Crop yield prediction under soil salinity using satellite derived vegetation indices

Field Crops Research

Volumn 192, Issue , 2016, Pages 134-143

  Satir, Onur ^a   Berberoglu, Suha ^b  

^a YUZUNCU YIL UNIVERSITY   (Turkey)

^b CUKUROVA UNIVERSITY   (Turkey)

Author keywords

Remote sensing; Soil salinity and yield loss; Turkey crop phenology; Vegetation index; Yield prediction

Indexed keywords

AGRICULTURAL LAND; COTTON; CROP YIELD; ENERGY CONSERVATION; FIELD METHOD; LAND MANAGEMENT; LANDSAT THEMATIC MAPPER; MAIZE; PHENOLOGY; REMOTE SENSING; SALINE SOIL; SATELLITE LASER RANGING; VEGETATION INDEX; WHEAT; YIELD RESPONSE;

MEDITERRANEAN REGION; TURKEY;

GOSSYPIUM HIRSUTUM; TRITICUM AESTIVUM; ZEA MAYS;

EID: 84964614573     PISSN: 03784290     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fcr.2016.04.028     Document Type: Article

References (41)

1. Amacher, JK., Koenig, R., Kitchen, B., 2000. Salinity and Plant Tolerance, All Archived Publications. Paper 43. http://digitalcommons.usu.edu/extension_histall/43.
2. Barton M.H., Buchberger S.G., Lange M.J. Estimation of error and compliance in surveys by kriging. J. Surv. Eng. 1999, 125:87-108.
3. Berberoğlu S. Optimising the remote sensing of mediterranean land cover. PhD Thesis 1999, University of Southampton School of Geography.
4. Berberoglu S., Lloyd C.D., Atkinson P.M., Curran P.J. The integration of spectral & texture information using neural networks for land cover mapping In the mediterranean. Comput. Geosci. 2000, 26:385-396.
5. Berberoglu S., Satir O., Atkinson P.M. Mapping percentage tree cover from envisat MERIS data using linear and nonlinear techniques. Int. J. Remote Sens. 2009, 30(18):4747-4766.
6. Boogaard, HL., Eerens, H., Supit, I., van Diepen, CA., Piccard, I., Kempenseers, P., 2002. Description of the MARS Crop Yield Forecasting System (MCYFS). METAMP-1/3. Alterra and VITO, Wageningen and Mol.
7. Cetin M., Ibrikci H., Kirda C., Kaman H., Karnez E., Ryan J., Topcu S., Oztekin E.M., Dingil M., Sesveren S. Using an electromagnetic sensor combined with geographic information systems to monitor soil salinity in an area of Southern Turkey irrigated with drainage water. Fres. Environ. Bull. 2012, 21:1133-1145.
8. Chen Z., Li S., Ren J., Gongi P., Zhang M., Wang L., Xiao S., Jiang D. Monitoring and management of agriculture with remote sensing. Advanges in Remote Sensing 2008, 397-421. Springer Science + Business Media B.V. S. Liang (Ed.).
9. Christiansen M.B. World environmental limitation to food and fibre culture. Breeding Plants for Less Favourable Environments 1982, John Wiley and Sons, New York, USA. M.B. Christiansen, C.F. LEWI˙S (Eds.).
10. Dinç U., Sari M., Şenol S., Kapur S., Sayin M., Derici M.R., Çavuşgil V., Gök M., Aydin M., Ekinci H., Ağca N., Schlichting E. Cukurova region soils. Lecture Book 1990, Cukurova University Press, Adana, Turkey, (No:26).
11. GWW 2010 and 2011 groundwater levels and salinity measurement results, water reports. Government Water Works of Turkey 2012, GWW, press, Adana, Turkey.
12. Gao B.C. NDWI -A Normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58:257-266.
13. Huang C., Wylie B., Yang L., Homer C., Zylstra G. Derivation of a tasselled cap transformation based on landsat 7 at-satellite reflectance. Int. J. Remote Sens. 2002, 23(8):1741-1748.
14. Huete A.R. A soil adjusted vegetation index (SAVI). Remote Sens. Environ. 1988, 17:37-53.
15. Kouadio L., Newlands N.K., Davidson A., Zhang Y., Chipanshi A. Assessing the performance of MODIS NDVI and EVI for seasonal crop yield forecasting at ecodistrict scale. Remote Sens. 2014, 6:10193-10214.
16. Lewis J.E., Rowland J., Nadeau A. Estimating maize production in Kenya using NDVI: some statistical considerations. Int. J. Remote Sens. 1998, 19(13):2609-2617.
17. Li A., Liang S., Wang A., Qin J. Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques. Photogramm. Eng. Remote Sens. 2007, 73:1149-1157.
18. + ATPases in cotton seedlings. Crop Sci. 1997, 37:190-197.
19. Lobo A., Chic O., Casterad A. Classification of Mediterranean crops with multisensory data: per-pixel versus per-object statistics and image segmentation. Int. J. Remote Sens. 1996, 17:2385-2400.
