• Journal of Climate Change Research
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• v.6 no.3
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• pp.209-221
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• 2015

Temperature is one of the most important factors affecting crop growth. The diurnal cycle of the scale factor [Tsc] for air temperature and the surface temperature of crop leaf and soil could be estimated by the following equation : $[Tsc]=0.5{\times}sin(X+C)+0.5$. The daily air temperature (E[Ti]) according to the E&E time [X] can be estimated by following equation using average (Tavg), maximum (Tm) and minimum (Tn) temperature : $E[Ti]=Tn+(Tm-Tn){\times}[0.5{\times}sin\;\{X+(9.646Tavg+703.65)\}+0.5]$. The crop leaf temperature in 24th June 2014 was high as the order of red pepper without mulching > red pepper with mulching > soybean under drought > soybean with irrigation > Chinese cabbage. The case in estimating crop leaf surface temperature using air temperature and soil surface temperature was lower in the deviation compared to the case using air temperature for Chinese cabbage and red pepper. These results can be utilized for the crop models as input data with estimation.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.901-912
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• 2022

The frequency of disasters is increasing due to global climate change, and unusual heavy rains and rainy seasons are occurring in Korea. Periodic monitoring and rapid detection are important because these weather conditions can lead to drought and flooding, causing secondary damage. Although research using optical images is continuously being conducted to determine the waterbody, there is a limitation in that it is difficult to detect due to the influence of clouds in order to detect floods that accompany heavy rain. Therefore, there is a need for research using synthetic aperture radar (SAR) that can be observed regardless of day or night in all weather. In this study, using Sentinel-1 SAR images that can be collected in near-real time as open data, the UNet model among deep learning algorithms that have recently been used in various fields was applied. In previous studies, waterbody detection studies using SAR images and deep learning algorithms are being conducted, but only a small number of studies have been conducted in Korea. In this study, to determine the applicability of deep learning of SAR images, UNet and the existing algorithm thresholding method were compared, and five indices and Sentinel-2 normalized difference water index (NDWI) were evaluated. As a result of evaluating the accuracy with intersect of union (IoU), it was confirmed that UNet has high accuracy with 0.894 for UNet and 0.699 for threshold method. Through this study, the applicability of deep learning-based SAR images was confirmed, and if high-resolution SAR images and deep learning algorithms are applied, it is expected that periodic and accurate waterbody change detection will be possible in Korea.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1225-1234
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• 2023

The intensity of crop water stress caused by moisture deficit is affected by growth and heat conditions. For more accurate detection of crop water stress state using remote sensing techniques, it is necessary to select vegetation indices sensitive to crop response and to understand their changes considering not only soil moisture deficit but also heat conditions. In this study, we measured the MERIS terrestrial chlorophyll index (MTCI) and chlorophyll/carotenoid index (CCI) under drought and heat wave conditions. The MTCI, sensitive to chlorophyll concentration, sensitively decreased on non-irrigation conditions and the degree was larger with heat waves. On the other hand, the CCI, correlated with photosynthesis efficiency, showed less sensitivity to water deficit but had decreased significantly with heat waves. After re-irrigation, the MTCI was increased than before damage and CCI became more sensitive to heat stress. These results are expected to contribute to evaluating the intensity of crop water stress through remote sensing techniques.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.535-544
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• 2021

