IJG  Vol.4 No.10 , December 2013
Estimation of Wheat Area in Córdoba, Argentina, with Multitemporal NDVI Data of SPOT-Vegetation
ABSTRACT

Determining the area occupied by a crop is a prerequisite for estimating agricultural production in a region, and such information at present can be generated from satellite remote sensing systems. This study uses multitemporal NDVI images of SPOT-VEGETATION with a 1 km spatial resolution to estimate the wheat area in Córdoba Province, Argentina, and to assess 2 generic digital classification procedures based on crop phenological behavior. The Characteristic Phenology Behavior Method (CPBM) uses the contrast each year between typical values of NDVI variation in the wheat crop season and those of the area not occupied by active vegetation; while the other procedure, the Reference Curve Method (RCM), assesses the correspondence in each pixel between the observed sequence of NDVI from seeding to physiological maturity, compared with 4 curves of reference. The CPBM 2 procedure, which uses the amplitude of NDVI between sowing and heading (ampsh), produces estimates that correlate better with the observed record, with approximately 70% (P < 0.01) of seeding area variability explained by this model in Río Segundo Department, but systematically overestimates the state reports at about 5%. On the other hand, RCM using Curve 1 explains 50% (P < 0.05) of interannual variability of the crop area. Comparing CPBM 1 and CPBM 2 procedures, it was found that replacing ampsh in the model with the minimum NDVI during physiological maturity stage (minpm) produced a deterioration of estimation, with R2 reduced to 0.37 (P < 0.05). The validation test with records from Marcos Juárez Department confirmed the prior predictive behavior. In this case, CPBM 2 produced a R2 of 0.61 (P < 0.01) that, besides the problem of overestimation, presented the linear relationship between observed and estimated with a slope that did not approach 1. RCM classification models reach a predictive performance similar to that shown in Río Segundo, with R2 values close to 0.50 (P < 0.05).


Cite this paper
A. Casa and G. Ovando, "Estimation of Wheat Area in Córdoba, Argentina, with Multitemporal NDVI Data of SPOT-Vegetation," International Journal of Geosciences, Vol. 4 No. 10, 2013, pp. 1355-1364. doi: 10.4236/ijg.2013.410132.
References
[1]   A. de la Casa, G. Ovando and A. Rodríguez, “Criteria Based on the Probability of Precipitation for Agroclimatic Risk Assessment of Rainfed Wheat Crop in Cordoba Province, Argentina,” Revista Brasileira de Agrometeorologia, Vol. 12, No. 2, 2004, pp. 333-339. (in Spanish)

[2]   H. Kerdiles, M. Grondona, R. Rodríguez and B. Seguin, “Frost Mapping Using NOAA AVHRR Data in the Pampean Region, Argentina,” Agricultural and Forest Meteorology, Vol. 79, No. 3, 1996, pp. 157-182.
http://dx.doi.org/10.1016/0168-1923(95)02253-8

[3]   A. de la Casa and G. Ovando, “Evaluation of Rainfed Wheat Yield in Córdoba Province, Argentina, in Relation to the Availability of Water and Frost Occurrence in Different Growth Stages,” Revista Argentina de Agrometeorología, Vol. 3-4, 2003/2004, pp. 47-58. (in Spanish)

[4]   S. M. Tavakkoli Sabour, P. Lohmann and U. Soergel, “Monitoring Agricultural Activities Using Multi-Temporal ASAR ENVISAT Data,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII, Part B7, WG VII/5 Processing of Multi-Temporal Data and Change Detection, Beijing, 2008, pp. 735-742.

[5]   U. Verma, D. S. Dabas, R. S. Hooda, M. H. Kalubarme, Y. Manoj, M. S. Grewal, M. P. Sharma and R. Prawasi, “Remote Sensing Based Wheat Acreage and Spectral-Trend Agrometeorological Yield Forecasting: Factor Analysis Approach,” Statistics and Applications, Vol. 9, 2011, pp. 1-13.

[6]   J. P. Ferrio, D. Villegas, J. Zarco, N. Aparicio, J. L. Araus and C. Royo, “Assessment of Durum Wheat Yield Using Visible and Near-infrared Reflectance Spectra of Canopies,” Field Crops Research, Vol. 94, No. 2-3, 2005, pp. 126-148. http://dx.doi.org/10.1016%2Fj.fcr.2004.12.002

[7]   J. L. Hatfield, A. A. Gitelson, J. S. Schepers and C. L. Walthall, “Application of Spectral Remote Sensing for Agronomic Decisions,” Agronomy Journal, Vol. 100, No. 3, 2008, pp. S117-S131.
http://dx.doi.org/10.2134%2Fagronj2006.0370c

