Status of accuracy in remotely sensed and in-situ agricultural water productivity estimates: A review

M.L. Blatchford*, C.M. Mannaerts, Y. Zeng

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

The scarcity of water and the growing global food demand has fevered the debate on how to increase agricultural production without further depleting water resources. Crop water productivity (CWP) is a performance indicator to monitor and evaluate water use efficiency in agriculture. Often in remote sensing datasets of CWP and its components, i.e. crop yield or above ground biomass production (AGBP) and evapotranspiration (ET a), the end-users and developers are different actors. The accuracy of the datasets should therefore be clear to both users and developers. We assess the accuracy of remotely sensed CWP against the accuracy of estimated in-situ CWP. First, the accuracy of CWP based on in-situ methods, which are assumed to be the user's benchmark for CWP accuracy, is reviewed. Then, the accuracy of current remote sensing products is described to determine if the accuracy benchmark, as set by in-situ methods, can be met with current algorithms. The percentage error of CWP from in-situ methods ranges from 7% to 67%, depending on method and scale. The error of CWP from remote sensing ranges from 7% to 22%, based on the highest reported performing remote sensing products. However, when considering the entire breadth of reported crop yield and ET a accuracy, the achievable errors propagate to CWP ranges of 74% to 108%. Although the remote sensing CWP appears comparable to the accuracy of in-situ methods in many cases, users should determine whether it is suitable for their specific application of CWP.

Original languageEnglish
Article number111413
Pages (from-to)1-20
Number of pages20
JournalRemote sensing of environment
Volume234
Early online date9 Oct 2019
DOIs
Publication statusPublished - 1 Dec 2019

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Crops
Productivity
productivity
crop
crops
Water
remote sensing
water
Remote sensing
Evapotranspiration
evapotranspiration
crop yield
in situ
agriculture
methodology
water shortages
aboveground biomass
water resources
water use efficiency
agricultural production

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-HYBRID

Cite this

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title = "Status of accuracy in remotely sensed and in-situ agricultural water productivity estimates: A review",
abstract = "The scarcity of water and the growing global food demand has fevered the debate on how to increase agricultural production without further depleting water resources. Crop water productivity (CWP) is a performance indicator to monitor and evaluate water use efficiency in agriculture. Often in remote sensing datasets of CWP and its components, i.e. crop yield or above ground biomass production (AGBP) and evapotranspiration (ET a), the end-users and developers are different actors. The accuracy of the datasets should therefore be clear to both users and developers. We assess the accuracy of remotely sensed CWP against the accuracy of estimated in-situ CWP. First, the accuracy of CWP based on in-situ methods, which are assumed to be the user's benchmark for CWP accuracy, is reviewed. Then, the accuracy of current remote sensing products is described to determine if the accuracy benchmark, as set by in-situ methods, can be met with current algorithms. The percentage error of CWP from in-situ methods ranges from 7{\%} to 67{\%}, depending on method and scale. The error of CWP from remote sensing ranges from 7{\%} to 22{\%}, based on the highest reported performing remote sensing products. However, when considering the entire breadth of reported crop yield and ET a accuracy, the achievable errors propagate to CWP ranges of 74{\%} to 108{\%}. Although the remote sensing CWP appears comparable to the accuracy of in-situ methods in many cases, users should determine whether it is suitable for their specific application of CWP.",
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Status of accuracy in remotely sensed and in-situ agricultural water productivity estimates : A review. / Blatchford, M.L.; Mannaerts, C.M.; Zeng, Y.

In: Remote sensing of environment, Vol. 234, 111413, 01.12.2019, p. 1-20.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Zeng, Y.

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