Aboveground biomass
The compressed files to be uploaded include three types: tif, csv, and py files.The compressed files to be uploaded include three types: tif, csv, and py files. These documents are directly related to our article.The following is a brief overview of the following files:
tif: 4160 training set images and 40 test set images.
csv: The AGB value corresponding to each image.
py：The code of data augmentation.
We hope that the documents we uploaded can make some contribution to the research of AGB estimation
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Aboveground biomass (AGB) is a vital indicator for studying the carbon sink in forest ecosystems. Semi-arid forests harbor substantial carbon storage but received little attention as high spatial-temporal heterogeneity. This study assessed the performance of different data sources (annual monthly time-series radar: Sentinel-1 (S1), annual monthly time-series optical: Sentinel-2 (S2), and single-temporal airborne LiDAR) and seven prediction approaches to map AGB in the semi-arid forests at the border between Gansu and Qinghai provinces in China.
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