Darren Ghent, John Remedios Earth Observation Science Department of Physics and Astronomy University...

Darren Ghent, John RemediosEarth Observation Science

Department of Physics and AstronomyUniversity of Leicester

20 April 2023 1NCEO Land Science

Land surface temperature (LST) and its applications

Outline

• Background

• LST improvements

• LST validation

• LST applications

• Outputs

• Next 12-months

• Phase-2


Background

What is land surface temperature (LST)?• LST is the radiative skin temperature (~20μm) of the land• It is an important climate variable derived from solar

radiation and is influenced by land/atmosphere boundary conditions

• It determines the emission of surface-to-atmosphere long-wave radiation

• It exerts control over the partitioning of energy into latent and sensible heat fluxes, and heat flux into the ground

• It influences land/atmosphere coupling – water and carbon cycling

• It can be retrieved from microwave and IR sensors• Although microwaves are able to penetrate clouds the signal

originating from Earth is stronger at IR wavelengths• Several issues with off-the-shelf LST products


Background

Consultation with users from several scientific fields which utilise LST data, both across Europe and the US elicited the following recommendations

•Availability –– Provision of level-3 gridded data at a variety of resolutions (0.5°, 1.0° for example)– Commonly used file formats are important to ensure maximum exploitation of LST

products– The processing of long time-series of LST

•Accuracy –– Improved cloud screening algorithms– Provision of uncertainty estimates– Multiple observation angles to improve atmospheric correction

•Validation –– Longer validation studies over a variety of surface regimes– An increased emphasis on radiance-based validation– An increase in the number of intercomparison studies

•Resolution –– A better resolution of the LST diurnal cycle– Higher resolution products for resolving agricultural fields and urban features for example– Combined LST products from instruments on Low Earth Orbit (LEO) satellites and

geostationary satellites


Phase-1

Phase-1 + Phase-2

Phase-2

Phase-1 + Phase-2

Improving auxiliary datasets

20 April 2023 NCEO Land Science 5

Operational AATSR LST (right) using operational biome (left) and fractional vegetation data (centre)

UoL AATSR LST (right) using Globcover biome (left) and CYCLOPES fractional vegetation data (centre)

Improving cloud masking


Improved cloud mask

Operational cloud

mask

Improving snow masking


RGB image (1st column); operational snow masking – orange (2nd column); operational cloud masking – green (3rd column); updated snow masking using a Bayesian approach - Water , Sea Ice , Land Ice , Sea Cloud , Land Cloud , Clear Land (4th column) over Ostrov Sakhalin, Russia, Jan 2006.

Assessment of data quality

Methodologies: Temperature-based; Radiance-based; Intercomparison

Temperature-based• Temperature method represents the conventional method for satellite LST validation. It involves the direct

comparison of concurrent ground measurements acquired at a field site with LST acquired from the satellite overpass.

• Creation of matchup database (MDB)

Radiance-based• Radiance method consists of calculating the ground LST from (TOA) brightness temperatures simulated

using surface emissivity and temperature data plus atmospheric profile data in a radiative transfer model.

• For each instrument top-of-atmosphere brightness temperatures (BTs) are simulated for the 11 and 12 μm channels using the fast radiative transfer model RTTOV-10.

• Utilised the ECMWF ERA-Interim profiles and 11 and 12 μm channel emissivities derived from CIMSS emissivity database (Seemann et al., 2008). Satellite-retrieved LST is taken as the input skin temperature to the model.

• Perturbations applied to the input skin temperature until the simulated BTs match the satellite retrieved BTs.

• LST error is the difference between satellite-retrieved LST and inverted skin temperature.

• Quality of the atmospheric profiles assessed through the difference Δ(T11–T12) between the satellite-retrieved BT differences (T11–T12) and the simulated BT differences (T11–T12).


