Canopy wetness and humidity prediction using satellite and synoptic-scale meteorological observations

A method for predicting canopy wetness and humidity from remotely-acquired meteorological and radiation data is described. This method employs a surface energy balance model to scale from the above-canopy macroclimate to in-canopy microclimate conditions. Above-canopy temperature, vapor pressure, and wind speed inputs were obtained from objective analyses of hourly measurements from the synoptic weather network, while downwelling long- and short-wave radiation forcings were estimated from standard satellite observations. Precipitation (irrigation + rainfall) was the only input acquired in-field. Model predictions compared well with measurements of nighttime dew accumulation and relative humidity made in irrigated potato crops grown in central Wisconsin. Maximum dew amount measured in full canopies over four nights was reproduced to within 0.05 to 0.1 mm. The practical utility of this method to disease management was assessed by processing modeled and measured canopy microclimate data from two weather stations over three growing seasons through the BLITECAST disease forecasting system. Given the uncertainties inherent in the measurement of humidity, the model reasonably reproduced disease severity values generated from in-situ measurements in all but one case, where the canopy had suffered partial defoliation. Because the model simulates the microclimate within a healthy, uniform canopy, it may in many cases produce more reliable regional forecasts for plant disease than would a single set of in-situ measurements.