Interpolation of Soil Moisture Content Aided by FDR Sensor Observations.

Automated soil moisture sensors are widely used in agronomy and environmental sciences. However, to sense properly the amount of water retained in the soil matrix, a calibration with gravimetrically observed values is required. This is laborious and not appropriate when the soil physical properties change during the wetting and drying cycles due to swelling and shrinkage, respectively. The objective of this study was to analyze how gravimetric soil moisture, θ, series can be interpolated from a minimum number of field observations, using daily automated sensor output as secondary information. Weekly observed soil moisture data from three depth intervals, and daily data from four sets for FDR sensors in two adjacent plots subject to different tillage practices were used to validate the method. A variogram analysis showed that soil moisture was more persistent in time and with depth in no-tillage and that at least two scales of temporal variation could be distinguished. Kriging with an external drift produced the largest model efficiency when using calibrated sensor measurements as secondary information. [ABSTRACT FROM AUTHOR]