Research Article
BibTex RIS Cite

Yield Estimation of Winter Wheat in Pre-harvest Season by Satellite Imagery Based Regression Models

Year 2020, Volume: 1 Issue: 2, 390 - 403, 31.12.2020

Ediz Ünal Hakan Yıldız Ali Mermer Metin Aydoğdu

Abstract

Early crop yield estimates could provide up-to-date information on supply, demand, stocks, and export availability through which governing bodies can make better agricultural management plans. This study aims to develop a yield model estimating pre-harvest winter wheat yield at both tillering and flowering stages using a multiple linear regression approach based on the relationship between actual yield and satellite derived crops’ phenological parameters. Four crop parameters (NDVI, Cumulative NDVI, LAI and FPAR) were regressed in combination to find the best applicable model. Regression results showed that correlations for all models among the variables of the flowering period are higher than that of tillering (0.63>0.53). The mean RMSE’s of the observed vs predicted yields for tillering period was 645.9 kg ha-1 and 574.5 kg ha-1 for flowering period. The optimal developed model which consists of NDVI and CNDVI variables provided 76% and 79% of predicting accuracy 3 and 1.5 months before harvest respectively.

Keywords

MODIS Remote sensing Vegetation index Wheat Yield prediction

Supporting Institution

General Directorate of Agricultural Research and Policies

Project Number

TAGEM/TBAD/12 A12/PO7/01

Thanks

This research study was supported by General Directorate of Agricultural Research and Policies through Agricultural Research Projects (Project No: TAGEM/TBAD/12 A12/PO7/01). We express our gratitude to all project staff for contributing the field studies and office work.

