Comment on Dong and Ochsner (2018): "Soil Texture often Exerts stronger Influence Than Precipitation on Mesoscale Soil Moisture Patterns"

Hydrological models require the determination of fitting parameters that are tedious and time consuming to acquire. A rapid alternative method of estimating the fitting parameters is to use pedotransfer functions. This paper proposes a reliable method to estimate soil moisture at -33 and -1500 kPa from soil texture and bulk density. This method reduces the saturated moisture content by multiplying it with two non-linear functions depending on sand and clay contents. The novel pedotransfer function has no restrictions on the range of the texture predictors and gives reasonable predictions for soils with bulk density that varies from 0.25 to 2.16 g cm-3. These pedotransfer functions require only five parameters for each pressure head. It is generally accepted that the introduction of organic matter as a predictor improves the outcomes; however it was found by using a porosity based pedotransfer model, using organic matter as a predictor only modestly improves the accuracy. The model was developed employing 18 559 samples from the IGBP-DIS soil data set for pedotransfer function development (Data and Information System of the International Geosphere Biosphere Programme) database that embodies all major soils across the United States of America. The function is reliable and performs well for a wide range of soils occurring in very dry to very wet climates. Climatical grouping of the IGBP-DIS soils was proposed (aquic, tropical, cryic, aridic), but the results show that only tropical soils require specific grouping. Among many other different non-climatical soil groups tested, only humic and vitric soils were found to require specific grouping. The reliability of the pedotransfer function was further demonstrated with an independent database from Northern Italy having heterogeneous soils, and was found to be comparable or better than the accuracy of other pedotransfer functions found in the literature. Key words: Pedotransfer functions, soil moisture, soil texture, bulk density, organic matter, grouping

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TIME OF SAMPLING AFTER AN IRRIGATION TO DETERMINE FIELD CAPACITY OF SOIL

Canadian Journal of Soil Science ◽

10.4141/cjss65-024 ◽

1965 ◽

Vol 45 (2) ◽

pp. 171-176 ◽

Cited By ~ 7

Author(s):

J. C. Wilcox

Keyword(s):

Soil Moisture ◽

Moisture Content ◽

Soil Texture ◽

Soil Moisture Content ◽

Field Capacity ◽

Percentage Error ◽

High Positive Correlation ◽

Positive Correlation

Drainage curves following irrigation were determined at six depths in eight soils having unrestricted drainage but varying widely in soil texture. The field capacities were determined under relatively high rates of evapotranspiration. The time after irrigation that it was necessary to wait before sampling the soil, to determine field capacity, was also determined. A high positive correlation was obtained between the log of field capacity in inches and the log of time after irrigation at which to sample the soil. The time varied from about 0.5 day with 1.5 in. field capacity to 4.0 days with 35 in. From the curves of soil moisture content versus time, the errors caused by sampling too soon or too late were determined. The percentage error (i.e. percent of field capacity) increased with an increase in the error in time of sampling; it decreased with an increase in field capacity in inches; and it was greater when sampling was too soon than when it was too late.

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Soil Moisture Estimate Uncertainties from the Effect of Soil Texture on Dielectric Semiempirical Models

Remote Sensing ◽

10.3390/rs12142343 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2343

Author(s):

Jing Liu ◽

Qinhuo Liu

Keyword(s):

Soil Moisture ◽

Water Content ◽

Soil Texture ◽

Dielectric Behavior ◽

Volumetric Water Content ◽

Frequency Range ◽

Dielectric Model ◽

Texture Effect ◽

Semi Empirical ◽

Semiempirical Models

Soil texture has been shown to affect the dielectric behavior of soil over the entire frequency range. Three universally employed dielectric semiempirical models (SEMs), the Dobson model, the Wang–Schmugge model and the Mironov model, as well as a new improved SEM known as the soil semi-empirical mineralogy-related-to-water dielectric model (SSMDM), incorporate a significant soil texture effect in different ways. In this paper, soil moisture estimate uncertainties from the effect of soil texture on these four SEMs are systematically and widely investigated over all soil texture cases at different frequencies between 1.4 and 18 GHz for volumetric water content levels between 0.0 and 0.4 m3/m3 from the perspective of two aspects: soil dielectric model discordance and soil texture discordance. Firstly, the effect of soil texture on these four dielectric SEMs is analyzed. Then, soil moisture estimate uncertainties due to the effect of soil texture are carefully investigated. Finally, the applicability of these SEMs is discussed, which can supply references for their choice. The results show that soil moisture estimate uncertainties are small and satisfy the 4% volumetric water content retrieval requirement in some cases. However, in other cases, it may contribute relatively significant uncertainties to soil moisture estimates and correspond to a difference that exceeds the 4% volumetric water content requirement, with potential for the largest deviations to exceed 0.22 m3/m3.

