This study aimed evaluating the ability of the FAO's WaPOR Product (FWP) to estimate reference evapotranspiration (RET) based on lysimetric data and RET equations and compare its accuracy with geostatistical methods in the Lake Urmia basin. Lysimetric RET was collected at two stations for 4 years. RET equations were then evaluated using the lysimetric data; the best equation was determined. The RET data obtained from FWP were evaluated at daily, monthly and annual scales using the results of the selected equation for the years 2010–2020. Finally, the accuracy of FWP in the spatial estimation of RET was compared with geostatistical methods. The results show that the FAO Penman–Monteith (FPM) equation was more accurate at both stations. Therefore, the results of the FPM were used to evaluate the reference evapotranspiration of FWP. The average nRMSE of the FWP to daily, monthly and annual data was 31, 25 and 16%, respectively. In general, 15% overestimation was observed in the FWP. Comparison of the FWP with geostatistical methods showed that the highest and lowest accuracy was observed in the experimental kriging method and FWP with an nRMSE value of 4.6 and 18%, respectively.
Appropriate determination of actual evapotranspiration (ETa) is crucial to improve crop water productivity and optimizing water resource consumption. Satellite data enables us to calculate ETa for a large spatial extent with higher granularity, but the temporal frequency of non-commercial satellite data is often a limitation. This research proposes a method that combines crop coefficients with satellite data to fill temporal data gaps and calculate ETa on a daily basis. The study was conducted on sugarcane crops in the Amirkabir Agro-industries area in the southern part of Khuzestan Province, southwestern Iran. First, Landsat-8 data with the 8- day temporal resolution is acquired to estimate Land Surface Temperature (LST) using Single-Channel Algorithm. The estimated LST is validated with the in-situ canopy temperature measurement via Infrared Thermometer (IRT). Then, the validated LST is used to predict the crop stress coefficient (Ks) based on its relationship with the crop water stress index (CWSI). The crop coefficient (Kc) is obtained from the Surface Energy Balance Algorithm for Land (SEBAL) algorithm. The predicted Ks and Kc with the 8-day temporal resolution are assumed to be constant during the eight days and are utilized to calculate daily ETa by multiplying by the daily reference evapotranspiration (ET0) obtained from local meteorological data. The calculated Ks based on the LST result showed that nRMSE ranged from 0.03 to 0.07 from April to September. The results indicate that the crop coefficients of sugarcane in the initial and mid-stage are 12% and 18%, respectively, higher than the proposed figures by the FAO56 guideline. The aggregated decadal and monthly ETa have shown remarkable similarity with the WaPOR datasets, represented by an RMSE of 8.7 and 1.93 mm, respectively. We think this naval approach can significantly overcome the challenge of remote sensing data availability with the desired higher temporal resolution.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.