[8]   T. Sakamoto, B. D. Wardlow, A. A. Gitelson, S. B. Verma and A. E. Suyker, T. J. Arkebauer, “A Two-Step Filtering Approach for Detecting Maize and Soybean Phenology with Time-Series MODIS Data,” Remote Sensing of Environment, Vol. 114, No. 10, 2010, pp. 2146-2159.
http://dx.doi.org/10.1016%2Fj.rse.2010.04.019

[9]   A. J. W. De Wit and J. G. P. W. Clevers, “Efficiency and Accuracy of Per-Field Classification for Operational Crop Mapping,” International Journal of Remote Sensing, Vol. 25, No. 20, 2004, pp. 4091-4112.
http://dx.doi.org/10.1080%2F01431160310001619580

[10]   B. D. Wardlow, S. L. Egbert and J. H. Kastens, “Analysis of Time-Series MODIS 250 m Vegetation Index Data for Crop Classification in the US Central Great Plains,” Remote Sensing of Environment, Vol. 108, No. 3, 2007, pp. 290-310. http://dx.doi.org/10.1016%2Fj.rse.2006.11.021

[11]   M. C. Hansen and R. S. DeFries, “Detecting Long-Term Global Forest Change Using Continuous Fields of Tree-Cover Maps From 8-km Advanced Very High Resolution Radiometer (AVHRR) Data for the Years 1982-99,” Ecosystems, Vol. 7, No. 7, 2004, pp. 695-716.
http://dx.doi.org/10.1007%2Fs10021-004-0243-3

[12]   J. H. Kastens, T. L. Kastens, D. L. A. Kastens, K. P. Price, E. A. Martinko and R.-Y. Lee, “Image Masking for Crop Yield Forecasting Using AVHRR NDVI Time Series Imagery,” Remote Sensing of Environment, Vol. 99, No. 3, 2005, pp. 341-356.
http://dx.doi.org/10.1016%2Fj.rse.2005.09.010

[13]   X. Zhan, R. A. Sohlberg, J. R. G. Townshend, C. DiMiceli, M. L. Carroll, J. C. Eastman, M. C. Hansen and R. S. DeFries, “Detection of Land Cover Changes Using MODIS 250m Data,” Remote Sensing of Environment, Vol. 83, No. 1-2, 2002, pp. 336-350.
http://dx.doi.org/10.1016%2FS0034-4257%2802%2900081-0

[14]   J. Chang, M. C. Hansen, K. Pittman, M. Carroll and C. Di Miceli, “Corn and Soybean Mapping in the United States Using MODIS Time-Series Data Sets,” Agronomy Journal, Vol. 99, No. 6, 2007, pp. 1654-1664.
http://dx.doi.org/10.2134%2Fagronj2007.0170

[15]   V. Sapino and C. Vidal, “Evaluation of Methods to Estimate the Area Occupied by Wheat in 2008 in Santa Fe Province Technical Information of Wheat and Other Winter Crops, Crop Season 2009,” Publicación Miscelánea No 113, INTA-Estación Experimental Agropecuaria Rafaela. Santa Fe, Argentina, 2009 (in Spanish).

[16]   C. Atzberger and P. H. C. Eilers, “A Time Series for Monitoring Vegetation Activity and Phenology at 10-Daily Time Steps Covering Large Parts of South America,” International Journal of Digital Earth, Vol. 4, No. 5, 2011, pp. 365-386.
http://dx.doi.org/10.1080%2F17538947.2010.505664

[17]   C. A. J. M. de Bie, M. R. Khan, V. U. Smakhtin, V. Venus, M. J. C. Weir and E. M. A. Smaling, “Analysis of Multi-Temporal SPOT NDVI Images for Small-Scale Land-Use Mapping,” International Journal of Remote Sensing, Vol. 32, No. 21, 2011, pp. 6673-6693.
http://dx.doi.org/10.1080%2F01431161.2010.512939

[18]   T. Murakami, S. Ogawa, N. Ishitsuka, K. Kumagai and G. Saito, “Crop Discrimination with Multitemporal SPOT/ HRV Data in the Saga Plains, Japan,” International Journal of Remote Sensing, Vol. 22, No. 7, 2001, pp. 1335-1348. http://dx.doi.org/10.1080%2F01431160151144378

[19]   J. P. Guerschman, J. M. Paruelo, C. Di Bella, M. C. Giallorenzi and F. Pacin, “Land Cover Classification in the Argentine Pampas Using Multi-Temporal Landsat TM Data,” International Journal of Remote Sensing, Vol. 24, No. 17, 2003, pp. 3381-3402.
http://dx.doi.org/10.1080%2F0143116021000021288

