to 3.054 mg m -3 , and DustTrak R measurements showed that PM 2.5 concentration ranged from 1.11 to 37.1 mg m-3 at 1 m height. An exponential relationship was obtained between PM 2.5 by DustTrak R and vehicle speeds with an average slope of -1.619 mg m -3 s -1 . The concentration of PM measured with the Sticky-tape decreased with increasing height of measurement. Both PM measurement techniques provided a good approximation of PM emissions at different vehicles speeds, unpaved roads and position of the instrument above ground level for a variety of unpaved roads. The low cost sticky tape method has the potential to further determine and abiotic (elemental composition) and biotic (fungus) particles in airborne PM.

Spatial estimation of surface soil texture using remote sensing data

Understanding the spatial distribution and variability of soil texture is essential for land use planning and other activities related to agricultural management and environmental protection. This study was conducted to evaluate Landsat Enhanced Thematic Mapper (ETM) remote sensing data as auxiliary variables for spatial estimation of surface soil texture using a limited number of soil samples taken from a site located in the city of PingduShandong ProvinceChina. Three methods of evaluating variability in surface soil texture were evaluated: (1) multiple stepwise regression (MSR) based on the relationship between surface soil sandsilt and clay contents and remote sensing data; (2) kriging of surface soil sandsilt and clay contents; (3) cokriging with remote sensing data. Correlation analysis showed that surface soil sandsilt and clay contents were significantly correlated with Landsat ETM digital number (DN) of six bands (Bands 1–5 and Band 7)and the DN of Band 7 explained most of the variability in soil sandsilt and clay contents. The DN of Band 7 was selected as auxiliary data for the estimation of surface soil texture. The cross-validation results indicated that both MSR and kriged estimates had low reliability due to the variations in landscape and the low-density sampling in the study area. Cokriging with remote sensing data significantly improves estimates of surface soil texture compared with MSR and kriging.

Impact of surface roughness and soil texture on mineral dust emission fluxes modeling

Dust production models (DPM) used to estimate vertical fluxes of mineral dust aerosols over arid regions need accurate data on soil and surface properties. The Laboratoire Inter‐Universitaire des Systemes Atmospheriques (LISA) data set was developed for Northern Africa, the Middle East, and East Asia. This regional data set was built through dedicated field campaigns and include, among others, the aerodynamic roughness length, the smooth roughness length of the erodible fraction of the surface, and the dry (undisturbed) soil size distribution. Recently, satellite‐derived roughness length and high‐resolution soil texture data sets at the global scale have emerged and provide the opportunity for the use of advanced schemes in global models. This paper analyzes the behavior of the ERS satellite‐derived global roughness length and the State Soil Geographic data base‐Food and Agriculture Organization of the United Nations (STATSGO‐FAO) soil texture data set (based on wet techniques) using an advanced DPM in comparison to the LISA data set over Northern Africa and the Middle East. We explore the sensitivity of the drag partition scheme (a critical component of the DPM) and of the dust vertical fluxes (intensity and spatial patterns) to the roughness length and soil texture data sets. We also compare the use of the drag partition scheme to a widely used preferential source approach in global models. Idealized experiments with prescribed wind speeds show that the ERS and STATSGO‐FAO data sets provide realistic spatial patterns of dust emission and friction velocity thresholds in the region. Finally, we evaluate a dust transport model for the period of March to July 2011 with observed aerosol optical depths from Aerosol Robotic Network sites. Results show that ERS and STATSGO‐FAO provide realistic simulations in the region.

