Can Basic Soil Quality Indicators and Topography Explain the Spatial Variability in Agricultural Fields Observed from Drone Orthomosaics?
Crop growth is often uneven within an agricultural parcel, even if it has been managed evenly. Aerial images are often used to determine the presence of vegetation and its spatial variability in field parcels. However, the reasons for this uneven growth have been less studied, and they might be conn...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/3/669 |
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| author | Roope Näsi Hannu Mikkola Eija Honkavaara Niko Koivumäki Raquel A. Oliveira Pirjo Peltonen-Sainio Niila-Sakari Keijälä Mikael Änäkkälä Lauri Arkkola Laura Alakukku |
| author_facet | Roope Näsi Hannu Mikkola Eija Honkavaara Niko Koivumäki Raquel A. Oliveira Pirjo Peltonen-Sainio Niila-Sakari Keijälä Mikael Änäkkälä Lauri Arkkola Laura Alakukku |
| author_sort | Roope Näsi |
| collection | DOAJ |
| description | Crop growth is often uneven within an agricultural parcel, even if it has been managed evenly. Aerial images are often used to determine the presence of vegetation and its spatial variability in field parcels. However, the reasons for this uneven growth have been less studied, and they might be connected to variations in topography, as well as soil properties and quality. In this study, we evaluated the relationship between drone image data and field and soil quality indicators. In total, 27 multispectral and RGB drone image datasets were collected from four real farm fields in 2016–2020. We analyzed 13 basic soil quality indicators, including penetrometer resistance in top- and subsoil, soil texture (clay, silt, fine sand, and sand content), soil organic carbon (SOC) content, clay/SOC ratio, and soil quality assessment parameters (topsoil biological indicators, subsoil macroporosity, compacted layers in the soil profile, topsoil structure, and subsoil structure). Furthermore, a topography variable describing water flow was used as an indicator. Firstly, we evaluated single pixel-wise linear correlations between the drone datasets and soil/field-related parameters. Correlations varied between datasets and, in the best case, were 0.8. Next, we trained and tested multiparameter non-linear models (random forest algorithm) using all 14 soil-related parameters as features to explain the multispectral (NIR band) and RGB (green band) reflectance values of each drone dataset. The results showed that the soil/field indicators could effectively explain the spatial variability in the drone images in most cases (R<sup>2</sup> > 0.5), especially for annual crops, and in the best case, the R<sup>2</sup> value was 0.95. The most important field/soil features for explaining the variability in drone images varied between fields and imaging times. However, it was found that basic soil quality indicators and topography variables could explain the variability observed in the drone orthomosaics in certain conditions. This knowledge about soil quality indicators causing within-field variation could be utilized when planning cultivation operations or evaluating the value of a field parcel. |
| first_indexed | 2024-03-11T07:03:18Z |
| format | Article |
| id | doaj.art-4259e27395784e378c4297df24431385 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-11T07:03:18Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-4259e27395784e378c4297df244313852023-11-17T09:04:27ZengMDPI AGAgronomy2073-43952023-02-0113366910.3390/agronomy13030669Can Basic Soil Quality Indicators and Topography Explain the Spatial Variability in Agricultural Fields Observed from Drone Orthomosaics?Roope Näsi0Hannu Mikkola1Eija Honkavaara2Niko Koivumäki3Raquel A. Oliveira4Pirjo Peltonen-Sainio5Niila-Sakari Keijälä6Mikael Änäkkälä7Lauri Arkkola8Laura Alakukku9Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), National Land Survey of Finland (NLS), FI-00521 Helsinki, FinlandDepartment of Agricultural Sciences, University of Helsinki, FI-00014 Helsinki, FinlandDepartment of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), National Land Survey of Finland (NLS), FI-00521 Helsinki, FinlandDepartment of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), National Land Survey of Finland (NLS), FI-00521 Helsinki, FinlandDepartment of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), National Land Survey