Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina

Information Processing in Agriculture

Volumn 2, Issue 2, 2015, Pages 73-84

  Lopresti, Mariano F ^a   Di Bella, Carlos M ^a,c   Degioanni, Américo J ^b  

^a INSTITUTO NACIONAL DE TECNOLOGÍA AGROPECUARIA INTA   (Argentina)

^b DEPARTAMENTO DE QUÍMICA   (Argentina)

^c Facultad de Ciencias Naturales e Instituto M Lillo   (Argentina)

Author keywords

Empirical models; MODIS; NDVI; Remote sensing; Wheat; Yield

Indexed keywords

CROPS; CULTIVATION; DIELECTRIC PROPERTIES; PLANTS (BOTANY); RADIOMETERS;

EMPIRICAL MODEL; MODIS; NDVI; WHEAT; YIELD;

REMOTE SENSING;

EID: 85016965709     PISSN: 22143173     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.inpa.2015.06.001     Document Type: Article

References (55)

1. [1] Ministry of Agriculture, Livestock and Fisheries of Argentina. Sistema Integrado de Información Agropecuaria. Link: http://www.siia.gov.ar/series. 2012.
2. [2] Delécolle, R., Maas, S.J., Guérif, M., Baret, F., Remote sensing and crop production models: present trends. ISPRS J Photogramm Remote Sens 47:2 (1992), 145–161.
3. [3] Clevers, J.G.P.W., A simplified approach for yield prediction of sugar beet based on optical remote sensing data. Remote Sens Environ 61:2 (1997), 221–228.
4. [4] Noureldin, N.A., Aboelghar, M.A., Saudy, H.S., Ali, A.M., Rice yield forecasting models using satellite imagery in Egypt. Egypt J Remote Sens Space Sci 16:1 (2013), 125–131.
5. [5] Sakamoto, T., Gitelson, A.A., Arkebauer, T.J., MODIS-based corn yield estimation model incorporating crop phenology information. Remote Sens Environ 131 (2013), 215–231.
6. [6] Kuri, F., Murwira, A., Murwira, K.S., Masocha, M., Predicting maize yield in Zimbabwe using dry dekads derived from remotely sensed vegetation condition index. Int J Appl Earth Obs Geoinf 33 (2014), 39–46.
7. [7] Son, N.T., Chen, C.F., Chen, C.R., Minh, V.Q., Trung, N.H., A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation. Agric For Meteorol 197 (2014), 52–64.
8. [8] Shao, Y., Campbell, J.B., Taff, G.N., Zheng, B., An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data. Int J Appl Earth Obs Geoinf 38 (2015), 78–87.
9. [9] Ippoliti GA, Epiphanio JCN, Shimabukuro YE. Utilização de sensoriamento remoto na previsão de área a ser plantada com culturas de verão em três municípios do estado de São Paulo. In: Proceedings of 9th Brazilian Symposium on Remote Sensing. Santos, Brazil; 1998. p. 25–37.
10. [10] Tsiligirides, T.A., Remote sensing as a tool for agricultural statistics: a case study of area frame sampling methodology in Hellas. Comput Electron Agric 20:1 (1998), 45–77.
11. [11] Clevers, J.G.P.W., Büker, C., van Leeuwen, H.J.C., Bouman, B.A.M., A framework for monitoring crop growth by combining directional and spectral remote sensing information. Remote Sens Environ 50:2 (1994), 161–170.
12. [12] Yadav, I.S., Srinivasa Rao, N.K., Reddy, B.M.C., Rawal, R.D., Srinivasan, V.R., Sujatha, N.T., et al. Acreage and production estimation of mango orchards using Indian Remote Sensing (IRS) satellite data. Sci Hortic 93:2 (2002), 105–123.
13. [13] Balaghi, R., Tychon, B., Eerens, H., Jlibene, M., Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. Int J Appl Earth Obs Geoinf 10:4 (2008), 438–452.
14. [14] Singh, R., Semwal, D.P., Rai, A., Chhikara, R.S., Small area estimation of crop yield using remote sensing satellite data. Int J Remote Sens 23:1 (2002), 49–56.
15. [15] Chuvieco Salinero, E., Análisis de imágenes: extracción de información temática. Teledetección ambiental, 2002, La observación de la tierra desde el espacio, Spain: Ariel, 315–471.
