Geostatistics Precision Agriculture Modeling on Moisture Root Zone Profiles in Clay Loam and Clay Soils, Using Time Domain Reflectometry Multisensors and Soil Analysis
Accurate measurement and understanding of the spatiotemporal distribution of soil water content (SWC) are crucial in various environmental and agricultural sectors. The present study implements a novel precision agriculture (PA) approach under sugarbeet field conditions of two moisture-irrigation tr...
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2025-07-01
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| author | Agathos Filintas |
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| description | Accurate measurement and understanding of the spatiotemporal distribution of soil water content (SWC) are crucial in various environmental and agricultural sectors. The present study implements a novel precision agriculture (PA) approach under sugarbeet field conditions of two moisture-irrigation treatments with two subfactors, clay loam (CL) and clay (C) soils, for geostatistics modeling (seven models’ evaluation) of time domain reflectometry (TDR) multisensor network measurements. Two different sensor calibration methods (M1 and M2) were trialed, as well as the results of laboratory soil analysis for geospatial two-dimensional (2D) imaging for accurate GIS maps of root zone moisture profiles, granular, and hydraulic profiles in multiple soil layers (0–75 cm depth). Modeling results revealed that the best-fitted semi-variogram models for the granular attributes were circular, exponential, pentaspherical, and spherical, while for hydraulic attributes were found to be exponential, circular, and spherical models. The results showed that kriging modeling, spatial and temporal imaging for accurate profile SWC <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi><mi>v</mi></mrow><mrow><mi>T</mi><mi>D</mi><mi>R</mi></mrow></msub></mrow></semantics></math></inline-formula> (m<sup>3</sup>·m<sup>−3</sup>) maps, the exponential model was identified as the most appropriate with TDR sensors using calibration M1, and the exponential and spherical models were the most appropriate when using calibration M2. The resulting PA profile maps depict spatiotemporal soil water variability with very high resolutions at the centimeter scale. The best validation measures of PA profile SWC <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi><mi>v</mi></mrow><mrow><mi>T</mi><mi>D</mi><mi>R</mi></mrow></msub></mrow></semantics></math></inline-formula> maps obtained were Nash-Sutcliffe model efficiency NSE = 0.6657, MPE = 0.00013, RMSE = 0.0385, MSPE = −0.0022, RMSSE = 1.6907, ASE = 0.0418, and MSDR = 0.9695. The sensor results using calibration M2 were found to be more valuable in environmental irrigation decision-making for a more accurate and timely decision on actual crop irrigation, with the lowest statistical and geostatistical errors. The best validation measures for accurate profile SWC <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi><mi>v</mi></mrow><mrow><mi>T</mi><mi>D</mi><mi>R</mi></mrow></msub></mrow></semantics></math></inline-formula> (m<sup>3</sup>·m<sup>−3</sup>) maps obtained for clay loam over clay soils. Visualizing the SWC results and their temporal changes via root zone profile geostatistical maps assists farmers and scientists in making informed and timely environmental irrigation decisions, optimizing energy, saving water, increasing water-use efficiency and crop production, reducing costs, and managing water–soil resources sustainably. |
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| spelling | doaj-art-2a78834cadb249deae2993c5abb20e5d2025-07-25T13:24:48ZengMDPI AGHydrology2306-53382025-07-0112718310.3390/hydrology12070183Geostatistics Precision Agriculture Modeling on Moisture Root Zone Profiles in Clay Loam and Clay Soils, Using Time Domain Reflectometry Multisensors and Soil AnalysisAgathos Filintas0Department of Agricultural Technologists, Campus Gaiopolis, University of Thessaly, 41500 Larisa, GreeceAccurate measurement and understanding of the spatiotemporal distribution of soil water content (SWC) are crucial in various environmental and agricultural sectors. The present study implements a novel precision agriculture (PA) approach under sugarbeet field conditions of two moisture-irrigation treatments with two subfactors, clay loam (CL) and clay (C) soils, for geostatistics modeling (seven models’ evaluation) of time domain reflectometry (TDR) multisensor network measurements. Two different sensor calibration methods (M1 and M2) were trialed, as well as the results of laboratory soil analysis for geospatial two-dimensional (2D) imaging for accurate GIS maps of root zone moisture profiles, granular, and hydraulic profiles in multiple soil layers (0–75 cm depth). Modeling results revealed that the best-fitted semi-variogram models for the granular attributes were circular, exponential, pentaspherical, and spherical, while for hydraulic attributes were found to be exponential, circular, and spherical models. The results showed that kriging modeling, spatial and temporal imaging for accurate profile SWC <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi><mi>v</mi></mrow><mrow><mi>T</mi><mi>D</mi><mi>R</mi></mrow></msub></mrow></semantics></math></inline-formula> (m<sup>3</sup>·m<sup>−3</sup>) maps, the exponential model was identified as the most appropriate with TDR sensors using calibration M1, and the exponential and spherical models were the most appropriate when using calibration M2. The resulting PA profile maps depict spatiotemporal soil water variability with very high resolutions at the centimeter scale. The best validation measures of PA profile SWC <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi><mi>v</mi></mrow><mrow><mi>T</mi><mi>D</mi><mi>R</mi></mrow></msub></mrow></semantics></math></inline-formula> maps obtained were Nash-Sutcliffe model efficiency NSE = 0.6657, MPE = 0.00013, RMSE = 0.0385, MSPE = −0.0022, RMSSE = 1.6907, ASE = 0.0418, and MSDR = 0.9695. The sensor results using calibration M2 were found to be more valuable in environmental irrigation decision-making for a more accurate and timely decision on actual crop irrigation, with the lowest statistical and geostatistical errors. The best validation measures for accurate profile SWC <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi><mi>v</mi></mrow><mrow><mi>T</mi><mi>D</mi><mi>R</mi></mrow></msub></mrow></semantics></math></inline-formula> (m<sup>3</sup>·m<sup>−3</sup>) maps obtained for clay loam over clay soils. Visualizing the SWC results and their temporal changes via root zone profile geostatistical maps assists farmers and scientists in making informed and timely environmental irrigation decisions, optimizing energy, saving water, increasing water-use efficiency and crop production, reducing costs, and managing water–soil resources sustainably.https://www.mdpi.com/2306-5338/12/7/183soil analysismoisture 2D GIS mappingTDR multisensors calibrationprecision agriculturegeostatistics modeling |
| spellingShingle | Agathos Filintas Geostatistics Precision Agriculture Modeling on Moisture Root Zone Profiles in Clay Loam and Clay Soils, Using Time Domain Reflectometry Multisensors and Soil Analysis Hydrology soil analysis moisture 2D GIS mapping TDR multisensors calibration precision agriculture geostatistics modeling |
| title | Geostatistics Precision Agriculture Modeling on Moisture Root Zone Profiles in Clay Loam and Clay Soils, Using Time Domain Reflectometry Multisensors and Soil Analysis |
| title_full | Geostatistics Precision Agriculture Modeling on Moisture Root Zone Profiles in Clay Loam and Clay Soils, Using Time Domain Reflectometry Multisensors and Soil Analysis |
| title_fullStr | Geostatistics Precision Agriculture Modeling on Moisture Root Zone Profiles in Clay Loam and Clay Soils, Using Time Domain Reflectometry Multisensors and Soil Analysis |
| title_full_unstemmed | Geostatistics Precision Agriculture Modeling on Moisture Root Zone Profiles in Clay Loam and Clay Soils, Using Time Domain Reflectometry Multisensors and Soil Analysis |
| title_short | Geostatistics Precision Agriculture Modeling on Moisture Root Zone Profiles in Clay Loam and Clay Soils, Using Time Domain Reflectometry Multisensors and Soil Analysis |
| title_sort | geostatistics precision agriculture modeling on moisture root zone profiles in clay loam and clay soils using time domain reflectometry multisensors and soil analysis |
| topic | soil analysis moisture 2D GIS mapping TDR multisensors calibration precision agriculture geostatistics modeling |
| url | https://www.mdpi.com/2306-5338/12/7/183 |
| work_keys_str_mv | AT agathosfilintas geostatisticsprecisionagriculturemodelingonmoisturerootzoneprofilesinclayloamandclaysoilsusingtimedomainreflectometrymultisensorsandsoilanalysis |