Agricultural water occupies 48% of water demand, and management of agricultural reservoirs is essential for water resources management within agricultural basins. For more efficient use of agricultural water, monitoring the distribution of water resources in agricultural reservoirs and agricultural basins is required. Therefore, in this study, three threshold determination methods (i.e., fixed threshold, Otsu threshold, Kittler-Illingworth (KI) threshold) were compared to detect terrestrial water bodies using Sentinel-1 images for 3 years from 2018 to 2020. The purpose of this study was to evaluate methods for determining threshold values to more accurately estimate the reservoir area. In addition, by analyzing the relationship between the water surface and water storage at the Edong, Gosam, and Giheung reservoirs, water storage based on the SAR image was estimated and validated with observations. The thresholding method for detecting a waterbody was found to be the most accurate in the case of the KI threshold, and the water storage estimated by the KI threshold indicated a very high agreement (r = 0.9235, KGE' = 0.8691). Although the seasonal error characteristics were not observed, the problem of underestimation at high water levels may occur; the relationship between the water surface and the water storage could change rapidly. Therefore, it is necessary to understand the relationship between the water surface area and water storage through ground observation data for a more accurate estimation of water storage. If the use of SAR data through water resources satellites becomes possible in the future, based on the results of this study, it is judged that it will be beneficial for monitoring water storage and managing drought.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of Korea Water Resources Association
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• v.44 no.10
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• pp.797-807
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• 2011

Phenomenon of vegetation recruitment on the sand bar is drastically rising in the streams and rivers in Korea. In the 1960s prior to industrialization and urbanization, most of the streams were consisted of sands and gravels, what we call, 'White River'. Owing to dam construction, stream maintenance, etc. carried out since the '70s, the characteristic of flow duration and sediment transport have been disturbed resulting in the abundance of vegetation in the waterfront, that is, 'Green River' is under progress. This study purposed to identify the correlation among water level, water temperature, rainfall, soil moisture and soil texture out of the factors which give an effect on the vegetation recruitment on the sand bar of unregulated stream. To this purpose, this study selected the downstream of Naeseong Stream, one of sand rivers in Korea, as the river section for test and conducted the monitoring and analysis for 289 days. In addition, this study analyzed the aerial photos taken from 1970 to 2009 in order to identify the aged change in vegetation from the past to the present. The range of the tested river section was 361 m in transverse length and about 2 km in longitudinal length. According to the survey analysis, the tested river section in Naeseong Stream was a gaining river showing the higher underground-water level by 20~30 m compared to Stream water level. The difference in the underground water temperature was less than $5^{\circ}C$ by day and season and the Stream temperature did not fall to $10^{\circ}C$ and less from May when the vegetation germination begins in earnest. The impact factor on soil moisture was the underground water level in the lower layer and the rainfall in the upper layer and it was found that all the upper and lower layer were influenced by soil particle size. The soil from surface to 1 m-underground out of 6 soil moisture-measured points was sand with the $D_{50}$ size of 0.07~1.37 mm and it's assumed that the capillary height possible in the particle size would reach around 14~43 cm. On the other hand, according to the result of space analysis on the tested river section of unregulated stream for 40 years, it was found that the artificial disturbance and drought promoted the vegetation recruitment and the flooding resulted in the frequency extinction of vegetation communities. Even though the small and large scales of recruitment and extinction in vegetation have been repeated since 1970, the present vegetation area increased clearly compared to the past. It's found that the vegetation area is gradually increasing over time.

• Journal of the Korean Wood Science and Technology
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• v.45 no.1
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• pp.43-52
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• 2017

