Hierarchical Stratification for Spatial Sampling and Digital Mapping of Soil Attributes

Author(s)

Melo, Derlei D. ; Cunha, Isabella A. ; Amaral, Lucas R.

• Document type: Journal article
• Source: AGRIENGINEERING; v. 7, n. 1, p. 17-pg., 2025-01-01.

Abstract

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-1 to 45.63 g kg-1, 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. (AU)

• FAPESP's process: 24/14044-4 - Detailed pedological mapping and management zones: characteristics, applications, and complementarity between approaches
• Grantee: Derlei Dias Melo
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 23/02592-4 - Sampling optimization and multivariate predictions for mapping soil fertility and soil quality
• Grantee: Derlei Dias Melo
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 22/03160-8 - Soil spatial variability mapping and optimized sampling supported by sensing techniques: bases for a more efficient and sustainable precision agriculture
• Grantee: Lucas Rios do Amaral
• Support Opportunities: Research Grants - Initial Project