Better placement of soil moisture point measurements guided by 2D resistivity tomography for improved irrigation scheduling

Soil Research ◽  
2011 ◽  
Vol 49 (6) ◽  
pp. 504 ◽  
Author(s):  
B. F. J. Kelly ◽  
R. I. Acworth ◽  
A. K. Greve
Keyword(s):  
Soil Moisture ◽  
Spatial Information ◽  
Root Zone ◽  
Irrigation Scheduling ◽  
Measurement Methods ◽  
Deep Drainage ◽  
Resistivity Tomography ◽  
Point Measurement ◽  
Irrigated Crops ◽  
2D Resistivity

Soil moisture beneath irrigated crops has traditionally been determined using point measurement methods such as neutron probes or capacitance systems. These approaches cannot measure soil moisture at depths beyond the root-zone of plants and have limited lateral coverage. It is shown that surface two-dimensional electrical resistivity tomography (ERT) can be used to map the spatial heterogeneity in soil moisture throughout a field under irrigated cotton. The case study demonstrates that ERT provides a better understanding of the pathways of water migration, and provides spatial information on how water storage changes throughout the growing season. We conclude that ERT should be integrated into farm water management surveys to delineate zones of excessive water loss due to deep drainage and to improve the positioning of point measurement methods for measuring soil moisture, thereby improving irrigation scheduling.

Detecting changes in root zone soil moisture from radar vegetation backscatter

2020 ◽  
Author(s):  
Coleen Carranza ◽  
Tim van Emmerik ◽  
Martine van der Ploeg
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Hydrological Cycle ◽  
Root Zone ◽  
Water Status ◽  
Roughness Parameter ◽  
Growing Season ◽  
Irrigation Scheduling ◽  
Vegetation Water Content ◽  
Vegetation Water

<p>Root zone soil moisture (θ<sub>rz</sub>) is a crucial component of the hydrological cycle and provides information for drought monitoring, irrigation scheduling, and carbon cycle modeling. During vegetation conditions, estimation of θ<sub>rz</sub> thru radar has so far only focused on retrieving surface soil moisture using the soil component of the total backscatter (σ<sub>soil</sub>), which is then assimilated into physical hydrological models. The utility of the vegetation component of the total backscatter (σ<sub>veg</sub>) has not been widely explored and is commonly corrected for in most soil moisture retrieval methods. However, σ<sub>veg </sub>provides information about vegetation water content. Furthermore, it has been known in agronomy that pre-dawn leaf water potential is in equilibrium with that of the soil. Therefore soil water status can be inferred by examining  the vegetation water status. In this study, our main goal is to determine whether changes in root zone soil moisture (Δθ<sub>rz</sub>) shows corresponding changes in vegetation backscatter (Δσ<sub>veg</sub>) at pre-dawn. We utilized Sentinel-1 (S1) descending pass and in situ soil moisture measurements from 2016-2018 at two soil moisture networks (Raam and Twente) in the Netherlands. We focused on corn and grass which are the most dominant crops at the sites and considered the depth-averaged θ<sub>rz</sub> up to 40 cm to capture the rooting depths for both crops. Dubois’ model formulation for VV-polarization was applied to estimate the surface roughness parameter (H<sub>rms</sub>) and σ<sub>soil </sub>during vegetated periods. Afterwards, the Water Cloud Model was used to derive σ<sub>veg</sub> by subtracting σ<sub>soil</sub> from S1 backscatter (σ<sub>tot</sub>). To ensure that S1 only measures vegetation water content, rainy days were excluded to remove the influence of intercepted rainfall on the backscatter. The slope of regression lines (β) fitted over plots of Δσ<sub>veg</sub> against Δθ<sub>rz</sub> were used investigate the dynamics over a growing season. Our main result indicates that Δσ<sub>veg </sub>- Δθ<sub>rz</sub> relation is influenced by crop growth stage and changes in water content in the root zone. For corn, changes in β’s over a growing season follow the trend in a crop coefficient (K<sub>c</sub>) curve, which is a measure of crop water requirements. Grasses, which are perennial crops, show trends corresponding to the mature crop stage. The correlation between soil moisture (Δθ) at specific soil depths (5, 10, 20, and 40 cm) and Δσ<sub>veg </sub> matches root growth for corn and known rooting depths for both corn and grass. Dry spells (e.g. July 2018) and a large increase in root zone water content in between two dry-day S1 overpass (e.g. from rainfall) result in a lower β, which indicates that Δσ<sub>veg</sub> does not match well with Δθ<sub>rz</sub>. The influence of vegetation on S1 backscatter is more pronounced for corn, which translated to a clearer Δσ<sub>veg</sub> - Δθ<sub>rz</sub> relation compared to grass. The sensitivity of Δσ<sub>veg</sub> to Δθ<sub>rz</sub> in corn means that the analysis may be applicable to other broad leaf crops or forested areas, with potential applications for monitoring  periods of water stress.</p>

