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An approach for delineating homogeneous within-field zones using proximal sensing and multivariate geostatistics

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  • Landrum, Carla
  • Castrignanò, Annamaria
  • Mueller, Tom
  • Zourarakis, Demetrio
  • Zhu, Junfeng
  • De Benedetto, Daniela

Abstract

At the landscape scale, the soil moisture distribution derives from the combination of hydrologic, pedologic, and geomorphic processes. This study uses multicollocated factorial cokriging to determine the spatial scale(s) at which soil properties and terrain attributes affect the soil moisture distribution and can be used to identify homogeneous zones in the field. Georeferenced sensing (e.g. geoelectric sensing and LiDAR) acquires real-time, non-invasive and high resolution data over large spatial extents that can be used in combination with spatial, temporal and scale-dependent information of primary interest. This study uses high resolution geoelectric and LiDAR data as auxiliary measures to supplement data obtained by the analysis of 127 soil cores taken from a 40hectare Central Kentucky (USA) karst landscape. Shallow and deep apparent electrical conductivities (EC) were measured using a Veris 3100 in tandem with soil moisture on three separate dates with increasing soil moisture contents ranging from plant wilting point up to field capacity. Terrain features were produced from 2010 LiDAR returns collected at ≤1m nominal pulse spacing. Exploratory statistics were used to identify 12 field characteristics that would be useful in determining the spatial distribution of soil moisture, including terrain features (slope and elevation), soil physical and chemical properties and geoelectric measurements (EC for each date). A linear model of coregionalization (LMC) was fitted to the matrix of direct and cross experimental variograms for the 12 characteristics. The LMC consisted of 3 basic components: nugget, spherical (short-range scale=40m) and exponential (long-range scale=250m) where each component explained 17%, 22% and 60% of the total measured variation, respectively. Results suggest that soil texture and organic matter affect the soil moisture variability. Mapping the long-range regionalized factor allows us to delineate the field into homogeneous zones. This study shows the potential for using proximal sensing and multivariate geostatistics to develop soil moisture management strategies under water stressed conditions.

Suggested Citation

  • Landrum, Carla & Castrignanò, Annamaria & Mueller, Tom & Zourarakis, Demetrio & Zhu, Junfeng & De Benedetto, Daniela, 2015. "An approach for delineating homogeneous within-field zones using proximal sensing and multivariate geostatistics," Agricultural Water Management, Elsevier, vol. 147(C), pages 144-153.
    • Handle: RePEc:eee:agiwat:v:147:y:2015:i:c:p:144-153
    DOI: 10.1016/j.agwat.2014.07.013
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    References listed on IDEAS

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    1. Hai Nguyen & Noel Cressie & Amy Braverman, 2012. "Spatial Statistical Data Fusion for Remote Sensing Applications," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1004-1018, September.
    2. Islam, Mohammad Monirul & Saey, Timothy & Meerschman, Eef & De Smedt, Philippe & Meeuws, Fun & Van De Vijver, Ellen & Van Meirvenne, Marc, 2011. "Delineating water management zones in a paddy rice field using a Floating Soil Sensing System," Agricultural Water Management, Elsevier, vol. 102(1), pages 8-12.
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    1. Landrum, Carla & Castrignanó, Annamaria & Zourarakis, Demetrio & Mueller, Tom, 2016. "Assessing the time stability of soil moisture patterns using statistical and geostatistical approaches," Agricultural Water Management, Elsevier, vol. 177(C), pages 118-127.
    2. Hodges, Blade & Tagert, Mary Love & Paz, Joel O. & Meng, Qingmin, 2023. "Assessing in-field soil moisture variability in the active root zone using granular matrix sensors," Agricultural Water Management, Elsevier, vol. 282(C).

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