Context and objectives
The BELSAR-Publication project was launched to capitalize on some of the more recent scientific results
obtained in the BELSAR-Science project, and to facilitate access to the BELSAR-Campaign dataset.
Within this framework, the two following objectives were pursued:
1. Improve the accuracy of the maize green area index and surface soil moisture retrieval algorithm
(developed in BELSAR-Science) using the Tor Vergata (ToV) model;
2. Document and disseminate the updated BELSAR-Campaign dataset through the publication of a
data paper in a peer-reviewed journal.
Project outcome
1. PAPER ON THE USE OF THE TOV MODEL FOR THE RETRIEVAL OF GAI AND SSM IN MAIZE
FIELDS TO BE SUBMITTED IN A PEER-REVIEWED JOURNAL/CONFERENCE
The algorithm envisaged at the start of the project did not work as expected. Work on it nevertheless
resulted in a conference paper entitled "Physics-Based ML and Polarimetric SAR for Soil Moisture
Retrieval" written in by Lorenzo G. Papale, Prof. Fabio Del Frate, Prof. Leila Guerriero, Prof. Giovanni
Schiavon from Tor Vergata University, and Jean Bouchat from UCLouvain. The paper has been presented
and will be published in the proceedings of the 2023 International Geoscience and Remote Sensing
Symposium (IGARSS) taking place in Pasadena (CA), USA.
2. DATA PAPER DOCUMENTING ALL THE DATA FROM BELSAR ALONGSIDE AN ANALYSISREADY
DATABASE ACCESSIBLE AS OPEN SOURCE DATA
A data paper was written in collaboration with Karlus A. C. de Macedo from MetaSensing BV and
submitted to Nature’s Scientific Data alongside the analysis-ready database and the codes used to build
it from the several sources of data from BELSAR-Campaign.
This data paper is available here: https://www.nature.com/articles/s41597-024-03320-1.
Project leader(s): | UCL - Environmental Sciences | |||||
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