DOI: 10.57760/sciencedb.21262
title: Spatial Datasets of Biomass of Different Grassland Types in Xinjiang from 2000 to 2023
title: 2000–2023年新疆天然草地不同草地类型生物量空间数据集

description: Located in the heart of Central Asia, Xinjiang is a typical mountain-oasis-desert composite ecosystem, shaped by its temperate continental arid and semi-arid climate combined with the unique geomorphological structure of "three mountains flanking two basins". This configuration has created complex and diverse grassland ecosystems with different vegetation types and characteristics at different altitudes in the mountainous vertical zones. However, upscaling limited observational biomass data to regional spatial datasets presents significant challenges. These challenges are further exacerbated by the predominant allocation of grassland biomass to below-ground components, which suffers from sparse sampling coverage, inconsistent measurement methods, shallow root sampling depths (typically &lt; 1 m) and, consequently, a critical lack of spatial data on below-ground biomass (BGB). These limitations introduce significant uncertainties into estimates of natural grassland productivity and carrying capacity in Xinjiang, while obscuring regional-scale carbon source/sink mechanisms. In this study, we first used a large amount of above-ground biomass (AGB) sample point data of different grassland types in Xinjiang obtained from field observation, combined with vegetation index products and meteorological data, and constructed an AGB estimation model of grassland by using the random forest algorithm to establish a spatial dataset of AGB of different grassland types in natural grassland in Xinjiang from 2000 to 2023. Second, we constructed a corresponding spatial BGB dataset by combining the AGB results with field-measured BGB of different grassland types (up to 1 m depth) and root-shoot ratio data. The creation and open-access provision of these spatially explicit AGB/BGB datasets for Xinjiang's natural grasslands provide critical baseline data for accurately assessing grassland production potential and carbon sequestration capacity, and optimizing management strategies for grassland resources. This work is particularly important for unlocking the ecological and economic potential of grassland ecosystems in Xinjiang.
description: Located in the heart of Central Asia, Xinjiang is a typical mountain-oasis-desert composite ecosystem, shaped by its temperate continental arid and semi-arid climate combined with the unique geomorphological structure of "three mountains flanking two basins". This configuration has created complex and diverse grassland ecosystems with different vegetation types and characteristics at different altitudes in the mountainous vertical zones. However, upscaling limited observational biomass data to regional spatial datasets presents significant challenges. These challenges are further exacerbated by the predominant allocation of grassland biomass to below-ground components, which suffers from sparse sampling coverage, inconsistent measurement methods, shallow root sampling depths (typically &lt; 1 m) and, consequently, a critical lack of spatial data on below-ground biomass (BGB). These limitations introduce significant uncertainties into estimates of natural grassland productivity and carrying capacity in Xinjiang, while obscuring regional-scale carbon source/sink mechanisms. In this study, we first used a large amount of above-ground biomass (AGB) sample point data of different grassland types in Xinjiang obtained from field observation, combined with vegetation index products and meteorological data, and constructed an AGB estimation model of grassland by using the random forest algorithm to establish a spatial dataset of AGB of different grassland types in natural grassland in Xinjiang from 2000 to 2023. Second, we constructed a corresponding spatial BGB dataset by combining the AGB results with field-measured BGB of different grassland types (up to 1 m depth) and root-shoot ratio data. The creation and open-access provision of these spatially explicit AGB/BGB datasets for Xinjiang's natural grasslands provide critical baseline data for accurately assessing grassland production potential and carbon sequestration capacity, and optimizing management strategies for grassland resources. This work is particularly important for unlocking the ecological and economic potential of grassland ecosystems in Xinjiang.