Comparison of Remote Sensing based Multi-Source ET Models over Cropland in a Semi-Humid Region of China

Abstract

The estimation of cropland evapotranspiration (ET) is essential for agriculture water management, drought monitoring, and yield forecast. Remote sensing-based multi-source ET models have been widely applied and validated in the semi-arid region of China. However, careful investigation of the models’ performances for different crop types (winter wheat and summer maize) over the semi-humid region is still necessary. This study used remote sensing data (Landsat 8 and ASTER) and compared three mainstream multi-source ET models: (i) the two-source energy balance model, i.e., TSEB; (ii) the Penman-Monteith based four-source model, i.e., 4s-PM; (iii) the Priestley Taylor-Jet Propulsion Laboratory ET algorithm, i.e., PT-JPL. The measurements of the eddy-covariance (EC) flux tower located in Guantao county of North China were used to validate the models. The results showed that the TSEB model performed the best in estimating latent heat flux (LE) of maize, with an RMSE of 75.0 W/m2 and an R2 of 0.9, and the 4s-PM model had the highest accuracy of LE estimation for wheat, with an RMSE of 61.0 W/m2 and an R2 of 0.91. The LE spatial distribution comparison indicated that the PT-JPL model had more capacity to exhibit crop ET heterogeneity. The major environmental factors affecting ET varied with crop types and crop growth stages. Without taking soil moisture into account, the 4s-PM and TSEB models overestimated LE under water deficit in the maturation stage of wheat. The plant moisture stress based on vegetation index in the PT-JPL model underestimated the evaporation in the maturation stage while the cropland was still wet.

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

“…In the investigated studies, most focused on quantifying traits using regression or RTM methods, whereas the usage of classification or anomaly detection was less pronounced. Among the papers reviewed in this study, about 72% were based on parametric approaches (e.g., Gerhards et al, 2016;Guan et al, 2017;Panigada et al, 2014), 22% on nonparametric linear approaches (e.g., Ainsworth et al, 2014;Sobejano-Paz et al, 2020;Thomas et al, 2017), about 17% on nonparametric nonlinear approaches (e.g., Camino et al, 2021;Gao et al, 2009;Zarco-Tejada et al, 2018), nearly 15% on surface energy balance models (e.g., Bayat et al, 2018;Bhattarai et al, 2019;Zhuang et al, 2020), 16% used radiative transfer models (e.g., Camino et al, 2018;Celesti et al, 2018) and only a few on hybrid approaches (e.g., De Grave et al, 2020;Delalieux et al, 2014). Note that several studies employed multiple methods.…”

Evaluating the SSEBop and RSPMPT Models for Irrigated Fields Daily Evapotranspiration Mapping with MODIS and CMADS Data

“…It was calculated mainly based on the surface aerodynamic roughness and the daily wind velocity from CMADS. The RSPMPT model can thoroughly explain how plants' transpiration rate varies with the increase or decrease in the environmental constraints under different conditions [35].…”

Comparative Analysis of Remote sensing based evapotranspiration models for seasonal crops in desert environment

“…Model estimates evaporation and transpiration as residual components of the energy balance for soil and plants respectively. The proposed the TSEB model byNorman et al (1995) has been revised for various land cover conditions byKustas et al (1999),Campbell et al (1998), Burchard et al (2019), and Zhuang et al (2020.…”