Possibility for Early Detection on Crop Water Stress Using Plural Vegetation Indices
![]() |
Moon, Hyun-Dong
(Department of Applied Plant Science, Chonnam National University)
Jo, Euni (Department of Applied Plant Science, Chonnam National University) Cho, Yuna (Department of Applied Plant Science, Chonnam National University) Kim, Hyunki (Department of Applied Plant Science, Chonnam National University) Kim, Bo-kyeong (Department of Applied Plant Science, Chonnam National University) Lee, Yuhyeon (Department of Applied Plant Science, Chonnam National University) Jeong, Hoejeong (Crop Production and Physiology Division, National Institute of Crop Science, Rural Development Administration) Kwon, Dongwon (Crop Production and Physiology Division, National Institute of Crop Science, Rural Development Administration) Cho, Jaeil (Department of Applied Plant Science, Chonnam National University) |
1 | Dash, J., P.J. Curran, M.J. Tallis, G.M. Llewellyn, G. Taylor, and P. Snoeij, 2010. Validating the MERIS Terrestrial Chlorophyll Index (MTCI) with ground chlorophyll content data at MERIS spatial resolution, International Journal of Remote Sensing, 31(20): 5513-5532. https://doi.org/10.1080/01431160903376340 DOI |
2 | Jeong H., R.-D. Jeong, J.-H. Ryu, D. Oh, S. Choi, and J. Cho, 2019. Preliminary growth chamber experiments using thermal infrared image to detect crop disease, Korean Journal of Agricultural and Forest Meteorology, 21(2): 111-116 (in Korean with English abstract). https://doi.org/10.5532/KJAFM.2019.21.2.111 DOI |
3 | Wong, C.Y., P. D'Odorico, M.A. Arain, and I. Ensminger, 2020. Tracking the phenology of photosynthesis using carotenoid-sensitive and near-infrared reflectance vegetation indices in a temperate evergreen and mixed deciduous forest, New Phytologist, 226(6): 1682-1695. https://doi.org/10.1111/nph.16479 DOI |
4 | Sun, P., A. Grignetti, S. Liu, R. Casacchia, R. Salvatori, F. Pietrini, F. Loreto, and M. Centritto, 2008. Associated changes in physiological parameters and spectral reflectance indices in olive (Olea europaea L.) leaves in response to different levels of water stress, International Journal of Remote Sensing, 29(6): 1725-1743. https://doi.org/10.1080/01431160701373754 DOI |
5 | Liu, X., L. Shao, H. Sun, S. Chen, and X. Zhang, 2013. Responses of yield and water use efficiency to irrigation amount decided by pan evaporation for winter wheat, Agricultural Water Management, 129: 173-180. https://doi.org/10.1016/j.agwat.2013.08.002 DOI |
6 | Banerjee, K. and P. Krishnan, 2020. Normalized Sunlit Shaded Index (NSSI) for characterizing the moisture stress in wheat crop using classified thermal and visible images, Ecological Indicators, 110: 105947. https://doi.org/10.1016/j.ecolind.2019.105947 DOI |
7 | Berger, K., M. Machwitz, M. Kycko, S.C. Kefauver, S.V. Wittenberghe, M. Gerhards, J. Verrelst, C. Atzberger, C. Tol, A. Damm, U. Rascher, I. Herrmann, V.S. Paz, S. Fahrner, R. Pieruschka, E. Prikaziuk, M.L. Buchaillot, A. Halabuk, M. Celesti, G. Koren, E.T. Gormus, M. Rossini, M. Foerster, B. Siegmann, A. Abdelbaki, G. Tagliabue, T. Hank, R. Darvishzadeh, H. Aasen, M. Garcia, I. Pocas, S. Bandopadhyay, M. Sulis, E. Tomelleri, O. Rozenstein, L. Filchev, G. Stancile, and M. Schlerf, 2022. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review, Remote Sensing of Environment, 280: 113198. https://doi.org/10.1016/j.rse.2022.113198 DOI |
8 | Gamon, J.A., K.F. Huemmrich, C.Y. Wong, I. Ensminger, S. Garrity, D.Y. Hollinger, A. Noormets, and J. Penuelas, 2016. A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers, Proceedings of the National Academy of Sciences, 113(46): 13087-13092. https://doi.org/10.1073/pnas.1606162113 DOI |
9 | Hanson, B., S. Orloff, and D. Peters, 2000. Monitoring soil moisture helps refine irrigation management, California Agriculture, 54(3): 38-42. https://doi.org/10.3733/ca.v054n03p38 DOI |
10 | Idso, S.B., R.D. Jackson, P.J. Pinter Jr., R.J. Reginato, and J.L. Hatfield, 1981. Normalizing the stress degree-day for environmental variability, Agricultural Meteorology, 24: 45-55. https://doi.org/10.1016/0002-1571(81)90032-7 DOI |
11 | Moeremans, B. and S. Dautrebande, 2000. Soil moisture evaluation by means of multi-temporal ERS SAR PRI images and interferometric coherence, Journal of Hydrology, 234(3-4): 162-169. https://doi.org/10.1016/S0022-1694(00)00251-1 DOI |
12 | Moran, M.S., 1994. Irrigation management in Arizona using satellites and airplanes, Irrigation Science, 15(1): 35-44. https://doi.org/10.1007/BF00187793 DOI |
13 | Sauer, T., P. Havlik, U.A. Schneider, E. Schmid, G. Kindermann, and M. Obersteiner, 2010. Agriculture and resource availability in a changing world: The role of irrigation, Water Resources Research, 46(6): 1-12. https://doi.org/10.1029/2009WR007729 DOI |
14 | Elliott, E., D. Deryng, C. Muller, K. Frieler, M. Konzmann, D. Gerten, M. Glotter, M. Florke, Y. Wada, N. Best, S. Eisner, B.M. Fekete, C. Folberth, I. Fostera, S.N. Gosling, I. Haddeland, N. Khabarov, F. Ludwig, Y. Masaki, S. Olin, C. Rosenzweig, A.C. Ruane, Y. Satoh, E. Schmid, T. Stacke, Q. Tang, and D. Wisser, 2013. Constraints and potentials of future irrigation water availability on agricultural production under climate change, Proceedings of the National Academy of Science, 111(9): 3239-3244. https://doi.org/10.1073/pnas.1222474110 DOI |
15 | Cerullo, G., D. Polli, G. Lanzani, S. De Silvestri, H. Hashimoto, and R.J. Cogdell, 2002. Photosynthetic light harvesting by carotenoids: detection of an intermediate excited state, Science, 298(5602): 2395-2398. https://doi.org/10.1126/science.1074685 DOI |
16 | Dash, J. and P.J. Curran, 2007. Evaluation of the MERIS terrestrial chlorophyll index (MTCI), Advances in Space Research, 39(1): 100-104. https://doi.org/10.1016/j.asr.2006.02.034 DOI |
17 | de Lima, I.P., R.G. Jorge, and J.L.P. de Lima, 2021. Remote sensing monitoring of rice fields: Towards assessing water saving irrigation management practices, Frontiers in Remote Sensing, 2: 762093. https://doi.org/10.3389/frsen.2021.762093 DOI |
![]() |