Continuous Daily Evapotranspiration Estimation at the Field-Scale over Heterogeneous Agricultural Areas by Fusing ASTER and MODIS Data

Zhenyan Yi,Hongli Zhao,Yunzhong Jiang

doi:10.3390/rs10111694

Abstract

Continuous daily evapotranspiration (ET) monitoring at the field-scale is crucial for water resource management in irrigated agricultural areas in arid regions. Here, an integrated framework for daily ET, with the required spatiotemporal resolution, is described. Multi-scale surface energy balance algorithm evaluations and a data fusion algorithm are combined to optimally exploit the spatial and temporal characteristics of image datasets, collected by the advanced space-borne thermal emission reflectance radiometer (ASTER) and the moderate resolution imaging spectroradiometer (MODIS). Through combination with a linear unmixing-based method, the spatial and temporal adaptive reflectance fusion model (STARFM) is modified to generate high-resolution ET estimates for heterogeneous areas. The performance of this methodology was evaluated for irrigated agricultural fields in arid and semiarid areas of Northwest China. Compared with the original STARFM, a significant improvement in daily ET estimation accuracy was obtained by the modified STARFM (overall mean absolute percentage error (MAP): 12.9% vs. 17.2%; overall mean absolute percentage error (RMSE): 0.7 mm d−1 vs. 1.2 mm d−1). The modified STARFM additionally preserved more spatial details than the original STARFM for heterogeneous agricultural fields, and provided field-to-field variability in water use. Improvements were further evident in the continuous daily ET, where the day-to-day dynamics of ET estimates were captured. ET data fusion provides a unique means of monitoring continuous daily crop ET values at the field-scale in agricultural areas, and may have value in supporting operational water management decisions.

Highlights

Evapotranspiration (ET)—the sum of land surface evaporation, vegetation transpiration, and evaporation of water intercepted by plant canopies—is a major component of the water cycle and energy exchange in the soil-plant-atmosphere-climate system [1]
RS-based approaches generally include empirical and semi-empirical methods [7], surface energy balance models (e.g., the surface energy balance algorithm for land (SEBAL), the surface energy balance system (SEBS), the mapping ET with internalized calibration (METRIC), the two-source energy balance model (TSEB) [8,9,10,11], the vegetation index combined with the Penman-Monteith (PM) or Priestley-Taylor (PT) method [12,13], and data assimilation combined with land surface models and hydrological models [14,15]
We developed a multiresolution modeling framework by combing spatial and temporal adaptive reflectance fusion model (STARFM) with a linear unmixing model (u-STARFM) to resolve the difficulties that STARFM presents from the mixed pixel of moderate resolution imaging spectroradiometer (MODIS) in heterogeneous areas

Summary

Introduction

Evapotranspiration (ET)—the sum of land surface evaporation, vegetation transpiration, and evaporation of water intercepted by plant canopies—is a major component of the water cycle and energy exchange in the soil-plant-atmosphere-climate system [1]. Gao et al (2006) proposed a data fusion technique named the “spatial and temporal adaptive reflectance fusion model” (STARFM), that simultaneously integrates the temporal advantage of MODIS-like images and the spatial advantage of Landsat-like images [26] This data fusion approach has lately received much attention, since it can provide high-resolution vegetation indices and land surface temperature estimates [27,28,29,30]. Considering heterogeneous landscapes with complex vegetation types, soil water, and meteorological conditions in small-scale agricultural irrigation areas, Bai et al (2017) applied ESTARFM to produce daily field-scale ET estimates based on Landsat and MODIS images for different crops [39]. The results were validated for multiple land-cover types, including cropland, residential areas, woodland, water, desert, desert steppe, and wetlands, using in situ observations from eddy covariance (EC) systems

Study Area

Satellite Data

A Brief Description of the MPDI-Integrated SEBS Model

STARFM

Validating the Quality of Meteorological Data

Evaluating the Performance of the MPDI-Integrated SEBS Model