Area-representative validation of remotely sensed high resolution soil moisture using a cosmic-ray neutron sensor

2020 ◽  
Author(s):  
Dragana Panic ◽  
Isabella Pfeil ◽  
Andreas Salentinig ◽  
Mariette Vreugdenhil ◽  
Wolfgang Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Root Zone ◽  
Spatial Scales ◽  
Temporal Frequency ◽  
Cosmic Ray ◽  
Irrigation Scheduling ◽  
Water Index ◽  
The Impact

<p>Reliable measurements of soil moisture (SM) are required for many applications worldwide, e.g., for flood and drought forecasting, and for improving the agricultural water use efficiency (e.g., irrigation scheduling). For the retrieval of large-scale SM datasets with a high temporal frequency, remote sensing methods have proven to be a valuable data source. (Sub-)daily SM is derived, for example, from observations of the Advanced Scatterometer (ASCAT) since 2007. These measurements are available on spatial scales of several square kilometers and are in particular useful for applications that do not require fine spatial resolutions but long and continuous time series. Since the launch of the first Sentinel-1 satellite in 2015, the derivation of SM at a spatial scale of 1 km has become possible for every 1.5-4 days over Europe (SSM1km) [1]. Recently, efforts have been made to combine ASCAT and Sentinel-1 to a Soil Water Index (SWI) product, in order to obtain a SM dataset with daily 1 km resolution (SWI1km) [2]. Both datasets are available over Europe from the Copernicus Global Land Service (CGLS, https://land.copernicus.eu/global/). As the quality of such a dataset is typically best over grassland and agricultural areas, and degrades with increasing vegetation density, validation is of high importance for the further development of the dataset and for its subsequent use by stakeholders.</p><p>Traditionally, validation studies have been carried out using in situ SM sensors from ground networks. Those are however often not representative of the area-wide satellite footprints. In this context, cosmic-ray neutron sensors (CRNS) have been found to be valuable, as they provide integrated SM estimates over a much larger area (about 20 hectares), which comes close to the spatial support area of the satellite SM product. In a previous study, we used CRNS measurements to validate ASCAT and S1 SM over an agricultural catchment, the Hydrological Open Air Laboratory (HOAL), in Petzenkirchen, Austria. The datasets were found to agree, but uncertainties regarding the impact of vegetation were identified.</p><p>In this study, we validated the SSM1km, SWI1km and a new S1-ASCAT SM product, which is currently developed at TU Wien, using CRNS. The new S1-ASCAT-combined dataset includes an improved vegetation parameterization, trend correction and snow masking. The validation has been carried out in the HOAL and on a second site in Marchfeld, Austria’s main crop producing area. As microwaves only penetrate the upper few centimeters of the soil, we applied the soil water index concept [3] to obtain soil moisture estimates of the root zone (approximately 0-40 cm) and thus roughly corresponding to the depth of the CRNS measurements. In the HOAL, we also incorporated in-situ SM from a network of point-scale time-domain-transmissivity sensors distributed within the CRNS footprint. The datasets were compared to each other by calculating correlation metrics. Furthermore, we investigated the effect of vegetation on both the satellite and the CRNS data by analyzing detailed information on crop type distribution and crop water content.</p><p>[1] Bauer-Marschallinger et al., 2018a: https://doi.org/10.1109/TGRS.2018.2858004<br>[2] Bauer-Marschallinger et al., 2018b: https://doi.org/10.3390/rs10071030<br>[3] Wagner et al., 1999: https://doi.org/10.1016/S0034-4257(99)00036-X</p>

