The global validation for SMAP (Soil Moisture Active Passive) Level-4 surface soil moisture using well-established core validation sites does not comprehensively account for all landscapes on earth’s surface due to the diverse nature of their intrinsic characteristics. Due to the inhibitive cost of implementing a standard validation site, this study presents an alternative sampling approach suitable for localized validation of SMAP data in non-complex terrains. It involves clustering a large heterogeneous study area to smaller units of non-complex terrains where landscape-defining characteristics are largely homogeneous, thus permitting the computation of areal soil moisture as a simple arithmetic mean of near-linear point measurements. This allows optimization of limited resources (a hand-held moisture sensor with site-specific calibrations) to balance the need for spatial representativeness of samples in the sampling unit and the need for temporal proximity of sampled measurements to the aggregation time of the satellite product being validated. For ease of movement, transect sampling is implemented along access roads that run across the sampling units to allow sufficient measurement replications within a reasonably short time. Validations with four different landscapes in Kenya show a good agreement between in situ measurements and SMAP with R2 of 0.76, 0.72, 0.80, and 0.82, and biases of −0.0246, +0.0113, 0.0004, and + 0.0035 m3 m−3, respectively for Mawego, Kuresoi, Sotik and Kapsuser sites. These results only marginally differ from those obtained with a spatially distributed sampling method, indicating the potential of the proposed sampling design for time and cost effectiveness in validations at non-complex terrains. An analysis of the temporal variability of SMAP soil moisture in the watershed is also presented, with an assessment of its significance in the selection of sampling sites for validation. Particularly, the concept of temporal stability of soil moisture as a basis for clustering validation sites is evaluated.

This work presents an improved gravimetric algorithm to derive reference soil moisture, with removal of some of the hypothesis on which its original expression was based, and addition of a new corrective term that takes into account the interdependence between temperature and non-unitary water density. The temperature correction term improves reference measurements by up to 0.55% of their values in the temperature range 10–35℃. The temperature-corrected reference measurements were applied to the calibration of a hand-held soil moisture meter (Lutron PMS-714) for three soil texture types: medium, fine, and very fine. Linear regression models were used to calibrate the meter for each soil type, and the resulting calibration equations were validated with field data sampled from Sondu-Miriu watershed in Western Kenya. The validation produced errors (RMSE = 0.022, 0.010, 0.010 m³/m³) that are significantly better than the meter’s reported factory calibration errors of ± 0.05 m³/m³. While calibrations did not improve correlation statistics (R² and RMSE), they did significantly reduce biases (+ 0.009, + 0.004, -0.001 m³/m³) compared to uncalibrated ones (-0.216, -0.181, -0.184 m³/m³). Additionally, the calibrated meter values compared well with Soil Moisture Active Passive (SMAP) surface moisture data, with errors (RMSE = 0.010, 0.007, 0.008 m³/m³) well within SMAP recommended value of ± 0.04 m³/m³. A spatial scalability test showed that the calibrations are adequately robust (with R² = 0.81, RMSE = 0.016 m³/m³, and Bias = + 0.005 m³/m³), permitting calibration equations derived from one site to be scaled out to other sites of similar soil texture regime.