Storm-wise sediment yield prediction using hillslope erosion model in semi-arid abundant lands

Evaluation of soil erosion by existing models is needed as an important tool for managerial purposes in designation of proper water and soil conservation techniques. The present study aimed to assess the applicability of hillslope erosion model (HEM) as one of the newest erosion models for prediction of storm-wise sediment yield in Khosbijan rangeland with 20% slope steepness by using soil erosion standard plots. In order to run the model, runoff depth, land surface cover, soil texture, slope steepness and length were determined for 16 storm events. The results showed that the uncalibrated HEM did not simulate the observed sediment yields properly. Calibration of soil erodibility parameter and developing regression between observed and estimated data indicated that the model was capable of predicting sediment yield in plots by applying soil erodibility parameter of 0.15 with determination coefficient of 0.64 and estimate error of 40%.&amp;nbsp;

Concentrated flow erodibility for physically based erosion models: Temporal variability in disturbed and undisturbed rangelands

Current physically based overland flow erosion models for rangeland application do not separate disturbed and undisturbed conditions in modeling concentrated flow erosion. In this study, concentrated flow simulations on disturbed and undisturbed rangelands were used to estimate the erodibility and to evaluate the performance of linear and power law equations that describe the relationship between erosion rate and several hydraulic parameters. None of the hydraulic parameters consistently predicted the detachment capacity well for all sites, however, stream power performed better than most of other hydraulic parameters. Using power law functions did not improve the detachment relation with respect to that of the linear function. Concentrated flow erodibility increased significantly when a site was exposed to a disturbance such as fire or tree encroachment into sagebrush steppe. This study showed that burning increases erosion by amplifying the erosive power of overland flow through removing obstacles and by changing the soil properties affecting erodibility itself. However, the magnitude of fire impact varied among sites due to inherent differences in site characteristics and variability in burn severity. In most cases we observed concentrated flow erodibility had a high value at overland flow initiation and then started to decline with time due to reduction of sediment availability. Thus we developed an empirical function to predict erodibility variation within a runoff event as a function of cumulative unit discharge. Empirical equations were also developed to predict erodibility variation with time postdisturbance as a function of readily available vegetation cover and surface soil texture data.

Combine Empirical Equation with Experiment for Prediction of Soil Saturated Hydraulic Conductivity

In order to improve simulation precision of saturated hydraulic conductivity, use RETC and PTFs function to simulate saturated hydraulic conductivity by measured research area surface soil (0-20cm) physicochemical property and soil texture data. The simulation results were assessed by the approach of consisting in presenting a graphical representation of model simulated compared with observed values and by mean square error. The main results indicate that the RETC-PTFs and local PTFs mean square errors are 1.19 and 0.40 respectively. The precision of RETC-PTFs is lower than local PTFs function obviously using soil physicochemical property and soil texture data. The RETC-PTFs is inappropriate to this area. Accuracy of each input data precision of RETC-PTFs play a big part in because of less data needed. More complete data not always get better simulating results.

Spring Wheat Yield and Quality Related to Soil Texture and Nitrogen Fertilization

Efficient N fertilization is crucial for economic wheat (Triticum aestivum L.) production and is of great agronomical and environmental significance. A study was conducted at 12 site‐years in eastern Canada to evaluate the effect of soil surface textural groups, N rate (0–200 kg N ha−1) and application timing on grain yield (GY), N uptake, nitrogen uptake efficiency (NUE), grain protein content (GPC), test weight, and thousand kernel weight (TKW). Chlorophyll meter readings (CMR) were taken at tillering and at flowering to assess in‐season wheat N nutrition. Fertilization and soil textural group effects were significant on all measured parameters and their interaction was significant on GPC, TKW, test weight, and CMR. Total N uptake and GPC ranged from 39 to 96 kg N ha−1 and from 13 to 18 g kg−1, respectively, and total N uptake increased proportionally to N rates. Applying N levels &gt;120 kg N ha−1 did not increase total yield, test weight, TKW, or CMR values. The variation in GY, N uptake, and GPC explained by the relative CMR taken at flowering was 87, 88, and 73%, respectively. This study demonstrates that in‐season wheat N nutrition can be monitored by CMR and that surface soil texture is an important parameter that influences wheat N response and wheat quality parameters. Applying half of the recommended rate (120 kg ha−1) at planting and the rest at tillering resulted in a high total yield, high grain N uptake, and the highest GPC price premium.

