AUTOMATIC IN SITU DETERMINATION OF FIELD CAPACITY USING SOIL MOISTURE SENSORS

2011 ◽  
Vol 61 (3) ◽  
pp. 416-424 ◽  
Author(s):  
Scott Fazackerley ◽  
Ramon Lawrence
Keyword(s):  
Soil Moisture ◽  
Field Capacity ◽  
Soil Moisture Sensors

scholarly journals Revisiting Field Capacity (FC): variation of definition of FC and its estimation from pedotransfer functions

2014 ◽  
Vol 38 (6) ◽  
pp. 1750-1764 ◽  
Author(s):  
Theophilo Benedicto Ottoni Filho ◽  
Marta Vasconcelos Ottoni ◽  
Muriel Batista de Oliveira ◽  
José Ronaldo de Macedo ◽  
Klaus Reichardt
Keyword(s):  
Moisture Content ◽  
Soil Properties ◽  
Field Capacity ◽  
Pedotransfer Functions ◽  
Soil Morphology ◽  
Soil Water Retention ◽  
Input Variables ◽  
Definition Of

Taking into account the nature of the hydrological processes involved in in situ measurement of Field Capacity (FC), this study proposes a variation of the definition of FC aiming not only at minimizing the inadequacies of its determination, but also at maintaining its original, practical meaning. Analysis of FC data for 22 Brazilian soils and additional FC data from the literature, all measured according to the proposed definition, which is based on a 48-h drainage time after infiltration by shallow ponding, indicates a weak dependency on the amount of infiltrated water, antecedent moisture level, soil morphology, and the level of the groundwater table, but a strong dependency on basic soil properties. The dependence on basic soil properties allowed determination of FC of the 22 soil profiles by pedotransfer functions (PTFs) using the input variables usually adopted in prediction of soil water retention. Among the input variables, soil moisture content θ (6 kPa) had the greatest impact. Indeed, a linear PTF based only on it resulted in an FC with a root mean squared residue less than 0.04 m³ m-3 for most soils individually. Such a PTF proved to be a better FC predictor than the traditional method of using moisture content at an arbitrary suction. Our FC data were compatible with an equivalent and broader USA database found in the literature, mainly for medium-texture soil samples. One reason for differences between FCs of the two data sets of fine-textured soils is due to their different drainage times. Thus, a standardized procedure for in situ determination of FC is recommended.

scholarly journals A comprehensive approach to analyze discrepancies between land surface models and in-situ measurements: a case study over the US and Illinois with SECHIBA forced by NLDAS

2012 ◽  
Vol 16 (11) ◽  
pp. 3973-3988 ◽  
Author(s):  
M. Guimberteau ◽  
A. Perrier ◽  
K. Laval ◽  
J. Polcher
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Field Capacity ◽  
Land Surface Models ◽  
Data Set ◽  
Wetness Index ◽  
Surface Models ◽  
The Us ◽  
Vegetation Parameters

Abstract. The purpose of this study is to test the ability of the Land Surface Model SECHIBA to simulate water budget and particularly soil moisture at two different scales: regional and local. The model is forced by NLDAS data set at 1/8th degree resolution over the 1997–1999 period. SECHIBA gives satisfying results in terms of evapotranspiration and runoff over the US compared with four other land surface models, all forced by NLDAS data set for a common time period. The simulated soil moisture is compared to in-situ data from the Global Soil Moisture Database across Illinois by computing a soil wetness index. A comprehensive approach is performed to test the ability of SECHIBA to simulate soil moisture with a gradual change of the vegetation parameters closely related to the experimental conditions. With default values of vegetation parameters, the model overestimates soil moisture, particularly during summer. Sensitivity tests of the model to the change of vegetation parameters show that the roots extraction parameter has the largest impact on soil moisture, other parameters such as LAI, height or soil resistance having a minor impact. Moreover, a new evapotranspiration computation including bare soil evaporation under vegetation has been introduced into the model. The results point out an improvement of the soil moisture simulation when this effect is taken into account. Finally, soil moisture sensitivity to precipitation variation is addressed and it is shown that soil moisture observations can be rather different, depending on the method of measuring field capacity. When the observed field capacity is deducted from the observed volumetric water profiles, simulated soil wetness index is closer to the observations.

