Analysis of soil water availability by integrating spatial and temporal sensor-based data

Pan, Luan; Adamchuk, Viacheslav I.; Martin, Derrel L.; Schroeder, Mark A.; Ferguson, Richard B.

doi:10.1007/s11119-013-9305-x

Cited by 26 publications

(23 citation statements)

References 13 publications

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“…This has been demonstrated by [34,61]. The data from soil moisture sensors located in the management units can also be used in generating dynamic application maps based on a relationship between the soil moisture depletion and the mapped EC values.…”

Section: Proximal Sensing and Mapping Of Soil Moisturementioning

confidence: 99%

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

et al. 2017

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Globally, the irrigation of crops is the largest consumptive user of fresh water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This paper discusses how incorporating adaptive decision support systems into precision irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil-plant-atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation.

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Section: Proximal Sensing and Mapping Of Soil Moisturementioning

confidence: 99%

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

et al. 2017

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“…Compared with abundant research on soil moisture content (either gravimetric water content w or volumetric water content θ), limited research has been done to understand the spatiotemporal variability or pattern of soil matric potential (ψ), which exhibits a nonlinear relationship with w or θ. However, the energy status of soil water, as represented by ψ , is often more critical to processes such as water flow and solute transport (Hayashi et al, 2009), slope instability and erosion (Rezaur et al, 2002), tree xylem sap flow (Meinzer et al, 2013), greenhouse gas emission (Castellano et al, 2010), and soil water availability to plants (Pan et al, 2013). A total of 12 studies on ψ spatiotemporal variability are summarized in Table 1, from which four knowledge gaps were identified:…”

Section: Summary Of Spatial Variability and Temporal Stability Of Pubmentioning

confidence: 99%

Spatiotemporal Patterns of Soil Matric Potential in the Shale Hills Critical Zone Observatory

Yang

Lin

2015

Vadose Zone Journal

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Spatiotemporal patterns of soil moisture are important for understanding landscape hydrologic processes. While the spatiotemporal characteristics of soil moisture content (q) have been frequently studied, those of soil matric potential (y) remain sparse. We investigated the spatial variability and temporal stability of y at multiple depths (10-100 cm) across a 7.9-ha forested catchment and their relationships with soil type, terrain, and season. The results from a 5.5-yr database consisting of 62 sites clearly showed a downward parabolic trend in y spatial variability with decreasing spatial mean y values (i.e., becoming drier) across all depths. The catchment's overall spatial variability of y generally increased with soil depth and was relatively high during summer and fall. Sand and silt contents were signiicant factors (p < 0.05) inluencing y in surface layers (0-20 cm), while y values in the subsurface (40-100 cm) were highly correlated with elevation. The temporal stability of y spatial pattern was generally higher in the surface soil than the subsoil but weaker in the spring than in other seasons. Moreover, relatively dry areas tended to have less evident y temporal stability at each depth, attributable to a higher sensitivity of y to changes in q when the soil is dry. Finally, at least one of the representative sites of the catchment mean y at each depth was found within a south-facing concave hillslope at mid-elevation, and these sites were different from the representative sites of the catchment mean q. The results of this study have implications for upscaling soil water from point-based observations to hillslope and catchment scales.Abbreviations: SLU, soil-landform unit.

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“…3). This type of information has been used for the delineation of irrigation management zones (IMZs; Pan et al 2013). As noted previously, the ECa map is subject to field conditions at the time of the sampling (Martini et al 2016).…”

Section: Hydrogeophysical Mapping and Eof Analysismentioning

confidence: 99%

“…The major limitation to implementing this technology often lies in the management of spatial datasets and the writing of irrigation prescription maps that address variables impacting yield and SWC (Evans et al 1996;Howell et al 2012). This requires efficient and accurate methods for measuring the subfield scale spatial variability of soil properties including porosity, saturated hydraulic conductivity, unsaturated hydraulic conductivity, available water, texture and depth (Hezarjaribi and Sourell 2007;Pan et al 2013;Ranney et al 2015). Managing irrigation rates and times based on hydraulic properties allows for irrigators to prescribe application depths based on the SWC below field capacity and above maximum allowable depletion.…”

Section: Introductionmentioning

confidence: 99%

Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management

et al. 2018

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Precision agriculture offers the technologies to manage for infield variability and incorporate variability into irrigation management decisions. The major limitation of this technology often lies in the reconciliation of disparate data sources and the generation of irrigation prescription maps. Here the authors explore the utility of the cosmic-ray neutron probe (CRNP) which measures volumetric soil water content (SWC) in the top ~ 30 cm of the soil profile. The key advantages of CRNP is that the sensor is passive, non-invasive, mobile and soil temperature-invariant, making data collection more compatible with existing farm operations and extending the mapping period. The objectives of this study were to: (1) improve the delineation of irrigation management zones within a field and (2) estimate spatial soil hydraulic properties to make effective irrigation prescriptions. Ten CRNP SWC surveys were collected in a 53-ha field in Nebraska. The SWC surveys were analyzed using Empirical Orthogonal Functions (EOFs) to isolate the underlying spatial structure. A statistical bootstrapping analysis confirmed the CRNP + EOF provided superior soil hydraulic property estimates, compared to other hydrogeophysical datasets, when linearly correlated to laboratory measured soil hydraulic properties (field capacitydigitalcommons.unl.edu F i n k e n b i n e r e t a l . i n P r e c i s i o n A g r i c u lt u r e , 2 0 1 8 2estimates reduced 20-25% in root mean square error). The authors propose a soil sampling strategy for better quantifying soil hydraulic properties using CRNP + EOF methods. Here, five CRNP surveys and 6-8 sample locations for laboratory analysis were sufficient to describe the spatial distribution of soil hydraulic properties within this field. While the proposed strategy may increase overall effort, rising scrutiny for agricultural water-use could make this technology cost-effective.

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Analysis of soil water availability by integrating spatial and temporal sensor-based data

Cited by 26 publications

References 13 publications

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Spatiotemporal Patterns of Soil Matric Potential in the Shale Hills Critical Zone Observatory

Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management

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