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http://dx.doi.org/10.7780/kjrs.2020.36.5.1.2

Crop Water Stress Index (CWSI) Mapping for Evaluation of Abnormal Growth of Spring Chinese Cabbage Using Drone-based Thermal Infrared Image  

Na, Sang-il (Climate Change and Agro-Ecology Division, National Institute of Agricultural Sciences, Rural Development Administration)
Ahn, Ho-yong (Climate Change and Agro-Ecology Division, National Institute of Agricultural Sciences, Rural Development Administration)
Park, Chan-won (Climate Change and Agro-Ecology Division, National Institute of Agricultural Sciences, Rural Development Administration)
Hong, Suk-young (Climate Change and Agro-Ecology Division, National Institute of Agricultural Sciences, Rural Development Administration)
So, Kyu-ho (Climate Change and Agro-Ecology Division, National Institute of Agricultural Sciences, Rural Development Administration)
Lee, Kyung-do (Climate Change and Agro-Ecology Division, National Institute of Agricultural Sciences, Rural Development Administration)
Publication Information
Korean Journal of Remote Sensing / v.36, no.5_1, 2020 , pp. 667-677 More about this Journal
Abstract
Crop water stress can be detected based on soil moisture content, crop physiological characteristics and remote-sensing technology. The detection of crop water stress is an important issue for the accurate assessment of yield decline. The crop water stress index (CWSI) has been introduced based on the difference between leaf and air temperature. In this paper, drone-based thermal infrared image was used to map of crop water stress in water control plot (WCP) and water deficit plot (WDP) over spring chinese cabbage fields. The spatial distribution map of CWSI was in strong agreement with the abnormal growth response factors (plant height, plant diameter, and measured value by chlorophyll meter). From these results, CWSI can be used as a good method for evaluation of crop abnormal growth monitoring.
Keywords
Crop Water Stress Index (CWSI); abnormal growth; thermal infrared; spring chinese cabbage; drone;
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Times Cited By KSCI : 12  (Citation Analysis)
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1 Jones, H.G., 1992. Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology, 2nd edition, Cambridge University Press, New York, NY, USA.
2 Kim, M.C., Y.H. Choi, J.G. Cho, S.K. Yun, J.H. Park, Y.J. Kim, J.K. Jeon, and S.B. Lee, 2019. Response of Crop Water Stress Index (CWSI) and Canopy Temperature of Apple Tree to Irrigation Treatment Schemes, Journal of the Korean Society of Agricultural Engineers, 61(5): 23-31 (in Korean with English abstract).   DOI
3 Korea Rural Economic Institute (KREI) Homepage. http://www.krei.re.kr/, Accessed on Jun. 2, 2020.
4 Korean Statistical Information Service (KOSIS) Homepage. https://www.kosis.kr/, Accessed on Jul. 8, 2020.
5 Lee, H.S., S.K. Kim, H.J. Lee, J.H. Lee, S.W. An, and S.G. Lee, 2019. Development of Crop Water Stress Index for Kimchi Cabbage Precision Irrigation Control, Korean Journal of Horticultural Science & Technology, 37(4): 490-498 (in Korean with English abstract).
6 Lee, S.G., H.J. Lee, S.K. Kim, C.S. Choi, S.T. Park, Y.A. Jang, and K.R. Do, 2015. Effects of Vernalization, Temperature, and Soil Drying Periods on the Growth and Yield of Chinese Cabbage, Korean Journal of Horticultural Science & Technology, 33(6): 820-828 (in Korean with English abstract).   DOI
7 Martinez, J., G. Egea, J. Aguera, and M. Pirez-Ruiz, 2016. A cost-effective canopy temperature measurement system for precision agriculture: A case study on sugar beet, Precision Agriculture, 18(1): 95-110.
