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.## 摘要 本研究针对非洲与欧洲区域，采用欧洲气象卫星应用组织LSA SAF（EUMESAT LSA SAF）的MLST [LSA-004] 地表温度（Land Surface Temperature, LST）数据，以及2019年欧空局气候变化倡议（ESA-CCI）土地覆盖分类数据，估算了土地覆盖类别变化对地表温度日循环的潜在变化。潜在变化量的估算采用了Duveiller等人2018年提出的时空替代法（space for time substitution）工作流程。本产品为2018年逐月聚合的日循环数据集，其中每15分钟的数值为对应月份该小时的月平均结果。 ## 维度 - 纬度：[-35.0, 80.0] - 经度：[-20.0, 52.0] - 时间：数组(['2018-01-01T00:00:00.000000000, '2018-01-01T00:15:00.000000000, ..., '2018-12-01T23:30:00.000000000, '2018-12-01T23:45:00.000000000]，共1152个元素，数据类型为datetime64[ns]。 ## 数据变量 - delta：特定土地覆盖转换对应的地表温度潜在变化量 - delta_error：特定土地覆盖转换的delta估算误差 - co-occurrence：局部移动窗口内两种土地利用类别的共现频率 - 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，特定移动窗口的地表温度预测值 - mov_window_rsquared_adjusted：各移动窗口对应线性模型的调整决定系数（Adjusted R²） ## 土地覆盖转换类型 本次研究涉及的土地覆盖转换包括："GRASS-MAN to GRASS-NAT"、"GRASS-MAN to TREES"、"SHRUB to GRASS-MAN"、"SHRUB to GRASS-NAT"、"SHRUB to TREES"、"TREES to GRASS-NAT"。 ### 转换类型说明： - 乔木（TREES）涵盖欧空局CCI分类类别：'TREES-BD、'TREES-BE、'TREES-ND、'TREES-NE - 灌丛（SHRUB）涵盖欧空局CCI分类类别：'SHRUBS-BD、'SHRUBS-BE、'SHRUBS-ND、'SHRUBS-NE ## 后处理 共现频率小于0.4的像元被设为缺失值（NaN）；delta值小于-20或大于20的像元被设为缺失值（NaN）。 ## 免责声明 本产品目前处于测试版阶段，后续版本将基于YAXArraysToolbox工具箱的新版本开发。 ## 可视化 本数据集可通过时空替代可视化器xcube进行可视化。 ## 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. 基于参数化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., Defourny, P.: 适用于气候模型的29年年度300米分辨率植物功能型时间序列. Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, 2023. [3] Duveiller, Gregory, Josh Hooker, Alessandro Cescatti. 植被覆盖变化潜在生物物理效应数据集构建. 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（Open-Earth-Monitor）网络基础设施项目已获得欧盟地平线欧洲研究与创新计划资助，资助协议编号为101059548。