Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery
The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a com...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/130976 |
| _version_ | 1855514379064180736 |
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| author | Pizarro, S. Pricope, N.G. Figueroa, D. Carbajal, C. Quispe, M. Vera, J. Alejandro, L. Achallma, L. Gonzalez, I. Salazar, W. Loayza, H. Cruz, J. Arbizu, C.I. |
| author_browse | Achallma, L. Alejandro, L. Arbizu, C.I. Carbajal, C. Cruz, J. Figueroa, D. Gonzalez, I. Loayza, H. Pizarro, S. Pricope, N.G. Quispe, M. Salazar, W. Vera, J. |
| author_facet | Pizarro, S. Pricope, N.G. Figueroa, D. Carbajal, C. Quispe, M. Vera, J. Alejandro, L. Achallma, L. Gonzalez, I. Salazar, W. Loayza, H. Cruz, J. Arbizu, C.I. |
| author_sort | Pizarro, S. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions. |
| format | Journal Article |
| id | CGSpace130976 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1309762025-12-08T10:29:22Z Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery Pizarro, S. Pricope, N.G. Figueroa, D. Carbajal, C. Quispe, M. Vera, J. Alejandro, L. Achallma, L. Gonzalez, I. Salazar, W. Loayza, H. Cruz, J. Arbizu, C.I. soil surveys machine learning The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions. 2023-06-20 2023-07-03T18:48:39Z 2023-07-03T18:48:39Z Journal Article https://hdl.handle.net/10568/130976 en Open Access MDPI Pizarro, S.; Pricope, N.G.; Figueroa, D.; Carbajal, C.; Quispe, M.; Vera, J.; Alejandro, L.; Achallma, L.; Gonzalez, I.; Salazar, W.; et al. 2023. Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery. Remote Sens. 15(12). ISSN 2072-4292. https://doi.org/ 10.3390/rs15123203 |
| spellingShingle | soil surveys machine learning Pizarro, S. Pricope, N.G. Figueroa, D. Carbajal, C. Quispe, M. Vera, J. Alejandro, L. Achallma, L. Gonzalez, I. Salazar, W. Loayza, H. Cruz, J. Arbizu, C.I. Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery |
| title | Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery |
| title_full | Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery |
| title_fullStr | Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery |
| title_full_unstemmed | Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery |
| title_short | Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery |
| title_sort | implementing cloud computing for the digital mapping of agricultural soil properties from high resolution uav multispectral imagery |
| topic | soil surveys machine learning |
| url | https://hdl.handle.net/10568/130976 |
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