20. Mass, 1984. Salt tolerance of plants, In: Christie, BR. (Ed.) Handbook of plant science in agriculture, Boca Raton, Florida.
21. Mather P.M. Computer Processing of Remotely-Sensed Images: An Introduction 1999, Wiley, New York, (ISBN 0-471-98550-3). Second edition.
22. Özyavuz M., Şatir O., Bilgili B.C. A change vector analysis technique to monitor land use/land cover in Yildiz mountains, Turkey. Fresenius Environ. Bull. 2011, 20(5):1190-1199.
23. Øvergaard S.I., Isaksson T., Korsaeth A. Prediction of wheat yield and protein using remote sensors on plots - part I: assessing near infrared model robustness for year and site variations. J. Near Infrared Spectrosc. 2013, 21(2):117-131.
24. Panda S.S., Ames D.P., Panigrahi S. Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sens. 2010, 2:673-696.
25. Ren J., Li S., Chen Q., Tang H. Regional yield prediction for winter wheat based on crop biomass estimation using multi-source data. Geosci. Remote Sens. Symp., IEEE Int. 2007, 805-808.
26. Richards L.A. Diagnosis and improvements of saline and alkali soils, U.S. Salinity Laboratory DA, US Dept. Agr. Hbk. 1954, 60:160.
27. Richardson A.J., Wiegand C.L. Distinguishing vegetation from soil background information. Photogramm. Eng. Remote Sens. 1977, 43(2):1541-1552.
28. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., 1973. Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium, (NASA SP-351 Washington, DC) 1, 309-317.
29. Satir, O., Berberoglu, S., Kapur, S., Nagano, T., Erdogan, M.A., Donmez, C., Satir, N.Y., Akça E., Tanaka, K., 2010. Soil Salinity Mapping Using CHRIS PROBA Hyperspectral Data, ESA Hyperspectral workshop, Frascati, Italy.
30. Şatir O., Berberoğlu S. Land use/cover classification techniques using optical remotely sensed data in landscape planning. Landscape Planning 2012, 21-54. Intech, Crotia. M. Özyavuz (Ed.).
31. Şatir O. Determining the agricultural land use suitability using remote sensing and geographical information system in Lower Seyhan Plane. PhD Thesis 2013, Cukurova University Natural and Applied Sciences Ins, Adana, Turkey.
32. Sawasawa Crop yieid estimation: integrating RS, GIS and management factors, a case study of Birkoor and Kortgiri Mandals - Nizamabad District, India. MSc Thesis 2003, ITS, Nederlands.
33. Shang J., Liu J., Ma B., Zhao T., Jiao X., Geng X., Huffman T., Kovacs J.M., Waters D. Mapping spatial variability of crop growth condurions using RapidEye data in Northern Ontario, Canada. Remote Sens. Environ. 2015, 168:113-125.
34. TSMS, 2010. Climate data, 1960-2010. Adana: Regional Directorate of State Meteorological Service, Turkish State Meteorological Service.
35. Thiam A., Eastman R.J. Chapter on vegetation indices. Manuel of the Idrisi Selva Edition 2012, 119-208. Clarke University, Worcester, MA, USA. R.J. Eastman (Ed.).
36. Vicente M.S., Prats J.M.C., Romo A. Early prediction of crop production using drought indices at different time-scales and remote sensing data: application in the Ebro Valley (North-east Spain). Int. J. Remote Sens. 2006, 27:511-518.
37. Wang X., Gassman P.W., Williams J.R., Potter S., Kemanian A.R. Modelling the impacts of soil management practices on runoff sediment yield, maize productivity and soil organic carbon using APEX. Soil Tillage Res. 2008, 101:78-88.
38. Wiegand C.L., Richardson A.J., Escobar D.E., Gerbermann A.H. Vegetation indices in crop assessments. Remote Sens. Environ. 1991, 35:105-119.
39. Yeler Monitoring the agricultural landscape by remote sensing. Lower Seyhan Plane Case Study, Msc. Thesis 2014, Cukurova University Natural and Applied Sciences Ins., Adana, Turkey.
40. Yousfi S., Kellas N., Saidi L., Benlakelal Z., Chaou L., et al. Comperative performance of remote sensing methods in assessing wheat performance under Mediterranean conditions. Agric. Water Manage. 2014, 164:137-147.
41. Zhao Y., Chen X.P., Cui Z.L., Lobell D.B. Using satellite remote sensing to understand maize yield gaps in the North China Plain. Field Crops Res. 2015, 183:31-42.