Abstract
Satellite remote sensing of Land and Water Surface Temperature (L/WST) has many applications in studies of terrestrial and aquatic ecology. Retrieval of L/WST requires a well calibrated radiometer and an accurate atmospheric correction. In the present study, the performance of the Thermal InfraRed Sensor (TIRS) on board Landsat 8 is evaluated for the retrieval of L/WST. libRadtran is used to retrieve atmospheric correction parameters based on atmospheric profiles of relative humidity and temperature from three global atmospheric models. Performance of single band retrievals is compared to typical MODTRAN results from the Atmospheric Correction Parameter Calculator (ACPC) and a split-window approach. A multi-temporal land masking method using imagery from the Operational Land Imager (OLI) on board Landsat 8 is demonstrated, and is used to automatically classify imagery in the matchup dataset in three classes of cloud cover. Two sources of in situ data covering the Belgian Coastal Zone (BCZ) are used for validation of the L/WST product: (1) fixed locations in the Flemish Banks measurement network and (2) underway data from regular RV Belgica campaigns. In the present study the single band methods outperformed the split-window approach, and consistent retrievals are found for the MODTRAN and libRadtran simulations. Typical single band surface temperature retrievals in quasi cloud-free conditions have Root Mean Squared Differences (RMSD) of 0.7 K and 1 K for Bands 10 and 11 with low bias, depending on the method and atmospheric profile source. For imagery with scattered clouds, RMSD values increase to 1 K and 2 K respectively with an approximately 0.5 K cold bias, likely caused by cloud proximity. The calibration efforts combined into Collection 1 allows for accurate absolute surface temperature retrievals from B10 on Landsat 8/TIRS for homogeneous targets with known emissivity, such as liquid water. The method is adapted to global processing and can be used for Land Surface Temperature retrieval with a suitable source of emissivity data.
Highlights
Land and Water Surface Temperature (L/WST) are essential variables for understanding the earth's climate and surface temperature is an important driver in ecology, biodiversity, and species distribution (Burrows et al, 2011; Doney et al, 2012; Cheung et al, 2009; Yang et al, 2013; Pachauri et al, 2014; Neukermans et al, 2018)
The present paper evaluates single band LST retrievals from both bands 10 and 11 in the L8/Thermal InfraRed Sensor (TIRS) Collection 1 Level 1 data
Collection 1 imagery from the Operational Land Imager (OLI) and Thermal InfraRed Sensor (TIRS) on board Landsat 8 were obtained from Google Earth Engine (GEE, Gorelick et al (2017))
Summary
Land and Water Surface Temperature (L/WST) are essential variables for understanding the earth's climate and surface temperature is an important driver in ecology, biodiversity, and species distribution (Burrows et al, 2011; Doney et al, 2012; Cheung et al, 2009; Yang et al, 2013; Pachauri et al, 2014; Neukermans et al, 2018). There clearly is an interest in operational use of Landsat data for WST monitoring in coastal and inland waters, and the TIRS on Landsat 8, is an ideal candidate for such a high resolution WST product. After the development of a stray light correction scheme by Gerace and Montanaro (2017), the split window approach of Du et al (2015) reportedly reached high fidelity comparable to that of MODIS This stray light correction is integrated in the Landsat Collection 1 dataset that is widely distributed by USGS. In most studies of LST retrieval and calibration of thermal bands (Schott et al, 2012; Cook et al, 2014), the radiative transfer modelling is performed using the MODerate resolution atmospheric TRANsmission program (MODTRAN) developed by Spectral Sciences, Inc. and the US Air Force (Berk et al, 1999). Multi-temporal land masking is developed for improved automated cloud-filtering of matchups, and L/ WST retrievals are compared to in situ temperature measurements from the Flemish Banks measurement network and underway data from Research Vessel Belgica collected in the Belgian Coastal Zone (BCZ)
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