Acknowledgement
본 논문은 주저자의 석사학위논문의 일부를 수정·보완한 것이며, 농촌진흥청 연구사업(과제번호: PJ0162342022)의 지원에 의해 이루어진 것임.
References
- Ali, A.M., I.Y. Savin, A. Poddubskiy, M. Abouelghar, N. Saleh, K. Adutaleb, M. ElShirbeny and P. Dokukin. 2020. Integrated method for rice cultivation monitoring using Sentinel-2 data and leaf area index. The Egyptian Journal of Remote Sensing and Space Sciences 24(3):431-441.
- Cao, J., Z. Zhang, F. Tao, L. Zhang, Y. Luo, J. Zhang, J. Han and J. Xie. 2021. Integrating multi-source data for rice yield prediction across China using machine learning and deep learning approaches. Agricultural and Forest Meteorology 297:108275. https://doi.org/10.1016/j.agrformet.2020.108275
- Cho, K.J. and Y.I. Kim. 2019. Simulation of Sentinel-2 product using airborne hyperspectral image and analysis of TOA and BOA reflectance for evaluation of Sen2Cor atmosphere correction: focused on agricultural land. Korean Journal of Remote Sensing 35(2):251-263. https://doi.org/10.7780/KJRS.2019.35.2.5
- ESA(European Space Agency). Resolution and Swath. http://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2/instrument-payload/resolution-and-swath.(Accessed September 22, 2021).
- Franch, B., A.S. Bautista, D. Fita, C. Rubio, D. Tarrazo-Serrano, A. Sanchez, S. Skakun, E. Vermote, I. Becker-Reshef and A. Uris. 2021. Within-field rice yield estimation based on Sentinel-2 satellite data. Remote Sensing 13(20):4095. https://doi.org/10.3390/rs13204095
- Haboudane, D., J.R. Miller, E. Pattey, P.J. Zarco-Tejada and I.B. Strachan. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90(3):337-352. https://doi.org/10.1016/j.rse.2003.12.013
- Hong, S.Y., J.N. Hur, J.B. Ahn, J.M.. Lee, B.K. Min, C.K. Lee, Y.H. Kim, K.D. Lee, S.H. Kim, G.Y. Kim and K.M. Shim. 2012. Estimating rice yield using MODIS NDVI and meteorological data in Korea. Korean Journal of Remote Sensing 28(5): 509-520. https://doi.org/10.7780/KJRS.2012.28.5.4
- Hong, S.Y., S.I. Na, K.D. Lee, Y.S. Kim and S.C. Baek. 2015. A study on estimating rice yield in DPRK using MODIS NDVI and rainfall data. Korean Journal of Remote Sensing 31(5):441-448. https://doi.org/10.7780/KJRS.2015.31.5.8
- Hyun, B.K., Y.K. Sonn, S.Y. Hong and K.T. Kim. 2019. Creation, classification and explanation of soil. In: Rural Development Administration(ed.) Crop soil management technology. Rural Development Administration, Jeonju, pp.8-41.
- Jang, S.E., A.S. Suh, P.G. Kim and J.I. Yun. 2000. Analysis of spectral reflectance characteristic change during growing status of rice plants using spectroradiometer. Journal of the Korean Association of Geographic Information Studies 3(3):12-19.
- Jensen, J.R. 2016. Introductory Digital Image Processing: A Remote Sensing Perspective 4th ed. (Im, J.H., H.G. Sohn, S.Y. Park, D.J. Kim, J.W. Choi, J.Y. Lee and C.J. Kim, Trans.). Sigma Press, Seoul, pp.327
- Ju, S., H. Lim, J.W. Ma, S. Kim, K. Lee, S. Zhao and J. Heo. 2021. Optimal county-level crop yield prediction using MODIS-based variables and weather data: a comparative study on machine learning models. Agricultural and Forest Meteorology 307: 108530. https://doi.org/10.1016/j.agrformet.2021.108530
- Kim, J.H., C.K. Lee, W.G.. Sang, P. Shin, H.S. Cho and M.C. Seo. 2017. Introduction to empirical approach to estimate rice yield and comparison with remote sensing approach. Korean Journal of Remote Sensing 33(5-2):733-740. https://doi.org/10.7780/KJRS.2017.33.5.2.12
- Kim, K.S., G.S. Moon and Y.J. Choung. 2020. Analysis on changes of remote sensing indices on each land cover before and after heavy rainfall using multi-temporal Sentinel-2 satellite imagery and daily precipitation data. Journal of the Korean Association of Geographic Information Studies 23(2):70-82. https://doi.org/10.11108/KAGIS.2020.23.2.070
- Kim, M.H., C.K. Lee, H.K. Park, J.E. Lee, B.C. Koo and J.C. Shin. 2008. A study on rice growth and yield monitoring using medium resolution Landsat imagery. Korean Journal of Crop Science 53(4): 388-393.
- KOSTAT(Statistics Korea). 2011. Development of yield estimation method for major crops using remote sensing techniques.
- KOSTAT(Statistics Korea). 2020. Paddy rice production by city and county(fine grain, 92.9%). Crop production survey.
- Lee, I.H. 2016. EasyFlow Regression Analysis. Hannarae. pp.459.