References

  • Acevedo E, Silva P and Silva H (2002). Wheat growth and physiology. FAO Plant Production and Protection Series (FAO), 0259–2525, no. 30, Food and Agriculture Organization of the United Nations, Rome.
  • Ahlrichs JS and Bauer ME (1983). Relation of Agronomic and Multispectral Reflectance Characteristics of Spring Wheat Canopies. Agronomy Journal 75(6): 987 – 993.
  • Aparicio N, Villegas D, Casadesus J, Araus JL and Royo C (2000). Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal 92: 83–91.
  • Babar MA, Reynolds MP, Van Ginkel M, Klatt AR, Raun WR and Stone ML (2006). Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Science 46: 578–588.
  • Becker RI, Vermote E, Lindeman M and Justice C (2010). A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment 114:1312-1323.
  • Boken VK and Shaykewich CF (2002). Improving an operational wheat yield model using phenological phase-based Normalized Difference Vegetation Index. International Journal of Remote Sensing 23: 4155−4168.
  • Campbell JB (1996). Introduction to Remote Sensing. Guilford Press, New York, NY, USA.
  • Chai T and Draxler RR (2014). Root mean square error (RMSE) or mean absolute error (MAE) – Arguments against avoiding RMSE in the literature. Geoscientific Model Development 7(3):1247-1250.
  • Coldwell JE, Rice DP and Nalepka RF (1977). Wheat yield forecasts using Landsat data. Proceedings of 11th International Symposium on Remote Sensing of Environment. Ann Arbor MI, pp. 1245–1254.
  • Dubey RP, Ajwani N, Kalubarme MH, Sridhar VN, Navalgund RR, Mahey RK, et al. (1994). Preharvest wheat yield and production estimation for the Punjab, India. International Journal of Remote Sensing 15: 2137−2144.
  • Doraiswamy PC, Moulin S, Cook PW and Stern A (2003). Crop yield assessment from remote sensing. Photogrammetric Engineering and Remote Sensing 69: 665−674.
  • Fischer RA (1975). Yield potential in dwarf spring wheat and the effect of shading. Crop Science 15: 607−613. GLAM (2018). Global Agricultural Monitoring Project. [online]. Website (http://pekko.geog.umd.edu/usda/test). [accessed; 18 March 2019].
  • Hanan NP, Prince S and Begue A (1995). Estimation of absorbed photosynthetically active radiation and vegetation net production efficiency using satellite data. Agriculture for Meteorology 76: 259-276.
  • Helene L (2012). GCARD2. Breakout session P1.1 National Food Security – Speaker Brief – The Wheat Initiative – an International Research Initiative for Wheat Improvement. Second Global Conference on Agricultural Research for Development. Punta del Este, Uruguay.
  • Huang J, Wang H, Dai Q and Han D (2014). Analysis of NDVI Data for Crop Identification and Yield Estimation. Selected Topics in Applied Earth Observations and Remote Sensing IEEE Journal 7(11): 4374-4384.
  • Jiang Z, Huete RA, Chen J, Chen Y, Li J, Yan G and Zhang X (2006). Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment 101: 366-378.
  • Justice CO and Becker-Reshef I (2007). Developing a strategy for global agricultural monitoring in the framework of Group on Earth Observations Report. FAO, Rome, Italy.
  • Pinter PJ, Jackson RD, Disco SB and Reginato RJ (1981). Multidate spectral reflectances as predictors of yield in water stressed wheat and barley. International Journal of Remote Sensing 2: 43−48.
  • Prince SD and Goward SN (1995). Global primary production: a remote sensing approach. Journal of Biography 22: 815-835.
  • Ren J, Chen Z, Zhou Q and Tang H (2008). Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong. China International Journal of Applied Earth Observation and Geoinformation 10: 403–413.
  • Royo C, Aparicio N, Villegas D, Casadesus J, Monneveux P and Araus JL (2003). Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions. International Journal of Remote Sensing 24: 4403–4419.
  • Serrano L, Filella I and Penuelas J (2000). Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science 40: 723–731.
  • TIGEM (2018). General Directorate of Agricultural Enterprises. [online]. Website http://www.tigem.gov.tr. [accessed 22 April 2019].
  • Tucker CJ (1979). Red and photographic infrared linear combination for monitoring vegetation. Remote Sensing of Environment 8(2): 127-150.
  • Tucker CJ, Elgin JH and McMurtrey JE (1980). Relationship of spectral data to grain yield variation. Photogrammetric Engineering and Remote Sensing 46(5): 657–666.
  • TUIK (2012). The Summary of Agricultural Statistics. Turkish Statistical Institute. Ankara, Turkey.
  • Ünal E and Debie CAJM (2017). Mapping Wheat Growing Areas of Turkey by Integrating Multi-Temporal NDVI Data and Official Crop Statistics. Journal of Field Crops Central Research Institute 26 (1): 11-23.
  • USGS (2018). USGS Earth Resources Observation and Science (EROS) Center. [online]. Website https://earthexplorer.usgs.gov/ [accessed; 05 March 2019].
  • Vermote EF, El Saleous NZ and Justice CO (2002). Atmospheric correction of MODIS data in the visible to middle infrared: first results. Remote Sensing of Environment 83: 97-111.
  • Wall L, Larocque D and Leger PM (2007). The early explanatory power of NDVI in crop yield modeling. International Journal of Remote Sensing 29: 2211−2225.
  • Wiegand CL, Richardson AJ, Escobar DE and Gerbermann AH (1991). Vegetation indices in crop assessment. Remote Sensing of Environment 35: 105–119.
  • White J and Edwards J (2008). Wheat growth and development. NSW Department of Primary Industries, Orange, Australia.
  • Zhao YS (2003). Methods and Theories of Remote Sensing Application and Analysis. Science Press. Beijing, China.

