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Article

An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping

by 1,2, 1,2,*, 3,4, 2,3, 1,2 and 1,2
1
State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
ICube, UdS, CNRS; 300 Boulevard Sebastien Brant, CS10413, 67412 Illkirch, France
4
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 761; https://doi.org/10.3390/rs11070761
Received: 25 February 2019 / Revised: 22 March 2019 / Accepted: 25 March 2019 / Published: 29 March 2019
(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))
Continuous high spatio-temporal resolution monitoring of evapotranspiration (ET) is critical for water resource management and the quantification of irrigation water efficiency at both global and local scales. However, available remote sensing satellites cannot generally provide ET data at both high spatial and temporal resolutions. Data fusion methods have been widely applied to estimate ET at a high spatio-temporal resolution. Nevertheless, most fusion methods applied to ET are initially used to integrate land surface reflectance, the spectral index and land surface temperature, and few studies completely consider the influencing factor of ET. To overcome this limitation, this paper presents an improved ET fusion method, namely, the spatio-temporal adaptive data fusion algorithm for evapotranspiration mapping (SADFAET), by introducing critical surface temperature (the corresponding temperature to decide soil moisture), importing the weights of surface ET-indicative similarity (the influencing factor of ET, which is estimated from remote sensing data) and modifying the spectral similarity (the differences in spectral characteristics of different spatial resolution images) for the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). We fused daily Moderate Resolution Imaging Spectroradiometer (MODIS) and periodic Landsat 8 ET data in the SADFAET for the experimental area downstream of the Heihe River basin from April to October 2015. The validation results, based on ground-based ET measurements, indicated that the SADFAET could successfully fuse MODIS and Landsat 8 ET data (mean percent error: −5%), with a root mean square error of 45.7 W/m2, whereas the ESTARFM performed slightly worse, with a root mean square error of 50.6 W/m2. The more physically explainable SADFAET could be a better alternative to the ESTARFM for producing ET at a high spatio-temporal resolution. View Full-Text
Keywords: evapotranspiration; fusion; multi-source satellite data; Landsat 8; MODIS; SADFAET evapotranspiration; fusion; multi-source satellite data; Landsat 8; MODIS; SADFAET
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MDPI and ACS Style

Wang, T.; Tang, R.; Li, Z.-L.; Jiang, Y.; Liu, M.; Niu, L. An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping. Remote Sens. 2019, 11, 761. https://doi.org/10.3390/rs11070761

AMA Style

Wang T, Tang R, Li Z-L, Jiang Y, Liu M, Niu L. An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping. Remote Sensing. 2019; 11(7):761. https://doi.org/10.3390/rs11070761

Chicago/Turabian Style

Wang, Tong, Ronglin Tang, Zhao-Liang Li, Yazhen Jiang, Meng Liu, and Lu Niu. 2019. "An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping" Remote Sensing 11, no. 7: 761. https://doi.org/10.3390/rs11070761

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