Abstract

The long-term estimation of grassland aboveground biomass (AGB) is important for grassland resource management in the Three-River Headwaters Region (TRHR) of China. Due to the lack of reliable grassland AGB datasets since the 1980s, the long-term spatiotemporal variation in grassland AGB in the TRHR remains unclear. In this study, we estimated AGB in the grassland of 209,897 km2 using advanced very high resolution radiometer (AVHRR), MODerate-resolution Imaging Spectroradiometer (MODIS), meteorological, ancillary data during 1982–2018, and 75 AGB ground observations in the growth period of 2009 in the TRHR. To enhance the spatial representativeness of ground observations, we firstly upscaled the grassland AGB using a gradient boosting regression tree (GBRT) model from ground observations to a 1 km spatial resolution via MODIS normalized difference vegetation index (NDVI), meteorological and ancillary data, and the model produced validation results with a coefficient of determination (R2) equal to 0.76, a relative mean square error (RMSE) equal to 88.8 g C m−2, and a bias equal to −1.6 g C m−2 between the ground-observed and MODIS-derived upscaled AGB. Then, we upscaled grassland AGB using the same model from a 1 km to 5 km spatial resolution via AVHRR NDVI and the same data as previously mentioned with the validation accuracy (R2 = 0.74, RMSE = 57.8 g C m−2, and bias = −0.1 g C m−2) between the MODIS-derived reference and AVHRR-derived upscaled AGB. The annual trend of grassland AGB in the TRHR increased by 0.37 g C m−2 (p < 0.05) on average per year during 1982–2018, which was mainly caused by vegetation greening and increased precipitation. This study provided reliable long-term (1982–2018) grassland AGB datasets to monitor the spatiotemporal variation in grassland AGB in the TRHR.

Highlights

  • The second step was upscaling grassland aboveground biomass (AGB) from ground observations to a 1 km spatial resolution based on gradient boosting regression tree (GBRT) model using all the input and output variables

  • The results show that the GBRT model yields the best validation performance with the highest

  • The results show that the GBRT model yields the best validation performance with the highest R2

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Summary

Introduction

Grassland aboveground biomass (AGB) is defined as the organic matter resulting from grass photosynthetic activity [1,2,3]. It is an important indicator of carbon cycling over grassland ecosystems and provides technical and theoretical support for grassland utilization decisions and resource management [4,5,6,7]. Grassland is a major component of the Three-River Headwaters Region (TRHR), which is an important nature reserve in the Qinghai-Tibet Plateau [8]. Spatiotemporal variation in AGB can reflect the carbon budget in grassland ecosystems [9,10].

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