Satellite Color Images, Vegetation Indices, and Metabolism Indices from Nordhorn, Germany from 1985 – 2023

The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document.The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications.In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description).Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.

‘德国马赛克（Germany Mosaic）’数据集是一套覆盖1984年至2023年全德国范围的Landsat卫星影像与矢量化分割片段时序数据集。影像数据按TK100分幅（TK100 sheet sections）进行划分，详细信息参见：1:10万比例尺地形图分幅（Blattschnitt der Topographischen Karte 1:100 000）。本数据集每年提供优化后的6波段影像，涵盖夏季（5月至7月）与秋季（8月至10月）两个时段，同时同步提供对应时段的植被指数，包括归一化植被指数（NDVI, Normalized Difference Vegetation Index）以及植被近红外反射率（NirV, Near-Infrared Reflectance of Vegetation）。此外，数据集每年还提供近似同质像元矢量化区域数据。影像数据的光谱特性与上述区域的形态学特征均以矢量属性形式收录，详细说明参见文档"Mosaic (1984–2023) - Data Description"。本文件中还提供了名为D-Mosaik_Sheet-Sections的矢量图层，用于展示所有分幅的覆盖范围与数据质量概况。‘德国马赛克’数据集同时可被视为一个时空数据立方体（spatial-temporal Data Cube），支持开展高级分析，并可集成至需要多维数据的工作流中。该数据结构支持用户执行各类操作，例如按特定时段查询数据、分析数十年间的变化趋势，或是对空间信息进行聚合以生成适配多样研究场景的定制化分析结果。在中纬度地区，植被的季节变化——进而引发影像数据的变化——通常比多年间的变化更为显著。本数据集的时间分割方案旨在覆盖完整的植被生长期（5月至10月），其中夏季与秋季的时段划分可捕捉自然生境的季节性代谢变化，同时也能记录绝大多数农业活动变化，包括播种与收获作业。根据天气条件的不同，单景影像数据将采用指定时段内的中值、均值或是质量最优的影像，详细说明参见文档"Mosaic (1984–2023) - Data Description"。遥感技术现已成为环境研究，尤其是景观分析领域不可或缺的工具。除传统应用场景外，‘德国马赛克’数据集还可支撑环境系统研究中的数字孪生（digital twins）开发。通过提供精细的时空数据，本数据集可用于构建虚拟生态系统模型，助力可持续管理相关的模拟、情景测试与预测分析。此外，遥感参数所捕捉的时空趋势，可作为生物学、生态学、地理学与流行病学研究中传统研究方法的有效补充。