Satellite based Vegetation Indices variables for Crop Water Footprint Assessment

H. Woldesellasse, Rajesh Govindan, Tareq Al-Ansari

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The global population has quadrupled over the last century. This has increased the global food demand and water sector. In the state of Qatar, the annual freshwater extraction from aquifers is four times the rate of natural recharge, and the depletion is driven by agriculture which represents only 1.6% of the total land area of Qatar, providing approximately 8-10% of domestic food consumption and contributing 0.1% to the domestic GDP. Considering the need for the sustainable intensification of food production systems, satellite technology has the ability to provide a frequent monitoring mechanism enabling the availability of physically-based spatial information useful for reliable environmental monitoring studies. The objective of this research paper is to assess the demand side water footprint of crops using satellite-driven technology in order to optimise the supply side irrigation requirements. The key vegetation indices, such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) estimated from the remotely acquired information from the Landsat satellite is used for the water footprint assessment. Finally, the water resources demand in agriculture is met by optimising the water supplied from the various decentralized treated sewage effluent plants (TSE). A mixed integer non-linear programming (MINLP) formulation was used to model the spatially-dependent demands, and the TSE plants allocated 80% and 20% of their capacity to fields 1 and 2 respectively. The findings of this paper are promising and a high correlation of -0.93 is found between NDVI and crop water demand, demonstrating that satellite images can be used to monitor the crop vegetation development. vegetation stress can be differentiated using NDVI, thereby demonstrating its applicability for agricultural and water sectors.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Pages1489-1494
Number of pages6
DOIs
Publication statusPublished - 1 Jan 2019

Publication series

NameComputer Aided Chemical Engineering
Volume46
ISSN (Print)1570-7946

Fingerprint

Crops
Satellites
Water
Sewage
Agriculture
Effluents
Monitoring
Nonlinear programming
Water resources
Aquifers
Irrigation
Availability

Keywords

  • Crop water footprint
  • NDVI
  • NDWI
  • Satellite remote sensing

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

Cite this

Woldesellasse, H., Govindan, R., & Al-Ansari, T. (2019). Satellite based Vegetation Indices variables for Crop Water Footprint Assessment. In Computer Aided Chemical Engineering (pp. 1489-1494). (Computer Aided Chemical Engineering; Vol. 46). Elsevier B.V.. https://doi.org/10.1016/B978-0-12-818634-3.50249-6

Satellite based Vegetation Indices variables for Crop Water Footprint Assessment. / Woldesellasse, H.; Govindan, Rajesh; Al-Ansari, Tareq.

Computer Aided Chemical Engineering. Elsevier B.V., 2019. p. 1489-1494 (Computer Aided Chemical Engineering; Vol. 46).

Research output: Chapter in Book/Report/Conference proceedingChapter

Woldesellasse, H, Govindan, R & Al-Ansari, T 2019, Satellite based Vegetation Indices variables for Crop Water Footprint Assessment. in Computer Aided Chemical Engineering. Computer Aided Chemical Engineering, vol. 46, Elsevier B.V., pp. 1489-1494. https://doi.org/10.1016/B978-0-12-818634-3.50249-6
Woldesellasse H, Govindan R, Al-Ansari T. Satellite based Vegetation Indices variables for Crop Water Footprint Assessment. In Computer Aided Chemical Engineering. Elsevier B.V. 2019. p. 1489-1494. (Computer Aided Chemical Engineering). https://doi.org/10.1016/B978-0-12-818634-3.50249-6
Woldesellasse, H. ; Govindan, Rajesh ; Al-Ansari, Tareq. / Satellite based Vegetation Indices variables for Crop Water Footprint Assessment. Computer Aided Chemical Engineering. Elsevier B.V., 2019. pp. 1489-1494 (Computer Aided Chemical Engineering).
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