DOI: 10.23728/b2share.5bc1b1eaaff745328a2132febb68b6b7
title: Hainich - Gross Primary Production (2019-2023)

description: Gross Primary Production (GPP) represents the total amount of carbon fixed by plants through photosynthesis in an ecosystem over a specific period. GPP data products are derived using a data-driven approach that integrates Earth observation data with in-situ carbon flux measurements. Specifically, GPP estimations combine Sentinel-2 multispectral imagery with carbon flux data from eddy covariance towers, employing the XGBoost machine learning algorithm for prediction. The resulting GPP maps are generated at a 10-meter spatial resolution and a temporal frequency up to 5 days, covering the period from March 2017 to December 2023. The temporal resolution is contingent upon 50% free-cloud conditions in the area of interest, with lower frequencies occurring during periods of high cloud coverage. The spatial extent of the GPP maps corresponds to the boundaries of long-term observation sites as recorded in the DEIMS-SDR registry (e.g., https://deims.org/5b409a72-2a45-4238-a501-e24f1a2900db). For sites where the boundary area is smaller than 1 km², or if only point coordinates are available in DEIMS-SDR, the maps are constrained to a 1 km x 1 km bounding box.
description: The methodology integrates multiple data sources via machine learning techniques to estimate Gross Primary Production (GPP) across different ecosystems. The process begins with data pre-processing, including the selection of sites based on criteria such available vegetation information, at least three full years of eddy covariance flux data. GPP and environmental data (e.g. air temperature, vapor pressure deficit, etc.) are extracted from the ICOS database across different ecosystem types. Then, different remote sensing (RS) indices (e.g NDVI, EVI, etc.) are estimated in GEE using Sentinel-2 MSI data as the mean value of the pixels found inside the climatological footprint 70 (an area were 70% of the GPP measurements are coming from). Both data coming from ICOS dataset and RS indices are used as predictors for the model. The data is split, with 70% used for training and 30% for testing. In the model setup, an XGBoost model is trained using the selected environmental and RS based indices. The model parameters are fine-tuned to improve accuracy. The remaining 30% of testing data is used to evaluate the model’s performance by comparing its predictions against in-situ GPP data. Error metrics like Mean Absolute Error (MAE), Root Mean Absolute Error (RMAE), and R² are provided. The maps computation phase applies the trained model to ecosystem boundaries from the DEIMS website to generate 5-day GPP maps.
description: Acknowledgement 

This work on the AGAME Gross Primary Production data product is funded by the European Space Agency (ESA, contract no. 4000143740/24/I-AG) in the frame of the GEOSS Platform Plus project (Horizon Europe, GA No. GA.Nr. 101039118). The work done is based on the requirements from eLTER contributing in addition to the eLTER Site Information Cluster. In-situ data for model calibration and validation has been derived from the ICOS Carbon Portal.