Temperature-based validation


Temperature-based validation at Gobabeb, Namibia, 2009: AATSR (1st column), Terra-MODIS (2nd column), Aqua-MODIS (3rd column), SEVIRI (4th column)

SiteDay Night

AATSR Terra-MODIS SEVIRI AATSR Terra-MODIS SEVIRIBias Sigma Bias Sigma Bias Sigma Bias Sigma Bias Sigma Bias Sigma

ARM Atqasuk 0.79 2.27 -2.32 3.80 - - -0.76 1.57 - - - -ARM Azores -11.48 5.27 -7.53 4.30 - - -5.44 2.94 -1.42 2.40 - -ARM Barrow -0.57 2.42 -6.05 2.53 - - -1.90 2.07 2.19 2.41 - -ARM Black Forest -4.40 2.33 -6.06 2.54 -2.54 2.67 2.64 3.35 2.90 1.46 1.78 2.95Cardington -1.21 1.74 -2.34 2.39 -6.69 1.40 0.21 2.46 -0.23 2.02 0.98 1.71Evora 0.36 2.91 -1.67 1.78 5.02 6.20 -1.83 1.16 -2.88 1.23 -2.43 4.27Gobabeb 1.99 2.53 -2.04 0.97 2.45 1.69 -2.84 1.17 -2.65 0.81 -0.51 1.08ARM Niamey 0.53 1.71 -6.30 3.57 - - -0.40 1.31 -3.58 1.67 - -ARM Oklahoma 0.07 1.87 -3.33 1.94 - - 0.15 1.70 -1.02 0.99 - -ARM Point Reyes -3.91 1.88 -2.57 3.07 - - -1.22 1.11 -0.82 1.56 - -ARM Shouxian 3.15 2.66 -6.26 2.51 - - 7.15 3.33 1.56 2.13 - -

Radiance-based validation

20 April 2023 10

Radiance-based LST Errors for the AATSR updated LST algorithm (left), Terra-MODIS (centre), Aqua-MODIS (right). LST errors derived for each month of 2006.

• Surface emissivities are taken from CIMSS dataset, which is derived from MODIS. Probable reason for larger absolute uncertainty in AATSR data.

• Δ(T11–T12) should be close to zero when the atmospheric temperature and water vapour profiles used in simulations represent the real atmospheric conditions and effect of the surface emissivities for the satellite observations. Only accurate profile associations (-1.0K < Δ(T11–T12) < 1.0K) were considered.

• Mean ΔLSTs are 0.63 K (AATSR), -0.3 K (Terra-MODIS) and -0.2 K (Aqua-MODIS).

NCEO Land Science

Instrument Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

AATSR 0.66 0.69 0.55 0.67 0.74 0.73 0.68 0.72 0.69 0.64 0.53 0.33Terra - MODIS -0.45 -0.24 -0.51 -0.40 -0.13 -0.20 -0.32 -0.03 -0.53 -0.13 -0.50 -0.61Aqua - MODIS -0.39 -0.26 -0.55 -0.49 0.05 0.04 0.10 0.22 0.24 0.11 -0.31 -0.49

LST intercomparison

Comparison of mean daytime LST composites for 2006: March (1st row), June (2nd row), September (3rd row), December (4th row); SEVIRI absolute LST (1st column); AATSR minus SEVIRI (2nd column); JULES minus SEVIRI (3rd column); Terra-MODIS minus SEVIRI (4th column)


LST applications

• Climate change: Urban heat, land/atm. coupling, surface energy balance, carbon cycle

• Modelling studies: Model validation, data assimilation• Land cover change: Desertification, change detection• Crop management: Irrigation, drought stress• Water management: Evapotranspiration, soil moisture

retrievals• Fire monitoring: Burned area mapping, fuel moisture

content• Geological applications: Geothermal anomalies,

volcanic activity


A decrease in the Tmax lag occurs as soil moisture decreases. There is a general increase in the LST diurnal cycle as soil moisture decreases.

