Multi-Modal AI Framework Integrating Satellite, Climate, and IoT Data for Soil Erosion Prediction: A Spatiotemporal Deep Learning Approach

Abstract

Soil erosion is a major environmental process that degrades agricultural productivity, disrupts watershed function, and contributes to downstream sedimentation. Its prediction is challenging because erosion is governed by interacting spatial and temporal factors, including vegetation cover, rainfall intensity, antecedent wetness, terrain slope, and soil moisture dynamics. Conventional erosion models such as the Revised Universal Soil Loss Equation provide useful baseline estimates but are limited in their ability to incorporate real-time environmental variability. This paper proposes a multi-modal artificial intelligence framework that integrates satellite observations, climate variables, and Internet of Things sensor data to predict soil erosion susceptibility. The framework combines convolutional neural networks for spatial feature learning from satellite imagery, long short-term memory networks for temporal modeling of climate and sensor sequences, and a fusion layer for final classification of erosion risk. Simulated results show that the proposed model outperforms satellite-only, climate-only, IoT-only, and RUSLE-based baselines in accuracy, recall, F1-score, and AUC. Feature importance analysis indicates that rainfall intensity, NDVI, antecedent precipitation, soil moisture, and slope are the dominant predictors. The study demonstrates the potential of multi-modal AI for near-real-time erosion mapping, early warning, and conservation planning in erosion-prone landscapes.[2][3][4][5][6][7][8][1]

Introduction

Soil erosion is a major form of land degradation driven by both long-term factors (slope, soil type, vegetation cover) and short-term triggers (rainfall, soil moisture, land disturbance). Traditional models like RUSLE are widely used but struggle to capture rapidly changing conditions, especially under climate change and extreme rainfall events.

To address this, the paper proposes a multi-modal AI framework that integrates three data sources: satellite imagery (land cover and vegetation), climate data (rainfall and weather patterns), and IoT sensor data (soil moisture and temperature). These inputs are fused using a deep learning architecture combining a CNN (spatial features), an LSTM (temporal patterns), and a fusion layer for final erosion risk prediction.

The system classifies erosion risk into four levels (low to very high) and is evaluated using accuracy, precision, recall, F1-score, and AUC. Simulation results show that the proposed fusion model significantly outperforms single-source and traditional approaches like RUSLE, achieving the highest predictive performance, especially for high-risk erosion zones.

Key influencing factors identified include rainfall intensity, antecedent precipitation, vegetation index (NDVI), soil moisture, and slope. The model also produces spatial erosion maps to support early warning and land conservation planning.

Conclusion

This paper presented a complete SCI-style manuscript for a multi-modal AI framework that integrates satellite, climate, and IoT data for soil erosion prediction. The proposed architecture uses CNN-based spatial feature extraction, LSTM-based temporal modeling, and fusion-based classification to produce erosion susceptibility estimates. Simulated results showed that the fusion model outperformed satellite-only, climate-only, IoT-only, and RUSLE-based baselines. The strongest predictors were rainfall intensity, antecedent precipitation, NDVI, soil moisture, and slope, confirming the physical plausibility of the framework. The model offers a scalable and interpretable basis for erosion hotspot detection, early warning, and conservation planning in erosion-prone environments.

References

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Copyright

Copyright © 2026 Lavish Patel, Prof. Atesh Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.