Year 2020, Volume: 1 Issue: 2, 390 - 403, 31.12.2020

Ediz Ünal Hakan Yıldız Ali Mermer Metin Aydoğdu

Abstract

Project Number

TAGEM/TBAD/12 A12/PO7/01

References

  • Acevedo E, Silva P and Silva H (2002). Wheat growth and physiology. FAO Plant Production and Protection Series (FAO), 0259–2525, no. 30, Food and Agriculture Organization of the United Nations, Rome.
  • Ahlrichs JS and Bauer ME (1983). Relation of Agronomic and Multispectral Reflectance Characteristics of Spring Wheat Canopies. Agronomy Journal 75(6): 987 – 993.
  • Aparicio N, Villegas D, Casadesus J, Araus JL and Royo C (2000). Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal 92: 83–91.
  • Babar MA, Reynolds MP, Van Ginkel M, Klatt AR, Raun WR and Stone ML (2006). Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Science 46: 578–588.
  • Becker RI, Vermote E, Lindeman M and Justice C (2010). A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment 114:1312-1323.
  • Boken VK and Shaykewich CF (2002). Improving an operational wheat yield model using phenological phase-based Normalized Difference Vegetation Index. International Journal of Remote Sensing 23: 4155−4168.
  • Campbell JB (1996). Introduction to Remote Sensing. Guilford Press, New York, NY, USA.
  • Chai T and Draxler RR (2014). Root mean square error (RMSE) or mean absolute error (MAE) – Arguments against avoiding RMSE in the literature. Geoscientific Model Development 7(3):1247-1250.
  • Coldwell JE, Rice DP and Nalepka RF (1977). Wheat yield forecasts using Landsat data. Proceedings of 11th International Symposium on Remote Sensing of Environment. Ann Arbor MI, pp. 1245–1254.
  • Dubey RP, Ajwani N, Kalubarme MH, Sridhar VN, Navalgund RR, Mahey RK, et al. (1994). Preharvest wheat yield and production estimation for the Punjab, India. International Journal of Remote Sensing 15: 2137−2144.
  • Doraiswamy PC, Moulin S, Cook PW and Stern A (2003). Crop yield assessment from remote sensing. Photogrammetric Engineering and Remote Sensing 69: 665−674.
  • Fischer RA (1975). Yield potential in dwarf spring wheat and the effect of shading. Crop Science 15: 607−613. GLAM (2018). Global Agricultural Monitoring Project. [online]. Website (http://pekko.geog.umd.edu/usda/test). [accessed; 18 March 2019].
  • Hanan NP, Prince S and Begue A (1995). Estimation of absorbed photosynthetically active radiation and vegetation net production efficiency using satellite data. Agriculture for Meteorology 76: 259-276.
  • Helene L (2012). GCARD2. Breakout session P1.1 National Food Security – Speaker Brief – The Wheat Initiative – an International Research Initiative for Wheat Improvement. Second Global Conference on Agricultural Research for Development. Punta del Este, Uruguay.
  • Huang J, Wang H, Dai Q and Han D (2014). Analysis of NDVI Data for Crop Identification and Yield Estimation. Selected Topics in Applied Earth Observations and Remote Sensing IEEE Journal 7(11): 4374-4384.
  • Jiang Z, Huete RA, Chen J, Chen Y, Li J, Yan G and Zhang X (2006). Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment 101: 366-378.
  • Justice CO and Becker-Reshef I (2007). Developing a strategy for global agricultural monitoring in the framework of Group on Earth Observations Report. FAO, Rome, Italy.
  • Pinter PJ, Jackson RD, Disco SB and Reginato RJ (1981). Multidate spectral reflectances as predictors of yield in water stressed wheat and barley. International Journal of Remote Sensing 2: 43−48.
  • Prince SD and Goward SN (1995). Global primary production: a remote sensing approach. Journal of Biography 22: 815-835.
  • Ren J, Chen Z, Zhou Q and Tang H (2008). Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong. China International Journal of Applied Earth Observation and Geoinformation 10: 403–413.
  • Royo C, Aparicio N, Villegas D, Casadesus J, Monneveux P and Araus JL (2003). Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions. International Journal of Remote Sensing 24: 4403–4419.
  • Serrano L, Filella I and Penuelas J (2000). Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science 40: 723–731.
  • TIGEM (2018). General Directorate of Agricultural Enterprises. [online]. Website http://www.tigem.gov.tr. [accessed 22 April 2019].
  • Tucker CJ (1979). Red and photographic infrared linear combination for monitoring vegetation. Remote Sensing of Environment 8(2): 127-150.
  • Tucker CJ, Elgin JH and McMurtrey JE (1980). Relationship of spectral data to grain yield variation. Photogrammetric Engineering and Remote Sensing 46(5): 657–666.
  • TUIK (2012). The Summary of Agricultural Statistics. Turkish Statistical Institute. Ankara, Turkey.
  • Ünal E and Debie CAJM (2017). Mapping Wheat Growing Areas of Turkey by Integrating Multi-Temporal NDVI Data and Official Crop Statistics. Journal of Field Crops Central Research Institute 26 (1): 11-23.
  • USGS (2018). USGS Earth Resources Observation and Science (EROS) Center. [online]. Website https://earthexplorer.usgs.gov/ [accessed; 05 March 2019].
  • Vermote EF, El Saleous NZ and Justice CO (2002). Atmospheric correction of MODIS data in the visible to middle infrared: first results. Remote Sensing of Environment 83: 97-111.
  • Wall L, Larocque D and Leger PM (2007). The early explanatory power of NDVI in crop yield modeling. International Journal of Remote Sensing 29: 2211−2225.
  • Wiegand CL, Richardson AJ, Escobar DE and Gerbermann AH (1991). Vegetation indices in crop assessment. Remote Sensing of Environment 35: 105–119.
  • White J and Edwards J (2008). Wheat growth and development. NSW Department of Primary Industries, Orange, Australia.
  • Zhao YS (2003). Methods and Theories of Remote Sensing Application and Analysis. Science Press. Beijing, China.
There are 33 citations in total.