Soil Water Measurement as Basis to Optimize Irrigation Scheduling: A Review

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 553f-554
Author(s):  
A.K. Alva ◽  
A. Fares
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Real Time ◽  
Soil Water Content ◽  
Root Zone ◽  
Soil Water Potential ◽  
Irrigation Scheduling ◽  
Rooting Depth ◽  
The Impact

Supplemental irrigation is often necessary for high economic returns for most cropping conditions even in humid areas. As irrigation costs continue to increase more efforts should be exerted to minimize these costs. Real time estimation and/or measurement of available soil water content in the crop root zone is one of the several methods used to help growers in making the right decision regarding timing and quantity of irrigation. The gravimetric method of soil water content determination is laborious and doesn't suite for frequent sampling from the same location because it requires destructive soil sampling. Tensiometers, which measure soil water potential that can be converted into soil water content using soil moisture release curves, have been used for irrigation scheduling. However, in extreme sandy soils the working interval of tensiometer is reduced, hence it may be difficult to detect small changes in soil moisture content. Capacitance probes which operate on the principle of apparent dielectric constant of the soil-water-air mixture are extremely sensitive to small changes in the soil water content at short time intervals. These probes can be placed at various depths within and below the effective rooting depth for a real time monitoring of the water content. Based on this continuous monitoring of the soil water content, irrigation is scheduled to replenish the water deficit within the rooting depth while leaching below the root zone is minimized. These are important management practices aimed to increase irrigation efficiency, and nutrient uptake efficiency for optimal crop production, while minimizing the impact of agricultural non-point source pollutants on the groundwater quality.

scholarly journals A Prior Estimation of the Spatial Distribution Parameter of Soil Moisture Storage Capacity Using Satellite-Based Root-Zone Soil Moisture Data

2019 ◽  
Vol 11 (21) ◽  
pp. 2580 ◽  
Author(s):  
Yifei Tian ◽  
Lihua Xiong ◽  
Bin Xiong ◽  
Ruodan Zhuang
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Storage Capacity ◽  
Spatial Information ◽  
Root Zone ◽  
River Catchment ◽  
Soil Moisture Storage ◽  
Soil Moisture Data ◽  
Moisture Storage ◽  
The Impact

Integration of satellite-based data with hydrological modelling was generally conducted via data assimilation or model calibration, and both approaches can enhance streamflow predictions. In this study, we assessed the feasibility of another approach that uses satellite-based soil moisture data to directly estimate the parameter β to represent the degree of the spatial distribution of soil moisture storage capacity in the semi-distributed Hymod model. The impact of using historical root-zone soil moisture data from the Soil Moisture Active Passive (SMAP) mission on the prior estimation of the parameter β was explored. Two different ways to incorporate the root-zone soil moisture data to estimate the parameter β are proposed, i.e., one is to derive a priori distribution of β , and the other is to derive a fixed value for β . The simulations of the Hymod models employing the two ways to estimate β are compared with the results produced by the original model, i.e., the one without employing satellite-based data to estimate the parameter β , at three study catchments (the Upper Hanjiang River catchment, the Xiangjiang River catchment, and the Ganjiang River catchment). The results illustrate that the two ways to incorporate the SMAP root-zone soil moisture data in order to predetermine the parameter β of the semi-distributed Hymod model both perform well in simulating streamflow during the calibration period, and a slight improvement was found during the validation period. Notably, deriving a fixed β value from satellite soil moisture data can provide better performance for ungauged catchments despite reducing the model freedom degrees due to fixing the β value. It is concluded that the robustness of the Hymod model in predicting the streamflow can be improved when the spatial information of satellite-based soil moisture data is utilized to estimate the parameter β .

scholarly journals An Optimal Irrigation Scheduling for Drip Irrigated Onion in A- Semi-Arid Region Using the Computer Program CROPWAT 8.0

