Model of soybean NDVI change based on time series

Zhang Zhitao, Yubin Lan, Wu Pute, Han Wenting

Abstract


Abstract: Normalized Difference Vegetation Index (NDVI) has been found to have good correlations with many physical properties of soybean surfaces. Due to the factors of air temperature, humidity, solar radiation, soil moisture, etc., NDVI of soybean varies dynamically in a day. The establishment of the soybean NDVI prediction model at different times in a day can effectively modify this variation. The soybean NDVI values are continuously monitored in hours during soybean seeding, flowering & podding and maturating stages by way of Green Seeker. Results show that the trend of NDVI change every day in the three stages is taken on as a reverse parabola. The NDVI value reaches to the maximum at 8 am or 9 am and decreases to its minimum at 2 pm before a moderate rise. A model for intraday and long-term NDVI change for soybean is built. The test of the model with independent data indicates that the precision meets the demands, with the root mean square error (RMSE) of each day being 3.95, 5.45 and 2.86 for the seeding stage, the bean podding stage and the maturation period, respectively. The prediction RMSEs of the soybean NDVI model for soybeans of the three stages for the fifth day are 5.75, 2.65 and 5.51, respectively and the prediction RMSEs for the sixth day are 9.74, 2.82 and 14.04, respectively according to the data from the first four days.
Keywords: model, NDVI, monitoring time, time series, atmospheric radiation, soybean
DOI: 10.3965/j.ijabe.20140705.007

Citation: Zhang Z T, Lan Y, Wu P T, Han W T. Model of soybean NDVI change based on time series. Int J Agric & Biol Eng, 2014; 7(5): 64－70.

Keywords


model, NDVI, monitoring time, time series, atmospheric radiation, soybean

Full Text:

PDF

References


Rouse J W, Haas R H, Schell J A, Deering D W. Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, Goddard Space Flight Center, Washington, D C. NASA SP-351, 1973; 1(1): 309–317.

Deering D W. Rangeland reflectance characteristics measured by aircraft and spacecraft sensors, Ph.D. Dissertation. Texas A&M Univ., 1978; 1–338.

Karlsen S R, Tolvanen A, Kubin E, Poikolainen J, Hogda K A, Johansen B,et al. MODIS NDVI-based mapping of the length of the growing season in northern Fennoscandia. International Journal of Applied Earth Observation and Geoinformation, 2008; 10: 253–266.

Alexandridis T K, Gitas I Z, Silleos N G. An estimation of the optimum temporal resolution for monitoring vegetation condition on a nationwide scale using MODIS/Terra data. International Journal of Remote Sensing, 2008; 29: 3589– 3607.

Ren J Q, Chen Z X, Zhou Q B, Tang H J. Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation, 2008; 10: 403–413.

Ciceka H, Sunoharab M, Wilkesb G, McNairnb H, Pickc F, Toppd E,et al. Using vegetation indices from satellite remote sensing to assess corn and soybean response to controlled tile drainage. Agricultural Water Management, 2010; 98: 261–270.

Doraiswamy P C, Sinclair T R, Hollinger S, Akhmedov B, Stern A, Prueger J. Application of MODIS derived parameters for regional crop yield assessment. Remote Sensing of Environment, 2005; 97: 192–202.

Mo X, Liu S, Lin Z, Xu Y, Xiang Y, Mcvicar T R. Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain. Ecological Modelling, 2005; 183: 301–322.

Fang H L, Liang S L, Hoogenboom G, Teasdale J, Cavigelli M. Corn-yield estimation through assimilation of remotely sensed data into the CSM-CERES maize model. International Journal of Remote Sensing, 2008; 29: 3011– 3032.

Liesenberg V, Galvao L S, Ponzoni F J. Variations in reflectance with seasonality and viewing geometry: implications for classification of Brazilian savanna physiognomies with MISR/Terra data. Remote Sensing of Environment, 2007; 107: 276–286.

Fábio M B, Lênio S G, Antonio R F, José C N E. Directional effects on NDVI and LAI retrievals from MODIS: A case study in Brazil with soybean. International Journal of Applied Earth Observation and Geoinformation, 2011; 13: 34–42.

Gamon J A, Field C B, Goulden M L, Griffin K L, Hartley A E, Joel G. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation type. Ecological, 1995; 58: 257– 266.

Gao B. NDWI a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment. Applications, 1996; 65: 28–41.

Carter G A. Reflectance wavebands and indices for remote estimation of photosynthesis and stomata conductance in pine canopies. Remote Sens. Environ, 1998; 63: 61-72.

Choudhury B J. Estimating gross photosynthesis using satellite and ancillary data: Approach and preliminary results. Remote Sens. Environ, 2001; 75:1-21.

Tian Y, Zhu Y, Cao W. Monitoring leaf photosynthesis with canopy spectral reflectance in rice. Photosynthetica, 2005; 43(4): 481-489.

Sellers P J. Canopy reflectance, photosynthesis, and transpiration, II: The role of biophysics in the linearity of their interdependence. Remote Sensing of Environment, 1987; 21: 143−183.

Anderson M C, Neale C M U, Li F, Norman J M, Kustas W P, Jayanthi H. Up scaling ground observation of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote Sensing of Environment, 2004; 92: 447– 464.

Chen D, Huang J, Thomas J. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands Remote Sensing of Environment, 2005; 98: 225-236.

Albert O, Toby N, Carlson b, Nadine B. Simulation of diurnal transpiration and photosynthesis of a water stressed soybean crop. Agricultural and Forest Meteorology, 1996; 81: 41-59.

Pettigrew W T, Hesketh J D, Peters D B, Woolley J T. Vapor pressure deficit effect on crop canopy photosynthesis. Photosyn. Res, 1990; 24: 27-34.

Lynn B H, Carlson T N. A stomatal resistance model illustrating plant vs. external control of transpiration. Agric. For. Meteorol, 1990; 52:5-43.

Carlson T N, Belles J E, Gillies R R. Transient water stress in a vegetation canopy: simulations and measurements. Remote Sens. Environ, 1991; 35: 175-186.

Hicke J A, Asner G P, Randerson J T, Tucker C. Trends in North American net primary productivity derived from satellite observations, 1982–1998. Global Biogeochem Cycles, 2002; 16: 1018–1032.

Wang Q, Adiku S, Tenhunen J, Granier A. On the Relationship of NDVI with leaf area index in a deciduous forest site. Remote Sens Environ, 2005; 94:244–255.

Calcante1 A, Mena1 A, Mazzetto F. Evaluation of “ground sensing” optical sensors for diagnosis of Plasmopara viticola on vines. Spanish Journal of Agricultural Research, 2012; 10(3): 619-630.

Jasper M. Applicability of ground-based remote sensors for crop N management in Sub Saharan Africa. Journal of Agricultural Science, 2012; 4(3): 175-188.

Wang L, Bai Y, Lu Y, Wang H, Yang L. NDVI analysis and yield estimation in winter wheat based on Green-Seeker. Acta Agronomica Sinica, 2012; 38(4): 747−753.




Copyright (c)



2023-2026 Copyright IJABE Editing and Publishing Office