• Journal of Bio-Environment Control
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• v.27 no.4
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• pp.302-311
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• 2018

Using cylindrical paper pot nursery method, three kinds of commercial tomatoes 'Dafnis', 'DOTAERANG DIA' and 'Maescala' were grafted onto a commercial rootstock 'B blocking'. From 10 to 40 days after graft-take, growth traits of seedlings were investigated by 0.5, 1.0 and 2.0S treatments of standard nutrient solution(S) for seedling growth, and top to root ratio(TRR), compactness(CP) and Dickson Quality Index(DQI) were calculated. Two weeks after transplanting of the seedlings under three different night temperature targeting to 10, 15, and $25^{\circ}C$, which were not precisely controlled, the relative growth rate (RGR) was investigated. The quantitative growth traits of grafted seedlings increased with increasing fertilizer concentration, and various range of seedling size could be produced. Compactness and DQI were significantly regressed (Adj $R^2=0.9480$). Short-term RGR after transplanting was higher at 1.0S treatment of standard nutrient solution at the seedling age of 30 days and 40 days after graft-take(DAGT). DQI and RGR were significantly regressed linearly at respective fertigation strength. Specially the diminishing slope of RGR was lower at 1.0S fertigation strength with the increase of DQI than others. The results indicate that DQI could be applied as a quality index of grafted tomato seedlings and the relation analysis between DQI and short-term RGR also could be used to determine the good quality seedlings of grafted tomato grown in cylindrical paper pot.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.1
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• pp.23-31
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• 2013

Microwave remote sensing can help monitor the land surface water cycle and crop growth. This type of remote sensing has great potential over conventional remote sensing using the visible and infrared regions due to its all-weather day-and-night imaging capabilities. In this paper, a ground-based multi-frequency (L-, C-, and X-band) polarimetric scatterometer system capable of making observations every 10 min was developed. This system was used to monitor the wheat over an entire growth cycle. The polarimetric scatterometer components were installed inside an air-conditioned shelter to maintain constant temperature and humidity during the data acquisition period. Backscattering coefficients for the crop growing season were compared with biophysical measurements. Backscattering coefficients for all frequencies and polarizations increased until dat of year 137 and then decreased along with fresh weight, dry weight, plant height, and vegetation water content (VWC). The range of backscatter for X-band was lower than for L- and C-band. We examined the relationship between the backscattering coefficients of each band (frequency/polarization) and the various wheat growth parameters. The correlation between the different vegetation parameters and backscatter decreased with increasing frequency. L-band HH-polarization (L-HH) is best suited for the monitoring of fresh weight (r=0.98), dry weight (r=0.96), VWC (r=0.98), and plant height (r=0.96). The correlation coefficients were highest for L-band observations and lowest for X-band. Also, HH-polarization had the highest correlations among the polarization channels (HH, VV and HV). Based on the correlation analysis between backscattering coefficients in each band and wheat growth parameters, we developed prediction equations using the L-HH based on the observed relationships between L-HH and fresh weight, dry weight, VWC and plant height. The results of these analyses will be useful in determining the optimum microwave frequency and polarizations necessary for estimating vegetation parameters in the wheat.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.