کاربرد تصاویر ماهواره‌ای چند زمانه در بهبود دقت مدل‌های پیش‌یابی فنولوژی ذرت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه تهران

2 گروه مهندسی ابیاری-دانشگاه تهران

3 دانشگاه علوم کشاورزی و منابع طبیعی گرگان

4 موسسه ژئوفیزیک دانشگاه تهران

چکیده

متداول­­ترین شیوه پیش­یابی مراحل فنولوژیکی گیاهان، استفاده از کمیت درجه-روز رشد تجمعی (AGDD) می­باشد. در تحقیق حاضر، مدلی برای تدقیق این روش با تلفیق دو نمایه AGDD و NDVI برای تخمین تاریخ شروع 8 مرحله فنولوژیکی گیاه ذرت رقم K407، با استفاده از داده­های یک دوره 9 ساله در منطقه کرج ارائه شده است. روش هموارسازی نوفه­ها در کاربست نمایه NDVI، ترکیبی از دو روش لجستیک دوگانه و رگرسیون وزنی (WLS-DL) می باشد. نتایج مدل تلفیقی با دو مدل مبتنی بر درجه-روز رشد و تاریخ­ کاشت مقایسه شد. یافته­های پژوهش نشان داد، مدل تلفیقی به طور متوسط، مقدار RMSE تاریخ­های شروع 7 مرحله ابتدایی فنولوژیکی (ظهور تا شیری شدن) را به ترتیب 7/1، 4/1، 8/0، 3/1، 4/2، 4/2 و 3/3 روز نسبت به مدل مبتنی بر تاریخ­های کاشت و 9/2، 7/1، 4/1، 9/2، 6/4، 9/2، 6/3 روز نسبت به مدل درجه- روز رشد، کمتر برآورد می نماید.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Application of multi temporal satellite images for improvement of maize phenology models prediction

نویسندگان [English]

  • Mahdi Ghamghami 1
  • Nozar Ghahreman 2
  • khalil Ghorbani 3
  • Parviz Irannejad 4
چکیده [English]

Crop phonological stages are commonly predicted by using accumulated growth degree days(AGDD).In this study a combined model of AGDD and remotely sensed NDVI has been developed for prediction of maize (cv. K407) phenology in Karaj using a nine year (2002 to 2010) dataset. For smoothing the existing noises of image processing, a combination of double logistic and weighing average (DL-WLS) approaches was employed. The results of combined phenology model were compared by two frequently used methods based on AGDD and date of sowing. The findings showed that in general, the developed model predicted the first 7 phenological stages of emergence to milky, more accurately comparing to other approaches (with average 2 and 2.5 days difference with observed dates, respectively) but was inaccurate for maturity stage. Our study highlights the need for further improvements in observations in the region.

کلیدواژه‌ها [English]

