References
- 김이현, 홍석영, 김명숙, 곽한강, 임상규, 2007. 농촌진흥청 국립농업과학원 농업환경부 시험연구보고서. 광학센서를 이용한 단백질함량 추정법 개발. pp.376-393.
- 미국 농무성 외국농업청(USDA FAS), http://www.pecad.fas.usda.gov/cropexplorer/.
- 안중배, 허지나, 심교문, 2010. 수치예보 모형을 이용한 역학적 규모축소 기법을 통한 농업기후지수 모사, 한국농림기상학회지, 12(1): 1-10. https://doi.org/10.5532/KJAFM.2010.12.1.001
- 홍석영, 최은영, 김건엽, 강신규, 김이현, 장용선, 2009. MODIS NDVI를 이용한 북한의 벼 수량 추정 연구, 2009 대한원격탐사학회 춘계학술대회 논문집, pp. 116-120.
- Ahn, J.B., C.K. Park, and E.S. Im, 2002. Reproduction of regional scale surface air temperature by estimating systematic bias of mesoscale numerical model, Journal of Korean Meteorological Society, 38(1): 69-80.
- Becker-Reshef, I., E. Vermote, M. Lineman, and C. Justice, 2010. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data, Remote Sensing of Environment, 114: 1312-1323. https://doi.org/10.1016/j.rse.2010.01.010
- Chang, J., M.C. Hansen, K. Pittman, M. Carroll, and C. DiMiceli, 2007. Corn and soybean mapping in the United States using MODIS time-series data sets, Agronomy Journal, 99: 1654-1664. https://doi.org/10.2134/agronj2007.0170
-
Cock, J.H. and S. Yoshida, 1972. Accumulation of
$^{14}C$ -labelled carbohydrate before flowering and its subsequent redistribution and respiration in the rice plant, Proceedings of the Crop Science Society of Japan, 41: 226-234. https://doi.org/10.1626/jcs.41.226 - Cressman, G.P., 1959: An operational objective analysis system. Mon. Wea. Rev., 87, 367-374. https://doi.org/10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2
- Doraiswamy, P.C, T.R. Sinclair, S. Hollinger, B. Akhmedov, A. Stern. and J. Prueger, 2005. Application of MODIS derived parameters for regional crop yield assessment, Remote Sensing of Environment, 97: 192-202. https://doi.org/10.1016/j.rse.2005.03.015
- FAO 작황 조기예보시스템 (GIEW), http://www.fao.org/giews/english/index.htm
- Hong, S.Y., J.T. Lee, S.K. Rim, and J.S. Shin, 1997. Radiometric estimates of grain yields related to crop aboveground net production (ANP) in paddy rice. Proc. of 1997 International Geoscience and Remote Sensing Symposium, Singapore, Aug. 3-8, pp. 1793-1795.
- Jordan, C.F., 1969. Derivation of leaf area index from quality of light on the forest floor, Ecology, 50: 663-666. https://doi.org/10.2307/1936256
- Myneni, R.B., G. Asrar, and F.G. Hall, 1992. A three dimensional radiative transfer modelsalgorithms- experiments. Remote Sensing of Environment, 51: 3-26.
- Narongrit, C. and K. Chankao, 2009. Development and validation of rice evapotranspiration model based on Terra/MODIS remotely sensed data, Journal of Food, Agriculture & Environment, 7: 684-689.
- Rasmussen M.S., 1997. Operational yield forecast using AVHRR NDVI data: reduction of environmental and inter-annual variability, International Journal of Remote Sensing, 18(5): 1059-1077. https://doi.org/10.1080/014311697218575
- Rouse, J.W, R.H. Haas, J.A. Schell, and D.W. Deering, 1973. Monitoring vegetation systems in the great plains with ETRA. In third ETRS Symposium, NASA SP-353. U.S. Govt. Printing Office, Washington D.C., 1: 309-317.
- Ren, J., Z. Chen, Q. Zhou, and H. Tang, 2008. Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China, International Journal of Applied Earth Observation and Geoinformation, 10: 403-413. https://doi.org/10.1016/j.jag.2007.11.003
- Sun, J., 2000. Dynamic monitoring and yield estimation of crops by mainly using the remote sensing technique in China, Photogrammetric Engineering and Remote Sensing, 66(5): 645-650.
