Evaluation of Satellite Imagery to Increase Crop Yield in Irrigated Agriculture

Michael HoffmannYaryna ButenkoSeydou Traore

DOI 10.7160/aol.2018.100304
No 3/2018, September
pp. 45-55

Hoffmann, M., Butenko, Y. and Traore, S. (2018) “Evaluation of Satellite Imagery to Increase Crop Yield in Irrigated Agriculture", AGRIS on-line Papers in Economics and Informatics, Vol. 10, No. 3, pp. 45-55. ISSN 1804-1930. DOI 10.7160/aol.2018.100304.

Abstract

The main aim of this study was to work out a satellite-imagery based approach that can be used to improve agricultural crop growing on a bigger-scale and on field level. Instead of working on small experimental fields, various vast farms have been selected, which were ready to cooperate for this study. Especially for the dry south of Ukraine, vegetation and soil indices provide useful information to improve crop development and productivity. However, many index variants produce similar results or unclear structures; therefore, their information content is restricted under practical conditions. The results analysis shows that a few indices are sufficient to regularly monitor irrigated fields. Talks with farmers revealed that advice is mainly needed to secure crop growth, leading to the decision to firstly select the indices NDVI and/or EVI. To detect failures in an early stage, we additionally used DIRT, NDRE, LAI, NMDI and OSAVI. NMDI could also be used to monitor irrigation activities. This article provides examples illuminating the implemented methodology.

Keywords

Remote sensing (RS), NDVI, vegetation index, irregular crop growth, satellite imagery, irrigation.

