Remote Sensing and Kriging with External Drift to Improve Sparse Proximal Soil Sensing Data and Define Management Zones in Precision Agriculture
The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been...
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MDPI AG
2023-12-01
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| Online Access: | https://www.mdpi.com/2624-7402/5/4/143 |
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| author | Hugo Rodrigues Marcos B. Ceddia Gustavo M. Vasques Vera L. Mulder Gerard B. M. Heuvelink Ronaldo P. Oliveira Ziany N. Brandão João P. S. Morais Matheus L. Neves Sílvio R. L. Tavares |
| author_facet | Hugo Rodrigues Marcos B. Ceddia Gustavo M. Vasques Vera L. Mulder Gerard B. M. Heuvelink Ronaldo P. Oliveira Ziany N. Brandão João P. S. Morais Matheus L. Neves Sílvio R. L. Tavares |
| author_sort | Hugo Rodrigues |
| collection | DOAJ |
| description | The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the <i>k</i>-means clustering algorithm: using data from proximal sensors (OK), proximal sensors combined with remote sensors (KED), and remote sensors. Seventy-two samples (0–10 cm in depth) were collected and analyzed in a laboratory (1 sample per hectare) for concentrations of clay, calcium, organic carbon, and magnesium to assess the capacity of the management zone maps created using analysis of variance. All zones created using the three data groups could distinguish the different treatment areas. The three data sources used to map management zones produced similar map zones, but the zone map using a combination of proximal and remote data did not show an improvement in defining the management zones, and using only remote sensing data lowered the significance levels of differentiating each zone compared to the OK and KED maps. In summary, this study not only underscores the global applicability of proximal and remote sensing techniques in precision agriculture but also sheds light on the nuances of their integration. The study’s findings affirm the efficacy of these advanced technologies in addressing the challenges posed by soil heterogeneity, paving the way for more nuanced and site-specific agricultural practices worldwide. |
| first_indexed | 2024-03-08T21:04:57Z |
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| language | English |
| last_indexed | 2024-03-08T21:04:57Z |
| publishDate | 2023-12-01 |
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| spelling | doaj.art-d8ca4a63acf349449f8df6722978b3532023-12-22T13:45:55ZengMDPI AGAgriEngineering2624-74022023-12-01542326234810.3390/agriengineering5040143Remote Sensing and Kriging with External Drift to Improve Sparse Proximal Soil Sensing Data and Define Management Zones in Precision AgricultureHugo Rodrigues0Marcos B. Ceddia1Gustavo M. Vasques2Vera L. Mulder3Gerard B. M. Heuvelink4Ronaldo P. Oliveira5Ziany N. Brandão6João P. S. Morais7Matheus L. Neves8Sílvio R. L. Tavares9Postgraduate Program in Agronomy—Soil Science, Federal Rural University of Rio de Janeiro, BR-465, Km 7, Seropédica 23897-000, RJ, BrazilAgroTechnologies and Sustainability Department, Federal Rural University of Rio de Janeiro, BR-465, Km 7, Seropédica 23897-000, RJ, BrazilBrazilian Agricultural Research Company (Embrapa) Soils, Rio de Janeiro 22460-000, RJ, BrazilSoil Geography and Landscape Group, Wageningen University, 6708 PB Wageningen, The NetherlandsSoil Geography and Landscape Group, Wageningen University, 6708 PB Wageningen, The NetherlandsBrazilian Agricultural Research Company (Embrapa) Soils, Rio de Janeiro 22460-000, RJ, BrazilBrazilian Agricultural Research Company (Embrapa) Cotton, Rua Osvaldo Cruz, 1143, Centenário 58428-095, CG, BrazilBrazilian Agricultural Research Company (Embrapa) Cotton, Rua Osvaldo Cruz, 1143, Centenário 58428-095, CG, BrazilPostgraduate Program in Agronomy—Soil Science, Federal Rural University of Rio de Janeiro, BR-465, Km 7, Seropédica 23897-000, RJ, BrazilBrazilian Agricultural Research Company (Embrapa) Soils, Rio de Janeiro 22460-000, RJ, BrazilThe precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the <i>k</i>-means clustering algorithm: using data from proximal sensors (OK), proximal sensors combined with remote sensors (KED), and remote sensors. Seventy-two samples (0–10 cm in depth) were collected and analyzed in a laboratory (1 sample per hectare) for concentrations of clay, calcium, organic carbon, and magnesium to assess the capacity of the management zone maps created using analysis of variance. All zones created using the three data groups could distinguish the different treatment areas. The three data sources used to map management zones produced similar map zones, but the zone map using a combination of proximal and remote data did not show an improvement in defining the management zones, and using only remote sensing data lowered the significance levels of differentiating each zone compared to the OK and KED maps. In summary, this study not only underscores the global applicability of proximal and remote sensing techniques in precision agriculture but also sheds light on the nuances of their integration. The study’s findings affirm the efficacy of these advanced technologies in addressing the challenges posed by soil heterogeneity, paving the way for more nuanced and site-specific agricultural practices worldwide.https://www.mdpi.com/2624-7402/5/4/143management zonesproximal soil sensingremote sensingprecision agriculturegeostatistics |
| spellingShingle | Hugo Rodrigues Marcos B. Ceddia Gustavo M. Vasques Vera L. Mulder Gerard B. M. Heuvelink Ronaldo P. Oliveira Ziany N. Brandão João P. S. Morais Matheus L. Neves Sílvio R. L. Tavares Remote Sensing and Kriging with External Drift to Improve Sparse Proximal Soil Sensing Data and Define Management Zones in Precision Agriculture AgriEngineering management zones proximal soil sensing remote sensing precision agriculture geostatistics |
| title | Remote Sensing and Kriging with External Drift to Improve Sparse Proximal Soil Sensing Data and Define Management Zones in Precision Agriculture |
| title_full | Remote Sensing and Kriging with External Drift to Improve Sparse Proximal Soil Sensing Data and Define Management Zones in Precision Agriculture |
| title_fullStr | Remote Sensing and Kriging with External Drift to Improve Sparse Proximal Soil Sensing Data and Define Management Zones in Precision Agriculture |
| title_full_unstemmed | Remote Sensing and Kriging with External Drift to Improve Sparse Proximal Soil Sensing Data and Define Management Zones in Precision Agriculture |
| title_short | Remote Sensing and Kriging with External Drift to Improve Sparse Proximal Soil Sensing Data and Define Management Zones in Precision Agriculture |
| title_sort | remote sensing and kriging with external drift to improve sparse proximal soil sensing data and define management zones in precision agriculture |
| topic | management zones proximal soil sensing remote sensing precision agriculture geostatistics |
| url | https://www.mdpi.com/2624-7402/5/4/143 |
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