RESUMO
Estimating gross primary production (GPP) of terrestrial ecosystems is important for understanding the terrestrial carbon cycle. However, existed nationwide GPP datasets are primarily driven by coarse spatial resolutions (≥500 m) remotely sensed data, which fails to capture the spatial heterogeneity of GPP across different ecosystem types at land surface. This paper introduces a new GPP dataset, Hi-GLASS GPP v1, with a fine spatial resolution (30-m) and monthly temporal resolution from 2016 to 2020 in China. The Hi-GLASS GPP v1 dataset is generated from 30-m Landsat data using a process based light use efficiency model. The Hi-GLASS GPP v1 model integrates a detailed map of maize plantations, a crucial C4 crop in China known for its higher photosynthetic efficiency compared to C3 crops. This inclusion helps correct the underestimation of GPP that typically occurs when all croplands are categorized as C3. The Hi-GLASS GPP v1 dataset demonstrates a robust correlation with GPP data derived from eddy covariance towers, thereby enabling a more accurate assessment of terrestrial carbon sequestration across China.
RESUMO
China is the world's second-largest maize producer, contributing 23% to global production and playing a crucial role in stabilizing the global maize supply. Therefore, accurately mapping the maize distribution in China is of great significance for regional and global food security and international cereals trade. However, it still lacks a long-term maize distribution dataset with fine spatial resolution, because the existing high spatial resolution satellite datasets suffer from data gaps caused by cloud cover, especially in humid and cloudy regions. This study aimed to produce a long-term, high-resolution maize distribution map for China (China Crop Dataset-Maize, CCD-Maize) identifying maize in 22 provinces and municipalities from 2001 to 2020. The map was produced using a high spatiotemporal resolution fused dataset and a phenology-based method called Time-Weighted Dynamic Time Warping. A validation based on 54,281 field survey samples with a 30-m resolution showed that the average user's accuracy and producer's accuracy of CCD-Maize were 77.32% and 80.98%, respectively, and the overall accuracy was 80.06% over all 22 provinces.