Evaluation of ASTER-Like Daily Land Surface Temperature by Fusing ASTER and MODIS Data during the HiWATER-MUSOEXE

Abstract

Land surface temperature (LST) is an important parameter that is highly responsive to surface energy fluxes and has become valuable to many disciplines. However, it is difficult to acquire satellite LSTs with both high spatial and temporal resolutions due to tradeoffs between them. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of thermal infrared (TIR) data or LST, but rarely both. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is the widely-used data fusion algorithm for Landsat and MODIS imagery to produce Landsat-like surface reflectance. In order to extend the STARFM application over heterogeneous areas, an enhanced STARFM (ESTARFM) approach was proposed by introducing a conversion coefficient and the spectral unmixing theory. The aim of this study is to conduct a comprehensive evaluation of the ESTARFM algorithm for generating ASTER-like daily LST by three approaches: simulated data, ground measurements and remote sensing products, respectively. The datasets of LST ground measurements, MODIS, and ASTER images were collected in an arid region of Northwest China during the first thematic HiWATER-Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) over heterogeneous land surfaces in 2012 from May to September. Firstly, the results of the simulation test indicated that ESTARFM could accurately predict background with temperature variations, even coordinating with small ground objects and linear ground objects. Secondly, four temporal ASTER and MODIS data fusion LSTs (i.e., predicted ASTER-like LST products) were highly consistent with ASTER LST products. Here, the four correlation coefficients were greater than 0.92, root mean square error (RMSE) reached about 2 K and mean absolute error (MAE) ranged from 1.32 K to 1.73 K. Finally, the results of the ground measurement validation indicated that the overall accuracy was high (R 2 = 0.92, RMSE = 0.77 K), and the ESTARFM algorithm is a highly recommended method to assemble time series images at ASTER spatial resolution and MODIS temporal resolution due to LST estimation error less than 1 K. However, the ESTARFM method is also limited in predicting LST changes that have not been recorded in MODIS and/or ASTER pixels.

Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land

“…The RMSEs (MAEs) for spring maize and sunflower were 1.47 (1.17 K) and 0.73 K (0.5 K), respectively. The overall discrepancy is close to what has been presented by G. Yang et al () with the RMSE ranging from 1.09 to 1.31 K, and the MAE from 1.41 to 1.72 K. The scatter points between the measured LST and the downscaled LST E are generally close to the 1:1 line (Figure ), with R 2 values of 0.83 and 0.92 for spring maize and sunflower, indicating the good performance of the downscaled LST E estimates. For spring wheat, the LST measurements were higher than the downscaled LST E estimates with the RMSE, MAE, and R 2 of 2.75 K, 2.56 K, and 0.55, respectively.…”

“…Weng et al () modified the STARFM algorithm and proposed the Spatiotemporal Adaptive Data Fusion Algorithm method to generate synthetic Landsat‐like daily thermal radiance and LST data by considering the annual temperature cycle. They found that the prediction accuracy for the whole study area ranged from 1.3 to 2.0 K. G. Yang et al () conducted a comprehensive evaluation of the ESTARFM algorithm for generating ASTER‐like daily LSTs by blending ASTER and MODIS images. The algorithm was also directly used to map daily ET at field scales and successfully achieved the data fusion products with root‐mean‐square error (RMSE) ranging from 1.11 to 1.81 mm/day (Bai et al, ; Cammalleri et al, ).…”

Evaluation of the Spatial Pattern of the Resolution-Enhanced Thermal Data for Urban Area

“…Therefore, more impacting factors, including impervious surface fractions, water indices, and albedo, were considered. Until years after 2010, more proposal studies and application reports of thermal sharpening methods were found in literature [8,11,15,35,42,43]. However, it is found that for most of them, the scale factor (or zoom factor) of downscaling is still limited (<10) and the aiming sharpening resolutions, especially for applications of thermal sharpening data, are 1 km [15,44], 90 m [35], or 30 m [42], which are the spatial resolutions of MODIS, ASTER, and TM/ETM+, respectively.…”

“…Voogt and Oke [34] have already criticized that the slow development of thermal remote sensing of urban areas is due largely to the qualitative description of thermal patterns. It is common to find in literature that, for comparison of LST spatial patterns or texture, people usually present a number of results in an illustration and then use a limited number of words for the description [35][36][37]. This revealed the lack of a widely acknowledged quantifying method for evaluation of the LST spatial pattern.…”

Continuous Daily Evapotranspiration Estimation at the Field-Scale over Heterogeneous Agricultural Areas by Fusing ASTER and MODIS Data

“…Gao et al (2006) proposed a data fusion technique named the "spatial and temporal adaptive reflectance fusion model" (STARFM), that simultaneously integrates the temporal advantage of MODIS-like images and the spatial advantage of Landsat-like images [26]. This data fusion approach has lately received much attention, since it can provide high-resolution vegetation indices and land surface temperature estimates [27][28][29][30]. This approach additionally appears to hold great utility for high-resolution TIR data for ET mapping.…”