2020 ◽  
pp. 96-105
Author(s):  
P. R. Anjitha Krishna ◽  
B. Maheshwara Babu ◽  
A. T. Dandekar ◽  
R. H. Rajkumar ◽  
G. Ramesh ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Root Zone ◽  
Management Strategies ◽  
Field Capacity ◽  
Irrigation Scheduling ◽  
Water Requirement ◽  
Estimation Model ◽  
Semi Arid Region ◽  
Semi Arid

Efficient utilization of available water resources requires appropriate management strategies considering the changing environmental conditions. The present study used a widely adopted crop water requirement estimation model-CROPWAT 8.0 for estimation and scheduling of irrigation requirement for onion crop grown under Vertisol in the Rabi season in the semi-arid region of Raichur district. The soil moisture at the root zone was not allowed to fall below 50% depletion. The irrigation events brought the soil moisture back to the field capacity level. The total water requirement for the 1st and 2nd seasons was 428.77 mm and 399.98 mm respectively at 90% irrigation efficiency. CROPWAT based two days irrigation scheduling scenario was found to be appropriate to maintain optimal soil moisture range within the crop root zone at different crop stages.

Soil water content sensors from laboratory calibration to field monitoring: discrepancies and uncertainties

2021 ◽  
Author(s):  
Angela Gabriela Morales Santos ◽  
Reinhard Nolz
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Root Zone ◽  
Sandy Loam ◽  
Irrigation Scheduling ◽  
Water Monitoring ◽  
Rooting Depth ◽  
Water Fraction ◽  
Laboratory Calibration

<p>Monitoring soil water status is one key option to optimise water use in agriculture. Soil moisture sensors are widely used for investigating available soil water to optimally adapt irrigation scheduling to crop water requirements. Although reliable measurements are subject to proper soil-specific calibration of sensors, meaningful calibration functions are not always available. Another question is the plausibility of soil water monitoring under field conditions. The objective of this study was to calibrate four multi-sensor capacitance probes in the laboratory and  to evaluate the calibrated water content readings under natural conditions in an irrigated field by means of a modelling approach.</p><p>The multi-sensor capacitance probes (SM1 by ADCON Telemetry) were of 90 cm length and contained nine sensors (S1 to S9) at 10 cm spacing. The digital output values were given in scaled frequency units (SFU). The laboratory calibration was carried out on sandy loam and sand. Measurements were undertaken by placing the probes inside a PVC tube backfilled with soil at different water contents. Soil samples were collected using metallic cylinders of 250 cm<sup>3</sup>, from which volumetric water content (θ) was determined gravimetrically. The sensor readings in soil were normalised by using sensor readings in air and water as lower and upper limit, respectively. The pairs of measured θ and normalised SFU were related to each other by curve fitting. For each soil type, eight sensor-specific calibration functions were developed that allowed the calculation of θ in cm<sup>3</sup> cm<sup>−</sup><sup>3</sup> from SM1 readings.</p><p>After calibration, the SM1 probes were installed in a field in Obersiebenbrunn, Lower Austria, where sandy loam is the main soil. Three of the probes monitored irrigated plots and the fourth a rainfed plot. To obtain reference values, one HydraProbe soil moisture sensor (Stevens Water Monitoring Systems) was installed in 20 cm depth, near each SM1. The average daily θ-values from the S2 (20 cm depth) contained in each SM1 probe were compared to the water fraction collected with the corresponding HydraProbe. Moreover, the SM1 θ-values were used to determine the daily soil water depletion in the root zone (Dr) for a rooting depth of 1 m. The obtained Dr datasets were compared to Dr simulated using CROPWAT 8.0 by FAO.</p><p>The field results showed that the SM1 probes were able to reproduce the HydraProbe dynamics of wetting and drying periods during the crop season. Nevertheless, a considerable difference was noted between the sensor measurements. The SM1 overestimated θ in the irrigated plots, whereas it underestimated θ in the rainfed plot. The discrepancies can be attributed mainly to the different physical mechanisms behind the sensors and to the unfeasible reproduction of field bulk density and soil structure in the laboratory. Furthermore, the operational frequency and permittivity response of the SM1 probes should be revised for future versions. The simulation results showed that the observed Dr values were more consistent with CROPWAT Dr results at the end of the simulation period, suggesting that the SM1 required several weeks to consolidate and give representative θ-values for the soil profile.</p>

scholarly journals Cotton Irrigation Scheduling Using a Crop Growth Model and FAO-56 Methods: Field and Simulation Studies