This study was fulfilled to verify the durations of cambial activity and analyze the associations of degree days and precipitation with the initiation of cambial activity and intra-annual wood formation for Pinus koraiensis and Chamaecyparis pisifera planted at Mt. Worak, respectively, by monitoring of their intra-annual cambial activities. And more, the reason was also analyzed why the DBH of Chamaecyparis pisifera known as planted in the same year could be classified as two groups (CPL: ${\phi}30cm$, CPS: ${\phi}15cm$). The intra-annual cambial activity was monitored using mini-cores (${\phi}2mm$) and they were collected in 2-week interval between April and October. However, between April and May and between middle September and October expected as the initiation and cessation of the cambial activity, respectively, it was fulfilled in 1-week interval. The average number of tree rings for PK (30) was less than CPS (37) and CPL (38), whereas the average ring width of PK (4.12 mm) was wider than CPS (1.84 mm) and CPL (3.97 mm). In the comparison of ring widths between CPL and CPS, CPL was 2.13 mm wider than CPS, however, excepting CPS 1 (0.83 mm), the average ring widths of CPS 2 (2.42 mm) and CPS 3 (2.73 mm) in the last 3 years were close to the average of CPL (2.71 mm). The initiation of cambial activity for PK was between 1 and 21 April, which was 1 week earlier than CPL and CPS (excepting CPS 1) and the cessation was between 1 and 22 September. The longest growing season therefore was 157.3 days (${\pm}3.3$) and it was longer than CPL ($145.7{\pm}6.6days$) and CPS ($148.0{\pm}15.1days$). In CP groups there were wide variations for the cessation of cambial activity and also there were the meaningful linear relationship between the growing seasons and the ring widths (r = 0.69, p < 0.064). The cambial activity in PK was initiated when degree days were between 99 and 134 and in CPS (excepting PCS 1) and CPL between 134 and 200. Excepting CPS 3, the false ring was observed in all samples collected on 21 July when drought stress was high due to low precipitation from June to the beginning of July.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.5
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• pp.255-264
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• 2017

To evaluate the water quality changes in agricultural reservoir covered with floating photovoltaic solar-tracking systems, the water quality variations with time and depth were monitored on both six sites for light blocking zones and four sites for light penetration zones after the installation of floating photovoltaic solar-tracking systems in Geumgwang reservoir at Anseong-si, Kyeonggi province. For one year with 16 monitoring events, water quality parameters [i.e., water temperature, pH, dissolved oxygen (DO), chlorophyll-a (Chl-a), and blue-green algae (BGA)] were monitored at depths of 0.3 m, 1 m, 3 m, and 5 m, while chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) were monitored at depths of 0.3 m. Statistically, the difference in all water quality parameters was not significantly different (p > 0.05) at the level of significance of 0.05. Based on these results, the water quality data from light blocking zones (site 1~6) and light penetration zones (site 7~10) were clustered, and were compared with time and depth. As a result, the difference in water temperature, pH, DO, COD, TN, TP, Chl-a, and BGA between light blocking zones and light penetration zones was not significant (p > 0.05) with different time and depth. For Chl-a and BGA, some data from light blocking zones greater than light penetration zones were temporary observed due to the severe drought, low water storage rate, and over growth of periphyton. However, this temporal phenomenon did not impact the water quality. Considering the small water surface area (${\leq}0.5%$) covered by floating photovoltaic solar-tracking systems, the mixing effect of whole Geumgwang reservoir caused by Ekman current and continuous discharge were more dominant than the effect of reduced solar irradiance. Further study is warranted to monitor the changes in water quality and aquatic ecosystems with greater water surface area covered by floating photovoltaic solar-tracking systems for a long time.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1465-1483
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• 2020

Evapotranspiration is a concept that includes the evaporation from soil and the transpiration from the plant leaf. It is an essential factor for monitoring water balance, drought, crop growth, and climate change. Actual evapotranspiration (AET) corresponds to the consumption of water from the land surface and the necessary amount of water for the land surface. Because the AET is derived from multiplying the crop coefficient by the reference evapotranspiration (ET0), an accurate calculation of the ET0 is required for the AET. To date, many efforts have been made for gridded ET0 to provide multiple products now. This study presents a comparison between the ET0 products such as FAO56-PM, LDAPS, PKNU-NMSC, and MODIS to find out which one is more suitable for the local-scale hydrological and agricultural applications in Korea, where the heterogeneity of the land surface is critical. In the experiment for the period between 2016 and 2019, the daily and 8-day products were compared with the in-situ observations by KMA. The analyses according to the station, year, month, and time-series showed that the PKNU-NMSC product with a successful optimization for Korea was superior to the others, yielding stable accuracy irrespective of space and time. Also, this paper showed the intrinsic characteristics of the FAO56-PM, LDAPS, and MODIS ET0 products that could be informative for other researchers.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.316-328
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• 2021

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.