The monitoring evapotranspiration over crops field can provide the information of crop water requirement for Agricultural Optimization, since soil water evaporation reflects the water content in the soils, which determined the water available to crops through crops root systems; meanwhile the transpiration reflects the water conditions in plants. Our recent developed hyperspectral imaging technique at near short wave infrared had demonstrated the ability to monitoring the vapor fluctuations with sensitivity at under 70-micrometer perceptible atmospheric water. This makes the hyperspectral imaging technique is suitable for monitoring the evapotranspiration over vegetation fields. With optimal optical-mechanics design, the current ruggedized setup is portable with the total mass under 20 kg, while keeping the adaptive spatial sampling rate around 8000~ 35,000, and adaptive spectral sampling rate over 20~40 in the wavelength range of 1100nm to 1300nm. A series of observations from different vegetation fields were carried out under different weather conditions. Beside the Evapotranspiration information, the classification of the fields through machine learning based on 3d convolutional neural networks are achieved. The evapotranspiration information from the snap shot imaging spectrometer are consistent with the theoretical modelling results based on MODTRAN.
|