Identification of Soil Types and Salinity Using MODIS Terra Data and Machine Learning Techniques in Multiple Regions of Pakistan

Abstract

Soil, a significant natural resource, plays a crucial role in supporting various ecosystems and serves as the foundation of Pakistan’s economy due to its primary use in agriculture. Hence, timely monitoring of soil type and salinity is essential. However, traditional methods for identifying soil types and detecting salinity are time-consuming, requiring expert intervention and extensive laboratory experiments. The objective of this study is to propose a model that leverages MODIS Terra data to identify soil types and detect soil salinity. To achieve this, 195 soil samples were collected from Lahore, Kot Addu, and Kohat, dating from October 2022 to November 2022. Simultaneously, spectral data of the same regions were obtained to spatially map soil types and salinity of bare land. The spectral reflectance of band values, salinity indices, and vegetation indices were utilized to classify the soil types and predict soil salinity. To perform the classification and regression tasks, the study employed three popular techniques in the research community: Random Forest (RF), Ada Boost (AB), and Gradient Boosting (GB), along with Decision Tree (DT), K-Nearest Neighbor (KNN), and Extra Tree (ET). A 70–30 test train validation split was used for the implementation of these techniques. The efficacy of the multi-class classification models for soil types was evaluated using accuracy, precision, recall, and f1-score. On the other hand, the regression models’ performances were evaluated and compared using R-squared (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrated that Random Forest outperformed other methods for both predicting soil types (accuracy = 65.38, precision = 0.60, recall = 0.57, and f1-score = 0.57) and predicting salinity (R2 = 0.90, MAE = 0.56, MSE = 0.98, RMSE = 0.97). Finally, the study designed a web portal to enable real-time prediction of soil types and salinity using these models. This web portal can be utilized by farmers and decision-makers to make informed decisions regarding soil, crop cultivation, and agricultural planning.

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“…Mean Squared Error (MSE) is one of the commonly used metrics in statistics, data analysis, and machine learning to measure the quality or accuracy of regression or prediction models. MSE measures the degree to which the model's predicted value approaches the actual value in squared form, and it pays more attention to large errors (Chen et al 2023;Farzana et al 2023;Haq et al 2023;Khairan et al 2023). MSE measures the mean of squares the difference between actual (observed) and predicted values (predicted) in a regression or prediction model.…”

“…RMSE is a statistical metric that measures the accuracy of a prediction model by calculating the square root of the mean of the squared difference between the actual value (observed) and the predicted value (predicted) in a regression or prediction model (Chen et al 2023;Farzana et al 2023;Haq et al 2023;Khairan et al 2023). The formula of this metric is as follows:…”

“…This provides insight into how well the model matches the actual data. R2-Score ranges from 0 to 1 (Chen et al 2023;Farzana et al 2023;Haq et al 2023;Khairan et al 2023), with the following interpretation: • R2-Score = 0: This indicates that the model cannot explain the variation at all and is no better than a model that only uses the average of the data as a prediction. • R2-Score = 1: This indicates that the model is perfect and able to explain all variations in the data correctly.…”