Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm

Abstract

Land surface temperature (LST) is one of the key parameters in hydrology, meteorology, and the surface energy balance. The National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm is adapted to Landsat-8 data to obtain the estimate of LST. The coefficients of the Enterprise algorithm were obtained by linear regression using the analog data produced by comprehensive radiative transfer modeling. The performance of the Enterprise algorithm was first tested by simulation data and then validated by ground measurements. In addition, the accuracy of the Enterprise algorithm was compared to the generalized split-window algorithm and the split-window algorithm of Sobrino et al. (1996). The validation results indicate the Enterprise algorithm has a comparable accuracy to the other two split-window algorithms. The biases (root mean square errors) of the Enterprise algorithm were 1.38 (3.22), 1.01 (2.32), 1.99 (3.49), 2.53 (3.46), and −0.15 K (1.11 K) at the SURFRAD, HiWATER_A, HiWATER_B, HiWATER_C sites and BanGe site, respectively, whereas those values were 1.39 (3.20), 1.0 (2.30), 1.93 (3.48), 2.53 (3.35), and −0.35 K (1.16 K) for the generalized split-window algorithm, 1.45 (3.39), 1.08 (2.41), 2.16 (3.67), 2.52 (3.58), and 0.02 K (1.12 K) for the split-window algorithm of Sobrino, respectively. This study provides an alternative method to estimate LST from Landsat-8 data.

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

“…It is interesting to discuss our results in comparison with those of other studies that utilized SURFRAD LST measurements and Landsat data for LST retrieval. Meng et al [52] estimated LST from Landsat-8 data using the NOAA Joint Polar Satellite System (JPSS) Enterprise algorithm and a hybrid LSE model [82,124]. At the SURFRAD sites, the LST RMSE by the Enterprise algorithm was 3.22 K. Considering our analyses, SWA presented close results to the above analysis under three different LSE models ranging from 2.79 K to 3.02 K. Yu et al [46] compared RTE, SWA, and SCA methods using Landsat 8 data with their LSE models reported in Section 4.3.…”

“…Besides, ancillary observations include direct and diffuse solar radiation, ultraviolet-B radiation, and meteorological parameters. Since SURFRAD stations provide unique in-situ LST information over rural sites, many researchers have used these data to validate satellite-based LST retrievals [15,46,52,53,56,[64][65][66][67]. In this study, five SURFRAD stations were considered as ground-based stations, and Table 1 reports information regarding the SURFRAD experimental sites.…”

“…The cross-validation method considers a well-validated LST product as a reference and compares the satellite-derived LST with the referenced (well-validated) LST derived from other satellites [7]. The T-based method, used by many researchers and also considered in this study, directly compares the satellite-based LST with ground-based LST measurements at the satellite overpass [46,[48][49][50][51][52][53][54][55]. The main advantage of the T-based method is that it enables evaluating the radiometric quality of the satellite sensor and the performance of LST retrieval methods depending on atmospheric and emissivity parameters.…”

“…In previous studies, many researchers have already examined the validation of different LST retrieval methods using Landsat data and in-situ LST measurements; however, they just considered one LSE model in the validation. Meng et al [52] used the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm and a hybrid LSE model to retrieve LST from Landsat-8 data. Yu et al [46] compared RTE, SWA, and SCA methods using Landsat 8 data and their NDVI-based LSE model reported in Section 4.3.…”

Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

“…A solution would be to carry out measurements that would acquire high-resolution urban land-surface temperature data (e.g., 7 m/pixel) [8,41]. Finally, there are several algorithms for calculating surface temperature, as mentioned in Section 2.4, and a comparison of the different results at the sites would be interesting, although the results should probably be very close [101].…”