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Evaluation of canopy temperature depression, transpiration, and canopy greenness in relation to yield of soybean at reproductive stage based on remote sensing imagery
- Hou, Mengjie, Tian, Fei, Zhang, Tong, Huang, Mengsi
- Agricultural water management 2019 v.222 pp. 182-192
- air temperature, canopy, filling period, flowering, models, prediction, remote sensing, seed yield, semiarid zones, soybeans, thermography, transpiration, water stress, yield forecasting
- Canopy temperature depression (CTD = canopy temperature (Tc) – air temperature (Ta)), transpiration (T), and canopy greenness (CG) have much to do with crop yields, and they have been widely used to estimate crop yields. However, the issues relating to the best measurement time to predict crop yields have seldom been addressed. Hence, the present study was conducted to identify the best measurement time and provide a new way to rapidly predict soybean yield prediction in a semiarid environment. Tc was measured during the reproductive stage under different water stress conditions using a handheld infrared thermal imager that allowed rapid acquisition of high-quality thermal and visible images. T was estimated using the three-temperature model (3 T model) based on thermography, and CG was estimated by analyzing visible images. The results indicate that yield is positively correlated with CG and T to a certain extent; however, it is negatively correlated with CTD. CTD and T at noon during the soybean reproductive period, especially at the flowering and podding stage, are effective in predicting soybean seed yield. During this period, each 1 °C increase in CTD at noon will on average reduce the yield of soybean by 273–304 kg/ha, and when the average T reaches about 1.1 mm/h, the yield no longer increases significantly. Moreover, there is a high correlation between CG (measured by SPAD, Soil-Plant Analysis Development) and the soybean yield during the reproductive stage, especially during the podding and pod-filling stage (R2 = 0.79), which indicates that chlorophyll-based analysis could be used to estimate soybean yield. Therefore, CTD, T, and CG measurements based on remote sensing can be used as key traits to predict soybean yield and make appropriate adaptions to water stress conditions in semiarid areas.