• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.393-404
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• 2022

Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.45-60
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• 2023

In this study, we analyzed the influence of middle-aged office workers' entrepreneurial competency and entrepreneurial opportunity recognition on entrepreneurial intention by mediating entrepreneurial opportunity evaluation. Sub-variables of entrepreneurial competency were classified into prior knowledge, positive attitude, and social network. For the empirical analysis of this study, an online survey using Naver Office was conducted for about 15 days (February 6, 2023 - February 20, 2023) targeting office workers across the country who are interested in starting a business, and a total of 262 copies were collected and missing values. For 250 copies excluding 12 copies, SPSS Ver.24.0 and PROCESS MACRO Model 4.0 were used for empirical analysis. The results of the analysis are as follows: First, the higher the prior knowledge of the founder's individual competency, social network, and entrepreneurial opportunity recognition, the higher the entrepreneurial opportunity evaluation and entrepreneurial intention. On the other hand, it was found that the positive attitude among entrepreneurs' individual competencies did not affect entrepreneurship opportunity evaluation and entrepreneurial intention. In addition, the magnitude of the influence on entrepreneurial opportunity evaluation and entrepreneurial intention was in the order of entrepreneurial opportunity recognition, prior knowledge, and social network. This is because the positive attitude of middle-aged office workers towards start-up has a negative image of start-up due to the shrinking start-up environment due to COVID-19, fear of failure due to lack of preparation for start-up, and successive cases of start-up failure due to cognitive bias errors due to overconfidence. implying that there is Second, it was found that the evaluation of entrepreneurship opportunities had a significant positive (+) effect on entrepreneurial intention in a situation where the entrepreneur's individual competency and entrepreneurial opportunity recognition were controlled. Third, the startup opportunity evaluation was shown to mediate between the prior knowledge of the entrepreneur's individual competency, social network and entrepreneurial opportunity recognition, and entrepreneurial intention, but it did not mediate between positive attitude and entrepreneurial intention. Fourth, among the factors influencing entrepreneurial opportunity evaluation and entrepreneurial intention, entrepreneurial opportunity recognition was found to be larger than founder's individual competency, confirming the importance of entrepreneurial opportunity recognition. Fifth, it was found that prior knowledge and network, which are individual capabilities of the founder, affect the evaluation of entrepreneurial opportunities and entrepreneurial intention, so that strengthening entrepreneurship education to recognize the importance of cultivating prior entrepreneurial knowledge and experience can revitalize middle-aged office workers' entrepreneurship. confirmed.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.