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Estimation of Chinese Cabbage Growth by RapidEye Imagery and Field Investigation Data
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 Title & Authors
Estimation of Chinese Cabbage Growth by RapidEye Imagery and Field Investigation Data
Na, Sangil; Lee, Kyoungdo; Baek, Shinchul; Hong, Sukyoung;
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 Abstract
Chinese cabbage is one of the most important vegetables in Korea and a target crop for market stabilization as well. Remote sensing has long been used as a tool to extract plant growth, cultivated area and yield information for many crops, but little research has been conducted on Chinese cabbage. This study refers to the derivation of simple Chinese cabbage growth prediction equation by using RapidEye derived vegetation index. Daesan-myeon area in Gochang-gun, Jeollabuk-do, Korea is one of main producing district of Chinese cabbage. RapidEye multi-spectral imagery was taken on the Daesan-myeon five times from early September to late October during the Chinese cabbage growing season. Meanwhile, field reflectance spectra and five plant growth parameters, including plant height (P.H.), plant diameter (P.D.), leaf height (L.H.), leaf length (L.L.) and leaf number (L.N.), were measured for about 20 plants (ten plants per plot) for each ground survey. The normalized difference vegetation index (NDVI) for each of the 20 plants was measured using an active plant growth sensor (Crop ) at the same time. The results of correlation analysis between the vegetation indices and Chinese cabbage growth data showed that NDVI was the most suited for monitoring the L.H. (r=0.958~0.978), L.L. (r=0.950~0.971), P.H. (r=0.887~0.982), P.D. (r=0.855~0.932) and L.N. (r=0.718~0.968). Retrieval equations were developed for estimating Chinese cabbage growth parameters using NDVI. These results obtained using the NDVI is effective provided a basis for establishing retrieval algorithm for the biophysical properties of Chinese cabbage. These results will also be useful in determining the RapidEye multi-spectral imagery necessary to estimate parameters of Chinese cabbage.
 Keywords
Chinese cabbage;Growth prediction equation;RapidEye;NDVI;
 Language
Korean
 Cited by
1.
Application of Highland Kimchi Cabbage Status Map for Growth Monitoring based on Unmanned Aerial Vehicle, Korean Journal of Soil Science and Fertilizer, 2016, 49, 5, 469  crossref(new windwow)
2.
Estimation of Highland Kimchi Cabbage Growth using UAV NDVI and Agro-meteorological Factors, Korean Journal of Soil Science and Fertilizer, 2016, 49, 5, 420  crossref(new windwow)
 References
1.
Ahn, J.H., Y.I. Hahm, Y.H. Om, and T.Y. Park, 1995. Prediction of chinese cabbage yield by statistical method, J. Korean Soc. Hort. Sci. 202-203 (in Korean).

2.
Ahn, J.H., K.D. Kim, and J.T. Lee, 2014. Growth modeling of chinese cabbage in an alpine area, Korean J. Agric. Forest Meteorol. 16(4):309-315 (in Korean). crossref(new window)

3.
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 Sens. Environ., 114:1312-1323. crossref(new window)

4.
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, Remote Sens. Environ., 28(5):509-520 (in Korean).

5.
Korean Statistical Information Service Homepage. http://www.kosis.kr/. Accessed 14 Aug. 2015.

6.
Lee, J.W., G.A. Park, H.K. Joh, K.H. Lee, S.I. Na, J.H. Park, and S.J. Kim, 2011. Analysis of relationship between vegetation indices and crop yield using KOMPSAT (KoreaMulti-Purpose SATellite)-2 imagery and field investigation data, J. Korean Soc. Agric. Engineers. 53(3):75-82 (in Korean).

7.
Na, S.I., J.H. Park, and J.K. Park, 2012. Development of Korean paddy Rice yield Prediction Model (KRPM) using meteorological element and MODIS NDVI, J. Korean Soc. Agric. Engineers. 54(3):141-148 (in Korean).

8.
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, Remote Sens. Environ., 29(5):461-476 (in Korean).

9.
Na, S.I., S.Y. Hong, Y.H. Kim, and K.D. Lee, 2014. Estimation of corn and soybean yields based on MODIS data and CASA model in Iowa and Illinois, USA, Korean J. Soil Sci. Fert. 47(2):92-99 (in Korean). crossref(new window)

10.
National Institution Horticultural and Herbal Science (NIHHS). 2009. Annual report. 2009.

11.
Ren J.Q., Z.X. Chen, Q.B. Zhou, and H.J. Tang, 2008. Regional yield estimation for winter wheat with MODISNDVI data in Shandong, China, Int. J. Appl. Earth Obs. Geoinf. 10:403-413. crossref(new window)

12.
Rural Development Administration (RDA), 2002. Standard farming manual 128, Cultivation of the Chinese Cabbage.

13.
Rojas, O., 2007. Operational maize yield model development and validation based on remote sensing and agro-meteorological data in Kenya, Int, J. Remote Sens., 28(17):3775-3793. crossref(new window)