| Revista: | Scientia Agricola |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000455484 |
| ISSN: | 0103-9016 |
| Autors: | Maldaner, Leonardo Felipe1 Molin, José Paulo1 Spekken, Mark1 |
| Institucions: | 1Universidade de Sao Paulo, Escola Superior de Agricultura "Luiz de Queiroz", Piracicaba, Sao Paulo. Brasil |
| Any: | 2022 |
| Volum: | 79 |
| Número: | 1 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Analítico, descriptivo |
| Resumen en inglés | The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature |
| Disciplines | Geografía |
| Paraules clau: | Cartografía, Agricultura de precisión, Densidad espacial, Mapas, Confiabilidad |
| Keyword: | Cartography, Maps, Precision agriculture, Spatial density, Reliability |
| Text complet: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
