Browse > Article
http://dx.doi.org/10.7780/kjrs.2015.31.1.2

Determining Canopy Growth Conditions of Paddy Rice via Ground-based Remote Sensing  

Jo, Seunghyun (Applied Plant Science, Chonnam National University)
Yeom, Jongmin (Satellite Applications Division, Korea Aerospace Research Institute)
Ko, Jonghan (Applied Plant Science, Chonnam National University)
Publication Information
Korean Journal of Remote Sensing / v.31, no.1, 2015 , pp. 11-20 More about this Journal
Abstract
This study aimed to investigate the canopy growth conditions and the accuracy of phenological stages of paddy rice using ground-based remote sensing data. Plant growth variables including Leaf Area Index (LAI) and canopy reflectance of paddy rice were measured at the experimental fields of Chonnam National University, Gwangju, Republic of Korea during the crop seasons of 2011, 2012, and 2013. LAI values were also determined based on correlations with Vegetation Indices (VIs) obtained from the canopy reflectance. Three phenological stages (tillering, booting, and grain filling) of paddy rice could be identified using VIs and a spatial index (NIR versus red). We found that exponential relationships could be applied between LAI and the VIs of interest. This information, as well as the relationships between LAI and VIs obtained in the present study, could be used to estimate and monitor the relative growth and development of rice canopies during the growing season.
Keywords
canopy growth; paddy rice; phenology; remote sensing; spatial index; vegetation index;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Ayyangar, R.S., P.P. Nagaeshwara Rao, and K.R. Rao, 1980. Crop cover and crop phenological information from red and infrared spectral responses, J. Indian Soc. Photo-Interpretation Remote Sensing, 8(1): 23-29.   DOI
2 Boschetti, M., D. Stroppiana, S. Bocchi, and P.A. Brivio, 2009. Multi-year monitoring of rice crop phenology through time series analysis of MODIS images, International Journal of Remote Sensing, 30: 4643-4662.   DOI
3 Boydell, B. and A.B. McBratney, 2002. Identifying potential within-field management zones from cotton-yield estimates, Precis. Agric, 3: 9-23.   DOI
4 Casanova, D., G.F. Epema, and J. Goudriaan, 1999. Monitoring rice reflectance at field level for estimating biomass and LAI, Field Crops Research, 55: 83-92.
5 Dobermann, A. and M.F. Pampolino, 1995. Indirect leaf area index measurement as a tool for characterizing rice growth at the field scale, Communications on Soil Science and Plant Analysis, 26: 1507-1523.   DOI
6 Haboudane, D., J.R. Miller, E. Pattey, P.J. Zarco-Tejada, and I. 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 Sens. Environ, 90: 337-352.   DOI
7 Hardke, J.T., 2014. Rice Production Handbook, University of Arkansas Division of agriculture Cooperative Extension Service, USA, MP-192, p. 206.
8 Huete, A., K. Didan, T. Miura, E.P. Rodriguez, X. Gao, and L.G. Ferreira, 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ, 83: 195-213.   DOI
9 Kimura, R., S. Okada, H. Miura, and M. Kamichika, 2004. Relationships among the leaf area index, moisture availability, and spectral reflectance in an upland rice field, Agricultural Water Management, 69 (2): 83-100.   DOI
10 Ko. J., S. Maas, S. Mauget, G. Piccinni, and D. Wanjura, 2006. Modeling water-stressed cotton growth using within-season remote sensing data, Agron. J, 98: 1600-1609.   DOI
11 Maas, S.J., 1992. GRAMI: a crop growth model that can use remotely sensed information. USDA, ARS-91, p. 77.
12 Motohka, T., K.N. Nasahara, A. Miyata, M. Mano, and S. Tsuchida, 2009. Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset, International Journal of Remote Sensing, 30: 4343-4357.
13 Nash, J.E. and J.V. Sutcliffe, 1970. River flow forecasting through conceptual models: Part I. A discussion of principles, J. Hydrol, 10 (3):282-290.   DOI
14 Pontailler, J.Y., G.J. Hymus, and B.G. Drake, 2003. Estimation of leaf area index using groundbased remote sensed NDVI measurements: validation and comparison with two indirect techniques, Can. J. Remote Sensing, 29 (3):381-387.   DOI
15 Rondeaux, G., M. Steven, and F. Baret, 1996. Optimization of soil-adjusted vegetation indices, Remote Sens. Environ, 55: 95-107.   DOI
16 Son, N.T., C.F. Chen, C.R. Chen, H.N. Due, and L.Y. Chang, 2014. A Phenology-Based Classification of Time-Series MODIS Data for Rice Crop Monitoring in Mekong Delta, Vietnam, Remote Sensing, 6(1): 135-156.
17 Rougean, J.-L. and F.M. Breon, 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements, Remote Sens. Environ, 51: 375-384.   DOI
18 Rouse, J.W., R.H. Haas, J.A. Schell, D.W. Deering, and J.C. Harlan, 1974. Monitoring the vernal advancements and retro gradation of natural vegetation, NASA/GSFC, Greenbelt, MD.
19 Sakamoto, T., M. Yokozawa, H. Toritani, M. Shibayama, N. Ishitsuka, and H. Ohno, 2005. A crop phenology detection method using timeseries MODIS data, Remote Sens. Environ, 96: 366-374.   DOI
20 Venkateswarlu, B., P.K. Rao, and A.V. Rao, 1976. Canopy analysis on the relationships between leaf area index and productivity in lowland rice, Oryza sativa, L, Plant and Soil, 45: 49-56.   DOI
21 Vina, A., A.A. Gitelson, D.C. Rundquist, G. Keydan, B. Leavitt, and J. Schepers, 2004. Monitoring Maize (Zea mays L.) Phenology with Remote Sensing, Agron. J, 96: 1139-1147.   DOI
22 Vina, A., A.A. Gitesle, A.L. Nguy-Robertson, and Y. Peng, 2011. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops, Remote Sens. Environ, 115:3468-3478.   DOI
23 Xiao, X., L. He, W. Salas, C. Li, B. Moore, R. Zhao, S. Frolking, and S. Boles, 2002. Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields, International Journal of Remote Sensing, 23:3595-3604.   DOI
24 Zarco-Tejada, P.J., S.L. Ustin, and M.L. Whiting, 2005. Temporal and spatial relationships between within-field yield variability in cotton and highspatial hyperspectral remote sensing imagery, Agron. J, 97: 641-653.   DOI
25 Yang, C., J.M. Bradford, and C.L. Wiegand, 2001. Airborne multispectral imagery for mapping variable growing conditions and yields of cotton, grain sorghum, and corn. Trans, ASAE, 44(6): 1983-1994.
26 Yang, C.M. and R.K. Chen, 2004. Modeling Rice Growth with Hyperspectral Reflectance Data, Crop Science Society of America, 44: 1283-1290.   DOI
27 Yun, S., B. Kang, S. Lim, W. Choi, J. Ko, S. Yoon, H. Ro, and H. Kim, 2011. Further understanding CH4 emissions from a flooded rice field exposed to experimental warming with elevated [$CO_2$], Agric. Forest Meteo, 154-155:75-83.
28 Zhang, X., M.A. Friedl, C.B. Schaaf, A.H. Strahler, J.C.F. Hodges, F. Gao, B.C. Reed, and A. Huete, 2003. Monitoring vegetation phenology using MODIS, Remote Sensing of Environment, 84: 471-475.   DOI