Neil C. Hansen’s research while affiliated with Brigham Young University–Hawaii and other places

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Publications (86)


The Conflicting Legacy of U.S. Irrigation
  • Preprint

December 2024

Robert B. Sowby

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Neil C Hansen

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Easton G Hopkins

Figure 2: Heat maps for the alternative data to VWC across the field in Rexburg using remote sensing.
Figure 5: Clustered irrigation zones based on expected zone. The clusters are relatively similar and follow the same general patterns as the predictions but could more reasonably be implemented as irrigation zones using VRI technology.
Figure 6: Partial dependence plots for influential covariates. The black line shows the marginal effect for the variable and the shaded area is the 95% credible interval for the marginal effect.
Figure 7: Feature importance for the covariates. Similar to the partial dependence plots, the most important features according to the importance measure is elevation, yield, and the NDVI for 2018 and 2019.
RAND index for the four different models for the 66 locations in the field.
Irrigation Zone Delineation by Coupling Neural Networks with Spatial Statistics
  • Article
  • Full-text available

October 2024

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18 Reads

The New England Journal of Statistics in Data Science

Variable rate irrigation (VRI) seeks to increase the efficiency of irrigation by spatially adjusting water output within an agricultural field. Central to the success of VRI technology is establishing homogeneous irrigation zones. In this research, we propose a fusion of statistical modeling and deep learning by using artificial neural networks to map irrigation zones from simple-to-measure predictors. We further couple our neural network model with spatial correlation to capture smooth variations in the irrigation zones. We demonstrate the effectiveness of our model to define irrigation zones for a farm of winter wheat crop in Rexburg, Idaho.

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Remote Sensing of Residential Landscape Irrigation in Weber County, Utah: Implications for Water Conservation, Image Analysis, and Drone Applications

October 2024

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10 Reads

Sustainability

Analyzing irrigation patterns to promote efficient water use in urban areas is challenging. Analysis of irrigation by remote sensing (AIRS) combines multispectral aerial imagery, evapotranspiration data, and ground-truth measurements to overcome these challenges. We demonstrate AIRS on eight neighborhoods in Weber County, Utah, using 0.6 m National Agriculture Imagery Program (NAIP) and 0.07 m drone imagery, reference evapotranspiration (ET), and water use records. We calculate the difference between the actual and hypothetical water required for each parcel and compare water use over three time periods (2018, 2021, and 2023). We find that the quantity of overwatering, as well as the number of customers overwatering, is decreasing over time. AIRS provides repeatable estimates of irrigated area and irrigation demand that allow water utilities to track water user habits and landscape changes over time and, when controlling for other variables, see if water conservation efforts are effective. In terms of image analysis, we find that (1) both NAIP and drone imagery are sufficient to measure irrigated area in urban settings, (2) the selection of a threshold value for the normalized difference vegetation index (NDVI) becomes less critical for higher-resolution imagery, and (3) irrigated area measurement can be enhanced by combining NDVI with other tools such as building footprint extraction, object classification, and deep learning.



Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

June 2024

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100 Reads

Agronomy

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Kirsten Sanders

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[...]

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Turfgrass irrigation consumes a large amount of the scarce freshwater in arid/semi-arid regions. Approximately 50% of this irrigation water is wasted. It has been suggested that determining patterns of spatial variability in soil moisture to modify applications with valve-in-head sprinkler technology can greatly reduce waste. Variable rate irrigation (VRI) studies in traditional agricultural settings have shown that VRI zones do not stay static temporally and need to be frequently redetermined. Electrical conductivity (ECa) data from Geonics EM38 surveys and data from Red, Green, Blue (RGB) and Thermal Infra-Red (Th.IR) drone surveys are less time-consuming and therefore expensive to collect than a dense field survey of soil moisture and grass health to produce accurate geostatistical maps. Drone flights and ECa surveys are compared here for their ability to accurately estimate spatial patterns of soil volumetric water content (VWC) using simple linear regression and z-score transformations for prediction—non-geostatistical approaches that require less data. Overall, ECa readings collected in the horizontal mode were the most consistent at capturing spatial patterns in soil moisture. Predictions from regression produced lower root mean squared errors (RMSEs) for the larger datasets. However, z-score transformation produced lower RMSEs when the sample number was very small and preserved the scale of values better than the regression approach. The results suggested that predictions from ECa and drone data were useful for capturing key features in soil moisture patterns for 2–3 weeks, suggesting that a periodic reassessment of zones is needed. Using ECa and drone data in an urban environment is more labor-intensive than in an agricultural field, so it is likely that automating periodic re-surveying of ECa data for zone definition would only be cost-effective for golf courses or high-income sports fields. Elsewhere, using static zones with variable rates applied to each zone may be more efficient.






Automated analysis of snowmelt from Sentinel-2 imagery to determine variable rate irrigation zones in the American Mountain West

June 2023

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19 Reads

Variable rate irrigation (VRI) is used to save water whilst maintaining crop yields in semiarid regions. A key problem is to be able to inexpensively determine spatial patterns in soil moisture so that VRI zones can be defined. In Southern Idaho, USA, the annual precipitation is low and most fall as winter snow. This research investigates whether snow melt patterns measured using freely available time-series Sentinel 2 imagery from Google Earth Engine can define useful VRI zones for two arable fields (Grace and Rexburg). The normalized difference snow index (NDSI) was computed for each 10 m pixel with snow for all winter images of the fields for 2018–2022. NDSI values were ranked within each image and average ranks were calculated for each month and over several years. The patterns of March NDSI were most similar to patterns in yield and soil moisture observed in previous years. Zones were determined using K-means classification of the mean ranks of March NDSI. Kruskal Wallis H tests showed consistent and significant differences between zones for key soil, plant, and topographic variables. For the Grace site, differences between zones were more consistent in their order of magnitude than VRI zones which were calculated using a labor-intensive method. For the Rexburg site, zones were shown to be better when based on snowmelt data from March 2018 to 2022 rather than just March 2019. It is important to base zones on several years of data because in some years there was no snow observed in the Grace field in March. In locations where the majority of soil moisture comes from snowmelt, basing VRI zones on several years of snowmelt patterns in March is a useful and inexpensive tool for deriving meaningful VRI zones. The code used to automatically extract suitable sentinel images and calculate the NDSI is included so that practitioners can use this approach in other locations.


