Estimating Calibration Variability in Evapotranspiration Derived from a Satellite-Based Energy Balance Model

Abstract

Computing evapotranspiration (ET) with satellite-based energy balance models such as METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) requires internal calibration of sensible heat flux using anchor pixels (“hot” and “cold” pixels). Despite the development of automated anchor pixel selection methods that classify a pool of candidate pixels using the amount of vegetation (normalized difference vegetation index, NDVI) and surface temperature (Ts), final pixel selection still relies heavily on operator experience. Yet, differences in final ET estimates resulting from subjectivity in selecting the final “hot” and “cold” pixel pair (from within the candidate pixel pool) have not yet been investigated. This is likely because surface properties of these candidate pixels, as quantified by NDVI and surface temperature, are generally assumed to have low variability that can be attributed to random noise. In this study, we test the assumption of low variability by first applying an automated calibration pixel selection process to 42 nearly cloud-free Landsat images of the San Joaquin area in California taken between 2013 and 2015. We then compute Ts (vertical near-surface temperature differences) vs. Ts relationships at all pixels that could potentially be used for model calibration in order to explore ET variance between the results from multiple calibration schemes where NDVI and Ts variability is intrinsically negligible. Our results show significant variability in ET (ranging from 5% to 20%) and a high—and statistically consistent—variability in dT values, indicating that there are additional surface properties affecting the calibration process not captured when using only NDVI and Ts. Our findings further highlight the potential for calibration improvements by showing that the dT vs. Ts calibration relationship between the cold anchor pixel (with lowest dT) and the hot anchor pixel (with highest dT) consistently provides the best daily ET estimates. This approach of quantifying ET variability based on candidate pixel selection and the accompanying results illustrate an approach to quantify the biases inadvertently introduced by user subjectivity and can be used to inform improvements on model usability and performance.

Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil

“…Allen et al [54] defined a subset of endmembers within the highest 5% of NDVI and the lowest 20% of T s to select the cold extreme condition, while endmembers within the lowest 10% NDVI and with the highest 20% T s were used to select the hot extreme condition. After [54], several additional criteria were proposed to improve and refine the endmember selection, sometimes with an increase in complexity and time processing [17,31,55,56,85]. In this research, we proposed an assessment of the automated calibration of the endmembers based on the Allen et al [54] methodology to accurately select the quantiles to estimate the surface energy components.…”

Review of approaches for the estimation of sensible heat flux in remote sensing-based evapotranspiration models

“…37 The utilization of available energy (R n − G) as an additional input along with NDVI and T s were useful in retrieving the candidate pixels with distinct variation. 38 By considering the soil moisture and available energy as influencing auxiliary parameters for the endmember selection process, Mohan et al 39 proposed the integration of synthetic aperture radar (SAR) derived soil moisture into SEBAL for anchor pixel selection process. The semiautomatic statistical approach by Allen et al, a fully automatic procedure based on exhaustive search algorithm by Bhattarai et al and the modified version ASEBAL with an automated endmember selection process are the recent related developments in the domain of anchor pixel selection process.…”

Editorial for the Special Issue “Remote Sensing of Evapotranspiration (ET)”

“…Considering the knowledge gaps in differences among final ET estimates resulting from subjectivity in selecting "hot" and "cold" pixel pair, Dhungel and Barber [39] tested the assumption of low variability of surface properties by first applying an automated calibration pixel selection process for a SEB model-Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC). Consequently, they computed vertical near-surface temperature differences (dT) vs. surface temperature (Ts) relationships at all pixels, which could potentially be used for model calibration to explore ET variance among the outcomes from multiple calibration schemes where normalized difference vegetation index (NDVI) and Ts variability are intrinsically negligible.…”