2017 ◽  
Vol 60 (6) ◽  
pp. 2023-2039 ◽  
Author(s):  
Kelly R. Thorp ◽  
Douglas J. Hunsaker ◽  
Kevin F. Bronson ◽  
Pedro Andrade-Sanchez ◽  
Edward M. Barnes
Keyword(s):  
Soil Moisture ◽  
Water Use ◽  
Root Zone ◽  
Crop Coefficient ◽  
Irrigation Scheduling ◽  
Crop Growth ◽  
Statistical Testing ◽  
Fine Tuning ◽  
Cotton Yield ◽  
Post Hoc

Abstract. Crop growth simulation models can address a variety of agricultural problems, but their use to directly assist in-season irrigation management decisions is less common. Confidence in model reliability can be increased if models are shown to provide improved in-season management recommendations, which are explicitly tested in the field. The objective of this study was to compare the CSM-CROPGRO-Cotton model (with recently updated ET routines) to a well-tested FAO-56 irrigation scheduling spreadsheet by (1) using both tools to schedule cotton irrigation during 2014 and 2015 in central Arizona and (2) conducting a post-hoc simulation study to further compare outputs from these tools. Two replications of each irrigation scheduling treatment and a water-stressed treatment were established on a 2.6 ha field. Irrigation schedules were developed on a weekly basis and administered via an overhead lateral-move sprinkler irrigation system. Neutron moisture meters were used weekly to estimate soil moisture status and crop water use, and destructive plant samples were routinely collected to estimate cotton leaf area index (LAI) and canopy weight. Cotton yield was estimated using two mechanical cotton pickers with differing capabilities: (1) a two-row picker that facilitated manual collection of yield samples from 32 m2 areas and (2) a four-row picker equipped with a sensor-based cotton yield monitoring system. In addition to statistical testing of field data via mixed models, the data were used for post-hoc reparameterization and fine-tuning of the irrigation scheduling tools. Post-hoc simulations were conducted to compare measured and simulated evapotranspiration, crop coefficients, root zone soil moisture depletion, cotton growth metrics, and yield for each irrigation treatment. While total seasonal irrigation amounts were similar among the two scheduling tools, the crop model recommended more water during anthesis and less during the early season, which led to higher cotton fiber yield in both seasons (p < 0.05). The tools calculated cumulative evapotranspiration similarly, with root mean squared errors (RMSEs) less than 13%; however, FAO-56 crop coefficient (Kc) plots demonstrated subtle differences in daily evapotranspiration calculations. Root zone soil moisture depletion was better calculated by CSM-CROPGRO-Cotton, perhaps due to its more complex soil profile simulation; however, RMSEs for depletion always exceeded 20% for both tools and reached 149% for the FAO-56 spreadsheet in 2014. CSM-CROPGRO-Cotton simulated cotton LAI, canopy weight, canopy height, and yield with RMSEs less than 21%, while the FAO-56 spreadsheet had no capability for such outputs. Through field verification and thorough post-hoc data analysis, the results demonstrated that the CSM-CROPGRO-Cotton model with updated FAO-56 ET routines could match or exceed the accuracy and capability of an FAO-56 spreadsheet tool for cotton water use calculations and irrigation scheduling. Keywords: Cottonseed, Crop coefficient, Decision support, Depletion, Evapotranspiration, Fiber, Management, Simulation, Soil moisture, Yield.

scholarly journals Irrigation Scheduling Using Soil Moisture Sensors

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Ruixiu Sui
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Management Practices ◽  
Root Zone ◽  
Weighted Average ◽  
Irrigation Scheduling ◽  
Time Interval ◽  
Soil Moisture Sensors

