Status of Rice Paddy Field and Weather Anomaly in the Spring of 2015 in DPRK

Hong, Suk Young; Park, Hye-Jin; Jang, Keunchang; Na, Sang-Il; Baek, Shin-Chul; Lee, Kyung-Do; Ahn, Joong‐Bae

doi:10.7745/kjssf.2015.48.5.361

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…그러나 그 신호는 대상체를 둘러싼 환경 즉, 일사량, 수분함량, 기온 등 환경변화에 매우 민감하기 때문에 정확하고 경험을 수반한 자료축적과 기술이 요구된다 (Hong, 1998). 최근 에는 무인비행체를 이용하여 들녘단위 주산지에 대한 정밀한 작물 재배면적 추정 기술 개발 연구도 활발하게 진행되 고 있다 (Na et al, 2016;Lee et al, 2016) 위성영상의 육안판독 또는 분류기법을 이용하여 지역 및 국가단위 논 면적을 추정한 사례는 다양하다 (Hong et al, 2001;Hong et al, 2012;Jeong et al, 2012;Hong et al, 2015).…”

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Estimation of Rice Cultivation Area by Threshold Method using Sentinel-1 Imagery in South Korea

Lee

Hong

Kim

et al. 2020

Korean J. Soil. Sci. Fert.

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This study was carried out to classify country-wide areas of paddy rice cultivation using sentinel-1 SAR (synthetic aperture radar) imagery. Rice production with adequate level of area is important for decision making of rice supply and demand policy. It is essential to grasp rice cultivation areas in advance for estimating rice production of the year. Time-series Sentinel-1A and 1B images acquired from early May to early July were processed to convert into sigma naught (dB) images using SNAP (SeNtinel application platform) toolbox provided by European Space Agency. Farm map and parcel map, which are spatial data of vector polygon, were used to stratify paddy field population for classifying rice paddy cultivation. To distinguish paddy rice from other crops grown in the paddy fields, threshold levels were determined for each image based on crop calendar and backscattering coefficient range of paddy rice and other crops, and applied for the images to make theme maps with "1" for rice and "0" for other crops. Theme map layers were stacked to make one image and the each threshold value was applied to the theme map for rice classification. The classified areas of rice cultivation in 2020 under the stratification of three different paddy field population, 1) whole farm map, 2) farm map with paddy and plastic film house, 3) parcel map edited by LX, were 716,683 ha, 621,778 ha, and 641,679 ha, respectively. Reference data with spatial information need to be developed using farmhouse management and farmland boundary information. Image classification methods need to be attempted for further studies.

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Estimation of Rice Cultivation Area by Threshold Method using Sentinel-1 Imagery in South Korea

Lee

Hong

Kim

et al. 2020

Korean J. Soil. Sci. Fert.

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Estimation of Highland Kimchi Cabbage Growth using UAV NDVI and Agro-meteorological Factors

Na¹,

Hong²,

Park³

et al. 2016

Korean Journal of Soil Science and Fertilizer

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For more than 50 years, satellite images have been used to monitor crop growth. Currently, unmanned aerial vehicle (UAV) imagery is being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of growth estimating equation for highland Kimchi cabbage using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main districts producing highland Kimchi cabbage. UAV imagery was taken in the Anbandeok ten times from early June to early September. Meanwhile, three plant growth parameters, plant height (P.H.), leaf length (L.L.) and outer leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation during growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, NDVI UAV and rainfall in the model explain 93% of the P.H. and L.L. with a root mean square error (RMSE) of 2.22, 1.90 cm. And NDVI UAV and accumulated temperature in the model explain 86% of the L.N. with a RMSE of 4.29. These lead to the result that the characteristics of variations in highland Kimchi cabbage growth according to NDVI UAV and other agro-meteorological factors were well reflected in the model.

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Status of Rice Paddy Field and Weather Anomaly in the Spring of 2015 in DPRK

Cited by 2 publications

References 6 publications

Estimation of Rice Cultivation Area by Threshold Method using Sentinel-1 Imagery in South Korea

Estimation of Rice Cultivation Area by Threshold Method using Sentinel-1 Imagery in South Korea

Estimation of Highland Kimchi Cabbage Growth using UAV NDVI and Agro-meteorological Factors

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