Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR Spectroscopy
Precision agriculture relies on highly detailed soil maps to optimize resource use. Proximal sensing methods, such as EMI, require a certain number of soil samples and laboratory analysis to interpolate the characteristics of the soil. NIR diffuse reflectance spectroscopy offers a rapid, low-cost al...
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MDPI AG
2024-12-01
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| Series: | Soil Systems |
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| Online Access: | https://www.mdpi.com/2571-8789/8/4/128 |
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| author | Leonardo Pace Simone Priori Monica Zanini Valerio Cristofori |
| author_facet | Leonardo Pace Simone Priori Monica Zanini Valerio Cristofori |
| author_sort | Leonardo Pace |
| collection | DOAJ |
| description | Precision agriculture relies on highly detailed soil maps to optimize resource use. Proximal sensing methods, such as EMI, require a certain number of soil samples and laboratory analysis to interpolate the characteristics of the soil. NIR diffuse reflectance spectroscopy offers a rapid, low-cost alternative that increases datapoints and map accuracy. This study tests and optimizes a methodology for high-detail soil mapping in a 2.5 ha hazelnut grove in Grosseto, Southern Tuscany, Italy, using both EMI sensors (GF Mini Explorer, Brno, Czech Republic) and a handheld NIR spectrometer (Neospectra Scanner, Si-Ware Systems, Menlo Park, CA, USA). In addition to two profiles selected by clustering, another 35 topsoil augerings (0–30 cm) were added. Laboratory analyses were performed on only five samples (two profiles + three samples from the augerings). Partial least square regression (PLSR) with a national spectral library, augmented by the five local samples, predicted clay, sand, organic carbon (SOC), total nitrogen (TN), and cation exchange capacity (CEC). The 37 predicted datapoints were used for spatial interpolation, using the ECa map, elevation, and DEM derivatives as covariates. Kriging with external drift (KED) was used to spatialize the results. The errors of the predictive maps were calculated using five additional validation points analyzed by conventional methods. The validation showed good accuracy of the predictive maps, particularly for SOC and TN. |
| format | Article |
| id | doaj-art-3ba7c6f273c74eb5bef2084655274ef0 |
| institution | DOAJ |
| issn | 2571-8789 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Soil Systems |
| spelling | doaj-art-3ba7c6f273c74eb5bef2084655274ef02025-08-20T02:57:21ZengMDPI AGSoil Systems2571-87892024-12-018412810.3390/soilsystems8040128Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR SpectroscopyLeonardo Pace0Simone Priori1Monica Zanini2Valerio Cristofori3Department of Agriculture and Forest Sciences (DAFNE), University of Tuscia, Via San Camillo de Lellis Snc, 01100 Viterbo, ItalyDepartment of Agriculture and Forest Sciences (DAFNE), University of Tuscia, Via San Camillo de Lellis Snc, 01100 Viterbo, ItalyDepartment of Agriculture and Forest Sciences (DAFNE), University of Tuscia, Via San Camillo de Lellis Snc, 01100 Viterbo, ItalyDepartment of Agriculture and Forest Sciences (DAFNE), University of Tuscia, Via San Camillo de Lellis Snc, 01100 Viterbo, ItalyPrecision agriculture relies on highly detailed soil maps to optimize resource use. Proximal sensing methods, such as EMI, require a certain number of soil samples and laboratory analysis to interpolate the characteristics of the soil. NIR diffuse reflectance spectroscopy offers a rapid, low-cost alternative that increases datapoints and map accuracy. This study tests and optimizes a methodology for high-detail soil mapping in a 2.5 ha hazelnut grove in Grosseto, Southern Tuscany, Italy, using both EMI sensors (GF Mini Explorer, Brno, Czech Republic) and a handheld NIR spectrometer (Neospectra Scanner, Si-Ware Systems, Menlo Park, CA, USA). In addition to two profiles selected by clustering, another 35 topsoil augerings (0–30 cm) were added. Laboratory analyses were performed on only five samples (two profiles + three samples from the augerings). Partial least square regression (PLSR) with a national spectral library, augmented by the five local samples, predicted clay, sand, organic carbon (SOC), total nitrogen (TN), and cation exchange capacity (CEC). The 37 predicted datapoints were used for spatial interpolation, using the ECa map, elevation, and DEM derivatives as covariates. Kriging with external drift (KED) was used to spatialize the results. The errors of the predictive maps were calculated using five additional validation points analyzed by conventional methods. The validation showed good accuracy of the predictive maps, particularly for SOC and TN.https://www.mdpi.com/2571-8789/8/4/128precision agriculturedigital soil mappingelectrical conductivityspectroscopysoil monitoring |
| spellingShingle | Leonardo Pace Simone Priori Monica Zanini Valerio Cristofori Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR Spectroscopy Soil Systems precision agriculture digital soil mapping electrical conductivity spectroscopy soil monitoring |
| title | Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR Spectroscopy |
| title_full | Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR Spectroscopy |
| title_fullStr | Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR Spectroscopy |
| title_full_unstemmed | Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR Spectroscopy |
| title_short | Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR Spectroscopy |
| title_sort | soil mapping of small fields with limited number of samples by coupling emi and nir spectroscopy |
| topic | precision agriculture digital soil mapping electrical conductivity spectroscopy soil monitoring |
| url | https://www.mdpi.com/2571-8789/8/4/128 |
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