Soil Texture Estimation Over a Semiarid Area Using TerraSAR-X Radar Data

In this letter, it is proposed to use TerraSAR-X data for analysis and estimation of soil surface texture. Our study is based on experimental campaigns carried out over a semiarid area in North Africa. Simultaneously with TerraSAR-X radar acquisitions, ground measurements (texture, soil moisture, and roughness) were made on different test fields. A strong correlation is observed between soil texture and a processed signal from two radar images, with the first acquired just after a rain event and the second corresponding to dry soil conditions, acquired three weeks later. An empirical relationship is proposed for the retrieval from radar signals of clay content percent. Soil texture mapping is proposed over the study site, which includes bare soils and olive groves.

Primary and Residual Effects of Abrams Tank Traffic on Prairie Soil Properties

Mechanized maneuver training can impact soil quality in many ways, most notably through soil displacement and compaction. To identify indicator variables for inclusion in monitoring programs on military lands, a replicated small‐plot study of tracked vehicle disturbance effects on tallgrass prairie soil properties was initiated on Fort Riley, KS, in 2003. A range of disturbances encompassing soil surface texture, environmental conditions, and traffic intensity were evaluated. Soil physical (rutting and compaction) and chemical (soil organic matter and nutrients) properties were monitored annually on curve and straightaway track areas through 2007. Rut depth remained significant (P ≤ 0.05) for curves of inside and outside tracks in both soil surface textures throughout the study and was more severe for repeated vs. single traffic events. Bulk density increased with disturbance under wet soil conditions, repeated traffic, and on curve areas but recovered to undisturbed levels within 1 to 3 yr. Soil compaction as measured by penetrometer resistance was more severe for traffic under wet soil conditions and remained significant (P ≤ 0.05) at depths of 5 to 10 cm in both soil surface textures throughout the study. Residual effects of tank traffic on soil chemical properties were generally mild, but soil C was reduced following disturbance under wet soil conditions, repeated traffic, and on curve areas and required 3 to 4 yr to recover to undisturbed levels. The results of this study emphasize the importance of suitable impact management to maintain the sustainability of tallgrass prairie for military training activities.

Multi-channel ground-penetrating radar to explore spatial variations in thaw depth and moisture content in the active layer of a permafrost site

Abstract. Multi-channel ground-penetrating radar (GPR) was applied at a permafrost site on the Tibetan Plateau to investigate the influence of surface properties and soil texture on the late-summer thaw depth and average soil moisture content of the active layer. Measurements were conducted on an approximately 85 × 60 m2 sized area with surface and soil textural properties that ranged from medium to coarse textured bare soil to finer textured, sparsely vegetated areas covered with fine, wind blown sand, and it included the bed of a gravel road. The survey allowed a clear differentiation of the various units. It showed (i) a shallow thaw depth and low average soil moisture content below the sand-covered, vegetated area, (ii) an intermediate thaw depth and high average soil moisture content along the gravel road, and (iii) an intermediate to deep thaw depth and low to intermediate average soil moisture content in the bare soil terrain. From our measurements, we found hypotheses for the permafrost processes at this site leading to the observed late-summer thaw depth and soil moisture conditions. The study clearly indicates the complicated interactions between surface and subsurface state variables and processes in this environment. Multi-channel GPR is an operational technology to efficiently study such a system at scales varying from a few meters to a few kilometers.

Porosity characterization of Argiudolls under different management systems in the Argentine Flat Pampa