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Comparative Analysis of High-Resolution Soil Moisture Simulations from the Soil, Vegetation, and Snow (SVS) Land Surface Model Using SAR Imagery Over Bare Soil

Water ◽

10.3390/w11030542 ◽

2019 ◽

Vol 11 (3) ◽

pp. 542 ◽

Cited By ~ 3

Author(s):

Mohammed Dabboor ◽

Leqiang Sun ◽

Marco Carrera ◽

Matthew Friesen ◽

Amine Merzouki ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Soil Texture ◽

Land Surface ◽

Goodness Of Fit ◽

Retrieval Algorithm ◽

Surface Model ◽

Land Surface Scheme ◽

Sar Imagery ◽

Surface Scheme

Soil moisture is a key variable in Earth systems, controlling the exchange of water andenergy between land and atmosphere. Thus, understanding its spatiotemporal distribution andvariability is important. Environment and Climate Change Canada (ECCC) has developed a newland surface parameterization, named the Soil, Vegetation, and Snow (SVS) scheme. The SVS landsurface scheme features sophisticated parameterizations of hydrological processes, including watertransport through the soil. It has been shown to provide more accurate simulations of the temporaland spatial distribution of soil moisture compared to the current operational land surface scheme.Simulation of high resolution soil moisture at the field scale remains a challenge. In this study, wesimulate soil moisture maps at a spatial resolution of 100 m using the SVS land surface scheme overan experimental site located in Manitoba, Canada. Hourly high resolution soil moisture maps wereproduced between May and November 2015. Simulated soil moisture values were compared withestimated soil moisture values using a hybrid retrieval algorithm developed at Agriculture andAgri-Food Canada (AAFC) for soil moisture estimation using RADARSAT-2 Synthetic ApertureRadar (SAR) imagery. Statistical analysis of the results showed an overall promising performanceof the SVS land surface scheme in simulating soil moisture values at high resolution scale.Investigation of the SVS output was conducted both independently of the soil texture, and as afunction of the soil texture. The SVS model tends to perform slightly better over coarser texturedsoils (sandy loam, fine sand) than finer textured soils (clays). Correlation values of the simulatedSVS soil moisture and the retrieved SAR soil moisture lie between 0.753–0.860 over sand and 0.676-0.865 over clay, with goodness of fit values between 0.567–0.739 and 0.457–0.748, respectively. TheRoot Mean Square Difference (RMSD) values range between 0.058–0.062 over sand and 0.055–0.113over clay, with a maximum absolute bias of 0.049 and 0.094 over sand and clay, respectively. Theunbiased RMSD values lie between 0.038–0.057 over sand and 0.039–0.064 over clay. Furthermore,results show an Index of Agreement (IA) between the simulated and the derived soil moisturealways higher than 0.90.

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Full Season Control of Japanese Beetle Grub Control with Merit, NH, 1994

Arthropod Management Tests ◽

10.1093/amt/21.1.352 ◽

1996 ◽

Vol 21 (1) ◽

pp. 352-352

Author(s):

Stanley R. Swier

Keyword(s):

Organic Matter ◽

Soil Moisture ◽

Soil Organic Matter ◽

Air Temperature ◽

Soil Ph ◽

Soil Texture ◽

Clay Soil ◽

Golf Course ◽

Silt Loam ◽

Japanese Beetle

Abstract The trial was conducted 10 May on a golf course rough, Amherst, NH. Plots were 10 X 10 ft, replicated 4 times, in a RCB design. Merit WP was applied in 4 gal water/1000 ft2 with a watering, can. Merit G granules were applied with a homemade salt shaker. Treatments were irrigated with 0.5 inch water after application. Plots were rated 30 Sep by counting the number of live grubs per 1 ft2. Conditions at the time of treatment were: air temperature 70°F; wind, 3 MPH; sky, clear; soil temperature, 1 inch, 60°F; thatch depth, 0.5 inch soil pH, 5.4; slope 0%; soil texture, silt loam, 47% sand, 50% silt, 3% clay; soil organic matter, 6.9%; soil moisture, 21.8%.

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

Earth System Science Data Discussions ◽

10.5194/essdd-6-811-2013 ◽

2013 ◽

Vol 6 (2) ◽

pp. 811-835 ◽

Cited By ~ 1

Author(s):

P. R. Kormos ◽

D. Marks ◽

C. J. Williams ◽

H. P. Marshall ◽

P. Aishlin ◽

...