Citations (61)


... A simple linear regression and zscore calibration approach for calibrating the ECa and drone data to give VWC data were also compared. This paper significantly expands on the initial study of Kerry et al. [66] by looking at information from several more temporal surveys of two of the fields and more detailed examination of the differences between prediction approaches. ...

Reference:

Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data
33. Assessing the ability of ECa and drone data to capture spatial patterns in soil moisture for more precise turfgrass irrigation
  • Citing Article
  • July 2023

... For height estimation, the three lowest RMSE values were achieved with MGRVI, H, and GLI from the RGB camera. Some past studies indicated that MGRVI showed a high correlation with plant growth parameters (Feng et al., 2022;Hammond et al., 2023), partially supporting the ability of MGRVI in accurate height estimation. However, the reason remains unclear, and further research is needed to clarify these details. ...

Assessing Within-Field Variation in Alfalfa Leaf Area Index Using UAV Visible Vegetation Indices

Agronomy

... Site-specific management can be used to address surface hardness and soil moisture variability through PTM practices (Burbrink & Straw, 2023). Periodic re-evaluation of irrigation zones is necessary to account for temporal changes in soil moisture and normalized difference vegetation index (NDVI) (Kerry et al., 2023). A deeper understanding of SSMUs in stadium football pitches is needed to further advance PTM (Braun et al., 2023;Kerry et al., 2023). ...

Spatial Analysis of Soil Moisture and Turfgrass Health to Determine Zones for Spatially Variable Irrigation Management

Agronomy

... As a temporary storage in soil crevices, soil moisture is an important variable in the water cycle process at the air-soil interface (Flint et al., 2023). Its spatio-temporal variation is crucial for flood risk assessment, drought evolution mechanisms, meteorological hydrology, and crop growth monitoring (Owusu et al., 2024). ...

Irrigation Zone Delineation and Management with a Field-Scale Variable Rate Irrigation System in Winter Wheat

Agronomy

... Mortality of seeded species can be reduced using drills equipped with metal discs to create a furrow for large seeded species, such as perennial grasses ( Young and Mckenzie 1982 ). These furrows serve to create microsites that are protected from direct sunlight and improve moisture availability ( Anderson et al. 2023 ). Alternatively, smaller seeded species, such as sagebrush, cannot tolerate deep burial and must be broadcast using airplanes or ground-based dispersal ( Shaw et al. 2005 ;Ott et al. 2016 ). ...

Furrows and, to a Lesser Extent, Seed Priming Improve Restoration Success in the Sagebrush Steppe

Rangeland Ecology & Management

... Achieving HR involves balancing the hydrological, ecological, and agricultural conditions using techniques such as terracing, check dams, and native afforestation [76]. Transverse ditches filled with gravel and sand can be used to effectively increase soil moisture [77]. To improve the sustainability of a watershed, it is essential to identify aspects such as the water quantity and quality, species, ecosystems, resilience to climate change, and local culture [78,79]. ...

Hydrologic restoration of anthropoentically altered springs in the Sheldon National Wildlife Refuge in the Great Basin, USA

Journal of Arid Environments

... These require an informed definition of an initial set of variables, a series of statistical steps for filtering and exploring these predictors, and model selection exercises that consider multiple sets of predictors (Cobos and Peterson 2023). Recently developed models have been useful to predict suitable areas for dryland farming (Ortiz Cano et al., 2023), and to aid future dryland ecosystem restoration (Weldemariam et al., 2022). ...

Ecological-niche modeling reveals current opportunities for Agave dryland farming in Sonora, Mexico and Arizona, USA

... Surprisingly, some studies found that DOC concentrations can even be higher a er a re than previously, although the organic matter load should have decreased due to J. Geosci. Insights., 2024, 2 is pyrogenic dissolved organic matter (PyDOM) bears unique chemical signatures relative to many other forms of DOM originating from unburned parent materials [32,33]. ...

Megafire affects stream sediment flux and dissolved organic matter reactivity, but land use dominates nutrient dynamics in semiarid watersheds

... The use of CS in NbS has been demonstrated to create positive outcomes by fostering public education [29,40], increasing buy-in from citizens on environmental stewardship [41,42] and supporting professional scientists in accomplishing research methods that are otherwise cost-prohibitive [43,44]. The opportunities to involve CS in NbS projects are as wide as the possibilities of NbS designs and programs as reflected in our search. ...

Citizen science reveals unexpected solute patterns in semiarid river networks

... In addition, considering that most small-scale farmlands are often characterised by challenging terrain featuring steep topography, it is essential to assess the influence of landscape variability on maize AGB (Polzin and Hughes, 2023). Therefore, landscape and landscape related variables that directly and indirectly influence crop growth such as soil moisture, slope, aspect, and elevation can provide a precise maize AGB estimation (Svedin et al., 2021, Fry and Guber, 2020, Goldenberg et al., 2022. In this regard, integrating drone-derived multispectral bands, with optimal vegetation indices, and biophysical landscape variables can provide better and precise estimates of maize AGB in small-scale farming systems. ...

Identifying Within-Field Spatial and Temporal Crop Water Stress to Conserve Irrigation Resources with Variable-Rate Irrigation

Agronomy