Hierarchical Stratification for Spatial Sampling and Digital Mapping of Soil Attributes
This study assessed whether stratifying agricultural areas into macro- and micro-variability regions allows targeted sampling to better capture soil attribute variability, thus improving digital soil maps compared to regular grid sampling. Allocating more samples where soil variability is expected o...
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MDPI AG
2025-01-01
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| Series: | AgriEngineering |
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| author | Derlei D. Melo Isabella A. Cunha Lucas R. Amaral |
| author_facet | Derlei D. Melo Isabella A. Cunha Lucas R. Amaral |
| author_sort | Derlei D. Melo |
| collection | DOAJ |
| description | This study assessed whether stratifying agricultural areas into macro- and micro-variability regions allows targeted sampling to better capture soil attribute variability, thus improving digital soil maps compared to regular grid sampling. Allocating more samples where soil variability is expected offers a promising alternative. We evaluated two sampling densities in two agricultural fields in Southeast Brazil: a sparse density (one sample per 2.5 hectares), typical in Precision Agriculture, and a denser grid (one sample per hectare), which usually provides reasonable mapping accuracy. For each density, we applied three designs: a regular grid and grids with 25% and 50% guided points. Apparent soil magnetic susceptibility (MSa) delimited macro-homogeneity zones, while Sentinel-2’s Enhanced Vegetation Index (EVI) identified micro-homogeneity, guiding sampling to pixels with higher Fuzzy membership. The attributes assessed included phosphorus (P), potassium (K), and clay content. Results showed that the 50% guided sample configuration improved ordinary kriging interpolation accuracy, particularly with sparse grids. In the six sparse grid scenarios, in four of them, the grid with 50% of the points in regular design and the other 50% directed by the proposed method presented better performance than the full regular grid; the higher improvement was obtained for clay content (RMSE of 54.93 g kg<sup>−1</sup> to 45.63 g kg<sup>−1</sup>, a 16.93% improvement). However, prior knowledge of soil attributes and covariates is needed for this approach. We therefore recommend two-stage sampling to understand soil properties’ relationships with covariates before applying the proposed method. |
| format | Article |
| id | doaj-art-a91ae90fc00d4b2c89b73f27403969d7 |
| institution | Kabale University |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | AgriEngineering |
| spelling | doaj-art-a91ae90fc00d4b2c89b73f27403969d72025-01-24T13:16:13ZengMDPI AGAgriEngineering2624-74022025-01-01711010.3390/agriengineering7010010Hierarchical Stratification for Spatial Sampling and Digital Mapping of Soil AttributesDerlei D. Melo0Isabella A. Cunha1Lucas R. Amaral2School of Agricultural Engineering, Universidade Estadual de Campinas—UNICAMP, Campinas 13083-875, SP, BrazilSchool of Agricultural Engineering, Universidade Estadual de Campinas—UNICAMP, Campinas 13083-875, SP, BrazilSchool of Agricultural Engineering, Universidade Estadual de Campinas—UNICAMP, Campinas 13083-875, SP, BrazilThis study assessed whether stratifying agricultural areas into macro- and micro-variability regions allows targeted sampling to better capture soil attribute variability, thus improving digital soil maps compared to regular grid sampling. Allocating more samples where soil variability is expected offers a promising alternative. We evaluated two sampling densities in two agricultural fields in Southeast Brazil: a sparse density (one sample per 2.5 hectares), typical in Precision Agriculture, and a denser grid (one sample per hectare), which usually provides reasonable mapping accuracy. For each density, we applied three designs: a regular grid and grids with 25% and 50% guided points. Apparent soil magnetic susceptibility (MSa) delimited macro-homogeneity zones, while Sentinel-2’s Enhanced Vegetation Index (EVI) identified micro-homogeneity, guiding sampling to pixels with higher Fuzzy membership. The attributes assessed included phosphorus (P), potassium (K), and clay content. Results showed that the 50% guided sample configuration improved ordinary kriging interpolation accuracy, particularly with sparse grids. In the six sparse grid scenarios, in four of them, the grid with 50% of the points in regular design and the other 50% directed by the proposed method presented better performance than the full regular grid; the higher improvement was obtained for clay content (RMSE of 54.93 g kg<sup>−1</sup> to 45.63 g kg<sup>−1</sup>, a 16.93% improvement). However, prior knowledge of soil attributes and covariates is needed for this approach. We therefore recommend two-stage sampling to understand soil properties’ relationships with covariates before applying the proposed method.https://www.mdpi.com/2624-7402/7/1/10precision agriculturedigital soil mappingmanagement zonessoil samplingremote sensingproximal sensing |
| spellingShingle | Derlei D. Melo Isabella A. Cunha Lucas R. Amaral Hierarchical Stratification for Spatial Sampling and Digital Mapping of Soil Attributes AgriEngineering precision agriculture digital soil mapping management zones soil sampling remote sensing proximal sensing |
| title | Hierarchical Stratification for Spatial Sampling and Digital Mapping of Soil Attributes |
| title_full | Hierarchical Stratification for Spatial Sampling and Digital Mapping of Soil Attributes |
| title_fullStr | Hierarchical Stratification for Spatial Sampling and Digital Mapping of Soil Attributes |
| title_full_unstemmed | Hierarchical Stratification for Spatial Sampling and Digital Mapping of Soil Attributes |
| title_short | Hierarchical Stratification for Spatial Sampling and Digital Mapping of Soil Attributes |
| title_sort | hierarchical stratification for spatial sampling and digital mapping of soil attributes |
| topic | precision agriculture digital soil mapping management zones soil sampling remote sensing proximal sensing |
| url | https://www.mdpi.com/2624-7402/7/1/10 |
| work_keys_str_mv | AT derleidmelo hierarchicalstratificationforspatialsamplinganddigitalmappingofsoilattributes AT isabellaacunha hierarchicalstratificationforspatialsamplinganddigitalmappingofsoilattributes AT lucasramaral hierarchicalstratificationforspatialsamplinganddigitalmappingofsoilattributes |