Calibration of METRIC Modeling for Evapotranspiration Estimation Using Landsat 8 Imagery Data

Derakhshandeh M., Tombul M.

Water Resources Management, cilt.36, sa.1, ss.315-339, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 1
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s11269-021-03029-5
  • Dergi Adı: Water Resources Management
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, ABI/INFORM, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, CAB Abstracts, Compendex, Environment Index, Geobase, INSPEC, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.315-339
  • Anahtar Kelimeler: METRIC method, Evapotranspiration, Energy balance, Land surface temperature, Landsat 8
  • İstanbul Gelişim Üniversitesi Adresli: Evet

Özet

© 2021, The Author(s), under exclusive licence to Springer Nature B.V.Water resources management needs efficient tools to estimate the rate of water loss through evapotranspiration (ET). High resolution spatial imagery has provided a valuable source of data which their implementation in well-tuned models has the potential of evapotranspiration rate estimations with satisfactory accuracy. Mapping evapotranspiration at high resolution with internalized calibration (METRIC) is basically an energy balance model which has shown a good performance in different applications. The model needs to be calibrated for various source of spatial data and with the introduction of new empirical correlations for numerous variables which are used in the model, the model is recalibrated for Landsat 8 multispectral image and applied to intensively cultivated agriculture lands in Alpu (Eskisehir, Turkey). In previous studies, the correlations from previous studies were referenced where the procedure was confusing for many users. In this work, a descriptive step by step procedure is also provided. The meteorological 24 h relative ET was then used to spread the instant ET (at image capture time) estimation into daily 24 h estimation. This approach reduces the errors from multiple correlations and to some extent the effect of short variations like partial cloud coverage.