Data, R Scripts and Random Forest Models for Winter Catch Crop Monitoring from Sentinel-2 NDVI Time Series in Germany

The data contains a zip-file with the following folders: a) data (agricultural parcels, filled and unfilled NDVI time series tables, feature extraction tables and prediction results) (csv, shp), b) model (random forest models for catch crop prediction) (rds), and c) R (R script files for Random Forest model training and prediction with RStudio) (r). The algorithms and models developed for this study were implemented via virtual Docker containers into the timeStamp software prototype which allows for large-scale automatized catch crop analysis on the parcel-level (www.timestamp.lup-umwelt.de). timeStamp saves the raster data from the GTS² archive as parcel-wise clipped image time series into a PostGIS database. All further processing steps were performed with the statistical computing language R (RStudio Team, 2020). For raster data manipulation within the PostGIS database and downloading NDVI time series, we used the packages rpostgis (Bucklin and Basille, 2019) and RPostgreSQL (Conway et al., 2017). For time series filling and predictors calculation, we used the packages zoo (Zeileis et al., 2020), hydroGOF (Zambrano-Bigiarini, 2020), tsoutliers (de Lacalle, 2019), and changepoint (Killick et al., 2016). For RF modelling, we used the package caret (Kuhn et al., 2020). The original data for NDVI time series calculation is from the GFZ Time Series System for Sentinel-2 by the German Research Centre for Geosciences, 2020 (https://gitext.gfz-potsdam.de/gts2). The predictors for Random Forest modelling calculated from the NDVI time series are described in the article in the reference section. For further information, we refer to the article mentioned in the references.本数据集包含一个压缩归档文件，内含以下三类文件夹：a) 数据文件夹（data）：存储农业地块、填充与未填充的归一化植被指数（Normalized Difference Vegetation Index, NDVI）时间序列表、特征提取表及预测结果文件，格式涵盖逗号分隔值（Comma-Separated Values, CSV）与形状文件（Shapefile, SHP）；b) 模型文件夹（model）：包含用于填闲作物预测的随机森林（Random Forest, RF）模型，格式为R数据序列化文件（R Data Serialization, RDS）；c) R脚本文件夹（R）：内含可借助RStudio开展随机森林模型训练与预测的R脚本文件，扩展名为.r。本研究开发的算法与模型通过虚拟Docker容器集成至timeStamp软件原型中，该原型支持实现地块级别的大规模自动化填闲作物分析，访问地址为www.timestamp.lup-umwelt.de。timeStamp将GTS²存档中的栅格数据按地块裁剪为影像时间序列，并存储至PostGIS数据库内。所有后续处理步骤均通过统计计算语言R（RStudio Team, 2020）完成。针对PostGIS数据库内的栅格数据操作与NDVI时间序列下载，本研究使用了rpostgis（Bucklin与Basille, 2019）与RPostgreSQL（Conway等, 2017）扩展包；针对时间序列填充与预测因子计算，使用了zoo（Zeileis等, 2020）、hydroGOF（Zambrano-Bigiarini, 2020）、tsoutliers（de Lacalle, 2019）及changepoint（Killick等, 2016）扩展包；针对随机森林建模，则使用了caret扩展包（Kuhn等, 2020）。用于NDVI时间序列计算的原始数据来源于德国地质研究中心（German Research Centre for Geosciences, GFZ）2020年发布的Sentinel-2 GFZ时间序列系统，访问地址为https://gitext.gfz-potsdam.de/gts2。从NDVI时间序列中提取的随机森林建模用预测因子已在参考文献部分的对应文章中予以说明，如需获取更多信息，请参阅参考文献中提及的相关文章。