Derived data and code for Multi-sensor assessment of cropland flood exposure following extreme rainfall

Abstract

This dataset provides aggregate derived data and numerical-figure reproduction code supporting the manuscript "Multi-sensor assessment of cropland flood exposure following extreme rainfall". The study describes the July–September 2023 event in Xiong'an, China, using multi-year rainfall and soil-moisture context, fixed cropland flood cohorts, optical water classifications, exploratory landscape strata and auxiliary InSAR line-of-sight summaries. The archive contains 31 CSV/JSON source-data files, two Python plotting scripts, a variable/file schema, methods and provenance, source references, licence information and SHA-256 checksums. Coverage includes CHIRPS rainfall maxima for 1991–2025, regional meteorological series for 2021–2025, ESA CCI analyses for 2021–2024, SMAP and IMERG event summaries for July–August 2023, and dated GFM and Sentinel-2 comparisons. Administrative and cropland statistics refer to the supplied 1,552.34-km² analysis boundary, not an extrapolation to the officially reported administrative area. The code reproduces manuscript Figures 1, 2 and 4 and the numerical panels 3b,c from the archived aggregates. Upstream satellite processing and the spatial map panel 3a require external inputs and are not reproduced by this release. Original satellite products, the supplied boundary geometry, map rasters and original InSAR/GNSS exports are not redistributed; provider sources are documented in the archive. The evidence is observational: dated re-detection is not proof of continuous inundation, satellite soil moisture is not field validation, and LOS change is not automatically vertical subsidence. Shared sensor lineage, overlapping footprints and reused controls limit independent replication. No independent local flood-accuracy or in-situ soil validation is supplied. Data and documentation: CC BY 4.0. Python code: MIT License; original third-party products retain their provider terms. Associated manuscript is prepared for submission; no publication or acceptance is asserted. Funding: 2025 Natural Science Foundation of Hebei Province, China (D2025403002); 2024 Hebei Province YanZhao Golden Platform Talent Program for Key Talents Project (B2024014). Funders provided financial support only. Contact: Weiguo Li, liweiguo@hgu.edu.cn.