Title:
Utilization of thermal infrared image for inversion of winter wheat yield and biomass.

Abstract:
The present paper utilizes thermal infrared image for inversion of winter wheat yield and biomass with different technology of irrigation (drip irrigation, sprinkler irrigation, flood irrigation). It is the first time that thermal infrared image is used for predicting the winter wheat yield and biomass. The temperature of crop and background was measured by thermal infrared image. It is necessary to get the crop background separation index (CBSI(L), CBSI(H)), which can be used for distinguishing the crop value from the image. CBSI(L) and CBSI(H) (the temperature when the leaves are wet adequately; the temperature when the stomata of leaf is closed completely) are the threshold values. The temperature of crop ranged from CBSI(L) to CBSI(H). Then the ICWSI was calculated based on relevant theoretical method. The value of stomata leaf has strong negative correlation with ICWSI proving the reliable value of ICWSI. In order to construct the high accuracy simulation model, the samples were divided into two parts. One was used for constructing the simulation model, the other for checking the accuracy of the model. Such result of the model was concluded as: (1) As for the simulation model of soil moisture, the correlation coefficient (R2) is larger than 0.887 6, the average of relative error (Er) ranges from 13.33% to 16.88%; (2) As for the simulation model of winter wheat yield, drip irrigation (0.887 6, 16.89%, -0.12), sprinkler irrigation (0.970 0, 14.85%, - 0.12), flood irrigation (0.969 0, 18.87%, -0.18), with the values of R2, Er and CRM listed in the parentheses followed by the individual term. (3) As for winter wheat biomass, drip irrigation (0.980 0, 13.70%, -0.13), sprinkler irrigation (0.95, 13.15%, -0.14), flood irrigation (0.970 0, 14.48%, -0.13), and the values in the parentheses are demonstrated the same as above. Both the CRM and Er are shown to be very low values, which points to the accuracy and reliability of the model investigated. The accuracy of model is high and reliable. The results indicated that thermal infrared image can be used potentially for inversion of winter wheat yield and biomass.