In Situ Comparison of Three Dielectric Soil Moisture Sensors in Drip Irrigated Sandy Soils

2005 ◽  
Vol 4 (4) ◽  
pp. 1037-1047 ◽  
Author(s):  
Finn Plauborg ◽  
Bo V. Iversen ◽  
Poul E. Laerke
Keyword(s):  
Soil Moisture ◽  
Sandy Soils ◽  
Soil Moisture Sensors

scholarly journals A comprehensive approach to analyze discrepancies between land surface models and in-situ measurements: a case study over US and Illinois with SECHIBA forced by NLDAS

2012 ◽  
Vol 9 (4) ◽  
pp. 5039-5083
Author(s):  
M. Guimberteau ◽  
A. Perrier ◽  
K. Laval ◽  
J. Polcher
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Field Capacity ◽  
Comprehensive Approach ◽  
Land Surface Models ◽  
Data Set ◽  
Wetness Index ◽  
Surface Models ◽  
Vegetation Parameters

Abstract. The purpose of this study is to test the ability of the Land Surface Model SECHIBA to simulate water budget and particularly soil moisture at two different scales: regional and mesoscale. The model is forced by NLDAS data set at eighth degree resolution over the 1997–1999 period. SECHIBA gives satisfying results in terms of evapotranspiration and runoff over US compared with four other land surface models, all forced by NLDAS data set for a common time period. The simulated soil moisture is compared to in-situ data from the Global Soil Moisture Database across Illinois by computing a soil wetness index. A comprehensive approach is performed to test the ability of SECHIBA to simulate soil moisture with a gradual change of the vegetation parameters closely related to the experimental conditions. With default values of vegetation parameters, the model overestimates soil moisture, particularly during summer. Sensitivity tests of the model to the change of vegetation parameters are performed and show that the roots extraction parameter has the largest impact on soil moisture, others parameters such as LAI, height or soil resistance having a minor impact. Moreover, a new computation of evapotranspiration including bare soil evaporation under vegetation has been introduced into the model. The results point out an improvement of the simulation of soil moisture when this effect is taken into account. Finally, uncertainties in forcing precipitation to simulate a realistic soil moisture are addressed and it is shown that soil moisture observations can be rather different depending on the method to measure field capacity. When the observed field capacity is deducted from the observed volumetric water profiles, simulated soil wetness index is closer to the observations. Excepted for one station, the monthly mean correlation is around 0.9 between observation and simulation.

INTERMITTENT INFILTRATION AND EVAPORATION FROM SOIL COLUMNS

1983 ◽  
Vol 63 (2) ◽  
pp. 303-314 ◽  
Author(s):  
M. A. MUSTAFA ◽  
R. DE JONG ◽  
H. N. HAYHOE ◽  
G. C. TOPP
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Clay Soil ◽  
Field Capacity ◽  
Richards Equation ◽  
Soil Columns ◽  
Successful Prediction ◽  
Deep Distribution ◽  
Satisfactory Prediction

Varying total amounts of water (160 and 320 mm) were infiltrated into 60-cm columns of air-dry saline sodic clay soil. The intervals between irrigation applications were varied from 5 to 20 days. The soil columns were subjected to a potential evaporation rate of 4.8 mm∙day−1 in a growth room. The cumulative evaporation followed a square root of time response, similar to that found by others for non-saline soils of coarser texture. An analytical solution of the Richards’ equation gave satisfactory (± 10%) prediction of cumulative evaporation at the end of the experiment as long as water was added in amounts of 40 mm or more per irrigation. The numerical solution to the Richards’ equation gave satisfactory estimates of evaporation for the latter stages of the experiment, but in the earlier stages it underestimated evaporation because of the too deep distribution of water in the soil given by this model. The neglect of hysteresis was invoked to explain the discrepancy between observed and predicted soil water content profiles. The "versatile soil moisture budget" empirical model also gave satisfactory prediction of evaporation but the successful prediction of water content profiles depended on "field capacity" values measured in situ. Key words: Soil moisture, modelling, water budgets, Richards’ equation

scholarly journals Different Rates of Soil Drying after Rainfall Are Observed by the SMOS Satellite and the South Fork in situ Soil Moisture Network

2015 ◽  
Vol 16 (2) ◽  
pp. 889-903 ◽  
Author(s):  
Wesley J. Rondinelli ◽  
Brian K. Hornbuckle ◽  
Jason C. Patton ◽  
Michael H. Cosh ◽  
Victoria A. Walker ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Soil Drying ◽  
Surface Soil Moisture ◽  
South Fork ◽  
The South ◽  
Near Surface ◽  
Soil Moisture Sensors ◽  
Smos Satellite

