Abstract
The ECO System Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a new space mission developed by NASA-JPL which launched on July 2018. It includes a multispectral thermal infrared radiometer that measures the radiances in five spectral channels between 8 and 12 μm. The primary goal of the mission is to study how plants use water by measuring their temperature from the vantage point of the International Space Station. However, as ECOSTRESS retrieves the surface temperature, the data can be used to measure other heat-related phenomena, such as heat waves, volcanic eruptions, and fires. We have cross-compared the temperatures obtained by ECOSTRESS, the Advanced Spaceborne Thermal Emission and Reflectance radiometer (ASTER) and the Landsat 8 Thermal InfraRed Sensor (TIRS) in areas where thermal anomalies are present. The use of ECOSTRESS for temperature analysis as well as ASTER and Landsat 8 offers the possibility of expanding the availability of satellite thermal data with very high spatial and temporal resolutions. The Temperature and Emissivity Separation (TES) algorithm was used to retrieve surface temperatures from the ECOSTRESS and ASTER data, while the single-channel algorithm was used to retrieve surface temperatures from the Landsat 8 data. Atmospheric effects in the data were removed using the moderate resolution atmospheric transmission (MODTRAN) radiative transfer model driven with vertical atmospheric profiles collected by the University of Wyoming. The test sites used in this study are the active Italian volcanoes and the Parco delle Biancane geothermal area (Italy). In order to test and quantify the difference between the temperatures retrieved by the three spaceborne sensors, a set of coincident imagery was acquired and used for cross comparison. Preliminary statistical analyses show a very good agreement in terms of correlation and mean values among sensors over the test areas.
Highlights
The estimation of land surface temperature (LST) using Thermal InfraRed (TIR) remote sensing data is a well-developed method offering a quick way to estimate reliable parameters of land surface physical processes on different scales with a positive cost–benefit ratio
The Temperature and Emissivity Separation (TES) algorithm has been considered for ASTER and estimates surface temperature and spectral emissivity images for multi and hyperspectral satellite images [18,19,20,21,22,23,24] using at least three thermal bands; for this reason, this methodology is typically used for ASTER and can be suitable for ECOSTRESS, as both have five TIR channels in the same spectral range
In order to compare the LSTs retrieved by the three sensors, which have different pixel spatial resolutions, all LSTs were resampled to 90 meters of spatial resolution using the nearest neighbor (NN) resampling method implemented in the Environment for Visualizing Images (ENVI) software
Summary
The estimation of land surface temperature (LST) using Thermal InfraRed (TIR) remote sensing data is a well-developed method offering a quick way to estimate reliable parameters of land surface physical processes on different scales with a positive cost–benefit ratio. The use of satellites offers the possibility of acquiring data in difficult or dangerous to access areas. 2019, 11, x FOR PEER REVIEW highlights the main surface changes which are potentially related to underground energy sources that tchoautlcdobueldribseksristokshteoahltehalatnhdanhdumhuamn aanctaivctiitvieisti.esT.hTehsetsutduydyaraeraesaschchoosesnenfoforrththisiswwoorrkk((FFiigguurree 11)) aarree tthhee mmaaiinn aaccttiivvee IIttaalliiaann vvoollccaannooeess:: MMtt. EEttnnaa((EEttnn)),,VVeessuuvviioo ((VVeess)),, SSoollffaattaarraa ((SSooll)),, SSttrroommbboollii ((SSttrr)),, aanndd VVuullccaannoo ((VVuull)). MMoorreeoovveerr,, aa ggeeootthheerrmmaall tteesstt ssiittee,, tthhee PPaarrccoo ddeellllee BBiiaannccaannee((PPddBB))aarreeaaiinnTTuussccaannyy,, wwaass sseelleecctteedd ininorodredretro atonalaynzaelythzee ctahpeabcilaiptyaboiflistayteloliftesdataetlalittoe ddeatetact tthoerdmeatel catnothmearlmieaslinannoonm-valoilecsaniinc naroena-sv.oAlclal nseiclecatreedasa.reAalsl asreelecchtaerdacatereriazsedarbeychhigarha-ctetemripzeerdatubyre hgiegohth-teermmpaelreantuerrgeygseooutrhceersm, caolmemneornglyy smoaurrkceeds,bcyotmhemrmonallymmanairfkeestdatbioynsthseurcmh aals fmuamnairfeosletast,isotneasmsuincghgarosufnudm, ahrootlseps,risntgesa,mvoinlcgangircoguansdv,ehnotst, scpraritnergss,,avnodlcmanuicdgpaosovlsen[1ts–,4c].raTtehress,eaanrdeamsumdupsot obles c[o1–m4m].
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