16. [16] Tucker, C.J., Holben, B.N., Elgin, J.H., McMurtrey, J.E., Relationship of spectral data to grain yield variation. Photogramm Eng Remote Sens 46:5 (1980), 657–666.
17. [17] Groten, S.M.E., NDVI-crop monitoring and early yield assessment of Burkina Faso. Int J Remote Sens 14:8 (1993), 1495–1515.
18. [18] Quarmby, N.A., Milnes, M., Hindle, T.L., Silleos, N., The use of multitemporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Int J Remote Sens 14:2 (1993), 199–210.
19. [19] Doraiswamy, P.C., Cook, P.W., Spring wheat yield assessment using NOAA AVHRR data. Can J Remote Sens 21:1 (1995), 43–51.
20. [20] Lewis, J.E., Rowland, J., Nadeau, A., Estimating maize production in Kenya using NDVI: some statistical considerations. Int J Remote Sens 19:13 (1998), 2609–2617.
21. [21] Boken, V.K., Shaykewich, C.F., Improving an operational wheat yield model using phenological phase-based Normalized Difference Vegetation Index. Int J Remote Sens 23:20 (2002), 4155–4168.
22. [22] Huete A, Justice C, van Leeuwen W. MODIS vegetation index (MOD 13)-Algorithm theoretical basis document. 1999. Link: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf. 2012.
23. [23] Ruiz-Huanca, P., Palacios-Vélez, E., Mejía-Saenz, E., Exebio-García, A., Oropeza-Mota, J.L., Bolaños-González, M., Estimación temprana del rendimiento de la cebada mediante uso de sensores remotos. Terra Latinoamericana 23:2 (2005), 167–174.
24. [24] Breunig, F.M., Galvão, L.S., Formaggio, A.R., Epiphanio, J.C.N., Directional effects on NDVI and LAI retrievals from MODIS: a case study in Brazil with soybean. Int J Appl Earth Obs Geoinf 13:1 (2011), 34–42.
25. [25] Cárcova, J., Abeledo, L.G., López Pereira, M., Análisis de la generación del rendimiento: Crecimiento, partición y componentes. Satorre, E.H., Benech Arnold, R.L., Slafer, G.A., de la Fuente, E.B., Miralles, D.J., Otegui, M.E., (eds.) Producción de Granos: Bases funcionales para su manejo, 2003, Facultad de Agronomía, UBA, Argentina, 75–98.
26. [26] Fischer, R.A., Yield potential in a dwarf spring wheat and the effect of shading. Crop Sci 15:5 (1975), 607–613.
27. [27] Lobell, D.B., Asner, G.P., Ortiz-Monasterio, J.I., Benning, T.L., Remote Sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agric Ecosyst Environ 94:2 (2003), 205–220.
28. [28] Becker-Reshef, I., Vermote, E., Lindeman, M., Justice, C., A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sens Environ 114:6 (2010), 1312–1323.
29. [29] Reeves, M.C., Zhao, M., Running, S.W., Usefulness and limits of MODIS GPP for estimating wheat yield. Int J Remote Sens 26:7 (2005), 1403–1421.
30. [30] Moriondo, M., Maselli, F., Bindi, M., A simple model of regional wheat yield based on NDVI data. Eur J Agron 26:3 (2007), 266–274.
31. [31] Kouadio, L., Duveiller, G., Djaby, B., El Jarroudi, M., Defourny, P., Tychon, B., Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data. Int J Appl Earth Obs Geoinf 18 (2012), 111–118.
32. [32] Doraiswamy, P.C., Hatfield, J.L., Jackson, T.J., Akhmedov, B., Prueger, J., Stern, A., Crop condition and yield simulations using Landsat and MODIS. Remote Sens Environ 92:4 (2004), 548–559.
33. [33] Yuping, M., Shili, W., Li, Z., Yingyu, H., Liwei, Z., Yanbo, H., et al. Monitoring winter wheat growth in North China by combining a crop model and remote sensing data. Int J Appl Earth Obs Geoinf 10:4 (2008), 426–437.
34. [34] Melo RW, Fontana DC, Berlato MA. Modelo agrometeorológico-espectral de estimativa de rendimento da soja para o estado do Rio Grande do Sul. In: Proceedings of 11th Brazilian Symposium on Remote Sensing. Belo Horizonte, Brazil; 2003. p. 173–9.