Details

Primary Language English
Subjects Agricultural Engineering
Journal Section Research Articles
Authors

Ediz Ünal 0000-0001-6463-2670

Hakan Yıldız 0000-0002-7627-7503

Ali Mermer

Metin Aydoğdu 0000-0001-6920-1976

Project Number TAGEM/TBAD/12 A12/PO7/01
Publication Date December 31, 2020
Submission Date July 9, 2020
Acceptance Date October 6, 2020
Published in Issue Year 2020 Volume: 1 Issue: 2

Cite

APA Ünal, E., Yıldız, H., Mermer, A., Aydoğdu, M. (2020). Yield Estimation of Winter Wheat in Pre-harvest Season by Satellite Imagery Based Regression Models. Turkish Journal of Agricultural Engineering Research, 1(2), 390-403.

26831

International peer double-blind reviewed journal

The articles in the Turkish Journal of Agricultural Engineering Research are open access articles and the articles are licensed under a Creative Commons Attribution 4.0 International License (CC-BY-NC-4.0)(https://creativecommons.org/licenses/by-nc/4.0/deed.en). This license allows third parties to share and adapt the content for non-commercial purposes with proper attribution to the original work. Please visit for more information this link https://creativecommons.org/licenses/by-nc/4.0/ 

Turkish Journal of Agricultural Engineering Research (TURKAGER) is indexed/abstracted in CABI, EBSCO, Information Matrix for the Analysis of Journals (MIAR), CAS Source Index (CASSI), Food Science & Technology Abstracts (FSTA), BASE, Directory Research Journals Indexing (DRJI), ROAD (Directory of Open Access Scholarly Resources), WorldCat, ResearchBible, Beluga-Catalogue of Hamburg Libraries, Advanced Science Index (ASI), Scientific Literature (Scilit), Scholar Article Journal Index (SAJI), IJIFACTOR Indexing, Electronic Journals Library (EZB), SJIF Master Journals List, International Institute of Organized Research (I2OR), International Services for Impact Factor and Indexing (ISIFI), ASOS INDEX, Cosmos, Technical Information Library (TIB), ROOTINDEXING, Scientific Indexing Services (SIS), Journal Tables of Contents, Quality Open Access Market.

Turkish Journal of Agricultural Engineering Research (TURKAGER) does not charge any application, publication, or subscription fees.

Publisher: Ebubekir ALTUNTAŞ

For articles citations to the articles of the Turkish Journal of Agricultural Engineering Research (TURKAGER), please click: