Ad Hoc Modeling of Root Zone Soil Water with Landsat Imagery and Terrain and Soils Data
Agricultural producers require knowledge of soil water at plant rooting depths,while many remote sensing studies have focused on surface soil water or mechanisticmodels that are not easily parameterized. We developed site-specific empirical models topredict spring soil water content for two Montana...
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| Format: | Article |
| Language: | English |
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MDPI AG
2008-01-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/8/1/314/ |
| _version_ | 1798040087411294208 |
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| author | Jon M. Wraith Rick L. Lawrence Joel B. Sankey |
| author_facet | Jon M. Wraith Rick L. Lawrence Joel B. Sankey |
| author_sort | Jon M. Wraith |
| collection | DOAJ |
| description | Agricultural producers require knowledge of soil water at plant rooting depths,while many remote sensing studies have focused on surface soil water or mechanisticmodels that are not easily parameterized. We developed site-specific empirical models topredict spring soil water content for two Montana ranches. Calibration data sample sizeswere based on the estimated variability of soil water and the desired level of precision forthe soil water estimates. Models used Landsat imagery, a digital elevation model, and asoil survey as predictor variables. Our objectives were to see whether soil water could bepredicted accurately with easily obtainable calibration data and predictor variables and toconsider the relative influence of the three sources of predictor variables. Independentvalidation showed that multiple regression models predicted soil water with average error(RMSD) within 0.04 mass water content. This was similar to the accuracy expected basedon a statistical power test based on our sample size (n = 41 and n = 50). Improvedprediction precision could be achieved with additional calibration samples, and rangemanagers can readily balance the desired level of precision with the amount of effort tocollect calibration data. Spring soil water prediction effectively utilized a combination ofland surface imagery, terrain data, and subsurface soil characterization data. Rancherscould use accurate spring soil water content predictions to set stocking rates. Suchmanagement can help ensure that water, soil, and vegetation resources are usedconservatively in irrigated and non-irrigated rangeland systems. |
| first_indexed | 2024-04-11T22:02:40Z |
| format | Article |
| id | doaj.art-df831b613f524c01803f9007d0681ff1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:02:40Z |
| publishDate | 2008-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-df831b613f524c01803f9007d0681ff12022-12-22T04:00:52ZengMDPI AGSensors1424-82202008-01-0181314326Ad Hoc Modeling of Root Zone Soil Water with Landsat Imagery and Terrain and Soils DataJon M. WraithRick L. LawrenceJoel B. SankeyAgricultural producers require knowledge of soil water at plant rooting depths,while many remote sensing studies have focused on surface soil water or mechanisticmodels that are not easily parameterized. We developed site-specific empirical models topredict spring soil water content for two Montana ranches. Calibration data sample sizeswere based on the estimated variability of soil water and the desired level of precision forthe soil water estimates. Models used Landsat imagery, a digital elevation model, and asoil survey as predictor variables. Our objectives were to see whether soil water could bepredicted accurately with easily obtainable calibration data and predictor variables and toconsider the relative influence of the three sources of predictor variables. Independentvalidation showed that multiple regression models predicted soil water with average error(RMSD) within 0.04 mass water content. This was similar to the accuracy expected basedon a statistical power test based on our sample size (n = 41 and n = 50). Improvedprediction precision could be achieved with additional calibration samples, and rangemanagers can readily balance the desired level of precision with the amount of effort tocollect calibration data. Spring soil water prediction effectively utilized a combination ofland surface imagery, terrain data, and subsurface soil characterization data. Rancherscould use accurate spring soil water content predictions to set stocking rates. Suchmanagement can help ensure that water, soil, and vegetation resources are usedconservatively in irrigated and non-irrigated rangeland systems.http://www.mdpi.com/1424-8220/8/1/314/empirical modelsprecision agriculturerangelandsite-specific agriculturesoil survey |
| spellingShingle | Jon M. Wraith Rick L. Lawrence Joel B. Sankey Ad Hoc Modeling of Root Zone Soil Water with Landsat Imagery and Terrain and Soils Data Sensors empirical models precision agriculture rangeland site-specific agriculture soil survey |
| title | Ad Hoc Modeling of Root Zone Soil Water with Landsat Imagery and Terrain and Soils Data |
| title_full | Ad Hoc Modeling of Root Zone Soil Water with Landsat Imagery and Terrain and Soils Data |
| title_fullStr | Ad Hoc Modeling of Root Zone Soil Water with Landsat Imagery and Terrain and Soils Data |
| title_full_unstemmed | Ad Hoc Modeling of Root Zone Soil Water with Landsat Imagery and Terrain and Soils Data |
| title_short | Ad Hoc Modeling of Root Zone Soil Water with Landsat Imagery and Terrain and Soils Data |
| title_sort | ad hoc modeling of root zone soil water with landsat imagery and terrain and soils data |
| topic | empirical models precision agriculture rangeland site-specific agriculture soil survey |
| url | http://www.mdpi.com/1424-8220/8/1/314/ |
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