  • NDVI
  • Double logistic
  • weighing regression
  • Phenology
  • maize
Ahmadi, M., Kamkar, B., Soltani, A., Zeinali, A. and Arabameri, R., (2010). Effect of planting date on length of phenologic spells for Wheat variety and its relation with yield. Researches of crop yields, 7(2),109-122 (in Farsi).
Arvor, D., Jonathan, M., Meirelles, M.S.P., Dubreuil, V., Lecerf, R., (2008). Comparison of Multitemporal MODIS-EVI Smoothing Algorithms and its Contribution to Crop Monitoring.  in Geoscience and Remote Sensing Symposium. IGARSS 2008. IEEE International , vol.2, no., pp.II-958-II-961, 7-11 July doi: 10. 1109/ IGARSS. 2008. 4779155
Baskerville, G.L. and P. Emin., (1969). Rapid estimation of heat accumulation from maximum and minimum temperatures. Ecology, 50,514–517.
Bolton, D.B. and  Friedl, M.A., (2013). Forecasting  crop  yield  using  remotely  sensed  vegetation  indices  and  crop phenology metrics, Agricultural and Forest Meteorology, 173, 74–84.
Chen, J., Jonsson, P., Tamura, M., Gu, Z., Matsushita, B., and Eklundh, L., (2004). A simple method for reconstructing a high quality NDVI time series data set based on the Savitzky-Golay filter, Remote Sens. Environ., 91, 332–344, 2004.
Curnel, Y. and Oger, R. (2007). Agrophenology indicators from remote sensing: state of the art. In: ISPRS Archives XXXVI-8/W48 Workshop proceedings: Remote sensing support to crop yield forecast and area estimates.
Dash, J., Lankester, T., Hubbard, S. and Curran, P. J. (2008). Signal to noise ratio forMTCI &NDVI time series data. Proceedings of the 2nd MERIS/(A)ATSR User Workshop, Frascati, Italy, 22–26 September 2008.
Davidson, A. and Csillag F., (2003). A comparison of three approaches for predicting C4 species coverof northern mixed grass prairie, Remote Sensing of Environment. 86, 70–82.
De Beurs, K.M. and Henebry, G.M. (2010). Spatio-Temporal Statistical Methods for Modelling Land Surface Phenology. In Phenological Research: Methods for Environmental and Climate Change Analysis; Hudson, I.L., Keatley, M.R., Eds.; Springer-Verlag: New York, NY, USA.
Deering, D.W. (1978). Rangeland reflectance characteristics measured by aircraft and spacecraft sensors. Ph.D. Dissertation, Texas A & M University, College Station, TX, 338 pp.
Diepen, C.A.; Wolf, J.; van Keulen, H. (1989). WOFOST: A simulation model of crop production. Soil Use Manage. 1989, 5, 16–24.
Dwyer, L.M., Stewart, D.W., Carrigan, L., Neave, B.L. Ma, P., and Balchin, D. (1999a). A general thermal index for maize. Agronomy Journal. 91, 946-949.
Dwyer, L.M., Stewart, D.W., Carrigan, L., Neave, B.L. Ma, P. and Balchin, D. (1999b). Guidelines for comparisons among different maize maturity rating systems. Agronomy Journal, 91, 946-949.
Hmimina, G., Dufrêne, E., Pontailler, J. Y., Delpierre, N., Aubinet, M., Caquet, B. (2013). Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements. Remote Sensing of Environment, 132,145–158
Hufkens, K., Friedl, M., Sonnentag, O., Braswell, B. H., Milliman, T. and Richardson, A. D. (2012). Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sensing of Environment, 117,307–321.
Jiang,  Z.,  Huete,  A.R.,  Didan,  K. and Miura,  T.,  (2008). Development of a two-ban enhanced vegetation index without a blue band. Remote Sens. Environ. 112, 3833–3845.
Jones, J.W., Tsuji, G.Y., Hoogenboom, G., Hunt, L.A., Thornton, P.K., Wilkens, P.W., Imamura, D.T., Bowen, W.T. and Singh, U. (1998). Decision Support System for Agrotechnology Transfer: DSSAT V3. In Understanding Options for Agricultural Production; Tsuji, G.Y., Hoogenboom, G.,Thornton, P., Eds.; Kluwer Academic Publishers: Boston, MA, USA, pp. 157–177
Kamble, B. and Kilic, A., (2013). Hubbard, K. Estimating Crop Coefficients Using Remote Sensing-Based Vegetation Index. Remote Sens. 5, 1588-1602.
Kroes, J.G., Dam, J.C.V., Groenendijk, P., Hendriks, R.F.A. and Jacobs, C.M.J. (2008). SWAP Version 3.2: Theory Description and User Manual; Alterra Report; Alterra: Wageningen, The Netherlands.
Kumudini S, Andrade F, Boote K, Brown G, Dzotsi K, Edmeades G, Gocken T, Goodwin M, Halter A, Hammer G. (2014). Predicting Maize Phenology: Intercomparison of Functions for Developmental Response to Temperature. Agronomy Journal, 106(6),2087-2097.
Lofton, J., Tubana, B.S., Kanke, Y., Teboh, J., Viator, H. and Dalen, M. (2012). Estimating Sugarcane Yield Potential Using an In-Season Determination of Normalized Difference Vegetative Index. Sensors, 12, 7529-7547.
McMaster, G.S. and Smika, D.E., (1988). Estimation and evaluation of winter wheat phenology  in the central Great Plains. Agric. For. Meteorol., 43, 1-18.
Saxton, K.E.; Porterand, M.A.; McMahon, T.A. (1992). Climatic impacts on dryland winter wheat by daily soil water and crop stress simulations. Agr. For. Meteorol., 58, 177–192.  
Shen, Y., Di, L., Wu, L., Yu, G., Tang, H., Yu, G. and Shao, Y. (2013). Real-time estimation of corn progress stages using hidden markov models with multisource features. Remote Sens., in review.
Stöckle, C.O., Donatelli, M. and Nelson, R. (2003). Cropsyst, a cropping systems simulation model. Eur. J. Agron., 18, 289–307.
Streck, N. A., LAGO, I., GABRIEL, L.F. and SAMBORANHA, F.K. (2008). Simulating maize phenology as a function of air temperature with a linear and a nonlinear model. Pesquisa Agropecuária Brasileira, 43, 449–455. 
Rocha, V.A. and Shaver, G.R., (2009). Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes, Agricultural and Forest Meteorology, 149, 1560–1563.
Rondeaux, G., Steven, M., and Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55,95–107.
Roth, G.W., and Yocum, J.O. (1997). Use of hybrid growing degree day ratings for corn in the northeastern USA. Journal of Production Agriculture, 10: 283-288.
Swets D.L., Reed B.C., Rowland  J.D. and Marko S.E., (1999). A Weighted Least-squares Approach to Temporal NDVI Smoothing. In: Proceedings Amr. Soc. Photogram. Rem. Sens. 17-21 May, Portland OR., ASPRS, Washington, D.C., pp. 526-536.
Teal R.K., Tubana B.S., Girma K., Freeman K.W., Arnall D.B., Walsh O. and Raun W.R. (2006). In-season prediction of corn grain yield potential using normalized difference vegetation index. Agron. J.98:1488–1494.
Van Dijk A., Callis S.L., Sakamoto C.M. and Decker W.L. (1985). Smoothing vegetation index profiles: an alternative method for reducing radiometric disturbance in NOAA/AVHRR data. Photogram. Engin. Rem. Sens., 53, pp. 1059-1067.
Viovy N., Arino O. and Belward A.S. (1992). The best index slope extraction (BISE): a method for reducing noise in NDVI time series. International Journal of remote sensing, 13(8), 1585-1590.
White, K., Pontius, J. and Schaberg, P. (2014). Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty. Remote Sensing of Environment, 148, 97–107.
Wu C., Gonsamo A., Gough C.M., Chen J.M. and Xu S. (2014). Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS, Remote Sensing of Environment, 147, 79–88.
Zhang, X., Friedl, M., Schaaf, M., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C. and Huete, A.R. (2003). A Monitoring vegetation phenology using MODIS. Remote Sens. Environ., 84, 471–475.
Ziaei, S.F., Khalili, A. and Ghahreman, N. (2009). Prediction of autumn Wheat phenology based on weather data in three climates of Iran. Agriculture,11(1), 71-86 (in Farsi).