Simultaneous estimation of soil moisture and soil organic matter from in situ dielectric measurements - part 1: Optimal estimation strategy

Abstract

Simultaneously estimating soil moisture (SM) and soil organic matter (OM) from microwave dielectric measurements has substantial value for sustainable agriculture and environmental monitoring, as both crop health and carbon sequestration depend heavily on these soil properties. However, existing dielectric mixing models often treat SM alone, neglecting the influence of OM and introducing discontinuities that complicate dual‐parameter optimization. Here, we propose a refined optimal estimation (OE) approach that seamlessly incorporates OM into dielectric modeling. Including OM in the dielectric modeling framework not only refines SM estimation but also provides a unique pathway for leveraging soil moisture sensors to assess soil carbon content. By carefully managing SM priors that contain OM‐related uncertainties, the ambiguity typically associated with jointly estimating SM and OM is significantly reduced. Field data from the SMAP Validation Experiment 2012 (SMAPVEX12) strongly agree with the simultaneously estimated SM (R = 0.805, RMSE = 0.086 cm3cm-3), as well as OM validated against laboratory-derived organic matter from loss-on-ignition (R = 0.850, RMSE = 0.059 g-1). These improvements have direct implications for practical agricultural water management and ecological stewardship, especially when optimizing irrigation strategies or tracking carbon stocks. By enabling more accurate, spatially explicit, and temporally dynamic estimates of SM and OM, this method broadens the capabilities of remote sensing tools, ultimately aiding both farmers seeking to enhance soil health and environmental managers charged with carbon accounting. To our knowledge, this is the first demonstration of simultaneous SM–OM retrieval from in-situ 50 MHz dielectric measurements using optimal estimation, extending prior OM-aware dielectric modeling beyond single-parameter (SM-only) inversions. Notably, jointly estimating SM and OM improves SM itself relative to SM-only inversion (ubRMSE: 0.097 → 0.074 m³ m⁻³), because explicitly accounting for OM variability prevents unmodeled OM from contaminating SM. © 2026 Elsevier B.V.