The potential change on Land Surface Temperature during the diurnal cycle produced by changes on Land Cover Classes. Africa and Europe 2018

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 daily cycle aggregate per month for the year 2018. 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: array(['2018-01-01T00:00:00.000000000', '2018-01-01T00:15:00.000000000','2018-01-01T00:30:00.000000000', ..., '2018-12-01T23:15:00.000000000','2018-12-01T23:30:00.000000000', '2018-12-01T23:45:00.000000000'],shape=(1152,), dtype='datetime64[ns]') 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. mov_window_cumulative_variance: Cumulative variance for the moving window. mov_window_predicted: Predicted value of LST for a specific moving window, given the current values of vegetation classes. mov_window_rsquared_adjusted: Adjusted R² of the linear model for each moving window. Transitions: "GRASS-MAN to GRASS-NAT", "GRASS-MAN to TREES", "SHRUB to GRASS-MAN", "SHRUB to GRASS-NAT", "SHRUB to TREES", "TREES to GRASS-NAT". Clarification of transitions: 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' 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. Visualization: The dataset can be visualized in the following link: Space-for-time visualizer xcube 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. Acknowledgment: 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.## 数据集摘要 本研究针对非洲与欧洲区域，利用EUMESAT LSA SAF提供的地表温度（Land Surface Temperature, LST）数据（MLST [LSA-004]，参考文献[1]）以及2019年欧洲空间局气候变化倡议（European Space Agency Climate Change Initiative, ESA-CCI）的土地覆盖类别数据（参考文献[2]），估算了土地覆盖类别变化对地表温度日循环的潜在影响。该潜在影响的估算采用了Duveiller等人2018年提出的时空替代法（space for time substitution）工作流（参考文献[3]）。本数据集为2018年的逐月日循环聚合产物，其中每15分钟的数值为对应月份该时刻的平均地表温度。 ## 维度信息 - 纬度（lat）：[-35.0, 80.0] - 经度（lon）：[-20.0, 52.0] - 时间（time）：数组(['2018-01-01T00:00:00.000000000', '2018-01-01T00:15:00.000000000', ..., '2018-12-01T23:45:00.000000000']，形状为(1152,)，数据类型为datetime64[ns] ## 数据变量 - delta：特定土地覆盖类型转换对应的地表温度潜在变化量 - delta_error：特定土地覆盖类型转换的delta估算值对应的误差 - co-occurrence：局部移动窗口内两种土地利用类别的共现值 - mov_window_cumulative_variance：移动窗口的累积方差 - mov_window_predicted：基于当前植被类别数值，特定移动窗口对应的预测地表温度值 - mov_window_rsquared_adjusted：各移动窗口对应线性模型的调整决定系数（Adjusted R²） ## 转换类型 支持以下土地覆盖类型转换："GRASS-MAN 转换至 GRASS-NAT"、"GRASS-MAN 转换至 TREES"、"SHRUB 转换至 GRASS-MAN"、"SHRUB 转换至 GRASS-NAT"、"SHRUB 转换至 TREES"、"TREES 转换至 GRASS-NAT"。 ### 转换类型说明 - 树木（TREES）：涵盖ESA-CCI分类体系下的'TREES-BD'、'TREES-BE'、'TREES-ND'与'TREES-NE'类别 - 灌丛（SHRUB）：涵盖ESA-CCI分类体系下的'SHRUBS-BD'、'SHRUBS-BE'、'SHRUBS-ND'与'SHRUBS-NE'类别 ## 后处理流程 将共现值小于0.4的像元赋值为缺失值（NaN）；将delta值小于-20或大于20的像元赋值为缺失值（NaN）。 ## 免责声明 本数据集目前处于测试版（beta）阶段，后续版本将基于YAXArraysToolbox包的新版本开发。 ## 可视化方式 可通过以下链接查看本数据集：Space-for-time visualizer xcube ## 源代码 https://github.com/dpabon/space4time_bdap_products ## 参考文献 [1] Ermida, S.L., Trigo, I.F., DaCamara, C.C., Pires, A.C., 2018. 基于参数模型与景观属性模拟地表温度方向效应的方法. 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.: 适用于气候模型的29年300米分辨率植物功能类型年度时间序列数据集. 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. 植被覆盖变化潜在生物物理效应的数据集制图. Scientific Data 5, no. 1 (2018年2月20日): 180014. https://doi.org/10.1038/sdata.2018.14. ## 致谢 "开放地球监测"网络基础设施（Open-Earth-Monitor Cyberinfrastructure）项目获欧盟地平线欧洲（Horizon Europe）研究与创新计划资助，资助协议编号为101059548。