Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe
Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/5/1011 |
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| author | Xiaoshuai Pei Kenneth A. Sudduth Kristen S. Veum Minzan Li |
| author_facet | Xiaoshuai Pei Kenneth A. Sudduth Kristen S. Veum Minzan Li |
| author_sort | Xiaoshuai Pei |
| collection | DOAJ |
| description | Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343⁻2222 nm), apparent electrical conductivity (EC<sub>a</sub>), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, EC<sub>a</sub>, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties. |
| first_indexed | 2024-04-11T14:09:07Z |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T14:09:07Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-44acd69febb64d529d4b00940ccf83e72022-12-22T04:19:48ZengMDPI AGSensors1424-82202019-02-01195101110.3390/s19051011s19051011Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor ProbeXiaoshuai Pei0Kenneth A. Sudduth1Kristen S. Veum2Minzan Li3Key Laboratory of Modern Precision Agriculture System Integration Research-Ministry of Education, China Agricultural University, Beijing 100083, ChinaUSDA-ARS Cropping Systems and Water Quality Research Unit, Columbia, MO 65211, USAUSDA-ARS Cropping Systems and Water Quality Research Unit, Columbia, MO 65211, USAKey Laboratory of Modern Precision Agriculture System Integration Research-Ministry of Education, China Agricultural University, Beijing 100083, ChinaOptical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343⁻2222 nm), apparent electrical conductivity (EC<sub>a</sub>), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, EC<sub>a</sub>, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties.https://www.mdpi.com/1424-8220/19/5/1011diffuse reflectance spectroscopyprecision agricultureprofile soil propertiesproximal soil sensingin-situ sensing |
| spellingShingle | Xiaoshuai Pei Kenneth A. Sudduth Kristen S. Veum Minzan Li Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe Sensors diffuse reflectance spectroscopy precision agriculture profile soil properties proximal soil sensing in-situ sensing |
| title | Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe |
| title_full | Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe |
| title_fullStr | Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe |
| title_full_unstemmed | Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe |
| title_short | Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe |
| title_sort | improving in situ estimation of soil profile properties using a multi sensor probe |
| topic | diffuse reflectance spectroscopy precision agriculture profile soil properties proximal soil sensing in-situ sensing |
| url | https://www.mdpi.com/1424-8220/19/5/1011 |
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