Irrigation is required to ensure crop production. Practical methods of use sensors to determine soil water status are needed in irrigation scheduling. Soil moisture sensors were evaluated and used for irrigation scheduling in humid region of the Mid-South US. Soil moisture sensors were installed in soil at depths of 15 cm, 30 cm, and 61 cm belowground. Soil volumetric water content was automatically measured by the sensors in a time interval of an hour during the crop growing season. Soil moisture data were wirelessly transferred onto internet through a wireless sensor network (WSN) so that the data could be remotely accessed online. Soil water content measured at the three depths were interpreted using a weighted average method to reflect the status of soil water in plant root zone. A threshold to trigger an irrigation event was determined with sensor-measured soil water content. An antenna mounting device was developed for operation of the WSN. Using the antenna mounting device, the soil moisture measurement was not be interrupted by crop field management practices.

scholarly journals Irrigation Scheduling Based on Wireless Sensors Output and Soil-Water Characteristic Curve in Two Soils

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1336 ◽  
Author(s):  
J.D. Jabro ◽  
W.B. Stevens ◽  
W.M. Iversen ◽  
B.L. Allen ◽  
U.M. Sainju
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Water ◽  
Root Zone ◽  
Sandy Loam ◽  
Characteristic Curve ◽  
Field Capacity ◽  
Irrigation Scheduling ◽  
Clay Loam ◽  
Soil Water Characteristic Curve

Data-driven irrigation planning can optimize crop yield and reduce adverse impacts on surface and ground water quality. We evaluated an irrigation scheduling strategy based on soil matric potentials recorded by wireless Watermark (WM) sensors installed in sandy loam and clay loam soils and soil-water characteristic curve data. Five wireless WM nodes (IRROmesh) were installed at each location, where each node consisted of three WM sensors that were installed at 15, 30, and 60 cm depths in the crop rows. Soil moisture contents, at field capacity and permanent wilting points, were determined from soil-water characteristic curves and were approximately 23% and 11% for a sandy loam, and 35% and 17% for a clay loam, respectively. The field capacity level which occurs shortly after an irrigation event was considered the upper point of soil moisture content, and the lower point was the maximum soil water depletion level at 50% of plant available water capacity in the root zone, depending on crop type, root depth, growth stage and soil type. The lower thresholds of soil moisture content to trigger an irrigation event were 17% and 26% in the sandy loam and clay loam soils, respectively. The corresponding soil water potential readings from the WM sensors to initiate irrigation events were approximately 60 kPa and 105 kPa for sandy loam, and clay loam soils, respectively. Watermark sensors can be successfully used for irrigation scheduling by simply setting two levels of moisture content using soil-water characteristic curve data. Further, the wireless system can help farmers and irrigators monitor real-time moisture content in the soil root zone of their crops and determine irrigation scheduling remotely without time consuming, manual data logging and frequent visits to the field.

AN ASSESSMENT OF THE CAPABILITY OF A COMPUTER PROGRAM TO ESTIMATE FALL SOIL MOISTURE AND DEEP PERCOLATION

1976 ◽  
Vol 56 (4) ◽  
pp. 357-362 ◽  
Author(s):  
J. C. VAN SCHAIK ◽  
D. S. CHANASYK ◽  
E. H. HOBBS
Keyword(s):  
Soil Moisture ◽  
Computer Program ◽  
Root Zone ◽  
Deep Drainage ◽  
Soil Moisture Storage ◽  
Moisture Contents ◽  
Growing Seasons ◽  
Actual Field ◽  
Moisture Storage ◽  
Summer Fallow

A computer program that estimates changes in soil moisture was used to calculate fall soil moisture contents and possible deep drainage. Generally good agreement was obtained between the calculated and measured total moisture contents under continuous wheat and grass after each of four and six growing seasons, respectively. Estimates of soil moisture storage and deep drainage under summer fallow showed discrepancies because unsaturated moisture flow was not included in the model. However, a comparison of actual field and estimated moisture data indicated that in two of five growing seasons, 3.7–7.5 cm of water could have been lost from the root zone of fallowed land because of deep drainage.

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