Abstract:
Soil moisture (SM) dynamics are integral to effective irrigation management, influencing both crop yield and the conservation of water resources within agricultural practices. Soil Organic matter is a pivotal factor in detecting the soil moisture. The capability to accurately detect irrigation events is essential for water use optimization, a critical concern in regions facing water scarcity where judicious irrigation can bolster crop health and advance sustainable farming practices. Traditional monitoring techniques, reliant on manual field surveys and intermittent data collection, are impeded by their limited scale and lack of continuous temporal coverage. Prior research has predominantly leaned on discrete point-based measurements or episodic remote sensing data, often
inadequate for the detailed, persistent monitoring that precision irrigation scheduling demands.
Addressing these limitations, our study introduces a cohesive framework for enhanced irrigation detection by using the organic matter of the bare soil, over extensive corn fields by integrating high-frequency satellite imagery with advanced machine learning techniques. Our comprehensive methodology encompasses the acquisition of Sentinel-1 satellite, SMAP and SMOS data,
validation with ground-truth references, and sophisticated preprocessing tactics to navigate the
common temporal and spatial discrepancies encountered in remote sensing analysis. We assessed
various machine learning models, notably. Linear Regression, Random Forest, SVR, and KNN,
with Liner Regression demonstrating the most accurate performance in predicting irrigation events
and along with other models Naïve Bayes model were used for the Bare Soil classification and
Soil Organic matter Detection. Mean Square Error (MSE) and Mean Absolute Error (MAE) are
the evaluation parameters for the irrigation detection and F1-Score, Precision, Recall, Support and
Accuracy are the evaluation parameters for the Soil Moisture estimation. However, satellite data
dependency may introduce atmospheric inconsistencies, and model adaptability across different
terrains or crop types may necessitate recalibration. Future directions broader environmental parameters, exploring deep learning potentials, and model validation across diverse agricultural settings. Ultimately, our vision is to develop a universally adaptable platform that delivers real time, actionable insights for stakeholders, revolutionizing irrigation practices on a global scale
