High Spatio- Temporal Resolution Land Surface Temperature Mission - a Copernicus Candidate Mission in Support of Agricultural Monitoring

Koetz, Benjamin; Bastiaanssen, W.G.M.; Berger, Michael; Defourney, Pierre; Bello, Umberto Del; Drusch, Matthias; Drinkwater, Mark R.; Duca, Ricardo; Fernandez, Valérie; Ghent, Darren; Guzinski, Radoslaw; Hoogeveen, J.; Hook, Simon J.; Lagouarde, Jean‐Pierre; Lemoine, Guido; Manolis, Ilias; Martimort, Philippe; Masek, Jeff; Massart, Michel; Notarnicola, Claudia; Sobrino, José A.; Udelhoven, Thomas

doi:10.1109/igarss.2018.8517433

Cited by 56 publications

(38 citation statements)

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“…Two overpass times have been considered to represent the nominal times of a morning and afternoon satellite overpass: 10:00 and 13:00 The 10:00 overpass corresponds to instruments like the Thermal Infrared Sensor (TIRS) onboard Landsat-8, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), MODIS onboard Terra or the Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel 3, and the afternoon overpass to instruments like MODIS onboard Aqua or VIIRS onboard the Suomi National Polar-Orbiting Partnership (Suomi-NPP) and Joint Polar Satellite System (JPSS) spacecrafts, for example. The afternoon overpass is also the preferred overpass time for missions in preparation, such as TRISHNA or LSTM [22,23]. The impact of acquisition time on the ability to detect water stress and then derive daily ET estimates in accordance with the actual stress level and the stability of temperature measurements was discussed by [54,55].…”

Section: Generation Of Series Of Observations With Different Revisit mentioning

confidence: 99%

“…LSTM has started an ESA preparatory phase study in 2018 to establish mission feasibility. To support agriculture management services, LSTM will have a spatial resolution of 30-50 m and a temporal resolution lower than three days [22]. The French Space Agency (CNES) and the Indian Space Research Organization (ISRO) have also started the design of a new satellite mission called TRISHNA, combining a high spatial resolution (50 m) and high revisit capacities (between two and three days) in the thermal infrared domain [23].…”

Section: Introductionmentioning

confidence: 99%

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Impact of the Revisit of Thermal Infrared Remote Sensing Observations on Evapotranspiration Uncertainty—A Sensitivity Study Using AmeriFlux Data

et al. 2019

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Thermal infrared remote sensing observations have been widely used to provide useful information on surface energy and water stress for estimating evapotranspiration (ET). However, the revisit time of current high spatial resolution (<100 m) thermal infrared remote sensing systems, sixteen days for Landsat for example, can be insufficient to reliably derive ET information for water resources management. We used in situ ET measurements from multiple Ameriflux sites to (1) evaluate different scaling methods that are commonly used to derive daytime ET estimates from time-of-day observations; and (2) quantify the impact of different revisit times on ET estimates at monthly and seasonal time scales. The scaling method based on a constant evaporative ratio between ET and the top-of-atmosphere solar radiation provided slightly better results than methods using the available energy, the surface solar radiation or the potential ET as scaling reference fluxes. On average, revisit time periods of 2, 4, 8 and 16 days resulted in ET uncertainties of 0.37, 0.55, 0.73 and 0.90 mm per day in summer, which represented 13%, 19%, 23% and 31% of the monthly average ET calculated using the one-day revisit dataset. The capability of a system to capture rapid changes in ET was significantly reduced for return periods higher than eight days. The impact of the revisit on ET depended mainly on the land cover type and seasonal climate, and was higher over areas with high ET. We did not observe significant and systematic differences between the impacts of the revisit on monthly ET estimates that are based on morning or afternoon observations. We found that four-day revisit scenarios provided a significant improvement in temporal sampling to monitor surface ET reducing by around 40% the uncertainty of ET products derived from a 16-day revisit system, such as Landsat for instance.

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Section: Generation Of Series Of Observations With Different Revisit mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of the Revisit of Thermal Infrared Remote Sensing Observations on Evapotranspiration Uncertainty—A Sensitivity Study Using AmeriFlux Data

et al. 2019

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“…This technique, called bi-linear in this work (simplest version of MLR [12]), supposes that the LST behaves linearly in two different shortwave features I (1) and I (2) :…”

Section: Bi-linearmentioning

confidence: 99%

“…Remotely sensed data from space in the thermal infrared (TIR) is quite well adapted for this purpose, but systems combining both high spatial resolution and revisit capacities are still missing. Different projects are currently under study such as TRISHNA (Thermal infraRed Imaging Satellite for High-resolution natural resource Assessment) developed in cooperation between French CNES (Centre National d'Etudes Spatiales) and Indian ISRO (Indian Space Research organisation) space agencies [1], LSTM (Land Surface Temperature Mission) at ESA [2] (European Space Agency), and HyspIRI (Hyperspectral InfraRed Imager) in NASA/JPL (National Aeronautics and Space Administration/Jet Propulsion Laboratory) (https://hyspiri.jpl.nasa.gov/) [3]. All of them are aiming at spatial resolutions in the Thermal InfraRed (TIR) close to 50 m. Nevertheless, the extreme fragmentation of the city structures with small size elements (such as buildings, streets, etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-Resolution Study of Thermal Unmixing Techniques over Madrid Urban Area: Case Study of TRISHNA Mission

