Soil moisture sensor location-allocation using spatial association of surface moisture data
Balancing cost and performance is typically required when deploying a soil moisture sensor array. The sensor array's performance is essentially dependent on the appropriate placement of the sensors, which is fundamentally a location-allocation problem. In this study, a novel approach based on s...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525001625 |
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| author | Dipankar Mandal Raj Khosla Louis Longchamps Deepak Joshi |
| author_facet | Dipankar Mandal Raj Khosla Louis Longchamps Deepak Joshi |
| author_sort | Dipankar Mandal |
| collection | DOAJ |
| description | Balancing cost and performance is typically required when deploying a soil moisture sensor array. The sensor array's performance is essentially dependent on the appropriate placement of the sensors, which is fundamentally a location-allocation problem. In this study, a novel approach based on spatial association of surface soil moisture (SASM) is presented. It proposes selecting a sub-sample of sensor locations that best represent the spatial distribution of soil moisture while maximizing the variance in soil moisture with the minimum number of sample sites. This approach was tested at two sites with maize cultivated fields in Colorado. Neutron probe readings were collected at 15 cm depth across 41 and 31 locations throughout the entire crop growing season in two maize fields in Colorado. The number of soil sensors were optimized in a range of 17–19 with optimum site configuration for all different data acquisition dates. A global measure of spatial association (GMSA) analysis indicated consistency in spatial pattern between reduced number of sub-samples and original samples. Strategic sensor placement, driven by insights into soil-water dynamics patterns and intrinsic field properties, is essential for informed decision-making in water management within an irrigated maize field. |
| format | Article |
| id | doaj-art-b2b3a4e9e63f4d94886e2b1ea669f614 |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-b2b3a4e9e63f4d94886e2b1ea669f6142025-08-20T02:16:29ZengElsevierSmart Agricultural Technology2772-37552025-08-011110092910.1016/j.atech.2025.100929Soil moisture sensor location-allocation using spatial association of surface moisture dataDipankar Mandal0Raj Khosla1Louis Longchamps2Deepak Joshi3Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA; Agro-geoinformatics Lab, School of Agro and Rural Technology, Indian Institute of Technology Guwahati, Assam 781039, IndiaDepartment of Agronomy, Kansas State University, Manhattan, KS 66506, USA; Corresponding author.Department of Soil and Crop Sciences Section, Cornell University, Ithaca, NY 14853, USADepartment of Agronomy, Kansas State University, Manhattan, KS 66506, USABalancing cost and performance is typically required when deploying a soil moisture sensor array. The sensor array's performance is essentially dependent on the appropriate placement of the sensors, which is fundamentally a location-allocation problem. In this study, a novel approach based on spatial association of surface soil moisture (SASM) is presented. It proposes selecting a sub-sample of sensor locations that best represent the spatial distribution of soil moisture while maximizing the variance in soil moisture with the minimum number of sample sites. This approach was tested at two sites with maize cultivated fields in Colorado. Neutron probe readings were collected at 15 cm depth across 41 and 31 locations throughout the entire crop growing season in two maize fields in Colorado. The number of soil sensors were optimized in a range of 17–19 with optimum site configuration for all different data acquisition dates. A global measure of spatial association (GMSA) analysis indicated consistency in spatial pattern between reduced number of sub-samples and original samples. Strategic sensor placement, driven by insights into soil-water dynamics patterns and intrinsic field properties, is essential for informed decision-making in water management within an irrigated maize field.http://www.sciencedirect.com/science/article/pii/S2772375525001625Precision irrigationSensor networkSpatial patternSensor placement |
| spellingShingle | Dipankar Mandal Raj Khosla Louis Longchamps Deepak Joshi Soil moisture sensor location-allocation using spatial association of surface moisture data Smart Agricultural Technology Precision irrigation Sensor network Spatial pattern Sensor placement |
| title | Soil moisture sensor location-allocation using spatial association of surface moisture data |
| title_full | Soil moisture sensor location-allocation using spatial association of surface moisture data |
| title_fullStr | Soil moisture sensor location-allocation using spatial association of surface moisture data |
| title_full_unstemmed | Soil moisture sensor location-allocation using spatial association of surface moisture data |
| title_short | Soil moisture sensor location-allocation using spatial association of surface moisture data |
| title_sort | soil moisture sensor location allocation using spatial association of surface moisture data |
| topic | Precision irrigation Sensor network Spatial pattern Sensor placement |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525001625 |
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