Keyword(s):

Soil Moisture ◽

Transition Zone ◽

Soil Texture ◽

Soil Depth ◽

Meteorological Data ◽

Dew Point ◽

Soil Profiles ◽

Data Set ◽

Hydrologic Condition ◽

Wide Range

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 dataset 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 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, air temperature, relative humidity, dew point temperature, and incoming solar and thermal radiation data. Sub-surface data included are hourly soil moisture data from multiple depths from 7 soil profiles within the catchment, and soil temperatures from multiple depths from 2 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. Though it represents only a single water year, the presentation of both above and below ground hydrologic condition makes it one of the most detailed and complete hydro-climatic datasets from the climatically sensitive rain-snow transition zone for a wide range of modeling and descriptive studies. Data are available at doi:10.1594/PANGAEA.819837.

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Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe

Water ◽

10.3390/w12113109 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3109

Author(s):

Roïya Souissi ◽

Ahmad Al Bitar ◽

Mehrez Zribi

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Soil Texture ◽

Surface Soil ◽

Root Zone ◽

Data Filtering ◽

Surface Soil Moisture ◽

The Mean ◽

Artificial Neural

This paper explores the accuracy in using an artificial neural network (ANN) to estimate root-zone soil moisture (RZSM) at multiple worldwide locations using only in situ surface soil moisture (SSM) as a training dataset. The paper also addresses the transferability of the trained ANN across climatic and soil texture conditions. Data from the International Soil Moisture Network (ISMN) were collected for several networks with variable soil texture and climate classes. Several scaling, feature extraction, and training approaches were tested. An artificial neural network employing rolling averages (ANNRAV) of SSM over 10, 30, and 90 days was developed. The results show that applying a standard scaling (SSCA) to the ANN input features improves the correlation, Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) for 52%, 91%, and 87%, respectively, of the tested stations, compared to MinMax scaling (MMSCA). Different training sets are suggested, namely, training on data from all networks, data from one network, or data of all networks excluding one. Based on these trainings, new transferability (TranI) and contribution (ContI) indices are defined. The results show that one network cannot provide the best prediction accuracy if used alone to train the ANN. They also show that the removal of the less contributing networks enhances performance. For example, elimination of the densest network (SCAN) from the training enhances the mean correlation by 20.5% and the mean NSE by 42.5%. This motivates the implementation of a data filtering technique based on the ANN’s performance. A median, max, and min correlation of 0.77, 0.96, and 0.65, respectively, are obtained by the model after data filtering. The performances are also analyzed with respect to the covered climatic regions and soil texture, providing insights into the robustness and limitations of the approach, namely, the need for complementary information in highly evaporative regions. In fact, the ANN using only SSM to predict RZSM has low performance when decoupling between the surface and root zones is observed. The application of ANN to obtain spatialized RZSM will require integrating remote sensing-based surface soil moisture in the future.

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The Influence of Soil Moisture, Soil Texture, Drying Conditions, and Exchangeable Cations on Soil Strength

Soil Science Society of America Journal ◽

10.2136/sssaj1965.03615995002900060017x ◽

1965 ◽

Vol 29 (6) ◽

pp. 641-645 ◽

Cited By ~ 23

Author(s):

C. J. Gerard

Keyword(s):

Soil Moisture ◽

Soil Texture ◽

Soil Strength ◽

Exchangeable Cations ◽

Drying Conditions

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Investigation of the Effect of Soil Moisture Content, Contact Surface Material and Soil Texture on Soil Friction and Soil Adhesion Coefficients

Acta Technologica Agriculturae ◽

10.2478/ata-2018-0009 ◽

2018 ◽

Vol 21 (2) ◽

pp. 44-50 ◽

Cited By ~ 2

Author(s):

Yousef Abbaspour-Gilandeh ◽

Fereshteh Hasankhani-Ghavam ◽

Gholamhosein Shahgoli ◽

Vali Rasooli Shrabian ◽

Mohammadreza Abbaspour-Gilandeh

Keyword(s):

Soil Moisture ◽

Moisture Content ◽

Contact Surface ◽

Soil Texture ◽

Soil Moisture Content ◽

Fluid Phase ◽

Surface Material ◽

Mean Values ◽

Contact Materials ◽

Draft Force

Abstract Soil friction and soil adhesion increase the implement draft force and energy consumption particularly in the tools that have larger contact area with soil. The main ways of lowering the total draft force of the tillage tools include the use of proper materials in tools structures as well as application of the tools in appropriate soil moisture content condition. This paper investigates the effects of soil moisture content, contact surface material and soil texture on soil friction and soil adhesion coefficients. To measure the coefficients of soil friction and soil adhesion, a measurement system was developed at the University of Mohaghegh Ardabili. Experiments for each soil texture were performed at five levels of soil moisture content and four contact materials of steel, cast iron, rubber, and teflon with three replications. Results have shown that in all soil types, the effects of soil moisture content and contact materials had a significant effect on the coefficient of both soil friction and soil adhesion at the probability level of 1%. The coefficient of friction increased with soil moisture content increment and reached its maximum and then had a drop in the fluid phase. Results have shown that the mean values of soil friction and soil adhesion coefficients were significantly different from the studied soils.

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