The potential change on Land Surface Temperature during the diurnal cycle produced by changes on Land Cover Classes

Summary

The potential change on Land Surface Temperature (LST) diurnal cycle produced by changes on Land Cover Classes was estimated for Africa and Europe using LST from EUMESAT LSA SAF (MLST [LSA-004], [1]), and land cover classes from ESA-CCI for the year 2019 [2]. The estimation of the potential change was estimated using the space for time substitution workflow proposed by Duveiller et al., 2018 [3].  The current product is a temporary aggregate for the month of August 2019. Where a value every 15 minutes was estimated as the average during the month for that hour.

Dimensions

lat: [-35.0, 80.0]

lon: [-20.0, 52.0]

time_of_day: ['00:00:00', '23:45:00']

transitions: ['BUILT to BARE', 'WATER_INLAND to BARE', 'TREES to BARE',  'GRASS to BARE', 'SHRUB to BARE', 'WATER_INLAND to BUILT', 'TREES to BUILT', 'GRASS to BUILT', 'SHRUB to BUILT', 'TREES to WATER_INLAND', 'GRASS to WATER_INLAND', 'SHRUB to WATER_INLAND', 'GRASS to TREES', 'SHRUB to TREES', 'SHRUB to GRASS'].

GRASS: Included ESA-CCI classes: 'GRASS-MAN' + 'GRASS-NAT'

TREES: Included ESA-CCI classes: 'TREES-BD' + 'TREES-BE' + 'TREES-ND' + 'TREES-NE'

SHRUB: Included ESA-CCI classes: 'SHRUBS-BD' + 'SHRUBS-BE' + 'SHRUBS-ND' + 'SHRUBS-NE'

Data Variables:

delta: The potential change of land surface temperature for a specific transition.

delta_error: The error associated to the estimation of delta for a specific transition.

co-occurrence: Co-occurrence of two land use classes in the local moving window.

Post-processing:

Pixels where co-occurrence < 0.4 were set as NaNs. Pixels with delta values lower than -20 and higher than 20 were set as NaNs.

Disclaimer:

The current product is in beta state. Future versions of the product will be based on a new version of the YAXArraysToolbox package.

Source code:

https://github.com/dpabon/space4time_bdap_products

References:

[1] Ermida, S.L., Trigo, I.F., DaCamara, C.C., Pires, A.C., 2018. A Methodology to Simulate LST Directional Effects Based on Parametric Models and Landscape Properties. Remote Sens. 10, 1114. https://doi.org/10.3390/rs10071114 [2] Harper, K. L., Lamarche, C., Hartley, A., Peylin, P., Ottlé, C., Bastrikov, V., San Martín, R., Bohnenstengel, S. I., Kirches, G., Boettcher, M., Shevchuk, R., Brockmann, C., and Defourny, P.: A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models, Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, 2023. [3] Duveiller, Gregory, Josh Hooker, and Alessandro Cescatti. “A Dataset Mapping the Potential Biophysical Effects of Vegetation Cover Change.” Scientific Data 5, no. 1 (February 20, 2018): 180014. https://doi.org/10.1038/sdata.2018.14.

Acknowledgement:

The Open-Earth-Monitor Cyberinfrastructure project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101059548.