Soil pore network characteristics are influenced by management and tillage practices. The objective of this work was to verify if the simultaneous use of the information obtained from tension infiltrometers and water release curves contribute to a better understanding of the impacts of different long-term management systems on the pore space of agricultural soils. The study was carried out on the Flat Pampa in Santa Fe, Argentina, in two types of typical Argiudolls with a silty-loam surface soil texture. The following treatments were evaluated: a) no-till with corn–wheat/soybean rotation (NT-R), and b) conventional tillage with wheat/soybean sequence (CT-S) at Gálvez; and a) no-till with corn–soybean–wheat/soybean rotation (NT-R), and b) no-till with wheat/soybean sequence (NT-S) at Videla. Tension values of 0, 1.5 and 3 cm were applied using tension infiltrometers with the aim of obtaining soil hydraulic conductivity measurements ( K 0, K 1.5, and K 3), and several hydraulic parameters (pore size, pore number, effective macroporosity, conducting macroporosity ( ε ( a, b) ), water flow and water flow decrease). Undisturbed soil cores were collected to determine water release curves (WRC) and soil bulk density (Db). The total macroporosity (Ma) and pore size frequency curve were determined from the fitted model of the WRC. Macropore connectivity was calculated using ε ( a, b) and Ma. In Gálvez, the Db values, K at all tensions, the number of effective pores, the mean pore radius and the effective macroporosity were significantly higher for NT-R. The conducting and total macroporosity values were similar in NT-R and CT-S, but the pores had better continuity in NT-R. In Videla, only K 0 and K 1.5 showed statistical differences in favor of NT-R. This treatment also had a greater number of effective pores, and higher effective, conducting and total macroporosity values, apart from the overall better pore connectivity. There were no significant differences between the NT-R and CT-S for Db and K 3. The evaluated indicators determined that the pore network characteristics are affected not only by tillage system, but also by the crops chosen for the rotation. When used jointly, tension infiltrometers and water release curves can be very useful tools for monitoring the evolution of the soils physical conditions.

An algorithm to estimate soil moisture over vegetated areas based on in situ and remote sensing information

An algorithm is proposed for estimating soil moisture over vegetated areas. The algorithm uses in situ and remote sensing information and statistical tools to estimate soil moisture at 1 km spatial resolution and at 20 cm depth over Puerto Rico. Soil moisture within the study region is characterized by spatial and temporal variability. The temporal variability for a given area exhibits long- and short-term variations that can be expressed by two empirical models. The average monthly soil moisture exhibits the long-term variability and is modelled by an artificial neural network (ANN), whereas the short-term variability is determined by hourly variation and is represented by a nonlinear stochastic transfer function model. Monthly vegetation index, land surface temperature, accumulated rainfall and soil texture are the major drivers of the ANN to estimate the monthly soil moisture. Radar, satellite and in situ observations are the major sources of information of the soil moisture empirical models. A self-organized ANN was also used to identify spatial variability to be able to determine a similar transfer function that best resembles the properties of a particular grid point and estimate the hourly soil moisture across the island. Validation techniques reveal an average absolute error of 3.34% of volumetric water content and this result shows that the proposed algorithm is a potential tool for estimating soil moisture over vegetated areas.

Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture

Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and soil texture. Moreover, the relationship between brightness temperature, soil moisture and the factors mentioned above is highly non-linear and ill-posed. Consequently, Artificial Neural Networks (ANNs) have been used to retrieve soil moisture from microwave data, but with limited success when dealing with data different to that from the training period. In this study, an ANN is tested for its ability to predict soil moisture at 1 km resolution on different dates following training at the same site for a specific date. A novel approach that utilizes information on the variability of soil moisture, in terms of its mean and standard deviation for a (sub) region of spatial dimension up to 40 km, is used to improve the current retrieval accuracy of the ANN method. A comparison between the ANN with and without the use of the variability information showed that this enhancement enables the ANN to achieve an average Root Mean Square Error (RMSE) of around 5.1% v/v when using the variability information, as compared to around 7.5% v/v without it. The accuracy of the soil moisture retrieval was further improved by the division of the target site into smaller regions down to 4 km in size, with the spatial variability of soil moisture calculated from within the smaller region used in the ANN. With the combination of an ANN architecture of a single hidden layer of 20 neurons and the dual-polarized brightness temperatures as input, the proposed use of variability and sub-region methodology achieves an average retrieval accuracy of 3.7% v/v. Although this accuracy is not the lowest as comparing to the research in this field, the main contribution is the ability of ANN in solving the problem of predicting “out-of-range” soil moisture values. However, the applicability of this method is highly dependent on the accuracy of the mean and standard deviation values within the sub-region, potentially limiting its routine application.