Anas Hesham, Ali Danandeh Mehr, Dursun Zafer Seker

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Accurate rainfall estimation is an essential task for optimal water resources planning and management. However, it is a challenging issue in areas with complex topography. To cope with such problems, remote sensing technology is increasingly used instead of local measurement techniques. This study evaluates the accuracy of five remotely sensed precipitation products over the Akdeniz Basin, Turkey at a monthly scale. The products include Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) with 0.25° × 0.25° spatial resolution, PERSIANN-Cloud Classification System (PERSIANN CCS) with 0.04 ° × 0.04° spatial resolution, PERSIANN- Climate Data Record (PERSIANN CDR) with 0.25° × 0.25° spatial resolution, Group InfraRed Precipitation with Station data (CHIRPS) which combines satellite observation with 0.05° × 0.05° spatial resolution and rain gauge stations, and Tropical Rainfall Measuring Mission product Multi-satellite Precipitation Analysis 3B42V7 (TMPA 3B42V7) with 0.25° × 0.25° spatial resolution. To assess the remotely sensed precipitation accuracy, root mean square error (RMSE) and Pearson correlation coefficient (R) between each product and the observed data from four ground-based stations during the period of 2003 to 2018 were calculated used. In addition, the precipitation values of the best product were corrected using a linear regression method. The results showed that TMPA 3B42V7 and CHIRPS have higher accuracy than those of PERSIANN products. The R for TMPA 3B42V7 and CHIRPS were 0.9 and 0.86, respectively which are corresponding to a strong linear correlation between satellite product and observed data. The results also showed that PERSIANN CCS and PERSIANN provide a weak correlation with observed data. Also, RMSE confirms the superiority of CHIRPS and TMPA 3B42V7 over the study area with 23 mm and 19.7 mm respectively while PERSIANN, PERSIANN CCS, and PERSIANN CDR were 53.7 mm, 51.8 mm, and 34.9 mm, respectively. After the correction of TMPA 3B42V7 using linear regression, the RMSE dropped from 19.7 mm to 15.7 mm. The evaluation proposes that TMPA 3B42V7 and CHIRPS can be promising precipitation products (after a bias correction) to be used as complementary to the ground-based stations for potential applications in the Akdeniz basin in Turkey.

Original languageEnglish
Title of host publication42nd Asian Conference on Remote Sensing, ACRS 2021
PublisherAsian Association on Remote Sensing
ISBN (Electronic)9781713843818
Publication statusPublished - 2021
Event42nd Asian Conference on Remote Sensing, ACRS 2021 - Can Tho, Viet Nam
Duration: 22 Nov 202126 Nov 2021

Publication series

Name42nd Asian Conference on Remote Sensing, ACRS 2021


Conference42nd Asian Conference on Remote Sensing, ACRS 2021
Country/TerritoryViet Nam
CityCan Tho

Bibliographical note

Publisher Copyright:
© ACRS 2021.All right reserved.


  • Akdeniz Basin
  • deep learning
  • Precipitation products
  • Rain gauges


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