Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands

Long-term monitoring of grasslands is pivotal for ensuring continuity of many environmental services and for supporting food security and environmental modelling. Remote sensing provides an irreplaceable source of information for studying changes in grasslands. Specifically, Spectral Mixture Analysis (SMA) allows for quantification of physically meaningful ground cover fractions of grassland ecosystems (i.e., green vegetation, non-photosynthetic vegetation, and soil), which is crucial for our understanding of change processes and their drivers. However, although popular due to straightforward implementation and low computational cost, ‘classical’ SMA relies on a single endmember definition for each targeted ground cover component, thus offering limited suitability and generalization capability for heterogeneous landscapes. Furthermore, the impact of irregular data density on SMA-based long-term trends in grassland ground cover has also not yet been critically addressed.
We conducted a s..., For the detailed description of the methodology used to derived the datasets please refer to the related publication: Lewińska K.E., Okujeni A., Kowalski K., Lehnamm F., Radeloff V.C., Leser U., Hostert P., \"Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands\". under review in Remote Sensing of Environment, Available as preprint: https://doi.org/10.31223/X5D43N, # Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands

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## Data description:

Pixel-level trends in green vegetation (`gv`), non-photosynthetic vegetation (`npv`), and soil (`soil`) ground cover fractions derived and analyzed in the course of a paper:
Lewińska K.E., Okujeni A., Kowalski K., Lehnamm F., Radeloff V.C., Leser U., Hostert P., (2025) Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands, Remote Sensing of Environment, 323, https://doi.org/10.1016/j.rse.2025.114736

The datasets are generated as GTifs and distributed as three `.tar` archives:

* `AR_LND`: comprising AR(1) trends derived based on the 1984-2021 Landsat (TM, ETM+, and OLI) time series
* `AR_LND`_baseline: comprising AR(1) trends derived based on 1984-2021 Landsat (TM, ETM+, and OLI) with the pixel-level probability of cloud-, snow-, and shade-free observation for the 2015...,

对草原开展长期监测，对保障诸多生态服务的可持续性、支撑粮食安全与环境建模均具有关键意义。遥感技术为草原变化研究提供了不可替代的信息来源。具体而言，光谱混合分析（Spectral Mixture Analysis, SMA）可量化草原生态系统中具有物理意义的地表覆盖组分占比，即绿色植被、非光合植被与土壤，这对理解变化过程及其驱动机制至关重要。然而，尽管经典光谱混合分析（'classical' SMA）因实现简便、计算成本低而广受应用，但它仅为每个目标地表覆盖组分采用单一端元定义，因此在异质景观中的适用性与泛化能力有限。此外，数据密度的不规则性对基于SMA的草原地表覆盖长期趋势的影响，尚未得到严谨的探讨。我们开展了[原文内容截断]，关于本数据集所采用方法的详细说明，请参阅相关学术论文：Lewińska K.E.、Okujeni A.、Kowalski K.、Lehnamm F.、Radeloff V.C.、Leser U.、Hostert P.，《欧洲草原地表覆盖组分长期趋势受数据密度与端元定义的影响》，该论文目前已被《Remote Sensing of Environment》（环境遥感）接收待刊，预印本链接：https://doi.org/10.31223/X5D43N。

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## 数据集说明：
本数据集包含基于论文研究推导并分析得到的绿色植被（`gv`）、非光合植被（`npv`）与土壤（`soil`）地表覆盖组分的像元级趋势：
Lewińska K.E.、Okujeni A.、Kowalski K.、Lehnamm F.、Radeloff V.C.、Leser U.、Hostert P.，(2025) 《欧洲草原地表覆盖组分长期趋势受数据密度与端元定义的影响》，《Remote Sensing of Environment》（环境遥感），第323卷，链接：https://doi.org/10.1016/j.rse.2025.114736

本数据集以GTifs格式生成，并打包为三个`.tar`压缩包进行分发：
* `AR_LND`：包含基于1984-2021年Landsat（TM、ETM+与OLI）时间序列推导得到的AR(1)趋势数据
* `AR_LND_baseline`：包含基于1984-2021年Landsat（TM、ETM+与OLI）时间序列推导得到的AR(1)趋势数据，附带2015年[原文内容截断]的像元级无云、无雪与无阴影观测概率