Cited by
- Prediction of Rice Yield in Korea using Paddy Rice NPP index - Application of MODIS data and CASA Model - vol.29, pp.5, 2013, https://doi.org/10.7780/kjrs.2013.29.5.2
- A Study on Estimating Rice Yield in DPRK Using MODIS NDVI and Rainfall Data vol.31, pp.5, 2015, https://doi.org/10.7780/kjrs.2015.31.5.8
- Status of Rice Paddy Field and Weather Anomaly in the Spring of 2015 in DPRK vol.48, pp.5, 2015, https://doi.org/10.7745/KJSSF.2015.48.5.361
- Satellite-based Hybrid Drought Assessment using Vegetation Drought Response Index in South Korea (VegDRI-SKorea) vol.57, pp.4, 2015, https://doi.org/10.5389/KSAE.2015.57.4.001
- Selection of Optimal Vegetation Indices for Estimation of Barley & Wheat Growth based on Remote Sensing - An Application of Unmanned Aerial Vehicle and Field Investigation Data - vol.32, pp.5, 2016, https://doi.org/10.7780/kjrs.2016.32.5.7
- Assessment of Multimodel Ensemble Seasonal Hindcasts for Satellite-Based Rice Yield Prediction vol.72, pp.3-4, 2016, https://doi.org/10.2480/agrmet.D-15-00019
- Exploring NDVI Gradient Varying Across Landform and Solar Intensity using GWR: a Case Study of Mt. Geumgang in North Korea vol.21, pp.4, 2013, https://doi.org/10.7319/kogsis.2013.21.4.073
- Regional-scale rice-yield estimation using stacked auto-encoder with climatic and MODIS data: a case study of South Korea pp.1366-5901, 2018, https://doi.org/10.1080/01431161.2018.1488291
- RapidEye 영상을 이용한 북한의 논 면적 산정 vol.45, pp.6, 2012, https://doi.org/10.7745/kjssf.2012.45.6.1194
- An Approach for Improvement of Goodness of Fit on the Estimation of Paddy Rice Yield Using Satellite(MODIS) Images vol.14, pp.11, 2012, https://doi.org/10.5762/kais.2013.14.11.5417
- 고해상도 위성영상과 LiDAR 자료를 활용한 해안지역에 인접한 농경지 추출에 관한 연구 vol.18, pp.1, 2012, https://doi.org/10.11108/kagis.2015.18.1.170
- Landsat 영상을 활용한 북한 주요도시의 도시화 지수 분석 vol.33, pp.4, 2015, https://doi.org/10.7848/ksgpc.2015.33.4.277
- MODIS와 기상자료 기반 회선신경망 알고리즘을 이용한 남한 전역 쌀 생산량 추정 vol.34, pp.5, 2016, https://doi.org/10.7848/ksgpc.2016.34.5.525
- 울폐산림의 엽면적지수 추정을 위한 적색경계 밴드의 효과 vol.33, pp.5, 2012, https://doi.org/10.7780/kjrs.2017.33.5.1.10
- 중국 동북3성에서의 옥수수 수확량과 위성기반의 식생 지수 및 농업기후요소와의 상관성 연구 vol.33, pp.5, 2012, https://doi.org/10.7780/kjrs.2017.33.5.2.10
- MODIS 전천후 기상자료 기반의 생물리학적 벼 수량 모형 개발 vol.33, pp.5, 2012, https://doi.org/10.7780/kjrs.2017.33.5.2.11
- 경험적 벼 작황예측 방법에 대한 소개와 원격탐사를 이용한 예측과의 비교 vol.33, pp.5, 2012, https://doi.org/10.7780/kjrs.2017.33.5.2.12
- MODIS NDVI와 기상자료를 이용한 미국 일리노이, 아이오와주 옥수수, 콩 수량 추정 vol.33, pp.5, 2012, https://doi.org/10.7780/kjrs.2017.33.5.2.13
- SSAE 알고리즘을 통한 2003-2016년 남한 전역 쌀 생산량 추정 vol.33, pp.5, 2017, https://doi.org/10.7780/kjrs.2017.33.5.2.3
- MODIS NDVI와 기상요인을 고려한 마늘·양파 주산단지 단수예측 모형 개발 vol.33, pp.5, 2012, https://doi.org/10.7780/kjrs.2017.33.5.2.5
- Selection of Optimal Vegetation Indices and Regression Model for Estimation of Rice Growth Using UAV Aerial Images vol.50, pp.5, 2012, https://doi.org/10.7745/kjssf.2017.50.5.409
- 작물모형의 생물계절 및 잠재수량 예측력 개선 방법 탐색: I. 유전 모수 정보 향상으로 콩의 개화시기 및 잠재수량 예측력 향상이 가능한가? vol.19, pp.4, 2012, https://doi.org/10.5532/kjafm.2017.19.4.203
- MODIS NDVI 및 기후정보 활용 산림생태계의 기후변화 민감성 분석 vol.21, pp.3, 2012, https://doi.org/10.11108/kagis.2018.21.3.001
- 농업관측을 위한 KOMPSAT-3 위성의 Spectral Band Adjustment Factor 적용성 평가 vol.34, pp.6, 2012, https://doi.org/10.7780/kjrs.2018.34.6.3.5
- UAV를 이용한 농경지 분광특성 및 식생지수 분석 vol.22, pp.4, 2019, https://doi.org/10.11108/kagis.2019.22.4.086
- 시계열 마스크 맵이 논벼 NDVI와 단수와의 관계에 미치는 영향 vol.36, pp.5, 2012, https://doi.org/10.7780/kjrs.2020.36.5.1.6
- KOMPSAT-3와 Landsat-8의 시계열 융합활용을 위한 교차검보정 vol.36, pp.6, 2012, https://doi.org/10.7780/kjrs.2020.36.6.2.4
- Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal vol.13, pp.7, 2012, https://doi.org/10.3390/rs13071391
- 우리나라 시군단위 벼 수확량 예측을 위한 다종 기상자료의 비교평가 vol.37, pp.2, 2012, https://doi.org/10.7780/kjrs.2021.37.2.12
- 벼 수량 자료의 추세분석을 통한 MODIS NDVI 및 기상자료 기반의 벼 수량 추정 모형 개선 vol.37, pp.2, 2012, https://doi.org/10.7780/kjrs.2021.37.2.2
- Growth Analysis of Rehmannia glutinosa using Destructive and Non-Destructive Methods vol.29, pp.4, 2021, https://doi.org/10.7783/kjmcs.2021.29.4.233