Sentinel 2 and 3 for Temperature Monitoring Over the Amazon

Jiménez, Juan Carlos; Gomis-Cebolla, José; Sabrino, J. A.; Sòria, G.; Skokovic, D.; Julien, Y.; García-Monteiro, Susana; Mattar, Cristián; Santamaría-Artigas, Andrés; Pasapera-Gonzales, Jose

doi:10.1109/igarss.2018.8518130

Cited by 2 publications

(4 citation statements)

References 9 publications

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“…Although they had few data points, their results showed a similar precision to that obtained for the rice paddy site. Similar results were also obtained for SLSTR LST at an Amazon site in [48], who obtained a bias of −1.3 K and a SD of 0.9 K. Zhang et al in [22] obtained a bias of −0.4 K and a SD of 0.9 K for a desert area in Wuhai, which are similar results to those obtained here with the same algorithm for bare soil. Yang et al in [49] trained nine SWAs to retrieved SLSTR LST.…”

Section: Discussionsupporting

confidence: 89%

“…It is suspected that the increased median difference is mainly caused by different day and night retrieval coefficients. These results cannot be directly compared with results obtained over other vegetated areas, e.g., the Amazon site [48] and Evora [20]. The Amazon site [48] was classified as closed to open broadleaved evergreen and/or semi-deciduous forest (biome 5) and yielded a SLSTR LST bias of −0.1 and a SD of 0.6 K for daytime and nighttime data.…”

Section: Discussionmentioning

confidence: 91%

“…These results cannot be directly compared with results obtained over other vegetated areas, e.g., the Amazon site [48] and Evora [20]. The Amazon site [48] was classified as closed to open broadleaved evergreen and/or semi-deciduous forest (biome 5) and yielded a SLSTR LST bias of −0.1 and a SD of 0.6 K for daytime and nighttime data. For Evora [20], the assigned biome was rainfed croplands (biome 2) and the SLSTR LST bias was −0.8 (−0.4) K and SD 0.7 (0.3) K for daytime (nighttime).…”

Section: Discussionmentioning

confidence: 91%

“…However, precision was low, with values of 1.9 K and 2.1 K for daytime and nighttime, respectively. In [48], a pyrgeometer and a thermal infrared (TIR) wide band radiometer for validating the SLSTR LST product over a forest site in the Amazon basin were used. An accuracy of −0.1 K and a precision of 0.6 K were estimated from the comparison with the wide band radiometer, while an accuracy and precision of 1.0 K were estimated from the comparison with the pyrgeometer, thereby reaching the GCOS thresholds.…”

Section: Operational Slstr Lst Productmentioning

confidence: 99%

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Validation of Sentinel-3 SLSTR Land Surface Temperature Retrieved by the Operational Product and Comparison with Explicitly Emissivity-Dependent Algorithms

et al. 2021

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Land surface temperature (LST) is an essential climate variable (ECV) for monitoring the Earth climate system. To ensure accurate retrieval from satellite data, it is important to validate satellite derived LSTs and ensure that they are within the required accuracy and precision thresholds. An emissivity-dependent split-window algorithm with viewing angle dependence and two dual-angle algorithms are proposed for the Sentinel-3 SLSTR sensor. Furthermore, these algorithms are validated together with the Sentinel-3 SLSTR operational LST product as well as several emissivity-dependent split-window algorithms with in-situ data from a rice paddy site. The LST retrieval algorithms were validated over three different land covers: flooded soil, bare soil, and full vegetation cover. Ground measurements were performed with a wide band thermal infrared radiometer at a permanent station. The coefficients of the proposed split-window algorithm were estimated using the Cloudless Land Atmosphere Radiosounding (CLAR) database: for the three surface types an overall systematic uncertainty (median) of –0.4 K and a precision (robust standard deviation) 1.1 K were obtained. For the Sentinel-3A SLSTR operational LST product, a systematic uncertainty of 1.3 K and a precision of 1.3 K were obtained. A first evaluation of the Sentinel-3B SLSTR operational LST product was also performed: systematic uncertainty was 1.5 K and precision 1.2 K. The results obtained over the three land covers found at the rice paddy site show that the emissivity-dependent split-window algorithms, i.e., the ones proposed here as well as previously proposed algorithms without angular dependence, provide more accurate and precise LSTs than the current version of the operational SLSTR product.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 91%

Section: Operational Slstr Lst Productmentioning

confidence: 99%

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Validation of Sentinel-3 SLSTR Land Surface Temperature Retrieved by the Operational Product and Comparison with Explicitly Emissivity-Dependent Algorithms

et al. 2021

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Development a split window algorithm to estimate land surface temperature from Sentinel -3 satellite data

حیدری¹,

هنزائی²

2020

jgit

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Land Surface Temperature (LST) is an important indicator of the study of energy balance models at the earth's surface and the interactions between the Earth and the atmosphere on a regional and global scale.In the last few decades, different algorithms have been developed to determine the land surface temperature using various satellite images. In this study, a new split window method for estimating the land surface temperature using Sentinel-3A Satellite Radiometer (SLSTR) images is presented. The advantage of the proposed method is the introduction of atmospheric water vapor into the split window algorithm, plays an important role in estimating the LST. The LST was calculated by using the proposed method and the split window three algorithms. . Then the results of the proposed method and three split window algorithms were compared with the ASTER, MODIS and Sentinel-3 LST products. The RMSE values of the proposed method for the study area of East Tehran were 3.49, 1.22 and 1.26 K, respectively, compared to the ASTER, MODIS and Sentinel-3 LST products which is lower than the RMSE obtained from other split window algorithms. Also the proposed method and three split window algorithms were implemented for northwest of Isfahan and Kermanshah. For Kermanshah study area the results were much better than the other two cases and the other methods so the RMSE of the proposed method was calculated to be 1.05 Kelvin while the RMSE of the other methods was 1. 19, 1.28 and 1.56 Kelvin.

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Sentinel 2 and 3 for Temperature Monitoring Over the Amazon

Cited by 2 publications

References 9 publications

Validation of Sentinel-3 SLSTR Land Surface Temperature Retrieved by the Operational Product and Comparison with Explicitly Emissivity-Dependent Algorithms

Validation of Sentinel-3 SLSTR Land Surface Temperature Retrieved by the Operational Product and Comparison with Explicitly Emissivity-Dependent Algorithms

Development a split window algorithm to estimate land surface temperature from Sentinel -3 satellite data

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