• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.4
• /
• pp.218-233
• /
• 2022

The long-term (1986~2020) predictability of the number of days of heat and cold damages for each growth stage of soybean is evaluated using the daily maximum and minimum temperature (Tmax and Tmin) data produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF). The Predictability evaluation methods for the number of days of damages are Normalized Standard Deviations (NSD), Root Mean Square Error (RMSE), Hit Rate (HR), and Heidke Skill Score (HSS). First, we verified the simulation performance of the Tmax and Tmin, which are the variables that define the heat and cold damages of soybean. As a result, although there are some differences depending on the month starting with initial conditions from January (01RUN) to May (05RUN), the result after a systematic bias correction by the Variance Scaling method is similar to the observation compared to the bias-uncorrected one. The simulation performance for correction Tmax and Tmin from March to October is overall high in the results (ENS) averaged by applying the Simple Composite Method (SCM) from 01RUN to 05RUN. In addition, the model well simulates the regional patterns and characteristics of the number of days of heat and cold damages by according to the growth stages of soybean, compared with observations. In ENS, HR and HSS for heat damage (cold damage) of soybean have ranged from 0.45~0.75, 0.02~0.10 (0.49~0.76, -0.04~0.11) during each growth stage. In conclusion, 01RUN~05RUN and ENS of PNU CGCM-WRF Chain have the reasonable performance to predict heat and cold damages for each growth stage of soybean in South Korea.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.4
• /
• pp.330-336
• /
• 2022

It is likely that the heading would occur early when air temperature increases. In 2022, however, the heading date was delayed unusually, e.g., by 3 to 5 days although temperature during the vegetative growth stage was higher than normal years. The objective of this study was to identify the cause of such event analyzing weather variables including average temperature, sunshine hours, and day-length for each growth stage. The observation data were collected for medium-late maturing varieties, which has been grown at crop yield experiment sites including Daegu, Andong, and Yesan. The difference in heading date was compared between growing seasons in 2021 and 2022 because crop management options, e.g., the cultivars and cultivation methods, were identical at those sites during the study period. It appeared that the heading date was delayed due to the difference in temperature responsiveness under a given day-length condition The effect of the temperature increase on the heading date differed between the periods during which when the day-length was more than 14.3 hours before and after the summer-solstice.. The effect of the temperature decrease during the period from which the day-length decreased to less than 14.3 hours to the heading date was relatively greater. This merits further studies to examine the response of rice to the temperature change under different day-length and sunshine duration in terms of heading.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.337-345
• /
• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.387-397
• /
• 2023

Plant height is a growth parameter that provides visible insights into the plant's growth status and has a high correlation with yield, so it is widely used in crop breeding and cultivation research. Investigation of the growth characteristics of crops such as plant height has generally been conducted directly by humans using a ruler, but with the recent development of sensing and image analysis technology, research is being attempted to digitally convert growth measurement technology to efficiently investigate crop growth. In this study, the canopy height of rice grown at various nitrogen fertilization levels was measured using a laser scanner capable of precise measurement over a wide range, and a comparative analysis was performed with the actual plant height. As a result of comparing the point cloud data collected with a laser scanner and the actual plant height, it was confirmed that the estimated plant height measured based on the average height of the top 1% points showed the highest correlation with the actual plant height (R² = 0.93, RMSE = 2.73). Based on this, a linear regression equation was derived and used to convert the canopy height measured with a laser scanner to the actual plant height. The rice growth curve drawn by combining the actual and estimated plant height collected by various nitrogen fertilization conditions and growth period shows that the laser scanner-based canopy height measurement technology can be effectively utilized for assessing the plant height and growth of rice. In the future, 3D images derived from laser scanners are expected to be applicable to crop biomass estimation, plant shape analysis, etc., and can be used as a technology for digital conversion of conventional crop growth assessment methods.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.258-266
• /
• 2023

This study analyzed methodologies for estimating carbon stocks of perennial woody crops and the research cases in overseas countries. As a result, we found that Australia, Bulgaria, Canada, and Japan are using the stock-difference method, while Austria, Denmark, and Germany are estimating the change in the carbon stock based on the gain-loss method. In some overseas countries, the researches were conducted on estimating the carbon stock change using image data as tier 3 phase beyond the research developing country-specific factors as tier 2 phase. In South Korea, convergence studies as the third stage were conducted in forestry field, but advanced research in the agricultural field is at the beginning stage. Based on these results, we suggest directions for the following four future researches: 1) securing national-specific factors related to emissions and removals in the agricultural field through the development of allometric equation and carbon conversion factors for perennial woody crops to improve the completeness of emission and removals statistics, 2) implementing policy studies on the cultivation area calculation refinement with fruit tree-biomass-based maturity, 3) developing a more advanced estimation technique for perennial woody crops in the agricultural sector using allometric equation and remote sensing techniques based on the agricultural and forestry satellite scheduled to be launched in 2025, and to establish a matrix and monitoring system for perennial woody crop cultivation areas in the agricultural sector, Lastly, 4) estimating soil carbon stocks change, which is currently estimated by treating all agricultural areas as one, by sub-land classification to implement a dynamic carbon cycle model. This study suggests a detailed guideline and advanced methods of carbon stock change calculation for perennial woody crops, which supports 2050 Carbon Neutral Strategy of Ministry of Agriculture, Food, and Rural Affairs and activate related research in agricultural sector.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.26 no.2
• /
• pp.127-139
• /
• 2024