- Lee, H.Y. and S.C. Noh. 2013. Advanced statistical analysis -theory and practice-2nd ed. Moonwoosa.
- Lee, J.B., J. Heo and H.G. Sohn. 2008. Study on correlation between timber age, image bands and vegetation indices for timber age estimation using Landsat TM image. Korean Journal of Remote Sensing 24(6): 583-590. https://doi.org/10.7780/KJRS.2008.24.6.583
- Lee, K.D., H.Y. An, C.W. Park, K.H. So, S.I. Na and S.Y. Jang. 2019. Estimation of rice grain yield distribution using UAV imagery. Journal of the Korean Society of Agricultural Engineers 61(4):1-10. https://doi.org/10.5389/KSAE.2019.61.4.001
- Lee, K.D., C.W. Park, S.I. Na, M.P. Jung and J.H. Kim. 2017. Monitoring on crop condition using remote sensing and model. Korean Journal of Remote Sensing 33(5-2):617-620. https://doi.org/10.7780/KJRS.2017.33.5.2.1
- Ma, J.W., K.D. Lee, K.Y. Choi and J. Heo. 2017. Rice yield estimation of South Korea from year 2003-2016 using Stacked Sparse AutoEncoder. 2017. Korean Journal of Remote Sensing 33(5-2):631-640. https://doi.org/10.7780/KJRS.2017.33.5.2.3
- Ma, J.W., C.H. Nguyen, K.D. Lee and J. Heo. 2016. Convolutional networks for rice yield estimation using MODIS and weather data: a case study for South Korea. 2016. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 34(5):525-534. https://doi.org/10.7848/KSGPC.2016.34.5.525
- Moon, H.G., T.Y. Choi, D.I. Kang and J.G. Cha. 2018. Study on the estimation of leaf area index(LAI) of using UAV vegetation index and tree height data. Journal of the Korean Association of Geographic Information Studies 21(4):158-174. https://doi.org/10.11108/KAGIS.2018.21.4.158
- Mosleh, M.K., Q.K. Hassan and E.H. Chowdhury. 2015. Application of remote sensors in mapping rice area and forecasting its production: A review. Sensors 15(1):769-791. https://doi.org/10.3390/s150100769
- Na, S.I., S.Y. Hong, H.Y. Ahn, C.W. Park, K.H. So and K.D. Lee. 2021. Detrending crop yield data for improving MODIS NDVI and meteorological data based rice yield estimation model. Korean Journal of Remote Sensing 37(2):199-209 . https://doi.org/10.7780/KJRS.2021.37.2.2
- Na, S.I., S.Y. Hong, Y.H. Kim, K.D. Lee and S.Y. Jang. 2013. Prediction of rice yield in Korea using paddy rice NPP index -application of MODIS data and CASA model-. Korean Journal of Remote Sensing 29(5):461-476. https://doi.org/10.7780/KJRS.2013.29.5.2
- Nazir, A., S. Ullah, Z.A. Saqib, A. Abbas, A. Ali, M.S. Iqbal, K. Hussain, M. Shakir and M. Shah. 2021. Estimation and forecasting of rice yield using phenology-based algorithm and linear regression model on Sentinel-II satellite data. Agriculture 11(10):1026. https://doi.org/10.3390/agriculture11101026
- Noureldin, N.A., M.A. Aboelghar, H.S. Saudy and A.M. Ali. 2013. Rice yield forecasting models using satellite imagery in Egypt. The Egyptian Journal of Remote Sensing and Space Sciences 16(1):125-131. https://doi.org/10.1016/j.ejrs.2013.04.005
- Nuarsa, I.W., F. Nishio and C. Hongo. 2012. Rice yield estimation using Landsat ETM+ data and field observation. Journal of Agricultural Science 4(3):45-56.
- Prada, M., C. Cabo, R. Hernandez-Clemente, A. Hornero, J. Majada and C. Martinez-Alonso. 2020. Assessing canopy responses to thinnings for sweet chestnut coppice with time-series vegetation indices derived from Landsat-8 and Sentinel-2 imagery. Remote Sensing 12(18):3068. https://doi.org/10.3390/rs12183068
- Sarma, A.A.L.N., T.V.L. Kumar and K. Koteswararao. 2008. Development of an agroclimatic model for the estimation of rice yield. The Journal of Indian Geophysical Uniton 12(2):89-96.
- Son, M.B., J.H. Chung, Y.G. Lee and S.J., Kim. 2021. A comparative analysis of vegetation and agricultural monitoring of Terra MODIS and Sentinel-2 NDVIs. Journal of the Korean Society of Agricultural Engineers 63(6):101-115. https://doi.org/10.5389/KSAE.2021.63.6.101
- Tasumi, M. 2003. Progress in operational estimation of regional evapotranspiration using satellite imagery. Ph.D. Thesis, University of Idaho, USA.
- Torre, D.M.G., J. Gao and C. Macinnis-Ng. 2021. Remote sensing-based estimation of rice yields using various models: a critical review. Geo-spatial Information Science 24(4):580-603. https://doi.org/10.1080/10095020.2021.1936656
- Yang, L., S. Deng and Z. Zhang. 2020. New spectral model for estimating leaf area index based on gene expression programming. Computers and Electrical Engineering 83: 106604. https://doi.org/10.1016/j.compeleceng.2020.106604