Temporal Reconstruction and Machine-Learning Emulation of a METRIC-Derived Daily Actual Evapotranspiration Benchmark Using MODIS and Climatic Data in a Semi-Arid Irrigation District
Irrigation and Drainage, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1002/ird.70251
- Dergi Adı: Irrigation and Drainage
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, Environment Index, Greenfile, INSPEC, Natural Science Collection (ProQuest)
- Anahtar Kelimeler: actual evapotranspiration, machine learning, METRIC, MODIS, semi-arid region
- Çukurova Üniversitesi Adresli: Evet
Özet
Accurate daily actual evapotranspiration (ETa) is important for irrigation-district water accounting, drought assessment, and seasonal water allocation in semi-arid agricultural regions. This study developed a district-scale machine-learning framework to emulate a temporally reconstructed METRIC-derived ETa benchmark for the Akarsu Irrigation District, Türkiye, using MODIS NDVI, land surface temperature and station-based climatic variables. The benchmark was anchored by 79 Landsat/METRIC anchor observations. Between satellite acquisitions, the short-grass reference evapotranspiration fraction (EToF) was linearly interpolated and combined with daily FAO-56 reference evapotranspiration (ETo), yielding 1476 reconstructed daily values for 2021–2024. We evaluated five machine-learning models and five predictor scenarios using random holdout, cross-validation and leave-one-water-year-out (LOWYO) validation. A leave-one-satellite-date-out sensitivity analysis additionally quantified reconstruction uncertainty. The full climatic scenario with XGBoost achieved a random-holdout R2 of 0.880 and RMSE of 0.447 mm day−1, whereas the LOWYO validation yielded mean R2 = 0.686 and mean NSE = 0.503. The results demonstrate strong internal predictive performance but weaker interannual transferability, supporting the framework for continuous district-scale ETa reconstruction, water accounting and operational assessment under comparable hydro-climatic and cropping conditions, while highlighting the importance of temporally independent validation.