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 10-35celcius. 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.