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.

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 the Cyclone Global Navigation Satellite System (CYGNSS) Level 3 soil moisture product in Guinea savanna agriculture over Benue State, Nigeria, from 2021 to 2023 using reference-free collocation diagnostics. Extended Triple Collocation (ETC) was applied to CYGNSS, the Soil Moisture Active Passive (SMAP) Enhanced Level 3 product, and European Centre for Medium-Range Weather Forecasts fifth-generation land reanalysis (ERA5-Land) 31-day centered anomalies to estimate model-derived correlation with a latent soil moisture anomaly signal, estimated error standard deviation, and signal-to-noise ratio (SNR). A covariance-pathway Quadruple Collocation (QC) analysis then introduced the European Space Agency Climate Change Initiative active microwave soil moisture product (ESA CCI ACTIVE) as a fourth, structurally distinct product to test whether the CYGNSS–SMAP pair exhibited significant direct error correlation. The regional ETC configuration gave CYGNSS an estimated latent correlation of $r=0.425$, an estimated error standard deviation of $0.036{\mathrm{m}}^3{\mathrm{m}}^{-3}$, and an SNR of $-6.56$ dB. In the common quadruplet sample, the SMAP-inclusive CYGNSS estimate was $r=0.423$, whereas the SMAP-independent configuration gave $r=0.386$, indicating a modest configuration-dependent inflation of $\Delta r=0.0368$. However, the QC cross-error correlation was not statistically significant ($r_{\epsilon }=0.0007$, 95% confidence interval (CI) [$-0.0270$, $0.0283$]). Performance was weakest under dry soils $(r=0.331)$, where drought detection is most important. Harmattan diagnostics showed that dry-season ETC failure was associated with reduced anomaly variance and selective CYGNSS decoupling from SMAP and ERA5-Land rather than numerical ill-conditioning alone. Skill was 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 indicate that uncorrected CYGNSS Level 3 soil moisture should not be used as a standalone year-round drought-monitoring product in Guinea savanna agriculture. Its strongest value is as part of environment-aware, bias-corrected, multi-sensor systems that account for vegetation, soil moisture state, precipitation history, land cover, and seasonality.