Compare Soil Scanner Data between Years

This article outlines various mathematical methods to quantify differences between soil scanner datasets and enhance decision-making for researchers and agronomists.

Soil scanners are essential tools for precision agriculture, enabling the collection of high-resolution data on soil properties such as moisture, organic matter, and nutrient levels. Comparing two soil scanner datasets is crucial for understanding changes over time, validating different scanning methods, or calibrating new devices. This article explores various mathematical approaches to measure deviation between two soil scanner datasets, providing actionable insights for researchers and agronomists.

Understanding Deviation in Soil Scanner Data

The deviation between two soil scanner datasets refers to the differences in measured values at the same locations, which may arise due to variations in measurement conditions, sensor calibration, or soil dynamics. The most common types of deviations include:

• Absolute Differences: Direct subtraction of values between datasets.

• Relative Differences: Comparison based on the magnitude of measurements.

• Error Metrics: Statistical measures like Mean Absolute Error (MAE) and Normalized Difference.

Two soil scanner datasets with potassium for 2024 and 2025 were chosen.

Initial soil scanner datasets

Choosing the Right Deviation Method

Method	Best for
Direct Difference	Simple visualization of positive/negative changes
Relative Difference	Comparing datasets with different scales
Normalized Difference	Standardized analysis across different datasets
Relative Deviation	Proportional differences, useful for trend analysis
Mean Absolute Error (MAE) per Pixel	Identifying areas with large absolute differences

Direct Difference Calculation

This Direct Difference method simply subtracts one dataset from the other to visualize the changes in soil attributes directly.

The usage of geopard.calculate_difference(dataset_1, dataset_2) with parameters explanation is documented here.

Pros:

• Clearly shows positive and negative changes.

• Easy to interpret and visualize.

Cons:

• The difference values may be hard to compare if datasets have different scales.

• High variation can dominate interpretation.

Direct Difference Calculation

Relative Difference Calculation

The Relative Difference method calculates the percentage change between the datasets based on the second dataset, offering another perspective on deviation.

The usage of geopard.calculate_relative_difference(dataset_1, dataset_2) with parameters explanation is documented here.

Pros:

• Good for understanding how much one dataset has changed in proportion to another.

• Normalizes differences across varying magnitudes.

Cons:

• Can become unstable when values in the second dataset are close to zero.

• Less intuitive when absolute differences are important.

Relative Difference Calculation

Normalized Difference Calculation

The Normalized Difference method normalizes the datasets by their global maximum value before computing differences, ensuring that variations are comparable across different scales.

The usage of geopard.calculate_normalized_difference(dataset_1, dataset_2) with parameters explanation is documented here.

Pros:

• Effective for datasets with different dynamic ranges.

• Reduces the impact of extreme values.

Cons:

• Small variations may appear exaggerated if not scaled properly.

Normalized Difference Calculation

Relative Deviation per Pixel

The Relative Deviation method calculates the deviation as a percentage relative to the first dataset. It helps in understanding proportional differences rather than absolute differences.

The usage of geopard.calculate_per_pixel_relative_deviation(dataset_1, dataset_2) with parameters explanation is documented here.

Pros:

• Useful when comparing datasets with different scales.

• Expresses deviation in an interpretable percentage format.

Cons:

• Can be misleading if the original values are very small.

Relative Deviation per Pixel

Mean Absolute Error (MAE) per Pixel

The Mean Absolute Error (MAE) method measures the absolute differences between corresponding values in two datasets. It provides a clear view of where the highest discrepancies occur.

The usage of geopard.calculate_per_pixel_mae(dataset_1, dataset_2) with parameters explanation is documented here.

Pros:

• Simple and intuitive.

• Highlights large differences clearly.

• Works well for datasets with similar scales.

Cons:

• Doesn't show the direction of the difference (i.e., positive or negative change).

• Sensitive to outliers.

Mean Absolute Error (MAE) per Pixel

Conclusion

Comparing soil scanner datasets requires a variety of mathematical approaches to extract meaningful differences. Whether using absolute metrics like MAE, relative deviations, or normalized comparisons, selecting the right method depends on the use case. By leveraging these techniques, agronomists and researchers can improve soil analysis, detect field variations, and enhance precision agriculture workflows.