Evaluation and Aggregation Properties of Thermal Infra-Red-Based Evapotranspiration Algorithms from 100 m to the km Scale over a Semi-Arid Irrigated Agricultural Area

Abstract

Evapotranspiration (ET) estimates are particularly needed for monitoring the available water of arid lands. Remote sensing data offer the ideal spatial and temporal coverage needed by irrigation water management institutions to deal with increasing pressure on available water. Low spatial resolution (LR) products present strong advantages. They cover larger zones and are acquired more frequently than high spatial resolution (HR) products. Current sensors such as Moderate-Resolution Imaging Spectroradiometer (MODIS) offer a long record history. However, validation of ET products at LR remains a difficult task. In this context, the objective of this study is to evaluate scaling properties of ET fluxes obtained at high and low resolution by two commonly used Energy Balance models, the Surface Energy Balance System (SEBS) and the Two-Source Energy Balance model (TSEB). Both are forced by local meteorological observations and remote sensing data in Visible, Near Infra-Red and Thermal Infra-Red spectral domains. Remotely sensed data stem from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS sensors, respectively, resampled at 100 m and 1000 m resolutions. The study zone is a square area of 4 by 4 km 2 located in a semi-arid irrigated agricultural zone in the northwest of Mexico. Wheat is the dominant crop, followed by maize and vegetables. The HR ASTER dataset includes seven dates between the 30 December 2007 and 13 May 2008 and the LR MODIS products were retrieved for the same overpasses. ET retrievals from HR ASTER products provided reference ET maps at LR once linearly aggregated at the km scale. The quality of this retrieval was assessed using eddy covariance data at seven locations within the 4 by 4 km 2 square. To investigate the impact of input aggregation, we first compared to the reference dataset all fluxes obtained by running TSEB and SEBS models using ASTER reflectances and radiances previously aggregated at the km scale. Second, we compared to the same reference dataset all fluxes obtained with SEBS and TSEB models using MODIS data. LR fluxes obtained by both models driven by aggregated ASTER input data compared well with the reference simulations and illustrated the relatively good accuracy achieved using aggregated inputs (relative bias of about 3.5% for SEBS and decreased to less than 1% for TSEB). Results also showed that MODIS ET estimates compared well with the reference simulation (relative bias was down to about 2% for Remote Sens. 2017, 9, 1178; doi:10.3390/rs9111178www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 1178 2 of 27 SEBS and 3% for TSEB). Discrepancies were mainly related to fraction cover mapping for TSEB and to surface roughness length mapping for SEBS. This was consistent with the sensitivity analysis of those parameters previously published. To improve accuracy from LR estimates obtained using the 1 km surface temperature product provided by MODIS, we tested three statistical and one deterministic aggregation rules for the most sens...

Challenges and uncertainty in plot-scale emissivity and surface temperature estimation using flux tower measurements

“…General approach: We estimate landscape-scale broadband ε using MODIS spectral ε as shown in Bahir et. al (2017) 45 .…”

The role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models

“…where ρc p is the volumetric heat capacity of air (J/K/m 3 ), T 0 (K) is the aerodynamic temperature at the effective canopy source height at which energy fluxes arise and T a (K) is the reference level air temperature. Despite substantial progress in global, regional and field scale ET mapping (Anderson et al, 2011;Bahir et al, 2017), implementation of SEB models is challenged by the uncertain specification of vegetation roughness and atmospheric stability variables for determining r aH and also due to the empirical adjustments to accommodate for the inequalities between LST and T 0 (Paul et al, 2014). Calculation of r aH requires addition of an external resistance (r ex ) to r aM , which involves the kB − 1 concept (Chen et al, 2019a(Chen et al, , 2019bChen et al, 2013;Su, 2002):…”

“…Aerodynamic resistance (hereafter r a ) expresses the efficiency of turbulent transport controlling the land-atmosphere (L-A) exchange of sensible heat (H) and water vapor between the source/sink height within a vegetation canopy and a reference height above the surface. It is the pivotal link that connects evapotranspiration (ET) with H through the surface energy balance (SEB) equation, and the estimation of r a is central in thermal remote sensing of ET at local to regional scales (Bahir et al, 2017;Bhattarai et al, 2018;Kustas et al, 2007). Advanced understanding of L-A interactions is a prerequisite for accurate monitoring and predictions of Earth-system responses to drought, climate warming and surface drying.…”

Evapotranspiration in the Tono Reservoir Catchment in Upper East Region of Ghana Estimated by a Novel TSEB Approach from ASTER Imagery

“…ASTER data was therefore used in our analysis owing to better spatial resolution. ASTER satellite imagery has provided several parameters for SEB models for the retrieval of reliable estimates for ET at both local and regional scales [12][13][14][15][16]. Land use and land cover derived from satellite imagery provide tools for predicting the spatial variation of vegetation and the effect of land use practices that control ET and soil moisture [17][18][19].…”