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This work is linked to the future Indian–French high spatio-temporal TRISHNA (Thermal infraRed Imaging Satellite for High-resolution natural resource Assessment) mission, which includes shortwave and thermal infrared bands, and is devoted amongst other things to the monitoring of urban heat island events. In this article, the performance of seven empirical thermal unmixing techniques applied on simulated TRISHNA satellite images of an urban scenario is studied across spatial resolutions. For this purpose, Top Of Atmosphere (TOA) images in the shortwave and Thermal InfraRed (TIR) ranges are constructed at different resolutions (20 m, 40 m, 60 m, 80 m, and 100 m) and according to TRISHNA specifications (spectral bands and sensor properties). These images are synthesized by correcting and undersampling DESIREX 2008 Airborne Hyperspectral Scanner (AHS) images of Madrid at 4 m resolution. This allows to compare the Land Surface Temperature (LST) retrieval of several unmixing techniques applied on different resolution images, as well as to characterize the evolution of the performance of each technique across resolutions. The seven unmixing techniques are: Disaggregation of radiometric surface Temperature (DisTrad), Thermal imagery sHARPening (TsHARP), Area-To-Point Regression Kriging (ATPRK), Adaptive Area-To-Point Regression Kriging (AATPRK), Urban Thermal Sharpener (HUTS), Multiple Linear Regressions (MLR), and two combinations of ground classification (index-based classification and K-means classification) with DisTrad. Studying these unmixing techniques across resolutions also allows to validate the scale invariance hypotheses on which the techniques hinge. Each thermal unmixing technique has been tested with several shortwave indices, in order to choose the best one. It is shown that (i) ATPRK outperforms the other compared techniques when characterizing the LST of Madrid, (ii) the unmixing performance of any technique is degraded when the coarse spatial resolution increases, (iii) the used shortwave index does not strongly influence the unmixing performance, and (iv) even if the scale-invariant hypotheses behind these techniques remain empirical, this does not affect the unmixing performances within this range of resolutions.

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“…Several projects are currently under study to develop thermal satellites with high enough spatial resolution and revisit capacities to characterize the microscale structure of SUHIs. These are the Indian (ISRO)-French (CNES) mission TRISHNA [13], the European (ESA) LSTM [14], and HyspIRI in NASA/JPL [15]. But such missions lack in their spatial resolution of about 50-60 m and are not able to discern the local LST on an urban object scale.…”

Section: Introductionmentioning

confidence: 99%

Night Thermal Unmixing for the Study of Microscale Surface Urban Heat Islands with TRISHNA-Like Data

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Urban Heat Islands (UHIs) at the surface and canopy levels are major issues in urban planification and development. For this reason, the comprehension and quantification of the influence that the different land-uses/land-covers have on UHIs is of particular importance. In order to perform a detailed thermal characterisation of the city, measures covering the whole scenario (city and surroundings) and with a recurrent revisit are needed. In addition, a resolution of tens of meters is needed to characterise the urban heterogeneities. Spaceborne remote sensing meets the first and the second requirements but the Land Surface Temperature (LST) resolutions remain too rough compared to the urban object scale. Thermal unmixing techniques have been developed in recent years, allowing LST images during day at the desired scales. However, while LST gives information of surface urban heat islands (SUHIs), canopy UHIs and SUHIs are more correlated during the night, hence the development of thermal unmixing methods for night LSTs is necessary. This article proposes to adapt four empirical unmixing methods of the literature, Disaggregation of radiometric surface Temperature (DisTrad), High-resolution Urban Thermal Sharpener (HUTS), Area-To-Point Regression Kriging (ATPRK), and Adaptive Area-To-Point Regression Kriging (AATPRK), to unmix night LSTs. These methods are based on given relationships between LST and reflective indices, and on invariance hypotheses of these relationships across resolutions. Then, a comparative study of the performances of the different techniques is carried out on TRISHNA synthesized images of Madrid. Since TRISHNA is a mission in preparation, the synthesis of the images has been done according to the planned specification of the satellite and from initial Aircraft Hyperspectral Scanner (AHS) data of the city obtained during the DESIREX 2008 capaign. Thus, the coarse initial resolution is 60 m and the finer post-unmixing one is 20 m. In this article, we show that: (1) AATPRK is the most performant unmixing technique when applied on night LST, with the other three techniques being undesirable for night applications at TRISHNA resolutions. This can be explained by the local application of AATPRK. (2) ATPRK and DisTrad do not improve significantly the LST image resolution. (3) HUTS, which depends on albedo measures, misestimates the LST, leading to the worst temperature unmixing. (4) The two main factors explaining the obtained performances are the local/global application of the method and the reflective indices used in the LST-index relationship.

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High Spatio- Temporal Resolution Land Surface Temperature Mission - a Copernicus Candidate Mission in Support of Agricultural Monitoring

Cited by 56 publications

References 10 publications

Impact of the Revisit of Thermal Infrared Remote Sensing Observations on Evapotranspiration Uncertainty—A Sensitivity Study Using AmeriFlux Data

Impact of the Revisit of Thermal Infrared Remote Sensing Observations on Evapotranspiration Uncertainty—A Sensitivity Study Using AmeriFlux Data

Multi-Resolution Study of Thermal Unmixing Techniques over Madrid Urban Area: Case Study of TRISHNA Mission

Night Thermal Unmixing for the Study of Microscale Surface Urban Heat Islands with TRISHNA-Like Data

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