International Journal of Biometeorology

, Volume 62, Issue 8, pp 1543–1556 | Cite as

Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment

  • A. Lashkari
  • N. Salehnia
  • S. Asadi
  • P. Paymard
  • H. Zare
  • M. Bannayan
Original Paper


The accuracy of daily output of satellite and reanalysis data is quite crucial for crop yield prediction. This study has evaluated the performance of APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), PERSIANN (Rainfall Estimation from Remotely Sensed Information using Artificial Neural Networks), TRMM (Tropical Rainfall Measuring Mission), and AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) precipitation products to apply as input data for CSM-CERES-Wheat crop growth simulation model to predict rainfed wheat yield. Daily precipitation output from various sources for 7 years (2000–2007) was obtained and compared with corresponding ground-observed precipitation data for 16 ground stations across the northeast of Iran. Comparisons of ground-observed daily precipitation with corresponding data recorded by different sources of datasets showed a root mean square error (RMSE) of less than 3.5 for all data. AgMERRA and APHRODITE showed the highest correlation (0.68 and 0.87) and index of agreement (d) values (0.79 and 0.89) with ground-observed data. When daily precipitation data were aggregated over periods of 10 days, the RMSE values, r, and d values increased (30, 0.8, and 0.7) for AgMERRA, APHRODITE, PERSIANN, and TRMM precipitation data sources. The simulations of rainfed wheat leaf area index (LAI) and dry matter using various precipitation data, coupled with solar radiation and temperature data from observed ones, illustrated typical LAI and dry matter shape across all stations. The average values of LAImax were 0.78, 0.77, 0.74, 0.70, and 0.69 using PERSIANN, AgMERRA, ground-observed precipitation data, APHRODITE, and TRMM. Rainfed wheat grain yield simulated by using AgMERRA and APHRODITE daily precipitation data was highly correlated (r2 ≥ 70) with those simulated using observed precipitation data. Therefore, gridded data have high potential to be used to supply lack of data and gaps in ground-observed precipitation data.


Crop model Gauge data Missing data Reanalysis Regional crop yield Satellite 


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Copyright information

© ISB 2018

Authors and Affiliations

  1. 1.School of Environmental Science and EngineeringSouthern University of Science and Technology of ChinaShenzhenChina
  2. 2.Faculty of Agriculture, Department of Water Engineering, P.O. Box 9177949207Ferdowsi University of MashhadMashhadIran
  3. 3.Faculty of AgricultureFerdowsi University of MashhadMashhadIran
  4. 4.Department of AgricultureIslamic Azad University, Mashhad BranchMashhadIran

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