References

  1. Arnold, J. G., Srinivasan, R., Muttiah, R. S. and Williams, J. R. (1998) “Large area hydrologic modelling and assessment, part I: model development”, Journal of American Water Resources Association, Vol. 34, pp. 73–89. E-ISSN 1752-1688. DOI 10.1111/j.1752-1688.1998.tb05961.x.
  2. Bannari, A., Morin, D., Bonn, F., and Huete, A. (1995) “A review of vegetation indices”, Remote Sensing Reviews, Vol. 13, No. 1, pp. 95-120. DOI 10.1080/02757259509532298.
  3. Barane, P. and Dwarakish, G. S. (2017) “Bridging the gap between potential and actual use of satellite data”, Plugin Repository [Online]. Available: https://github.com/PrathamGitHub/ NITK_RS-GIS_17/blob/master/Information.pdf [Accessed: 23 Feb. 2018].
  4. Bastiaanssen, W.G.M., Molden, D.J. and Makin, I.W. (2000) “Remote sensing for irrigated agriculture: Examples from research and possible applications”, Agricultural Water Management, Vol. 46, No. 2, pp.137-155. ISSN 0378-3774. DOI 10.1016/S0378-3774(00)00080-9.
  5. Benedetti, R. and Rossini, P. (1993) “On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna”, Remote Sensing of Environment, Vol. 45, No. 3, pp. 311-326. ISSN 0034-4257. DOI 10.1016/0034-4257(93)90113-C.
  6. Brom, J. (2015) “Raster Best Fit Scatterplot for QGIS” [Online]. Available: http://www.jbrom. smoothcollie.eu/ [Accessed: 23 Feb. 2018].
  7. CGIAR-CSI (The CGIAR Consortium for Spatial Information) (2017) [Online]. Available: http://srtm.csi.cgiar.org / [Accessed: 23 Feb. 2018].
  8. Chavez, P. S. (1996) “Image-based atmospheric corrections - revisited and improved”, Photogrammetric Engineering and Remote Sensing, Vol. 62, No. 9, pp. 1025-1036.
  9. Congedo, L. (2016) “Semi-Automatic Classification Plugin Documentation”. pp.198. [Online]. Available: https://www.researchgate.net/publication/307593091_Semi- Automatic_Classification_Plugin_Documentation_Release_6011. [Accessed: 23 Feb. 2018]. DOI 10.13140/RG.2.2.29474.02242/1.
  10. Congedo, L. (2017) “Landsat 8 Surface Reflectance: a Comparison of DOS1 Correction and USGS High Level Data Products”. [Online]. Available: https://fromgistors.blogspot.com/2015/01/landsat- 8-surface-reflectance.html?spref=yml [Accessed: 23 Feb. 2018].
  11. Corredor-Llano, X. C. (2017) “Cloud masking”, [Online]. Available: http://plugins.qgis.org/plugins/ CloudMasking/ [Accessed: 23 Feb. 2018].
  12. De Solan, B., Lesergent, A.D., Gouache,. D. and Baret, F. (2012) “Current use and potential of satellite imagery for crop production management” [Online]. Available: www.google.com.ua/l? ursa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjG1NXM367XA hVPPFAKHaSVBz0QFggxMAA&url=http%3A%2F%2Fec.europa.eu%2FDocsRoom%2Fdocume nts%2F432%2Fattachments%2F1%2Ftranslations%2Fen%2Frenditions%2Fnative&usg=AOvVaw 3QmcBqsid0qerTe-Hfiqs2 [Accessed: 23 Feb. 2018].
  13. Hogg, E. H., Barr, A. G. and Black, T. A. (2013) "A simple soil moisture index for representing multi-year drought impacts on aspen productivity in the western Canadian interior”, Agricultural and Forest Meteorology, Vol. 178–179, No. 15, pp. 173-182, ISSN 0168-1923. DOI 10.1016/j.agrformet.2013.04.025.
  14. Huete, A. R. (1988) “A soil-adjusted vegetation index (SAVI)”, Remote Sensing of Environment, Vol. 25, No. 3, pp. 259-309. DOI 10.1016/0034-4257(88)90106-X.
  15. IDB (Index Data Base) (2017) “Simple Ratio 1600/820 Moisture Stress Index (MSI)”, [Online]. Available: https://www.indexdatabase.de/db/i.php [Accessed: 23 Feb. 2018].
  16. Iowa State University (2017) “Corn Diagnostic Guide”, [Online]. Available: https://www.aganytime. com/dekalb/tools/Documents/CornDiagnosticGuide.pdf [Accessed: 23 Feb. 2018].
  17. Langner, H., Böttger, H. and Schmidt, H. (2016) “Difference Index with Red Threshold”, ResearchGate 09, [Online]. Available: https://www.researchgate.net/search.Search. html?type=publication&query=Difference%20Index%20with%20Red%20Threshold [Accessed: 23 Feb. 2018].
  18. Mandanici, E. and Bitelli, G. (2016) “Preliminary Comparison of Imagery for a Combined Use”, Remote Sensing., Vol. 8, pp. 1014; [Online]. Available: www.mdpi.com/journal/remotesensing [Accessed: 23 Feb. 2018]. ISSN 2072-4292. DOI 10.3390/rs8121014.