of Finland (NLS), FI-00521 Helsinki, FinlandNatural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, FinlandDepartment of Agricultural Sciences, University of Helsinki, FI-00014 Helsinki, FinlandDepartment of Agricultural Sciences, University of Helsinki, FI-00014 Helsinki, FinlandDepartment of Agricultural Sciences, University of Helsinki, FI-00014 Helsinki, FinlandDepartment of Agricultural Sciences, University of Helsinki, FI-00014 Helsinki, FinlandCrop growth is often uneven within an agricultural parcel, even if it has been managed evenly. Aerial images are often used to determine the presence of vegetation and its spatial variability in field parcels. However, the reasons for this uneven growth have been less studied, and they might be connected to variations in topography, as well as soil properties and quality. In this study, we evaluated the relationship between drone image data and field and soil quality indicators. In total, 27 multispectral and RGB drone image datasets were collected from four real farm fields in 2016–2020. We analyzed 13 basic soil quality indicators, including penetrometer resistance in top- and subsoil, soil texture (clay, silt, fine sand, and sand content), soil organic carbon (SOC) content, clay/SOC ratio, and soil quality assessment parameters (topsoil biological indicators, subsoil macroporosity, compacted layers in the soil profile, topsoil structure, and subsoil structure). Furthermore, a topography variable describing water flow was used as an indicator. Firstly, we evaluated single pixel-wise linear correlations between the drone datasets and soil/field-related parameters. Correlations varied between datasets and, in the best case, were 0.8. Next, we trained and tested multiparameter non-linear models (random forest algorithm) using all 14 soil-related parameters as features to explain the multispectral (NIR band) and RGB (green band) reflectance values of each drone dataset. The results showed that the soil/field indicators could effectively explain the spatial variability in the drone images in most cases (R<sup>2</sup> > 0.5), especially for annual crops, and in the best case, the R<sup>2</sup> value was 0.95. The most important field/soil features for explaining the variability in drone images varied between fields and imaging times. However, it was found that basic soil quality indicators and topography variables could explain the variability observed in the drone orthomosaics in certain conditions. This knowledge about soil quality indicators causing within-field variation could be utilized when planning cultivation operations or evaluating the value of a field parcel.https://www.mdpi.com/2073-4395/13/3/669spatial variabilitywithin-field variabilitysoil mechanical resistancesoil physical qualitydroneUAV |
| spellingShingle | Roope Näsi Hannu Mikkola Eija Honkavaara Niko Koivumäki Raquel A. Oliveira Pirjo Peltonen-Sainio Niila-Sakari Keijälä Mikael Änäkkälä Lauri Arkkola Laura Alakukku Can Basic Soil Quality Indicators and Topography Explain the Spatial Variability in Agricultural Fields Observed from Drone Orthomosaics? Agronomy spatial variability within-field variability soil mechanical resistance soil physical quality drone UAV |
| title | Can Basic Soil Quality Indicators and Topography Explain the Spatial Variability in Agricultural Fields Observed from Drone Orthomosaics? |
| title_full | Can Basic Soil Quality Indicators and Topography Explain the Spatial Variability in Agricultural Fields Observed from Drone Orthomosaics? |
| title_fullStr | Can Basic Soil Quality Indicators and Topography Explain the Spatial Variability in Agricultural Fields Observed from Drone Orthomosaics? |
| title_full_unstemmed | Can Basic Soil Quality Indicators and Topography Explain the Spatial Variability in Agricultural Fields Observed from Drone Orthomosaics? |
| title_short | Can Basic Soil Quality Indicators and Topography Explain the Spatial Variability in Agricultural Fields Observed from Drone Orthomosaics? |
| title_sort | can basic soil quality indicators and topography explain the spatial variability in agricultural fields observed from drone orthomosaics |
| topic | spatial variability within-field variability soil mechanical resistance soil physical quality drone UAV |
| url | https://www.mdpi.com/2073-4395/13/3/669 |
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