Regional Wheat Yield Estimation by Integration of Remotely Sensed Soil Moisture into a Crop Model. Issue 6 (2nd November 2019)

• Record Type: Journal Article
• Authors: Fahad, Muhammad; Ahmad, Ishfaq; Rehman, Mariam; Waqas, Muhammad Mohsin; Gul, Farhana
• Is Part Of: Canadian journal of remote sensing. Volume 45:Issue 6(2019)
• Page Start: 770
• Page End: 781
• Publication Date: 2019-11-02
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2019.1692651
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: A field study was conducted to estimate the regional wheat yield by integration of remotely sensed soil moisture index into CERES-Wheat model. The calibration and evaluation of model was performed using experimental data and then applied on the area of Faisalabad district for yield estimation. Area of Faisalabad district was divided into 7929 cells for independent simulations. The weather data of the wheat season were used uniformly to all cells, while site-specific soil data were used for each cell. Recommended crop management practices were used in the model for all cells. Median normalized difference water index (NDWI) were used to estimate the irrigation amount for each cell. The estimated yield was validated with observed yield of 25 random farms. Model calibration results showed a good agreement between observed and simulated values of grain yield (RMSE = 284.8 kg ha −1 ). The validation of model at regional scale showed a close association with simulated and observed yield of 25 farms ( R 2 = 0.71). The regional yield estimation results indicated that grain yield varies from 1500 to 3593 kg ha −1 in Faisalabad district. The estimated mean yield was 2979 kg ha −1, which was 5.2% higher than the yield reported by Crop Reporting Service (CRS), Punjab.