[20]   J. T. Musick, O. R. Jones, B. A. Stewart and D. A. Dusek, “Water-Yield Relationships for Irrigated and Dryland Wheat in the US Southern Plains,” Agronomy Journal, Vol. 86, No. 6, 1994, pp. 980-986.
http://dx.doi.org/10.2134%2Fagronj1994.00021962008600060010x

[21]   E. Martellotto, A. Salinas, H. Salas, E. Lovera, J. Giubergia, V. Capuccino, C. López, O. Signorile, S. Lingua, S. álvarez, M. Cantarero and G. Viotti, “Wheat: A Contribution to the Sustainability of Production Systems. Evaluation of Cultivars and Management Strategies in 8 Sites at Córdoba Province,” Boletín No 9, Ediciones Instituto Nacional de Tecnología Agropecuaria, EEA Manfredi INTA, Córdoba, 2005 (in Spanish).

[22]   T. Jacobs, P. Claes, B. Smets, T. Van Roey and H. Eerens, “Vgtextract: The Free Vegetation Extraction Tool. Software User Guide,” AGRICAB FP 7 SICA 282621, Flemish Institute of the Technological Research (VITO), 2012.

[23]   C. Bainotti, J. Fraschina, J. Salines, E. Alberione, M. Cuniberti, B. Masiero, G. Donaire, D. Gómez, J. Nisi, M. Formica, O. Berra, S. Macagno and L. Mir, “Evaluation of Wheat Cultivars in the EEA Marcos Juárez. Crop Season 2007,” INTA-EEA Marcos Juárez, Marcos Juárez, Cba., Argentina, 2007 (in Spanish).

[24]   C. Bainotti, J. Fraschina, J. Salines, E. Alberione, D. Gómez, G. Donaire, J. Nisi, B. Masiero, B. Conde, M. Cuniberti, L. Mir, S. Macagno and O. Berra, “Evaluation of Wheat Cultivars in the EEA Marcos Juárez. Crop Season 2008/2009,” INTA-EEA Marcos Juárez, Marcos Juárez, Cba., Argentina, 2009 (in Spanish).

[25]   C. Bainotti, J. Fraschina, J. Salines, E. Alberione, D. Gómez, G. Donaire, J. Nisi, B. Masiero, B. Conde, C. Gutiérrez, M. Cuniberti, L. Mir, S. Macagno, O. Berra, H. Paolini and F. Reartes, “Evaluation of Wheat Cultivars in the EEA Marcos Juárez. Crop Season 2010,” INTA-EEA Marcos Juárez, Marcos Juárez, Cba., Argentina, 2011 (in Spanish).

[26]   Integrated Agricultural Information System (SIIA), Ministry of Agriculture, Livestock and Fisheries of Argentina, 2013. http://www.siia.gov.ar/index.php/series-por-provincia/cordoba

[27]   D. M. Howard, B. K. Wylie and L. L. Tieszen, “Crop Classification Modelling Using Remote Sensing and Environmental Data in the Greater Platte River Basin, USA,” International Journal of Remote Sensing, Vol. 33, No. 19, 2012, pp. 6094-6108.
http://dx.doi.org/10.1080%2F01431161.2012.680617

[28]   L. Zhong, T. Hawkins, G. Biging and P. Gong, “A Phenology Based Approach to Map Crop Types in the San Joaquin Valley, California,” International Journal of Remote Sensing, Vol. 32, No. 22, 2011, pp. 7777-7804.
http://dx.doi.org/10.1080%2F01431161.2010.527397

[29]   Agriculture Ministry of Córdoba Province, “Statistics of Crop Acreage and Yields,” Córdoba, Argentina, 2010.
http://magya.cba.gov.ar/Umsiia.aspx#anterior

[30]   M. Turker and A. Ozdarici, “Field-Based Crop Classification Using SPOT4, SPOT5, IKONOS and Quickbird Imagery for Agricultural Areas: A Comparison Study,” International Journal of Remote Sensing, Vol. 32, No. 24, 2011, pp. 9735-9768.
http://dx.doi.org/10.1080%2F01431161.2011.576710

[31]   A. J. Stern, P. C. Doraiswamy and P. W. Cook, “Spring Wheat Classification in an AVHRR Image by Signature Extension From a Landsat TM Classified Image,” Photogrammetric Engineering & Remote Sensing, Vol. 67, No. 2, 2001, pp. 207-211.

[32]   D. B. Lobell and G. P. Asner, “Cropland Distributions from Temporal Unmixing of MODIS Data,” Remote Sensing of Environment, Vol. 93, No. 3, 2002, pp. 412-422.
http://dx.doi.org/10.1016%2Fj.rse.2004.08.002

 
 
Top