Data associated with the article "Long-term phenological shifts in coastal saltmarsh vegetation reveal complex responses to climate change"

In the publication associated with this dataset, we investigate the spatial phenology of salt marsh vegetation in the Dutch Western Scheldt estuary over the past three decades (1993–2022). Landsat-derived EVI2 (two-band enhanced vegetation index) data were exported from Google Earth Engine (GEE) and processed with the R package phenofit to extract pixel-wise phenological indicators and assess long-term vegetation dynamics. Open-source maps of emersion duration, elevation, vegetation, and ecotopes from Rijkswaterstaat were processed in ArcGIS Pro, while meteorological data from KNMI and drought index time series from the Global SPEI database (SPEIbase) were processed in R. After integrating all datasets, we generated pixel-based and formatted data tables and shapefiles, which were then used for further analysis and visualization in R. This dataset contains the processed data and R code used to produce all figures in the article, along with descriptive files detailing the original data sources.

本数据集配套的学术论文中，我们针对1993—2022年这30年间荷兰西斯海尔德河口（Western Scheldt estuary）的盐沼植被空间物候学特征展开了研究。由陆地卫星（Landsat）反演得到的增强型两波段植被指数（EVI2，two-band enhanced vegetation index）数据从谷歌地球引擎（Google Earth Engine，GEE）导出，并借助R语言工具包phenofit进行处理，以逐像素提取物候指标并评估植被长期动态变化。来自荷兰公共工程与水管理署（Rijkswaterstaat）的淹露时长、高程、植被类型及生态单元开源地图数据，均在ArcGIS Pro中完成处理；而荷兰皇家气象研究所（KNMI）提供的气象数据，以及全球SPEI数据库（SPEIbase）的干旱指数时序数据，则通过R语言完成处理。在整合所有数据集后，我们生成了逐像素格式化数据表与形状文件（shapefiles），随后将其用于R语言环境下的后续分析与可视化工作。本数据集包含用于生成论文中所有图表的预处理数据与R代码，以及详述原始数据来源的说明文档。