There is a global realization in all governmental setups of the need to provoke the efficient appraisal of crop water budgeting in order to manage water resources efficiently. This study aims to use the satellite remote sensing techniques to determine the water deficit in the crop rich Lower Bari Doab Canal (LBDC) command area. Crop classification was performed using multi-temporal NDVI profiles of Landsat-8 imagery by distinguishing the crop cycles based on reflectance curves. The reflectance-based crop coefficients (Kc) were derived by linear regression between normalized difference vegetation index (NDVI) cycles of the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and MYD13Q1 products and Food and Agriculture Organization (FAO) defined crop coefficients. A MODIS 250 m NDVI product of the last 10 years (2004-2013) was used to identify the best performing crop cycle using Fourier filter method. The meteorological parameters including rainfall and temperature substantiated the reference evapotranspiration (ET0) calculated using the Hargreaves method. The difference of potential ET and actual ET, derived from the reflectance-based Kc calculated using reference NDVI and current NDVI, generates the water deficit. Results depict the strong correlation between ET, temperature and rainfall, as the regions having maximum temperature resulted in high ET and low rainfall and vice versa. The derived Kc values were observed to be accurate when compared with the crop calendar. Results revealed maximum water deficit at middle stage of the crops, which were observed to be particularly higher at the tail of the canal command. Moreover, results also depicted that kharif (summer) crops suffer higher deficit in comparison to rabi (winter) crops due to higher ET demand caused by higher temperature. Results of the research can be utilized for rational allocation of canal supplies and guiding farmers towards usage of alternate sources to avoid crop water stress.
Optimization of Canal Management Based on Irrigation Performance Analysis Using Satellite Measurements KEY POINTS• A remote sensing-based decision support system (DSS) was developed for an irrigation system covering 756,000 hectares in Punjab, Pakistan.• The DSS provides insight into irrigation requirements based on actual evapotranspiration and soil moisture contents.Results showed that delivered canal water varied among divisions, secondary canals called distributaries, and crop seasons, implying the system's potential for water distribution improvements.• Soil moisture estimates confirmed that deficit irrigation was generally practiced for the study year 2017-2018, but areas with conjunctive use are wetter.• The inequity in delivery may be minimized by reallocating water between divisions. Inequity within a division can be minimized by modifying the rotation plan of the division.• Key limitations of the study could be addressed with ground validation and analyzing more than 1 year of historical satellite data. In Punjab, irrigation managers recognize this methodology as a potential tool for improving equity and productivity at the distributary level. A follow-up would be to operationalize the remote sensing-based DSS by extending the model to provide forecasts.
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