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Daily Evapotranspiration Estimations by Direct Calculation and Temporal Upscaling Based on Field and MODIS Data

Authors :
Yazhen Jiang
Junrui Wang
Yafei Wang
Source :
Remote Sensing, Vol 14, Iss 16, p 4094 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Daily evapotranspiration (ET) integration is essential to various applications of agricultural water planning and management, ecohydrology, and energy balance studies. The constant reference evaporative fraction (EFr) temporal upscaling method has been proven to be efficient in extrapolating instantaneous ET to a daily timescale. Unlike upscaling methods, the direct calculation (DC) method developed in our previous study directly estimates daily ET without calculating instantaneous ET. The present study aimed to compare daily estimations of ET using the EFr and DC methods based on field and MODIS data at a site from the ChinaFLUX network. The estimation results were validated by eddy covariance (EC) ET both with and without the correction of energy imbalance. Based on field data, the results show that (i) the DC method performed with higher accuracy when compared to uncorrected EC measurements, while daily ET from both methods was overestimated; (ii) the DC method still performed better after the EC ET was corrected by the Residual Energy scheme, and the overestimations were significantly decreased; (iii) both methods performed best when compared with corrected ET by the Bowen Ratio scheme. The results from satellite data reveal that (i) the constant EFr method overestimated daily ET by a mean-bias-error (MBE) of 5.6 W/m2, and a root-mean-square error (RMSE) of 18.6 W/m2; and (ii) the DC method underestimated daily ET by a smaller MBE of −4.8 W/m2 and an RMSE of 22.5 W/m2. Therefore, the DC method has similar or better performance than the widely used constant EFr upscaling method and can estimate daily ET directly and effectively.

Details

Language :
English
ISSN :
14164094 and 20724292
Volume :
14
Issue :
16
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.bf42a6edd88446f5aa4d2a223c5921b6
Document Type :
article
Full Text :
https://doi.org/10.3390/rs14164094