Global Cropland Land Surface Temperature dataset(2017-2022)
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2025
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| _version_ | 1866902266404929536 |
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| author | Yuan, Zijin Zhang, Zengmian Chen, Bo Wang, Heou Cao, Mengmeng Mao, Kebiao |
| author_facet | Yuan, Zijin Zhang, Zengmian Chen, Bo Wang, Heou Cao, Mengmeng Mao, Kebiao |
| contents | <p><span lang="EN-US">The LST dataset contains the surface temperature data of cultivated land worldwide during the period from 2003 to 2022. The unit is in degrees Celsius, with a time resolution of daily and a spatial resolution of 1000 meters.</span><a name="OLE_LINK5"></a><span lang="EN-US">It is produced by</span><span><span lang="EN-US"> </span></span><span><span lang="EN-US">combing</span></span><span><span lang="EN-US"> </span></span><span><span lang="EN-US">Fengyun and MODIS </span></span><span><span lang="EN-US">daily data</span></span><span><span lang="EN-US">, </span></span><span><span lang="EN-US">and meteorological station data</span></span><span><span lang="EN-US"> to </span></span><span><span lang="EN-US">reconst</span></span><span><span lang="EN-US">ruct</span></span><span><span lang="EN-US"> real </span></span><span><span lang="EN-US">LST</span></span><span><span lang="EN-US"> under cloud coverage</span></span><span><span lang="EN-US"> in </span></span><span><span lang="EN-US">dai</span></span><span><span lang="EN-US">ly </span></span><span><span lang="EN-US">LST images, </span></span><span><span lang="EN-US">and then a regression analysis model is constructed to further improve accuracy in </span></span><span><span lang="EN-US">six natural subregions with different climatic conditions</span></span><span><span lang="EN-US">. The accuracy analysis shows that the reconstruction result is closely correlated with the in-situ measurements, with an average MAE of 1-2 K.</span></span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17060846 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Global Cropland Land Surface Temperature dataset(2017-2022) Yuan, Zijin Zhang, Zengmian Chen, Bo Wang, Heou Cao, Mengmeng Mao, Kebiao Land Surface Temperature Global Cropland <p><span lang="EN-US">The LST dataset contains the surface temperature data of cultivated land worldwide during the period from 2003 to 2022. The unit is in degrees Celsius, with a time resolution of daily and a spatial resolution of 1000 meters.</span><a name="OLE_LINK5"></a><span lang="EN-US">It is produced by</span><span><span lang="EN-US"> </span></span><span><span lang="EN-US">combing</span></span><span><span lang="EN-US"> </span></span><span><span lang="EN-US">Fengyun and MODIS </span></span><span><span lang="EN-US">daily data</span></span><span><span lang="EN-US">, </span></span><span><span lang="EN-US">and meteorological station data</span></span><span><span lang="EN-US"> to </span></span><span><span lang="EN-US">reconst</span></span><span><span lang="EN-US">ruct</span></span><span><span lang="EN-US"> real </span></span><span><span lang="EN-US">LST</span></span><span><span lang="EN-US"> under cloud coverage</span></span><span><span lang="EN-US"> in </span></span><span><span lang="EN-US">dai</span></span><span><span lang="EN-US">ly </span></span><span><span lang="EN-US">LST images, </span></span><span><span lang="EN-US">and then a regression analysis model is constructed to further improve accuracy in </span></span><span><span lang="EN-US">six natural subregions with different climatic conditions</span></span><span><span lang="EN-US">. The accuracy analysis shows that the reconstruction result is closely correlated with the in-situ measurements, with an average MAE of 1-2 K.</span></span></p> |
| title | Global Cropland Land Surface Temperature dataset(2017-2022) |
| topic | Land Surface Temperature Global Cropland |
| url | https://doi.org/10.5281/zenodo.17060846 |