Soil mapping is essential for sustainable land management, precision agriculture, and environmental monitoring. This synoptic review evaluates the evolution of classical and modern geostatistical methods, spanning 2000 to 2024, and their integration with machine learning (ML) and remote sensing (RS) technologies. Key techniques such as spatial autocorrelation, variogram analysis, kriging and its variants, Bayesian models, Markov Chains, geostatistical simulations, and ensemble modeling were assessed in the context of digital soil mapping (DSM). Emphasis was placed on hybrid approaches that fuse geostatistics with ML algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), along with the enrichment of spatial models using RS data. Hybrid approaches combining geostatistics with ML algorithms (e.g., RF, Boost, SVM, ANN) demonstrate promise in addressing spatial uncertainty, while RS data enhances covariate enrichment and near-real-time applications. Although advancements in variogram estimation and kriging techniques have optimized sampling strategies, and improved prediction accuracy, challenges persist in computational efficiency and uncertainty quantification. However, emerging trends such as, Bayesian methods, multiscale modeling, and sensor networks show the potential to mitigate these limitations. Future research should focus on improving model accuracy, interpretability, transparency, and explainability. Additionally, addressing data quality issues, scalability, and increasing sampling density to support robust spatial analysis and uncertainty quantification are major research gaps and future directions for soil mapping and information systems. This review is a valuable resource for researchers, practitioners, and policymakers engaged in data-driven land and soil management.

Spatial heterogeneity is a universal phenomenon existing in ecological systems at all scales. In the humid tropics, soil sampling and characterization are complex due to high spatial variability. The inability of researchers to capture this spatial variability inhibits the effective management of resources in terrestrial ecosystems. Hence, simple sampling approaches that are labor and cost-efficient but generate representative samples are required to optimize soil sampling and characterization. In this paper, we used a combination of descriptive statistics and geostatistics to characterize the spatial variability of soil under two land uses. One-way ANOVA and a Student t-test were also used to assess the effectiveness of three distinct sampling designs at capturing the variation in the soil’s physicochemical properties. The wetland soils generally exhibited low spatial variability, whereas the variability of the agroforest ranged from low (coefficient of variance (CV) < 25%) to highly variable (CV > 75%). Results show that in the wetland, CVs of samples obtained using the random sampling design (RSD) and response surface directed design (RSDD) were highly correlated (pearson correlation coefficient (r) = 0.98). Similarly, in the agroforest, the CVs of the stratified random sampling design (SRSD) and RSDD gave almost the same accuracy (r = 0.97). Thus, it is recommended that in this and similar humid-tropical wetland and agroforest ecosystems, the RSD and the RSDD, which require the least samples, can be used, respectively. However, more comprehensive testing is required on a broader range of soils before its more widespread application in other climatic conditions.

Soil bacterial communities are vital for ecosystem functioning in the humid tropics, yet their response to land-use change remains poorly understood. This knowledge gap is exacerbated by the lack of long-term studies. We employed a space-for-time substitution approach to assess the effects of land-use intensification on soil bacterial communities across a gradient of anthropogenic disturbance in Trinidad. Three sub-watersheds (Arouca = pristine, Maracas = intermediate, Tacarigua = intensive) were selected, each containing adjacent forest, grassland, and agricultural land uses. We combined geophysical soil apparent electrical conductivity (ECa-directed) sampling with 16S rDNA gene amplicon sequencing to characterize bacterial communities and their relationships with soil and landscape properties. Soil properties were the primary determinant of bacterial community structure, explaining 56% of the variation (p < 0.001), with pH, clay content, hygroscopic water, and nutrient availability as key drivers. Bacterial α-diversity differed significantly among sub-watersheds (p < 0.01), with Tacarigua exhibiting lower richness and diversity compared to Arouca and Maracas, but not across land uses. While a core microbiome of ten bacterial families was ubiquitous across land uses, indicating a stable foundational community, land-use intensification significantly altered β-diversity (p < 0.01 among sub-watersheds). Agricultural soils showed the greatest divergence from forest soils (p < 0.05), with a marked decline in key Proteobacterial families (e.g., Xanthomonadaceae, Pseudomonadaceae) involved in nutrient cycling and plant growth promotion. Although inherent soil properties shape the core microbiome, land-use intensification acts as a strong secondary filter, shifting soil bacterial communities toward more stress-resistant Firmicutes with potentially less diverse functions. Our findings demonstrate the utility of integrating space-for-time substitution with molecular profiling to predict long-term microbial responses to environmental change in vulnerable tropical ecosystems.