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Soil Moisture Mapping and Pattern Classification Using Geospatial and Machine Learning Techniques
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DOI：10.3390/land15060945.png)
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Abstract
En 中文
Accurate assessment of soil moisture is essential for enhancing irrigation efficiency and promoting sustainable agriculture. This study was conducted at Punjab Agricultural University, Ludhiana (PAU), to investigate the spatial and depth-wise variability of soil moisture across 30 field sites by using field measurements, geospatial-based (inverse distance weighting: IDW) interpolation, and machine learning techniques. Soil moisture was recorded at four depth intervals, including 0–15 cm, 15–30 cm, 30–45 cm, and 45–60 cm. The surface layer (0–15 cm) exhibited the highest variability due to evaporation and irrigation timing, with values ranging from 4.5% to 16.0%. Deeper layers showed more stable moisture retention, particularly at sites with intensive irrigation and crop cover, such as L11 (wheat), L22 (Gobhi Sarson), and L25 (wheat), where the moisture levels exceeded 14% at 45–60 cm depth, supporting suitability for deep-rooted crops. Supervised machine learning models, namely decision tree (DT), random forest (RF), and logistic regression (LR), were employed to classify soil moisture into low, medium, and high categories. The highest classification accuracy (88.9%) was achieved by the decision tree at 30–45 cm and logistic regression at 15–30 cm. Shallow layers exhibited frequent misclassification between medium and high classes, indicating surface-induced variability. Unsupervised clustering using K-means (k = 4) and hierarchical methods effectively delineated distinct soil moisture zones aligned with land use, irrigation history, and crop cover. The combination of geospatial analysis, depth-specific field data, and machine learning models provides an integrated framework for precision soil moisture assessment. This approach supports site-specific irrigation scheduling and water resource optimization, which are particularly critical for groundwater-stressed regions like Punjab. The novelty of this study lies in integrating depth-specific field-based soil moisture observations with geospatial interpolation and machine learning-based classification and clustering approaches to improve subsurface moisture characterization for precision irrigation management.
Keywords:
soil moisture
geospatial analysis
machine learning
Punjab
Journal
IF:
3.2
Papers:
1.3W
Citations:
2.5W
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