Enhancing Soil Organic Matter Estimation Using Advanced Machine Learning Algorithms: A Comparative Study of PolSAR and IoT Sensor Data

Monitoring soil organic matter (SOM) is a cornerstone of sustainable agriculture and food authenticity verification, yet traditional laboratory-based assessments are too resource-intensive for regional-scale deployment. This study presents a rigorous comparative evaluation of two sensing modalities: a terrestrial IoT sensor network and polarimetric synthetic aperture radar (PolSAR). To address the lack of laboratory-certified labels, we engineered the Soil Fertility Index (SFI) and Component-based Soil Quality Index (CSQI) as continuous biophysical proxies.Our methodology employs a dual-phase machine learning strategy: first, unsupervised latent discovery was used to identify dominant geophysical regimes within the radar backscatter. Second, an ensemble of supervised regression algorithms, including XGBoost and LightGBM, was deployed using a 10-fold cross-validation protocol to estimate soil integrity across the study site. The Geophysical Handshake novelty, which mathematically harmonizes ground-level dielectric measurements with satellite-based polarimetric scattering signatures, is central to our framework.To ensure the resilience of our findings, we implemented a strict identity-feature audit to eliminate data leakage. Results reveal a significant performance disparity: while the IoT track provided stable local insights, the PolSAR-based models demonstrated superior predictive power, achieving a Blind-Test 𝑅2 of 0.9908 and an RMSE of 0.33, with a negligible cross-validation standard deviation of 0.0003. This study proves that the integration of continuous proxy engineering and supervised radar modeling provides the synoptic, vegetation-penetrative X-ray required for resilient and scalable SOM estimation across non-instrumented landscapes.