An improved method of soil moisture meter calibration for satellite data validation at watershed scale

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.