1 / 14

Remote Sensing-based Crop Water Productivity for Irrigation Management in Asia

Learn how remote sensing can be used to measure and improve irrigation performance for better investments and management decisions. Explore the pySEBAL method, which converts satellite images into valuable information such as water use, soil moisture, crop growth, and water productivity. Gain insights into factors that affect water use, yield, and water productivity, and discover how remote sensing can be a powerful diagnostic tool for planning and managing mega irrigation systems in Asia.

mlentz
Télécharger la présentation

Remote Sensing-based Crop Water Productivity for Irrigation Management in Asia

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Remote sensing based crop water productivity for investment in and management of mega irrigation systems in Asia Xueliang Cai, Wim Bastiaanssen

  2. Background Water productivity concept is widely accepted and the remote sensing approach produces beautiful maps. …So what? How water productivity can be operationalized to help make better investment and management decisions?

  3. Background • Asian Development Bank (ADB) is working with many countries to invest hundred of millions USD in irrigation; • But how should the irrigation performance be measured? And how this measurement can lead to improved investments and management?

  4. Our method pySEBAL: a crop water productivity tool to convert satellite images to water use, soil moisture, crop growth, and water productivity Remote sensing (Crop biomass, leaf area index, nitrogen, yield) Crop water productivity (ETa, ET deficit, Evaporation, Transpiration, soil moisture) Diagnostic analysis Performance indicator

  5. Water productivity maps for performance assessment India, Rice • Good CWP (average 1.47 kg/m3) • Moderate variability (CV = 0.14)

  6. Factors affecting water use, yield, and WP Distance to the main dam

  7. Factors affecting water use, yield, and WP Soil type

  8. Factors affecting water use, yield, and WP Crop calendar and intensity

  9. Factors affecting water use, yield, and WP Fertilizer (type and rate)

  10. Factors affecting water use, yield, and WP Seed

  11. Establishing the investment target Assessing potential BC = Ta / ETa • Big and uneven potential for on-farm water management improvement

  12. Establishing the investment target Determining priority

  13. Final remarks • Crop water productivity needs to move beyond being research and policy concept • Operationalization of CWP requires looking at the elements contributing to CWP (crop, water, and biophysical and managerial factors) • Remote sensing approach is mostly used for monitoring and performance assessment, but it can also be a powerful diagnostic tool for planning and management • Remote sensing works better with ground information

  14. Dank ye! x.cai@un-ihe.org

More Related