Climate change has increased extreme weather events likewise heatwaves, heavy rain, and drought. Unlike other natural disaster, drought is a slowly developing phenomenon and thus drought damage increases as the drought continues. Therefore, it is necessary to understand the characteristics and mechanism of drought occurrence. Agricultural drought occurs when the water supply needed by crops becomes insufficient due to lack of soil water. Therefore, soil water is used as a key variable affecting agricultural drought. In this study, we examined the spatio-temporal distribution and trends of drought across the Korean Peninsula by determining the soil available water content (SAWC) through a model that integrated soil, meteorological, and crop data. Moreover, an investigation into the correlation between meteorological variables and the SAWC was conducted to assess how meteorological characteristics influence the nature of drought occurrences. During the soybean cultivation period, the average SAWC was lowest in 2018 at 88.6% and highest in 2021 at 103.2%. Analysis of the spatial distribution of SAWC by growth stage revealed that the lowest SAWC occurred during the flowering stage (S3) in 2018, during the leaf extension stage (S2) in 2019, during the seedling stage (S1) in 2020, again during the flowering stage (S3) in 2021, and during the seedling stage (S1) in 2022. Based on the average SAWC across different growth stages, the frequency of upland drought was the highest at 22 times during the S3 in 2018. The lowest SAWC was primarily influenced by a significant negative correlation with rainfall and evapotranspiration, whereas the highest SAWC showed a significant positive correlation with rainfall and relative humidity, and a significant negative correlation with reference evapotranspiration.

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

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.20 no.1
• /
• pp.1-15
• /
• 2015

Impacts of Sea Surface Temperature (SST) assimilation to the prediction of upper ocean temperature is investigated by using a regional ocean forecasting system, in which 3-dimensional optimal interpolation is applied. In the present study, Sea Surface Temperature and Sea Ice Analysis (OSTIA) dataset is adopted for the daily SST assimilation. This study mainly compares two experimental results with (Exp. DA) and without data assimilation (Exp. NoDA). When comparing both results with OSTIA SST data during Sept. 2011, Exp. NoDA shows Root Mean Square Error (RMSE) of about $1.5^{\circ}C$ at 24, 48, 72 forecast hour. On the other hand, Exp. DA yields the relatively lower RMSE of below $0.8^{\circ}C$ at all forecast hour. In particular, RMSE from Exp. DA reaches $0.57^{\circ}C$ at 24 forecast hour, indicating that the assimilation of daily SST (i.e., OSTIA) improves the performance in the early SST prediction. Furthermore, reduction ratio of RMSE in the Exp. DA reaches over 60% in the Yellow and East seas. In order to examine impacts in the shallow costal region, the SST measured by eight moored buoys around Korean peninsula is compared with both experiments. Exp. DA reveals reduction ratio of RMSE over 70% in all season except for summer, showing the contribution of OSTIA assimilation to the short-range prediction in the coastal region. In addition, the effect of SST assimilation in the upper ocean temperature is examined by the comparison with Argo data in the East Sea. The comparison shows that RMSE from Exp. DA is reduced by $1.5^{\circ}C$ up to 100 m depth in winter where vertical mixing is strong. Thus, SST assimilation is found to be efficient also in the upper ocean prediction. However, the temperature below the mixed layer in winter reveals larger difference in Exp. DA, implying that SST assimilation has still a limitation to the prediction of ocean interior.

• Asian Journal of Atmospheric Environment
• /
• v.7 no.2
• /
• pp.120-128
• /
• 2013

The concentration and composition of particulate matter (PM) in the atmosphere can directly reflect the environmental pollution. The atmospheric pollution in some Cameroonian cities is increasing with the industrial development and urbanization. Air pollution is inherently complex, containing PM of varied size and composition. This PM exists as a dynamic cloud interacting with sunlight and is modified by the meteorology. The reflectometer and the EDXRF spectrometry are applied to determine the concentration of some specific elements at four sites in the city of Yaound$\acute{e}$. The particular aim of the present work is to put in place data base on air pollution in urban area and elaborate regulations on the emissions issued to industrial and vehicle activities. This study provides an overview of the concentration of black carbon and some specific elements in the air, which have impacts on human health. The measurement was done by distinguishing the size of particle. So that, the particle with aerodynamic diameter between $2.5-10{\mu}m$ (so-called coarse particle) and aerodynamic diameter < $2.5{\mu}m$ (so-called fine particle) were considered to obtain more information about levels of the inhalable fraction of the location. The results obtained in four locations of the city of Yaound$\acute{e}$ show that the black carbon concentration is very considerable, the element sulfur is a major pollutant and the concentration of fine particle is very greater. The results obtained of fine and coarse filters range from $5-17{\mu}g/m^3$ and $10-18{\mu}g/m^3$ for the black carbon. S, Cu, Zn, Pb, Cd, As, Se and Hg are the specific findings of this work. The pollutants with a greater concentration are S, Pb, and Zn. These later seem to be non-uniformly, non-regular in some location and high compared to other countries. This work allows us to make a potential relation between pollutants and emission sources. In this framework, some suggestions have been proposed to reduce emissions for an improvement of the air quality in the environment and thus, the one of the city of Yaound$\acute{e}$.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.17 no.1
• /
• pp.91-106
• /
• 2014

This study tries to cluster the 795 standard watersheds of Korea Water Resources Unit Map using multivariate statistical analysis technique. The 30 factors of watershed characteristics related to topography, stream, meteorology, soil, land cover and hydrology were selected for comprehensive analysis. From the factor analysis, 16 representative factors were selected. The significant factors in order were the pedological feature, scale and geological location and meteorological and hydrological features of the watershed. As a next step, the 73 gauged watersheds were selected for cluster analysis. They are scattered properly to the whole country and the discharge data were within a confidential level. Based on the 73 watersheds, the other ungaged watersheds were clustered by applying the 16 factors and calculating Euclidian distances. The clustering results showed that the similarity between standard watersheds within the same river basin were 87%, 69%, 41%, 52%, and 27% for Han, Nakdong, Geum, Seomjin, and Yeongsan river basins respectively.