DOI: 10.57760/sciencedb.ecodb.00204
title: A 30m spatial resolution primary productivity dataset for Northeast China in 2022
title: 2022年中国东北30m空间分辨率初级生产力数据集

description: Accurately estimating Gross Primary Productivity (GPP) in terrestrial ecosystems is crucial for gaining a deeper understanding of the carbon cycle within the ecosystem and for predicting climate change. Although many GPP datasets are available, they often have low resolution, typically around 500 meters or lower, which restricts their effectiveness in monitoring fragmented croplands and areas with high heterogeneity. In this study, we utilized optical satellite data from Landsat-8/9 and Sentinel-2A/B, along with meteorological data from ERA5, to generate a GPP dataset with a spatial resolution of 30 meters for three provinces in Northeast China in 2022. This dataset was developed based on the Vegetation Photosynthesis Model (VPM) and exhibited robust validation results when compared with SIF data and other existing GPP datasets. It provides a 30m resolution GPP product that significantly enhances the precision of carbon cycle research in Northeast China in 2022. This research underscores the feasibility of producing high spatial resolution GPP products using Landsat-8/9 and Sentinel-2A/B optical satellite data. The resulting dataset offers a more refined GPP estimate for studies related to the terrestrial carbon cycle.The final GPP dataset obtained through the VPM model has a spatial resolution of 30 meters and a temporal resolution of 8 days. It comprises 46 images stored in TIFF format, with each file representing the average GPP over 8 days. For example, 'NEC_GPP_2022001' represents the average GPP from January 1 to January 8, 2022, and so on. The last image 'NEC_GPP_2022361' represents the average GPP for the 5 days from December 27 to December 31, 2022. The unit is g C m-2 day-1
description: Accurately estimating Gross Primary Productivity (GPP) in terrestrial ecosystems is crucial for gaining a deeper understanding of the carbon cycle within the ecosystem and for predicting climate change. Although many GPP datasets are available, they often have low resolution, typically around 500 meters or lower, which restricts their effectiveness in monitoring fragmented croplands and areas with high heterogeneity. In this study, we utilized optical satellite data from Landsat-8/9 and Sentinel-2A/B, along with meteorological data from ERA5, to generate a GPP dataset with a spatial resolution of 30 meters for three provinces in Northeast China in 2022. This dataset was developed based on the Vegetation Photosynthesis Model (VPM) and exhibited robust validation results when compared with SIF data and other existing GPP datasets. It provides a 30m resolution GPP product that significantly enhances the precision of carbon cycle research in Northeast China in 2022. This research underscores the feasibility of producing high spatial resolution GPP products using Landsat-8/9 and Sentinel-2A/B optical satellite data. The resulting dataset offers a more refined GPP estimate for studies related to the terrestrial carbon cycle.The final GPP dataset obtained through the VPM model has a spatial resolution of 30 meters and a temporal resolution of 8 days. It comprises 46 images stored in TIFF format, with each file representing the average GPP over 8 days. For example, 'NEC_GPP_2022001' represents the average GPP from January 1 to January 8, 2022, and so on. The last image 'NEC_GPP_2022361' represents the average GPP for the 5 days from December 27 to December 31, 2022. The unit is g C m-2 day-1