LST and hydrology


EO and JULES LST vs. in situ LST (left); in situ LST vs. in situ soil moisture (centre) - KNP, South Africa

• Surface energy balance models can estimate evapotranspiration (ET) from this partitioning of energy available at the land surface

• LST retrieval can be a valuable metric in the estimation of ET and soil water without the need for additional data such as precipitation or soil texture, since varying soil moisture conditions yield distinctive soil and canopy thermal signatures

• 0.5K LST error can result in a 10% error in sensible heat flux (Brutsaert et al., 1993)• 1.0K LST error can lead to a 10% error in ET (Moran and Jackson, 1991)• LST Errors between 1.0K and 3.0K can lead to errors as much as 100Wm -2 in heat

fluxes (Kustas and Norman, 1996)

AATSR LST anomaly for April 2011 with respect to 2003-2011 climatology

LST and data assimilation


• Not only can LST act as a proxy for soil water it can constrain the simulation of soil water in models through data assimilation (DA)

• Assimilation of LST observations into land surface models has been shown to constrain soil moisture (below-left) and ET (below-right) (Ghent et al., 2010; 2011)

• Other examples of DA include:– Assimilation of MSG LST into JULES over the Sahel (Phil Harris, CEH)

providing mesoscale structure critical for storm initiation– Using adJULES with LST data to constrain parameters related to root

zone soil moisture (SWELTER-21 Changing Water Cycle project - CEH/Met Office/Leicester/Reading)

Outputs


Publications:• Ghent et al. (2010) Assimilation of land-surface temperature into the

land surface model JULES with an Ensemble Kalman Filter, JGR, 115, D19112

• Ghent et al. (2011) Data assimilation into land-surface models: the implications for climate feedbacks, IJRS, 32, 617-632

• Ghent and Remedios. Land surface temperature evaluation in a mixed tree/grass landscape (submitted to RSE)

• Ghent et al. Improving the AATSR land surface temperature retrieval with higher resolution auxiliary datasets (in preparation)

• Ghent et al. Radiance-based validation of land surface temperature from AATRS, MODIS and SEVIRI (in preparation)

AATSR Level-3 product (left) at user-defined spatial resolution

Daytime July 2007 at 0.05°

AATSR LST data portal (below)

Next 12-months

• To examine the LST long-term time series in comparison to the global, in situ, land surface air temperature series for climate.

• LST long-time series vs. land surface conditions (soil temperature, soil moisture) with respect to biome and vegetation cover.

• Provide error uncertainties and quality

controlled land surface temperature data for assimilation into JULES.


Phase-2

• Climate quality, multi-dimensional LST time series from current and future missions (SLSTR, MSG-3/4, NPP)– Significant algorithm improvements, with consistent application to

current and future sensors for LST time series (dual-view angular dependences, snow and ice). Continue improvements into cloud and snow/ice masking

– First comprehensive validation matchup database for LST data quality; comparisons to air temperature (for climate) and soil temperature (for JULES verification)

– Combined geostationary satellites (SEVIRI, GOES, MTSAT) and polar-orbiters (AATSR, MODIS, AVHRR) to give near-global, diurnal, climate quality LST.

– LST overlap analysis between current missions and planned missions, and algorithm support for future missions: AATSR and SLSTR (MODIS and VIIRS on NPP); SEVIRI on MSG-2 and MSG-3/4


Phase-2

• Optimise LST for JULES model verification and assimilation of LST:– Global gridded product at different spatial resolutions for model validation

/ assimilation into JULES– Support JULES assimilation of LST– Investigate the Canopy Heat Capacity model in JULES for fully vegetated

surfaces with respect to LST from satellite– Global estimates of drought stress and simulation of evapotranspiration.– Calculate soil heat fluxes from LST and verify with JULES– Derive soil moisture from a combination of soil temperature and LST

• LST in the urban environment:– Estimation of sub-pixel LST in urban environments using for example

empirical relationships for land-cover classes– Investigate relationships between diurnal LST anomalies and heat waves– Time series of urban LST vs. rural LST.

• This work will put NCEO at the centre of the Sentinel-3 scientific data processing (and potentially that for MSG).


Darren Ghent, John Remedios Earth Observation Science Department of Physics and Astronomy University...

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