Beget M E, Di Bella C M. 2007. Flooding: The effect of water depth on the spectral response of grass canopies. J Hydrol, 335: 285–294.
Boschetti M, Stroppiana D, Brivio P A, Bocchi S. 2009. Multi-year monitoring of rice crop phenology through time series analysis of MODIS images. Int J Remote Sens, 30: 4643–4662.
Bouman B A M, Kropff M J, Tuong T P, Wopereis M C S, ten Berge H F M, van Laar H H. 2001. ORYZA2000: Modeling Lowland Rice. Manila, Philippines: International Rice Research Institute.
Delbart N, Toan T L, Kergoat L, Fedotova V. 2006. Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982– 2004). Remote Sens Environ, 101: 52–62.
Digkuhn M, Gal P L. 1996. Effect of drainage date on yield and dry matter partitioning in irrigated rice. Field Crops Res, 46: 117–126.
Doktor D, Bondeau A, Koslowski D, Badeck F. 2009. Influence of heterogeneous landscapes on computed green-up dates based on daily AVHRR NDVI observations. Remote Sens Environ, 113: 2618–2632.
He Y, Wang B, Jiang L G, Meng C L, Ou Z R, Xu S H. 2013. Refined climatic division of rice planting distribution using GIS in Guangxi, China. Chin J Rice Sci, 27(6): 658–664. (in Chinese with English abstract)
Huang J F, Wang F M, Wang X Z. 2010. Hyperspectral Experiment for Paddy Rice Remote Sensing. Hangzhou: Zhejiang University Press: 7–15. (in Chinese)
Ishii A, Kuroda E, Shimono H. 2011. Effect of high water temperature during vegetative growth on rice growth and yield under a cool climate. Field Crops Res, 121(1): 88–95.
Jakubauskas M, Legates D R, Kastens J H. 2002. Crop identification using harmonic analysis of time-series AVHRR NDVI data. Comput Electron Agric, 37: 127–139.
Karnieli A. 2003. Natural vegetation phenology assessment by ground spectral measurements in two semi-arid environments. Int J Biometeorol, 47: 179–187.
Kimball J S, McDonald K C, Running S W, Frolking S E. 2004. Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests. Remote Sens Environ, 90: 243–258.
Le Toan T, Ribbes F, Wang L, Floury N, Ding K, Kong J A, Fujita M, Kurosu T. 1997. Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results. IEEE T Geosci Remote, 35: 41–56.
Moldenhauer K, Slaton N. 2001. Rice growth and development. In: Slaton N A. Rice Production Handbook. University of Arkansas, Division of Agriculture, Cooperative Extension Service, Little Rock, A: 7–14.
Motohka T, Nasahara K N, Oguma H, Tsuchida S. 2010. Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sens, 2: 2369–2387.
Nagai S, Nasahara K N, Muraoka H, Akiyam T, Tscuchida S. 2010. Field experiments to test the use of the normalized-difference vegetation index for phenology detection. Agric Forest Meteorol, 150: 152–160.
Peng D L, Huete A R, Huang J F, Wang F M, Sun H S. 2011. Detection and estimation of mixed paddy rice cropping patterns with MODIS data. Int J Appl Earth Obs Geoinform, 13: 13–23.
Piao S L, Fang J Y, Zhou L M, Ciais P, Zhu B. 2006. Variations in satellite-derived phenology in China’s temperate vegetation. Glob Change Biol, 12: 672–685.
Potter C, Tan P, Steinbach M, Klooster S, Kumar V, Myneni R, Genovese V. 2003. Major disturbances events in terrestrial ecosystems detected using global satellite data sets. Glob Change Biol, 9: 1005–1021.
Richardson A D, Braswell B H, Hollinger D Y, Jenkins J P, Ollinger S V. 2009. Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol Appl, 19: 1417–1428.
Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N, Ohno H. 2005. A crop phenology detection method using time-series MODIS data. Remote Sens Environ, 96: 366–374.
Shimono H. 2011. Earlier rice phenology as a result of climate change can increase the risk of cold damage during reproductive growth in northern Japan. Agric Ecosyst Environ, 144(1): 201– 207.
Soudani K, le Maire G, Dufrêne E, Fran?ois C, Delpierre N, Ulrich E, Cecchini S. 2008. Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Remote Sens Environ, 112: 2643–2655.
St?ckli R, Vidale P L. 2004. European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset. Int J Remote Sens, 25: 3303–3330.
Tucker C J, Slayback D A, Pinzon J E, Los S O, Myneni R B, Taylor M G. 2001. Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. Int J Biometeorol, 45: 184–190.
Upadhyay G, Ray S S, Panigrahy S. 2008. Derivation of crop phenological parameters using multi-date SPOT-VGT-NDVI data: A case study for Punjab. J Ind Soc Remote Sens, 36: 37–50.
Verbesselt J, Hyndman R, Zeileis A, Culvenor D. 2010. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sens Environ, 114: 2970– 2980.
Vaesen K, Gilliams S, Nackaerts K, Coppin P. 2001. Ground- measured spectral signatures as indicators of ground cover and leaf area index: The case of paddy rice. Field Crops Res, 69(1): 13–25.
Vrieling A, de Beurs K M, Brown M E. 2011. Variability of African farming systems from phenological analysis of NDVI time series. Climatic Change, 109: 455–477.
White M A, de Beurs K M, Didan K, Inouye D W, Richardson A D, Jensen O P, Okeefe J, Zhang G, Nemani R R, van Leeuwen W J D, Brown J F, de Wit A, Schaepman M, Lin X, Dettinger M, Bailey A S, Kimball J, Schwartz M D, Baldocchi D D, Lee J T, Lauenroth W K. 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Glob Change Biol, 15: 2335–2359.
Wopereis M C S, Defoer T, Idinoba P, Diack S, Dugué M. 2009. Curriculum for Participatory Learning and Action Research (PLAR) for Integrated Rice Management (IRM) in Inland Valleys of Sub-Saharan Africa: Technical Manual. Africa Rice Center, Cotonou, Benin: 26–32.
Xiao X M, Boles S, Frolking S, Salas W, Moore III B, Li C S, He L H, Zhao R. 2002a. Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data. Int J Remote Sens, 23: 3009–3022.
Xiao X M, He L H, Salas W, Li C S, Moore III B, Zhao R, Frolking S, Boles S. 2002b. Quantitative relationships between field- measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields. Int J Remote Sens, 23: 3595–3604.
Xiao X M, Boles S, Liu J Y, Zhuang D F, Frolking S, Li C S, Salas W, Moore III B. 2005. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sens Environ, 95(4): 480–492.
Xiao X M, Boles S, Frolking S, Li C S, Babu J Y, Salas W, Moore III B. 2006. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens Environ, 100(1): 95–113.
Zhang X, Friedl M A, Schaaf C B, Strahler A H, Hodeges J C F, Gao F, Reed B C, Huete A. 2003. Monitoring vegetation phenology using MODIS. Remote Sens Environ, 84: 471–475.
Zhang H C, Zhang J, Gong J L, Chang Y, Li M, Gao H, Dai Q G, Huo Z Y, Xu K, Wei H Y. 2013. The Productive advantages and formation mechanisms of ‘indica rice to japonica rice’. Sci Agric Sin, 46: 686–704. (in Chinese with English abstract)