35. [35] Rizzi R, Rudorff BFT. Estimativa da produtividade de soja por meio de um modelo agrometeorológico-espectral: estudo de caso no Rio Grande do Sul. In: Proceedings of 12th Brazilian Symposium on Remote Sensing. Goiânia, Brazil; 2005. p. 237–44.
36. [36] Ren, J., Chen, Z., Zhou, Q., Tang, H., Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. Int J Appl Earth Obs Geoinf 10:4 (2008), 403–413.
37. [37] Mkhabela, M.S., Bullock, P., Raj, S., Wang, S., Yang, Y., Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agric For Meteorol 151:3 (2011), 385–393.
38. [38] Bolton, D.K., Friedl, M.A., Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric For Meteorol 173 (2013), 74–84.
39. 137Cs technique. Soil Tillage Res 69:1 (2003), 127–137.
40. [40] INTA-CIRN. Mapa de suelos de la provincia de Buenos Aires, Escala 1:500000. Argentina: Edipubli; 1989, p. 544.
41. [41] Riquier, J., Bramao, D.L., Cornet, J.P., A new system of soil appraisal in terms of actual and potential productivity, 1970, FAO, AGL: TESR/70/6.
42. [42] Sofield, I., Evans, L.T., Cook, M.G., Wardlaw, I.F., Factors influencing the rate and duration of grain filling in wheat. Aust J Plant Physiol 4:5 (1977), 785–797.
43. [43] Andriani, J.M., Consumo de agua de los cultivos. Para mejorar la producción, El agua en los sistemas productivos. INTA EEA Oliveros 13 (2000), 27–30.
44. [44] Palacios, E.V., Palacios, L.S., Rodríguez, J.C., Palacios, J.E.S., Earth observation as a support to improve water use in irrigated agriculture. J Agric Sci Technol 4:6 (2010), 1–9.
45. [45] Leica Geosystems Geospatial Imaging. ERDAS IMAGINE 9.1; 2006.
46. [46] InfoStat Group, Faculty of agricultural sciences. 2007, UNC, Córdoba, Argentina.
47. [47] Genovese, G., Vignolles, C., Nègre, T., Passera, G., A methodology for a combined use of normalised difference vegetation index and CORINE land cover data for crop yield monitoring and forecasting. A case study on Spain. Agronomie 21:1 (2001), 91–111.
48. [48] Kastens, J.H., Kastens, T.L., Kastens, D.L.A., Price, K.P., Martinko, E.A., Lee, R.-Y., Image masking for crop yield forecasting using AVHRR NDVI time series imagery. Remote Sens Environ 99:3 (2005), 341–356.
49. [49] Abbate PE, Cantarero MG. Efectos del estrés hídrico en trigo. 2007. Link: http://www.engormix.com/MA-agricultura/trigo/articulos/efectos-estres-hidrico-trigo-t1694/998-p0.htm. 2012.
50. [50] Labus, M.P., Nielsen, G.A., Lawrence, R.L., Engel, R., Long, D.S., Wheat yield estimates using multi-temporal NDVI satellite imagery. Int J Remote Sens 23:20 (2002), 4169–4180.
51. [51] Ma, H., Huang, J., Zhu, D., Liu, J., Su, W., Zhang, C., et al. Estimating regional winter wheat yield by assimilation of time series of HJ-1 CCD NDVI into WOFOST-ACRM model with Ensemble Kalman Filter. Math Comput Model 58:3 (2013), 759–770.
52. [52] Salazar, L., Kogan, F., Roytman, L., Use of remote sensing data for estimation of winter wheat yield in the United States. Int J Remote Sens 28:17 (2007), 3795–3811.
53. [53] Grohs, D.S., Bredemeier, C., Poletto, N., Mundstock, C.M., Validação de modelo para predição do potencial produtivo de trigo com sensor óptico ativo. Pesq Agropec Bras 46:4 (2011), 446–449.
54. [54] Fontana, D.C., Potgieter, A.B., Apan, A., Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images. Appl GIS 3:7 (2007), 1–16.
55. [55] Government of Spain, MAGRAMA. Fundamentación científica, Sistema de Vigilancia y Alerta de la Evolución Actual de los Cultivos y Aprovechamientos. Link: http://www.magrama.gob.es/es/agricultura/temas/sistema-de-vigilancia-y-alerta-de-la-evolucion-anual-de-los-cultivos-y-aprovechamientos/fundamentacion_cientifica.aspx. 2013.