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...
| Autores principales: | , , , , , , , , , , , , |
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| Formato: | info:eu-repo/semantics/article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/20.500.12955/2290 https://doi.org/10.3390/rs15123203 |
| _version_ | 1855028750073200640 |
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| author | Pizarro Carcausto, Samuel Edwin Pricope, Narcisa G. Figueroa Venegas, Deyanira Antonella Carbajal Llosa, Carlos Miguel Quispe Huincho, Miriam Rocío Vera Vilchez, Jesús Emilio Alejandro Méndez, Lidiana Rene Achallma Mendoza, Lino González Tovar, Izamar Estrella Salazar Coronel, Wilian Loayza, Hildo Cruz Luis, Juancarlos Alejandro Arbizu Berrocal, Carlos Irvin |
| author_browse | Achallma Mendoza, Lino Alejandro Méndez, Lidiana Rene Arbizu Berrocal, Carlos Irvin Carbajal Llosa, Carlos Miguel Cruz Luis, Juancarlos Alejandro Figueroa Venegas, Deyanira Antonella González Tovar, Izamar Estrella Loayza, Hildo Pizarro Carcausto, Samuel Edwin Pricope, Narcisa G. Quispe Huincho, Miriam Rocío Salazar Coronel, Wilian Vera Vilchez, Jesús Emilio |
| author_facet | Pizarro Carcausto, Samuel Edwin Pricope, Narcisa G. Figueroa Venegas, Deyanira Antonella Carbajal Llosa, Carlos Miguel Quispe Huincho, Miriam Rocío Vera Vilchez, Jesús Emilio Alejandro Méndez, Lidiana Rene Achallma Mendoza, Lino González Tovar, Izamar Estrella Salazar Coronel, Wilian Loayza, Hildo Cruz Luis, Juancarlos Alejandro Arbizu Berrocal, Carlos Irvin |
| author_sort | Pizarro Carcausto, Samuel Edwin |
| collection | Repositorio INIA |
| 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 | info:eu-repo/semantics/article |
| id | INIA2290 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INIA22902023-08-31T17:37:25Z Implementing cloud computing for the digital mapping of agricultural soil properties from high resolution UAV multispectral imagery Pizarro Carcausto, Samuel Edwin Pricope, Narcisa G. Figueroa Venegas, Deyanira Antonella Carbajal Llosa, Carlos Miguel Quispe Huincho, Miriam Rocío Vera Vilchez, Jesús Emilio Alejandro Méndez, Lidiana Rene Achallma Mendoza, Lino González Tovar, Izamar Estrella Salazar Coronel, Wilian Loayza, Hildo Cruz Luis, Juancarlos Alejandro Arbizu Berrocal, Carlos Irvin Soil mapping UAV Google Earth Engine Machine learning Cloud computing https://purl.org/pe-repo/ocde/ford#4.01.06 Soil surveys Reconocimiento de suelos Unmanned aerial vehicles Vehículos aéreos no tripulados 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. 2023-08-31T17:37:23Z 2023-08-31T17:37:23Z 2023-06-20 info:eu-repo/semantics/article Pizarro, S.; Pricope, N. G.; Figueroa, D.; Carbajal, C.; Quispe, M.; Vera, J.; ... & Arbizu, C. I. (2023). Implementing cloud computing for the digital mapping of agricultural soil properties from high resolution UAV multispectral imagery. Remote Sensing, 15(12), 3203. doi: 10.3390/rs15123203 2072-4292 https://hdl.handle.net/20.500.12955/2290 https://doi.org/10.3390/rs15123203 eng urn:issn:2072-4292 Remote sensing info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ application/pdf application/pdf MDPI CH Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | Soil mapping UAV Google Earth Engine Machine learning Cloud computing https://purl.org/pe-repo/ocde/ford#4.01.06 Soil surveys Reconocimiento de suelos Unmanned aerial vehicles Vehículos aéreos no tripulados Machine learning Pizarro Carcausto, Samuel Edwin Pricope, Narcisa G. Figueroa Venegas, Deyanira Antonella Carbajal Llosa, Carlos Miguel Quispe Huincho, Miriam Rocío Vera Vilchez, Jesús Emilio Alejandro Méndez, Lidiana Rene Achallma Mendoza, Lino González Tovar, Izamar Estrella Salazar Coronel, Wilian Loayza, Hildo Cruz Luis, Juancarlos Alejandro Arbizu Berrocal, Carlos Irvin 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 mapping UAV Google Earth Engine Machine learning Cloud computing https://purl.org/pe-repo/ocde/ford#4.01.06 Soil surveys Reconocimiento de suelos Unmanned aerial vehicles Vehículos aéreos no tripulados Machine learning |
| url | https://hdl.handle.net/20.500.12955/2290 https://doi.org/10.3390/rs15123203 |
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