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Deep learning-driven soil texture classifier using Landsat 8 images
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DOI：10.1016/j.rsase.2025.101568.png)
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Abstract
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Soil texture characterization is a crucial criterion for agricultural development and environmental sustainability. Conventional methods rely extensively on field samples and often provide insufficient data for large-scale applications. This study classifies soil into six texture classes by integrating remote sensing data from 6 Landsat 8 optical bands, 7 derived indices, 8 topographic attributes from Digital Elevation Model (DEM) and 9 textural features from Gray Level Co-occurrence Matrix (GLCM). The study region is Chittoor district, Andhra Pradesh encompassing roughly 15,349 Km2 of area and soil classes include Fine Loamy, Loamy, Loamy Skeletal, Coarse Loamy, Fine, Clayey Skeletal. The feature importance analysis using Random Forest (RF) algorithm revealed that the topographic attributes and specific Landsat 8 bands are among the most influential features for distinguishing soil classes. These features are fed to the deep learning models (Convolutional Neural Network (CNN), Recurrent Neural Network (RNN)) and hybrid models (CNN-RF, RNN-RF) for soil texture classification. Legacy soil data (1:50,000 scale), comprising 1800 samples of six soil classes was used to train the models. The results indicate that the RNN-RF model achieved the highest average accuracy of 99.2 % and an F1-Score of 98.5 %. The CNN-RF model provided a slightly lower average accuracy of 98.2 % followed by RNN (98.1 %) and CNN (97.4 %). The RNN-RF model outperformed other models in classifying soil classes, particularly for Fine Loamy and Coarse Loamy classes. These findings highlight the effectiveness of integrating remote sensing and hybrid deep learning models for large scale soil texture classification.
Keywords:
Soil texture classification
Remote sensing
Topographic attributes
Texture features
Hybrid models
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