Evaluation of the S-NPP VIIRS land surface temperature product using ground data acquired by an autonomous system at a rice paddy

Niclòs, Raquel; Pérez-Planells, Lluís; Coll, César; Valiente, José

doi:10.1016/j.isprsjprs.2017.10.017

Cited by 26 publications

(9 citation statements)

References 38 publications

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“…All differences are within ±5 K except three cases (23)(24)(25). The largest deviations are only observed for the ACT-center atmospheric correction, most likely due to the presence of some clouds in the scene, responsible for the heterogeneity in the atmosphere for that date.…”

Section: Local Validation Of Sbacmentioning

confidence: 84%

“…The largest deviations are only observed for the ACT-center atmospheric correction, most likely due to the presence of some clouds in the scene, responsible for the heterogeneity in the atmosphere for that date. For these specific three cases (23)(24)(25), LST results using ACT-site shows an underestimation above −2.0 K whereas SBAC overestimates in less than 0.9 K. Average results for SBAC show practically no mean bias (−0.1 K) and small standard deviation (±1.6 K), yielding a root mean square error (RMSE) of ±1.6 K. Note thermal ETM+ data are provided with a noise equivalent temperature <0.3 K [58]. Moreover, differences between observed and estimated LST show no correlation with water vapor content (R 2 = 0.09) nor brightness temperature (R 2 = 0.3).…”

Section: Local Validation Of Sbacmentioning

confidence: 99%

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A New Single-Band Pixel-by-Pixel Atmospheric Correction Method to Improve the Accuracy in Remote Sensing Estimates of LST. Application to Landsat 7-ETM+

et al. 2018

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Monitoring Land Surface Temperature (LST) from satellite remote sensing requires an accurate correction of the atmospheric effects. Although thermal remote sensing techniques have advanced significantly over the past few decades, to date, single-band pixel-by-pixel atmospheric correction of full thermal images is unsolved. In this work, we introduce a new Single-Band Atmospheric Correction (SBAC) tool that provides pixel-by-pixel atmospheric correction parameters regardless of the pixel size. The SBAC tool uses National Centers of Environmental Prediction (NCEP) profiles as inputs and, as a novelty, it also accounts for pixel elevation through a Digital Elevation Model (DEM). Application of SBAC to 19 Landsat 7-ETM+ scenes shows the potential of the proposed pixel-by-pixel atmospheric correction to capture terrain orography or atmospheric variability within the scene. LST estimation yields negligible bias and an RMSE of ±1.6 K for the full dataset. The Landsat Atmospheric Correction Tool (ACT) is also considered for comparison. SBAC-ACT LST deviations are analyzed in terms of distance to the image center, surface elevation, and spatial distribution of the atmospheric water content. Differences within 3 K are observed. These results give us the first insight of the potential of SBAC for the operational pixel-by-pixel atmospheric correction of full thermal images. The SBAC tool is expected to help users of satellite single-channel thermal sensors to improve their LST estimates due to its simplicity and robustness.

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Section: Local Validation Of Sbacmentioning

confidence: 84%

Section: Local Validation Of Sbacmentioning

confidence: 99%

A New Single-Band Pixel-by-Pixel Atmospheric Correction Method to Improve the Accuracy in Remote Sensing Estimates of LST. Application to Landsat 7-ETM+

et al. 2018

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“…The retrieval accuracy for microwave sensors is far worse, probably larger than 2.5 K (Prigent, Jimenez, and Aires 2016). Other validation studies for AATSR (Coll et al 2012) and VIIRS (Li, Sun, et al 2014;Niclos et al 2018) LST products.…”

Section: Land Surface Temperaturementioning

confidence: 98%

Remote sensing of earth’s energy budget: synthesis and review

Liang

Wang

et al. 2019

International Journal of Digital Earth

148

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The Earth's climate is largely determined by its energy budget. Since the 1960s, satellite remote sensing has been used in estimating these energy budget components at both the top of the atmosphere (TOA) and the surface. Besides the broadband sensors that have been traditionally used for monitoring Earth's Energy Budget (EEB), data from a variety of narrowband sensors aboard both polar-orbiting and geostationary satellites have also been extensively employed to estimate the EEB components. This paper provides a comprehensive review of the satellite missions, state-of-the art estimation algorithms and the satellite products, and also synthesizes current understanding of the EEB and spatiotemporal variations. The TOA components include total solar irradiance, reflected shortwave radiation/planetary albedo, outgoing longwave radiation, and energy imbalance. The surface components include incident solar radiation, shortwave albedo, shortwave net radiation, longwave downward and upwelling radiation, land and sea surface temperature, surface emissivity, all-wave net radiation, and sensible and latent heat fluxes. Some challenges, and outlook such as virtual constellation of different satellite sensors, temporal homogeneity tests of long time-series products, algorithms ensemble, and products intercomparison are also discussed.

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“…This site has been extensively used for LST validation purposes [15,[25][26][27][28]. Previous studies demonstrated a high thermal homogeneity for this site at different spatial resolutions [27,[29][30][31] and concluded that it is suitable for validating satellite LST with in-situ measurements. For full vegetation cover, these studies found a standard deviation (SD) lower than 0.5 K for 33 × 33 ASTER pixels (~9 km 2 ) centered on the study area and for a Landsat TM5 scene (~16 km 2 ).…”

Section: Study Site and Ground Data 21 Sitementioning

confidence: 99%

“…For full vegetation cover, these studies found a standard deviation (SD) lower than 0.5 K for 33 × 33 ASTER pixels (~9 km 2 ) centered on the study area and for a Landsat TM5 scene (~16 km 2 ). In [30], the authors analyzed the variability of 11 × 11 ASTER pixels (1 km 2 ) centered on the study area, and obtained a SD < 0.3 K. In [27], the thermal variability of the area was studied for the three land covers present at the site with hand-held radiometer measurements along transects (~300 m long) through the station parcel on different dates: the SD values obtained were 0.5 K, 0.4 K, and 0.9 K for full vegetation, flooded soil, and bare soil, respectively. This site has been extensively used for LST validation purposes [15,[25][26][27][28].…”

Section: Study Site and Ground Data 21 Sitementioning

confidence: 99%

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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Evaluation of the S-NPP VIIRS land surface temperature product using ground data acquired by an autonomous system at a rice paddy

Cited by 26 publications

References 38 publications

A New Single-Band Pixel-by-Pixel Atmospheric Correction Method to Improve the Accuracy in Remote Sensing Estimates of LST. Application to Landsat 7-ETM+

A New Single-Band Pixel-by-Pixel Atmospheric Correction Method to Improve the Accuracy in Remote Sensing Estimates of LST. Application to Landsat 7-ETM+

Remote sensing of earth’s energy budget: synthesis and review

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

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