• Ecology and Resilient Infrastructure
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• v.5 no.4
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• pp.257-263
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• 2018

This study is to develop a GSTM (Green infrastructure Surface Temperature Measurment) equipment for reducing the surface temperature of GI by using LID Method. The tests were conducted including GI products such as Greening block, Pervious Block, Soil Block and so on. The GSTM equipment developed by considering the literature surveys are characterized as follows. The non-contact infrared temperature sensor was used to measure the surface temperature, and it was improved to measure the overall average temperature including the center and the corner temperature of the specimen. The developed GSTM equipment was used to compare performance of asphalt and GI products. As a result, the Greening Block show a high difference of $18.4^{\circ}C$ and it contributes to the decrease of surface temperature.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.329-339
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• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.