Abstract Soil moisture affects the spatial variation of land–atmosphere interactions through its influence on the balance of latent and sensible heat fluxes. Wetter soils are more prone to flooding because a smaller fraction of rainfall can infiltrate into the soil. The Soil Moisture Ocean Salinity (SMOS) satellite carries a remote sensing instrument able to make estimates of near-surface soil moisture on a global scale. One way to validate satellite observations is by comparing them with observations made with sparse networks of in situ soil moisture sensors that match the extent of satellite footprints. The rate of soil drying after significant rainfall observed by SMOS is found to be higher than the rate observed by a U.S. Department of Agriculture (USDA) soil moisture network in the watershed of the South Fork Iowa River. This leads to the conclusion that SMOS and the network observe different layers of the soil: SMOS observes a layer of soil at the soil surface that is a few centimeters thick, while the network observes a deeper soil layer centered at the depth at which the in situ soil moisture sensors are buried. It is also found that SMOS near-surface soil moisture is drier than the South Fork network soil moisture, on average. The conclusion that SMOS and the network observe different layers of the soil, and therefore different soil moisture dynamics, cannot explain the dry bias. However, it can account for some of the root-mean-square error in the relationship. In addition, SMOS observations are noisier than the network observations.

scholarly journals Performance Assessment of Five Different Soil Moisture Sensors under Irrigated Field Conditions in Oklahoma

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3786 ◽  
Author(s):  
Sumon Datta ◽  
Saleh Taghvaeian ◽  
Tyson Ochsner ◽  
Daniel Moriasi ◽  
Prasanna Gowda ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Cropping Systems ◽  
Irrigation Management ◽  
Clay Content ◽  
Field Capacity ◽  
Irrigation Scheduling ◽  
Irrigated Agriculture ◽  
Undisturbed Soil ◽  
Soil Moisture Sensors ◽  
Rosetta Model

Meeting the ever-increasing global food, feed, and fiber demands while conserving the quantity and quality of limited agricultural water resources and maintaining the sustainability of irrigated agriculture requires optimizing irrigation management using advanced technologies such as soil moisture sensors. In this study, the performance of five different soil moisture sensors was evaluated for their accuracy in two irrigated cropping systems, one each in central and southwest Oklahoma, with variable levels of soil salinity and clay content. With factory calibrations, three of the sensors had sufficient accuracies at the site with lower levels of salinity and clay, while none of them performed satisfactorily at the site with higher levels of salinity and clay. The study also investigated the performance of different approaches (laboratory, sensor-based, and the Rosetta model) to determine soil moisture thresholds required for irrigation scheduling, i.e., field capacity (FC) and wilting point (WP). The estimated FC and WP by the Rosetta model were closest to the laboratory-measured data using undisturbed soil cores, regardless of the type and number of input parameters used in the Rosetta model. The sensor-based method of ranking the readings resulted in overestimation of FC and WP. Finally, soil moisture depletion, a critical parameter in effective irrigation scheduling, was calculated by combining sensor readings and FC estimates. Ranking-based FC resulted in overestimation of soil moisture depletion, even for accurate sensors at the site with lower levels of salinity and clay.

scholarly journals Estimation of field capacity from ring infiltrometer-drainage data

2014 ◽  
Vol 38 (6) ◽  
pp. 1765-1771 ◽  
Author(s):  
Theophilo Benedicto Ottoni Filho ◽  
Marta Vasconcelos Ottoni ◽  
Muriel Batista de Oliveira ◽  
José Ronaldo de Macedo
Keyword(s):  
Field Capacity ◽  
Soil Science ◽  
Matric Potential ◽  
Volumetric Soil Water Content ◽  
Double Ring ◽  
Infiltration Test ◽  
Drainage Data ◽  
Volumetric Soil Water

Field capacity (FC) is a parameter widely used in applied soil science. However, its in situ method of determination may be difficult to apply, generally because of the need of large supplies of water at the test sites. Ottoni Filho et al. (2014) proposed a standardized procedure for field determination of FC and showed that such in situ FC can be estimated by a linear pedotransfer function (PTF) based on volumetric soil water content at the matric potential of -6 kPa [θ(6)] for the same soils used in the present study. The objective of this study was to use soil moisture data below a double ring infiltrometer measured 48 h after the end of the infiltration test in order to develop PTFs for standard in situ FC. We found that such ring FC data were an average of 0.03 m³ m- 3 greater than standard FC values. The linear PTF that was developed for the ring FC data based only on θ(6) was nearly as accurate as the equivalent PTF reported by Ottoni Filho et al. (2014), which was developed for the standard FC data. The root mean squared residues of FC determined from both PTFs were about 0.02 m³ m- 3. The proposed method has the advantage of estimating the soil in situ FC using the water applied in the infiltration test.

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