Maria Fantappiè
Dr. Maria Fantappiè PhD
Address: CREA, Via Di Lanciola 12/A – 50125 – Firenze, Italy.
http://orcid.org/0000-0001-6223-1099
Dr. Maria Fantappiè is a senior researcher at CREA Research Center for Agriculture and Environment, Italy. She is a soil scientist, and she holds a PhD in Environmental Agronomy. She is representative for Italy of the INSII – GSP – FAO network, the network of Global Soil Partnership – International Network of Soil Information Institutions, as she supports CREA in the holding and maintaining of the national soil database of Italy. Her main topics of research include digital soil mapping, Italian soil geodatabase, soil organic carbon modelling, climate change, land use and management change, soil erosion modelling, pedology, participatory research. She has been Pillar 4 representative for Europe of the FAO-GSP. She has coordinated for EJP SOIL Programme (H2020, GA 862295), the working package 6 dedicated to the “Supporting harmonised soil information and reporting” and contributing to the establishment of a continuous dialogue between policy and research on the topics of soil data standardization, harmonisation, soil health monitoring, digital soil mapping, soil data sharing and soil data governance. She has coordinated a focus group on 'How to harmonise public and private datasets for mapping and monitoring soil carbon dynamics?' during the CREDIBLE carbon farming project, and she is currently involved in MRV4SOC and Soilwise projects.
Sessions
In the framework of carbon farming, remote sensing (RS) is used to produce soil property maps for the upscaling of soil carbon stocking potentials, as modeled in in-field simulations. Reducing the uncertainty of the soil maps produced is a key element to obtain reliable upscaling products and finally calculate credible carbon credit potentials of the interested territories. The MRV4SOC project have tested the in-situ simulation of a RothC model in 3 demonstration sites in central-northern Italy, to calculate carbon credits of croplands and agroforestry. The soil input parameters used were soil organic carbon stock of the first 30 cm depth, and soil textural fractions, clay and sand. The MRV4SOC project has tested different procedures to produce soil map input parameters by remote sensing, such as: 1) a calibration of the bare soil composite of Europe SoilSuite (SRC), 2) a calibration of Sentinel and ENMAP spectral libraries using local field and lab-collected soil spectral libraries with VNIR spectroradiometers, and 3) a calibration of the SRC using a data augmentation strategy. AI models used for each calibration cases were: for case 1) quantile random forest (QRF) models, for case 2) XGBoost models, and for case 3) QRF and convolutional neural networks (CNN). The three strategies showed high calibration performance, but validation performance differs based on the model and strategy used, as well as the uncertainty of products.
Authors: Carlos Lozano Fondon, carlos.lozanofondon@crea.gov.it; Kevin Küelh, kevin.kuehl@dlr.de; Dimitra Palanza, dpalant@auth.gr; Konstantinos Karyotis, kkaryiotis@auth.gr; Costanza Andrenelli, mariacostanza.andrenelli@crea.gov.it; Roberto Barbetti, roberto.barbetti@crea.gov.it; Maria Fantappiè, maria.fantappie@crea.gov.it.