Quantifying Uncertainty in Satellite-Retrieved Land Surface Temperature from Cloud Detection Errors

Bulgin, Claire E.; Merchant, Christopher J.; Ghent, Darren; Klüser, Lars; Popp, Thomas; Poulsen, C.; Sogacheva, Larisa

doi:10.3390/rs10040616

Cited by 16 publications

(12 citation statements)

References 30 publications

(50 reference statements)

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“…One of the main (and harder to account for) sources of uncertainty in any LST retrieval is the presence of undetected clouds [96], which usually introduce negative biases into IR LST products. If the cloud mask algorithm fails to detect a cloudy or partly cloudy pixel, the cloud top temperature instead of the surface temperature is measured (or a mix of the two), which then causes the observed negative biases.…”

Section: Discussionmentioning

confidence: 99%

An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations

et al. 2019

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A new all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. It is the first all-weather LST product based on visible and infrared observations combining clear-sky LST retrieved from the Spinning Enhanced Visible and Infrared Imager on Meteosat Second Generation (MSG/SEVIRI) infrared (IR) measurements with LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a microwave (MW)-based LST product derived from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which offers the potential to obtain a global, nearly gap-free LST product.

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Section: Discussionmentioning

confidence: 99%

An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations

et al. 2019

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“…There is in general good agreement at low uncertainty values and consistent behaviour across many sites, which provides some evidence that the satellite uncertainty model is capturing most of the main sources of uncertainty. Future work will focus on the upscaling uncertainty, uncertainty due to instrument calibration, and assess the feasibility of integrating the most advanced approaches, such as [60], to cloud detection uncertainty into the uncertainty model for LST presented here. Such work would be intended to address any underestimation at low uncertainties and overestimation at high uncertainties.…”

Section: Discussionmentioning

confidence: 99%

“…However, this is perhaps the most challenging uncertainty component to quantify and no operational LST algorithm has confronted this to date. A first attempt has been made in [60], which exploits simulated data to estimate an average impact on the LST of misclassification with the probability of any given pixel being misclassified extracted both from previous cloud masking assessment and from the probability of a pixel being subject to cloud contamination. They found there is a dependence on the dominant land cover classification, with uncertainties ranging from 0.09 K for cropland up to 1.95 K over permanent snow and ice.…”

Section: Other Sources Of Uncertainty Not Quantifiedmentioning

confidence: 99%

A New Approach to Defining Uncertainties for MODIS Land Surface Temperature

et al. 2019

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The accuracy of land surface temperature (LST) observations is critical to many applications. Any observation of LST is subject to incomplete knowledge, so an accurate assessment of the uncertainty budget is critical. We present a comprehensive and consistent approach to determining an uncertainty budget for LST products. We apply this approach to the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the Aqua satellite. In order to generate the uncertainty model, a new implementation of the generalised split-window algorithm is applied, in which retrieval coefficients are categorised by viewing angle and water vapour. Validation of the LST against in situ data shows a mean absolute bias of 0.37 K for daytime and 0.73 K for nighttime. The average standard deviation per site is 1.53 K for daytime and 1.21 K for nighttime. Uncertainties from the implemented model are estimates in their own right and are also validated. We do this by comparing the standard deviation of the differences between the satellite and in situ LSTs, and the total uncertainties of the validation matchups. We show that the uncertainty model provides a good fit. Our approach offers a framework for quantifying uncertainties for LST that is equally applicable across different sensors and different retrieval approaches.

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“…One of the main (and harder to account for) sources of uncertainty in any LST retrieval is the presence of undetected clouds [97]. Moreover, undetected clouds usually introduce negative biases in IR LST products.…”

Section: Discussionmentioning

confidence: 99%

An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations

Martins¹,

Trigo²,

Ghilain³

et al. 2019

Preprint

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An all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. The product is based on clear-sky LST retrieved from MSG/SEVIRI infrared (IR) measurements, complemented by LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a Microwave (MW) based LST product derived from AMSR-E measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which offers the potential to obtain a global, nearly gap-free LST product.

show abstract

Quantifying Uncertainty in Satellite-Retrieved Land Surface Temperature from Cloud Detection Errors

Cited by 16 publications

References 30 publications

An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations

An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations

A New Approach to Defining Uncertainties for MODIS Land Surface Temperature

An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations

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