CYGNSS Soil Moisture Performance in Guinea Savanna Region: Extended and Quadruple Collocation Evidence from Benue State, Nigeria

Reliable soil moisture information is essential for agricultural drought warning, but tropical smallholder regions often lack ground networks for validating satellite products. This study evaluates CYGNSS Level 3 soil moisture in Guinea savanna agriculture over Benue State, Nigeria, from 2021 to 2023. Extended Triple Collocation was applied to CYGNSS, SMAP Enhanced Level 3, and ERA5-Land anomalies. Quadruple Collocation then used ESA CCI ACTIVE as a fourth product to quantify CYGNSS-SMAP error dependence. The standard SMAP-inclusive configuration gives CYGNSS a correlation with unknown true soil moisture of r = 0.425, an error standard deviation of 0.036m³m⁻³, and a signal-to-noise ratio of −6.56 dB. Quadruple Collocation identifies a CYGNSS-SMAP cross-error correlation of 0.325 and reduces the SMAP-independent CYGNSS estimate to r = 0.386, indicating that SMAP-inclusive validation overstates retrieval skill. Performance is weakest under dry soils (r = 0.331), where drought detection is most important, and location-level ETC convergence fails during Harmattan conditions as anomaly variance collapses. Skill is higher over cropland (r = 0.447), shrubland or grassland (r = 0.455), and moderate precipitation conditions (r = 0.630), but lower over tree cover (r = 0.342). These findings show that uncorrected CYGNSS Level 3 soil moisture is not sufficient for standalone year-round drought monitoring in Guinea savanna agriculture. Its value is strongest in bias-corrected, multi-sensor systems that account for vegetation, soil moisture state, precipitation history, land cover, and seasonality.