  19. Matsushita, B., Yang, W., Chen, J., Onda,Y. and Qiu, G. (2007) “Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: a case study in high-density Cypress forest”, Sensors (Basel), Vol. 5, No. 7, pp. 2636–2651. ISSN 1424-8220. DOI 10.3390/s7112636.
  20. Meera Gandhi, G., Parthiban, S., Thummalu, N. and Christy, A. (2015) “NDVI: Vegetation change detection using remote sensing and GIS – A case study of Vellore District”, Procedia Computer Science, Vol. 57, pp.1199 – 1210. ISSN 1877-0509. DOI 10.1016/j.procs.2015.07.415.
  21. NASA and USGS (2017) "Landsat". [Online]. Available: http://eoedu.belspo.be/en/satellites/ landsat.htm [Accessed: 23 Feb. 2018].
  22. Panda, S., Ames, D. and Panigrahi, S. (2010) “Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques”, Remote Sensing, Vol. 2, pp. 673-696. ISSN 2072-4292. DOI 10.3390/rs2030673.
  23. Pandolfo, A., Antunes, P. and Gong, X. (2017) “Using Landsat TM data for soil moisture mapping” [Online]. Available: https://mafiadoc.com/vegetation-mapping-with_59cceec81723ddd52081b506. html [Accessed: 23 Feb. 2018].
  24. Potić, I., Bugarski, M. and Matić-Varenica, J. (2017) “Soil Moisture Determination using Remote Sensing data for the property protection and increase of agriculture production”, Paper presented at the “ Worldbank conference on land and poverty”, The World Bank, Washington DC, March 20-24, 2017.
  25. Rondeaux, G., Steven, M. D. and Baret, F. (1996) “Optimization of Soil-Adjusted Vegetation Indices”, Remote Sensing of Environment, Vol. 55, No. 2, pp. 95-107. ISSN 0034-4257. DOI 10.1016/0034-4257(95)00186-7.
  26. Roujean, J.-L. and Breon, F.-M. (1995) “Estimating PAR absorbed by vegetation from bidirectional reflectance measurements”, Remote Sensing of Environment, Vol. 51, No. 3, pp. 375–384. ISSN 0034-4257. DOI 10.1016/0034-4257(94)00114-3.
  27. Sánchez, N., González-Zamora, A., Piles, M. and Martínez-Fernández, J. (2016) “A new soil moisture agricultural drought index (SMADI) integrating MODIS and SMOS products: a case of study over the Iberian Peninsula”, Remote Sensing, Vol. 8, No. 4, ISSN 2072-4292. DOI 10.3390/rs8040287.
  28. Sentek Systems website (2015) “Comparisons-between-NDVI”, [Online]. Available: http://www. senteksystems.com/2015/11/25/ [Accessed: 23 Feb. 2018].
  29. Tilling, A. K., O'Leary, G. J., Ferwerda, J. G., Jones, S. D., Fitzgerald, G. J., Rodriguez, D. and Belford, R. (2007) “Remote Sensing of nitrogen and water stress in wheat”, Field Crops Research, 2007, Vol. 104, No. 1-3, pp. 77-85. ISSN 0378-4290. DOI 10.1016/j.fcr.2007.03.023.
  30. USGS (2017) "Landsat and Agriculture - Case Studies on the Uses and Benefits of Landsat Imagery in Agricultural Monitoring and Production" [Online]. Available: https://pubs.usgs.gov/of/2017/1034/ ofr20171034.pdf [Accessed: 23 May. 2018].
  31. USGS (2017) “Landsat Provides Near Real-Time Global Agricultural Analysis” [Online]. Available: https://landsat.usgs.gov/landsat-global-agricultural-analysis [Accessed: 23 May. 2018].
  32. USGS websites (2017) [Online]. Available: https://landsat.usgs.gov and http://earthexplorer.usgs. gov/ [Accessed: 23 Feb. 2018].
  33. Wang, L. and Qu, L.L. (2007) “NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing”, Geophysical Research Letters, Vol. 34, No. 20, E-ISSN 1944-8007. DOI 10.1029/2007GL031021.
  34. Wang, Q., Adikua, S., Tenhunena, J. and Granierb, A. (2005) “On the relationship of NDVI with Leaf Area Index in a deciduous forest site”, Remote Sensing of Environment, Vol. 94, No. 2, pp. 244-255. ISSN 0034-4257. DOI 10.1016/j.rse.2004.10.006.
  35. Wang, Q., Blackburn, G. A., Onojeghuo, A. O., Dash, J., Zhou, L., Zhang, Y. and Atkinson, P. M. (2017) “Fusion of Landsat 8 OLI and Sentinel-2 MSI Data”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, issue 7. ISSN 0196-2892. DOI 10.1109/TGRS.2017.2683444.
  36. Weather Online [Online]. Available: https://www.weatheronline.co.uk/reports/climate/Ukraine.htm [Accessed: 23 Feb. 2018].

Full paper

  Full paper (.pdf, 2.09 MB).