8 Na, S.I., K.D. Lee, S.C. Baek, and S.Y. Hong, 2015. Estimation of Chinese cabbage growth by RapidEye imagery and field investigation data, Korean Journal of Soil Science and Fertilizer, 48(5): 556-563 (in Korean with English abstract).   DOI
9 Na, S.I., C.W. Park, and K.D. Lee, 2016. Application of highland kimchi cabbage status map for growth monitoring based on unmanned aerial vehicle, Korean Journal of Soil Science and Fertilizer, 49(5): 469-479 (in Korean with English abstract).   DOI
10 Na, S.I., C.W. Park, K.H. So, H.Y. Ahn, and K.D. Lee, 2018. Development of Biomass Evaluation Model of Winter Crop Using RGB Imagery Based on Unmanned Aerial Vehicle, Korean Journal of Remote Sensing, 34(5): 709-720 (in Korean with English abstract).   DOI
11 Na, S.I., C.W. Park, K.H. So, H.Y. Ahn, and K.D. Lee, 2019a. Selection on Optimal Bands to Estimate Yield of the Chinese Cabbage Using Drone-based Hyperspectral Image, Korean Journal of Remote Sensing, 35(3): 375-387 (in Korean with English abstract).   DOI
12 Na, S.I., C.W. Park, K.H. So, H.Y. Ahn, and K.D. Lee, 2019b. Photochemical Reflectance Index (PRI) Mapping using Drone-based Hyperspectral Image for Evaluation of Crop Stress and its Application to Multispectral Imagery, Korean Journal of Remote Sensing, 35(5-1): 637-674 (in Korean with English abstract).   DOI
13 Yun, S.K., S.J. Kim, E.Y. Nam, J.H. Kwon, Y.S. Do, S.Y. Song, M.Y. Kim, Y.H. Choi, G.S. Kim, and H.S. Shin, 2020. Evaluation of Water Stress Using Canopy Temperature and Crop Water Stress Index (CWSI) in Peach Trees, Protected Horticulture and Plant Factory, 29(1): 20-27 (in Korean with English abstract).   DOI
14 Pou, A., M.P. Diago, H. Medrano, J. Baluja, and J. Tardaguila, 2014. Validation of thermal indices for water status identification in grapevine, Agricultural Water Management, 134: 60-72.   DOI
15 Torres-Sanchez, J., J.M. Pena, A.I. de Castro, and F. Lopez-Granados, 2014. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV, Computers and Electronics in Agriculture, 103: 104-113.   DOI
16 Woebbecke, D.M., G.E. Meyer, K. Von Bargen, and D.A. Mortensen, 1995. Color indices for weed identification under various soil, residue, and lighting conditions, Transactions of the ASAE, 38(1): 259-269.   DOI
17 Choi, Y.H., M.Y. Kim, W.H. Oh, J.G. Cho, S.K. Yun, S.B. Lee, Y.J. Kim, and J.K. Jeon, 2019. Statistical Analysis of Determining Optimal Monitoring Time Schedule for Crop Water Stress Index (CWSI), Journal of the Korean Society of Agricultural Engineers, 61(6): 73-79 (in Korean with English abstract).
18 Zhang, Z., J. Bian, W. Han, Q. Fu, S. Chen, and T. Cui, 2018. Cotton moisture stress diagnosis based on canopy temperature characteristics calculated from UAV thermal infrared image, Transactions of the Chinese Society Agricultural Engineers, 34(15): 77-84.
19 Agricultural Weather Information Service Homepage. http://weather.rda.go.kr/, Accessed on Jul. 15, 2020.
20 Bellvert, J., J. Marsal, J. Girona, and P.J. Zarco-Tejada, 2015. Seasonal evolution of crop water stress index in grapevine varieties determined with high-resolution remote sensing thermal imagery, Irrigation Science, 33(2): 81-93.   DOI
21 DeJonge, K.C., S. Taghvaeian, T.J. Trout, and L.H. Comas, 2015. Comparison of canopy temperature-based water stress indices for maize, Agricultural Water Management, 156: 51-62.   DOI
22 Han, M., H. Zhang, K.C. DeJonge, L.H. Comas, and T.J. Trout, 2016. Estimating maize water stress by standard deviation of canopy temperature in thermal imagery, Agricultural Water Management, 177: 400-409.   DOI
23 Idso, S.B., R.D. Jackson, P.J.J. Pinter, R.J. Reginato, and J.L. Hatfield, 1981. Normalizing the stress degree-day parameter for environmental variability, Agricultural Meteorology, 24: 45-55.   DOI
24 Idso, S.B., R.D. Jackson, and R.J. Reginato, 1977. Remote sensing of crop yields, Science, 196(4285): 19-25.   DOI
25 Jackson, R.D., S.B. Idso, R. Reginato, and P.J. Pinter, 1981. Canopy temperature as a crop water stress indicator, Water Resources Research, 17(4): 1133-1138.   DOI