The number of samples used in the calibration data set affects the quality of the generated
predictive models using visible, near and shortwave infrared (VIS–NIR–SWIR) spectroscopy …

Abstract The United Nations (UN) emphasizes the pivotal role of sustainable agriculture in
addressing persistent starvation and working towards zero hunger by 2030 through global …

Using machine learning and earth observation data to capture real-world variability in
spatial predictive map** depends on sample size, design, and spatial extent …

Abstract Machine learning techniques are widely employed to generate digital soil maps.
The map accuracy is partly determined by the number and spatial locations of the …

Remote sensing and soil spectroscopy applications are valuable techniques for soil property
estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil …

In this chapter we review the statistical theory for soil map**. We focus on models
considered most suitable for practical implementation and use with soil profile data and …

Diffuse reflectance spectroscopy (DRS) is emerging as a rapid and cost-effective alternative
to routine laboratory analysis for many soil properties. However, it has primarily been …

Spatial soil information is essential for informed decision-making in a wide range of fields.
Digital soil map** (DSM) using machine learning algorithms has become a popular …

Site-specific estimation of lime requirement requires high-resolution maps of soil organic
carbon (SOC), clay and pH. These maps can be generated with digital soil map** models …

Due to high rate of nutrient removal by cotton plants, the productive cotton-growing soils of
Australia are becoming depleted of exchangeable (exch.) cations. For long-term …