Assessing the Effects of Spatial Scales on Regional Evapotranspiration Estimation by the SEBAL Model and Multiple Satellite Datasets: A Case Study in the Agro-Pastoral Ecotone, Northwestern China

Abstract

Evapotranspiration (ET) estimation is important for understanding energy exchanges and water cycles. Remote sensing (RS) is the main method used to obtain ET data over large scales. However, owing to surface heterogeneities and different model algorithms, ET estimated from RS products with different spatial resolutions can cause significant uncertainties, whose causes need to be thoroughly analyzed. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) model was selected to explore spatial resolution influences on ET simulations. Three satellite datasets (Landsat Thematic Mapper (TM), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High-Resolution Radiometer (AVHRR)) were selected to independently estimate ET in SEBAL model to identify the influence of the spatial scale on ET estimation, and analyze the effects and causes of scale aggregation. Results indicated that: (1) the spatial distributions of ET estimated from the three satellite datasets were similar, with the MODIS-based ET having the largest uncertainty; and (2) aggregating input parameters had limited changes in the net radiation and soil heat fluxes. However, errors in the sensible heat and latent heat fluxes were relatively larger, which were caused by changes in the selection of hot and cold pixels and the NDVI and surface albedo parameters during scale aggregation. The scale errors caused by the model mechanisms were larger than those caused by the land use/cover pattern in the SEBAL model. Overall, this study highlights the impact of spatial scale on ET and provides a better understanding of the scale aggregation effect on ET estimation by RS.

Urban heat mitigation by green and blue infrastructure: Drivers, effectiveness, and future needs

“…The greatest variability in the reported LST is influenced by spatial resolution because RS platforms (especially satellites) capture data over larger spatial scales, which may include a mix of different land cover types. 126 Consequently, the integration of temperature values from various surfaces, including hot urban areas or exposed surfaces, can lead to higher average temperature readings. 127 Therefore, while RS-based measurements offer extensive coverage and the ability to provide spatially detailed information, their accuracy should be validated and calibrated with ground-based measurements.…”

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

“…Since the two sites are quite different in climate, topography, and soil, comparing them would better assess the applicability of the data fusion methods under different climates and ecosystems. The DGS (39 • 29 N, 110 • 11 E) has an altitude of 1300 m Above Sea Level (ASL) and is located at the boundary of the Ordos Plateau and the Mu Us Desert, which belongs to the Agricultural-Pastoral Ecotone of Northern China (APENC)-one of the four largest agricultural-pastoral ecotones in the world, with an extremely fragile and sensitive ecosystem [51][52][53]. Under the influence of temperate continental climate, the annual average temperature at the DGS is 4.2 • C [54][55][56], and the annual precipitation is 351 mm, 80% of which happens from May to August [57,58].…”

Research Progress on Quantitative Inversion of Evapotranspiration based on Remote Sensing Technology

“…Surface evapotranspiration (ET) is a process in which surface water absorbs solar radiation energy and vaporizes it for transmission to the atmosphere [1], including evaporation of surface water [2] and transpiration of vegetation [3][4]. Studies have shown that more than 60% of the precipitation that reaches the earth's surface through condensation is vaporized into the atmospheric water vapor through surface moisture and vegetation transpiration, and this proportion can be as high as 90% or more due to the lower precipitation and larger evapotranspiration in arid regions [5].…”