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| DOI：10.13522/j.cnki.ggps.2017.0201 |
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| A Data-driven Model and Its Uncertainty Analysis for Estimating Leaf Temperature of Maize under Drip Irrigation |
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MA Lihua , HU Xiaotao, WANG Ping, WANG Zhenchang
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College of Resources and Environments, Southwest University, Chongqing 400715, China; Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A& F University, Yangling 712100, China; College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
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| Abstract: |
| 【Objective】 Leaf temperature affects plant physiological development and this paper is to elucidate the responsive change in leaf temperature of maize under drip irrigation to soil moisture and meteorological factors. 【Method】A field experiment was conducted with the maize growing in soil boxes and the soil moisture of the boxes controlled by drip irrigation. During the experiment, leaf temperature, soil moisture and meteorological factors were measured. The change in the leaf temperature with soil water and meteorological factors, as well as its associated uncertainty were analyzed using the artificial neural network (ANN) model and the multivariable linear regression model (MLR). 【Result】①The ANN model is superior to the MLR model for calculating the leaf temperature, with the determining coefficient of the former being r2 =0.8 and of the latter being r2=0.9 when the soil moisture was in the range of 40% and 60%. When the soil moisture increased to 80%, the determining coefficient of the MLR was r2=0.5 and that of the ANN was r2=0.7. ②The uncertainty analysis revealed that for the meteorological factors, the leaf temperature was most sensitive to air temperature followed by net radiation and air humidity, while for soil moisture, the leaf temperature was most sensitive to water content in 30~40 cm. 【Conclusion】 Uncertainty analysis using Monte Carlo method showed that the proposed models are able to quantify the changes in maize leaf temperature with soil moisture and meteorological factors. The d-factor analysis indicated that the impact of soil moisture on leaf temperature is depth-dependent with the leaf temperature most sensitive to 30~40 cm water content. |
| Key words: leaf temperature; soil water content; meteorological factor; model; uncertainty analysis |
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