Estimation of Seasonal Evapotranspiration for Crops in Arid Regions Using Multisource Remote Sensing Images

Mingxing Cha,Mengmeng Li,Xiaoqin Wang

doi:10.3390/rs12152398

Abstract

An accurate estimation of evapotranspiration (ET) from crops is crucial in irrigation management, crop yield assessment, and optimal allocation of water resources, particularly in arid regions. This study explores the estimation of seasonal evapotranspiration for crops using multisource remote sensing images. The proposed estimation framework starts with estimating daily evapotranspiration (ETd) values, which are then used to calculate ET estimates during the crop growing season (ETs). We incorporated Landsat images into the surface energy balance algorithm over land (SEBAL) model, and we used the trapezoidal and sinusoidal methods to estimate the seasonal ET. The trapezoidal method used multitemporal ETd images, while the sinusoidal method employs time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images and multitemporal ETd images. Experiments were implemented in the agricultural lands of the Kai-Kong River Basin, Xinjiang, China. The experimental results show that the obtained ETd estimates using the SEBAL model are comparable with those from the Penman–Monteith method. The ETs obtained using the trapezoidal and sinusoidal methods both have a relatively high spatial resolution of 30 m. The sinusoidal method performs better than the trapezoidal method when using low temporal resolution Landsat images. We observed that the omission of Landsat images during the middle stage of crop growth has the greatest impact on the estimation results of ETs using the sinusoidal method. Based on the results of the study, we conclude that the proposed sinusoidal method, with integrated multisource remote sensing images, offers a useful tool in estimating seasonal evapotranspiration for crops in arid regions.

Highlights

Water is an essential resource, especially for agriculture in arid and semiarid regions [1,2]
These methods can be grouped into four categories: (1) empirical methods using statistically-derived relationships between estimates during the crop growing season (ETs) and vegetation indices; (2) residual surface energy balance models, such as single and dual-source models, the Surface Energy Balance System (SEBS) [37], Surface Energy Balance Index (SEBI) [38], and the surface energy balance algorithm over land (SEBAL) [39]; (3) physically-based methods based on the Penman–Monteith (PM) [40,41] and Priestley–Taylor (PT) [42] equations; and (4) data assimilation methods with heat diffusion equation and radiometric surface temperature sequences
Temporal-Spatial Variation of ETinst and ETd Obtained by the SEBAL Model

Summary

Introduction

Water is an essential resource, especially for agriculture in arid and semiarid regions [1,2]. References [34,35,36] reviewed the available methods for ET estimation based on different structural complexities, theories, and assumptions These methods can be grouped into four categories: (1) empirical methods using statistically-derived relationships between ET and vegetation indices; (2) residual surface energy balance models, such as single and dual-source models, the Surface Energy Balance System (SEBS) [37], Surface Energy Balance Index (SEBI) [38], and the surface energy balance algorithm over land (SEBAL) [39]; (3) physically-based methods based on the Penman–Monteith (PM) [40,41] and Priestley–Taylor (PT) [42] equations; and (4) data assimilation methods with heat diffusion equation and radiometric surface temperature sequences. The spatial resolution of remote sensing images affects the accuracy of ET estimates. In order to improve the accuracy of ET estimates, new approaches would have to be developed that utilize remote sensing images with a high spatial resolution at a regional scale

Objectives

Methods

Results

Discussion

Conclusion