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<p>Gross primary productivity (GPP) is a fundamental indicator of terrestrial carbon uptake and ecosystem functioning, yet integrated analyses of productivity and its environmental drivers are often hindered by inconsistencies in spatial resolution, projection, masking conventions, and temporal aggregation across datasets. Here, we present a scale-consistent 0.1° dataset of annual forest GPP and associated environmental drivers for mainland China spanning 1990–2018. The dataset integrates a flux-calibrated NIRv-based GPP product with climatic variables (temperature, precipitation, shortwave radiation, and soil moisture), topographic attributes, nighttime lights, and an annually derived forest fragmentation index (FFI), all harmonized to a unified WGS84 grid using reproducible preprocessing and consistent masking conventions. The dataset includes forest-cover fraction and transition products describing contemporary forest cover (2018), persistent forest cover (1990–2018), forest gain, loss, and transition products. This dataset standardizes preprocessing steps that are typically implemented inconsistently across studies, reducing methodological variability and enabling reproducible ecosystem analyses. Technical validation, including comparisons with the original NIRv-based GPP product and an independent consistency check using eddy covariance flux tower observations, indicates that aggregation to 0.1° preserves the overall behavior of the original product without introducing systematic degradation while enabling direct integration with harmonized environmental drivers. Comparative analyses show that harmonization reduces inconsistencies arising from mixed resolutions and maintains cross-variable coherence. The FFI introduces a landscape structural dimension, enabling joint analysis of productivity dynamics and spatial configuration within a unified framework. This dataset provides a nationally consistent, multi-decadal analytical foundation for integrated forest-climate studies, supporting reproducible modelling, cross-regional comparisons, and ecosystem studies.</p>

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A scale-consistent 0.1° dataset of forest productivity and environmental drivers for China (1990–2018)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月16日

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  • Pedro Cabral,
  • Chenxi Zhu,
  • Guojie Wang

摘要

Gross primary productivity (GPP) is a fundamental indicator of terrestrial carbon uptake and ecosystem functioning, yet integrated analyses of productivity and its environmental drivers are often hindered by inconsistencies in spatial resolution, projection, masking conventions, and temporal aggregation across datasets. Here, we present a scale-consistent 0.1° dataset of annual forest GPP and associated environmental drivers for mainland China spanning 1990–2018. The dataset integrates a flux-calibrated NIRv-based GPP product with climatic variables (temperature, precipitation, shortwave radiation, and soil moisture), topographic attributes, nighttime lights, and an annually derived forest fragmentation index (FFI), all harmonized to a unified WGS84 grid using reproducible preprocessing and consistent masking conventions. The dataset includes forest-cover fraction and transition products describing contemporary forest cover (2018), persistent forest cover (1990–2018), forest gain, loss, and transition products. This dataset standardizes preprocessing steps that are typically implemented inconsistently across studies, reducing methodological variability and enabling reproducible ecosystem analyses. Technical validation, including comparisons with the original NIRv-based GPP product and an independent consistency check using eddy covariance flux tower observations, indicates that aggregation to 0.1° preserves the overall behavior of the original product without introducing systematic degradation while enabling direct integration with harmonized environmental drivers. Comparative analyses show that harmonization reduces inconsistencies arising from mixed resolutions and maintains cross-variable coherence. The FFI introduces a landscape structural dimension, enabling joint analysis of productivity dynamics and spatial configuration within a unified framework. This dataset provides a nationally consistent, multi-decadal analytical foundation for integrated forest-climate studies, supporting reproducible modelling, cross-regional comparisons, and ecosystem studies.