Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European Alps
In this article, we present a new concept for predicting satellite-derived land surface temperature (LST) under cloudy skies over vegetated areas in the Alps. Although many different reconstruction methods have been developed, they require rarely available inputs, or they restore missing pixels from...
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| Format: | Article |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/9699062/ |
| _version_ | 1831543417615679488 |
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| author | Paulina Bartkowiak Mariapina Castelli Alice Crespi Georg Niedrist Damiano Zanotelli Roberto Colombo Claudia Notarnicola |
| author_facet | Paulina Bartkowiak Mariapina Castelli Alice Crespi Georg Niedrist Damiano Zanotelli Roberto Colombo Claudia Notarnicola |
| author_sort | Paulina Bartkowiak |
| collection | DOAJ |
| description | In this article, we present a new concept for predicting satellite-derived land surface temperature (LST) under cloudy skies over vegetated areas in the Alps. Although many different reconstruction methods have been developed, they require rarely available inputs, or they restore missing pixels from clear-sky observations with low spatial resolution (1–5 km), which makes them unreliable in heterogenous ecosystems. Given these limitations, we propose a station-based procedure to predict cloud-covered grids from 1-km Terra MODIS LST at 250 m spatial resolution. First, we explored correlations between ground-measured LST and air temperature in conjunction with other geo-biophysical variables under cloudy-sky conditions derived from ESRA clear-sky radiation model. Considering a high site dependency driven by different landcovers, in-situ data were aggregated into three groups (forest, permanent crops, grassland) and then, models were established. Next, the regressions were applied to 250-m gridded predictors to estimate cloud-covered LST pixels for six Terra MODIS LST images in 2014. While for permanent crops and forest group linear modelling was the most efficient, neural networks achieved the best performance for grasslands. The reconstructions showed reasonable LST distribution considering landscape heterogeneity of the region. The results were validated against timeseries of ground-measured LST in 2014. The models achieved reliable performance with an average R<sup>2</sup> of 0.84 and root-mean-square error of 2.12 °C. Despite some limitations, mainly due to diversified character of cloudy-sky conditions and high heterogeneity of gridded predictors, the method can effectively reconstruct overcast MODIS data at subpixel level, which shows great potential for producing cloud-free LSTs in complex ecosystems |
| first_indexed | 2024-12-17T00:50:54Z |
| format | Article |
| id | doaj.art-1fd1d656b25742139ac7100300b6eab8 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-17T00:50:54Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-1fd1d656b25742139ac7100300b6eab82022-12-21T22:09:46ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01152037205710.1109/JSTARS.2022.31473569699062Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European AlpsPaulina Bartkowiak0https://orcid.org/0000-0002-1172-5942Mariapina Castelli1Alice Crespi2Georg Niedrist3Damiano Zanotelli4https://orcid.org/0000-0002-7700-5761Roberto Colombo5https://orcid.org/0000-0003-3997-0576Claudia Notarnicola6https://orcid.org/0000-0003-1968-0125Institute for Earth Observation, Eurac Research, Bozen-Bolzano, ItalyInstitute for Earth Observation, Eurac Research, Bozen-Bolzano, ItalyInstitute for Earth Observation, Eurac Research, Bozen-Bolzano, ItalyInstitute for Alpine Environment, Eurac Research, Bozen-Bolzano, ItalyFaculty of Science and Technology, Free University of Bozen-Bolzano, Bozen-Bolzano, ItalyDepartment of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, ItalyInstitute for Earth Observation, Eurac Research, Bozen-Bolzano, ItalyIn this article, we present a new concept for predicting satellite-derived land surface temperature (LST) under cloudy skies over vegetated areas in the Alps. Although many different reconstruction methods have been developed, they require rarely available inputs, or they restore missing pixels from clear-sky observations with low spatial resolution (1–5 km), which makes them unreliable in heterogenous ecosystems. Given these limitations, we propose a station-based procedure to predict cloud-covered grids from 1-km Terra MODIS LST at 250 m spatial resolution. First, we explored correlations between ground-measured LST and air temperature in conjunction with other geo-biophysical variables under cloudy-sky conditions derived from ESRA clear-sky radiation model. Considering a high site dependency driven by different landcovers, in-situ data were aggregated into three groups (forest, permanent crops, grassland) and then, models were established. Next, the regressions were applied to 250-m gridded predictors to estimate cloud-covered LST pixels for six Terra MODIS LST images in 2014. While for permanent crops and forest group linear modelling was the most efficient, neural networks achieved the best performance for grasslands. The reconstructions showed reasonable LST distribution considering landscape heterogeneity of the region. The results were validated against timeseries of ground-measured LST in 2014. The models achieved reliable performance with an average R<sup>2</sup> of 0.84 and root-mean-square error of 2.12 °C. Despite some limitations, mainly due to diversified character of cloudy-sky conditions and high heterogeneity of gridded predictors, the method can effectively reconstruct overcast MODIS data at subpixel level, which shows great potential for producing cloud-free LSTs in complex ecosystemshttps://ieeexplore.ieee.org/document/9699062/Cloudy-sky conditionsland surface temperaturemachine learningreconstruction |
| spellingShingle | Paulina Bartkowiak Mariapina Castelli Alice Crespi Georg Niedrist Damiano Zanotelli Roberto Colombo Claudia Notarnicola Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European Alps IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Cloudy-sky conditions land surface temperature machine learning reconstruction |
| title | Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European Alps |
| title_full | Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European Alps |
| title_fullStr | Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European Alps |
| title_full_unstemmed | Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European Alps |
| title_short | Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European Alps |
| title_sort | land surface temperature reconstruction under long term cloudy sky conditions at 250 m spatial resolution case study of vinschgau x002f venosta valley in the european alps |
| topic | Cloudy-sky conditions land surface temperature machine learning reconstruction |
| url | https://ieeexplore.ieee.org/document/9699062/ |
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