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

Tong Wang,Yazhen Jiang,Meng Liu,Lu Niu,Zhao-Liang Li,Ronglin Tang

doi:10.3390/rs11070761

Abstract

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.

Highlights

Evapotranspiration (ET), including soil evaporation and vegetation transpiration, is defined as the movement of water from the land surface into air and continuously acquiring ET at a high spatio-temporal resolution at field or sub-field scales is of critical significance for agricultural and hydrological cycle modelling, irrigation water efficiency quantification and agricultural water resource management [1]
ET at a high spatio-temporal resolution is estimated by different ET models using intermediate variables that are closely related to ET, such as the reference ET fraction (ETrF), normalized differential vegetation index (NDVI) and land surface temperature (LST)
It should be noted that existing methods have the following limitations: most fusion methods applied to ET are initially used to integrate the land surface reflectance, spectral index and LST; these methods cannot completely consider the influencing factor of ET including remote sensing and atmospheric characteristics [31] [33,34]

Summary

Introduction

Evapotranspiration (ET), including soil evaporation and vegetation transpiration, is defined as the movement of water from the land surface into air and continuously acquiring ET at a high spatio-temporal resolution at field or sub-field scales is of critical significance for agricultural and hydrological cycle modelling, irrigation water efficiency quantification and agricultural water resource management [1]. Moderate-resolution sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), provide imagery for ET estimation on a daily basis but the relatively coarse spatial resolution hinders the application of ET for the quantification of irrigation water efficiency and water resource management at field, local or basin scales. To overcome this limitation, previous studies have proposed several methods, mainly including traditional downscaling methods and data fusion methods [2]. It should be noted that existing methods have the following limitations: most fusion methods applied to ET are initially used to integrate the land surface reflectance, spectral index and LST; these methods cannot completely consider the influencing factor of ET including remote sensing and atmospheric characteristics [31] (especially some critical issues, such as soil moisture [32] and vegetation distribution) [33,34]

Objectives

Methods

Results

Conclusion