Canopy Wetness and Humidity Prediction Using Satellite and Synoptic-Scale Meteorological Observations.

Area of Science:

• Agricultural Meteorology
• Plant Pathology
• Remote Sensing

Background:

• Accurate microclimate data is crucial for predicting crop diseases.
• Traditional methods of measuring canopy humidity can be limited in scope and accuracy.

Purpose of the Study:

• To develop and validate a method for predicting canopy wetness and humidity using remote sensing and meteorological data.
• To assess the utility of the model's predictions for plant disease management.

Main Methods:

• Employed a surface energy balance model to link macroclimate to in-canopy microclimate.
• Utilized above-canopy meteorological data (temperature, vapor pressure, wind speed) and satellite-derived radiation data.
• Incorporated precipitation (irrigation + rainfall) as the sole in-field input.

Main Results:

• Model predictions closely matched measurements of nighttime dew accumulation (0.05 to 0.1 mm accuracy).
• Disease severity forecasts derived from modeled data were comparable to those from in-situ measurements.
• The model demonstrated reasonable accuracy in predicting disease severity across multiple growing seasons.

Conclusions:

• The developed method reliably predicts in-canopy microclimate conditions, including dew accumulation and humidity.
• The model offers a potentially more accurate and reliable tool for regional plant disease forecasting than single-point measurements.
• This approach can enhance disease management strategies by providing more consistent and spatially relevant microclimate predictions.