PHD POSITION: MULTI AND HYPERSPECTRALA REMOTE SENSING DATA FOR RETRIEVING CROP BIOPHYSICAL VARIABLES AND TOPSOIL PROPERTIES IN AGRICULTURAL FIELDS

 

The PhD is jointly supported by:

Sapienza Università di Roma Scuola Ingegneria Aerospaziale (SIA) and Dip. Di Ingegneria Astronautica, Elettrica e Energetica (DIAEE) and

the CNR Institute for Environmental Monitoring (IMAA)

 

The PhD scholarship, offered in the PhD course of Energy and Environment (https://www.uniroma1.it/it/pagina/dottorati-di-ricerca) 37° cycle, will be carried in the framework of the ESA project Dragon5 (http://dragon5.esa.int/) that shall consider the combine use of the Sentinels and the Chinese satellite data. The project partners are the Key Laboratory of Digital Earth Science Aerospace Information Research Institute, Chinese Academy of Sciences (AIR-CAS) and National Agriculture Information Engineering Technical Research Center (NERCITA). During the PhD stages in China are foreseen.

 

The goal of the research that will be carried out by the candidate is the concerns the use of multiplatform, multifrequency EO data and the use of biophysical models for retrieving crop biophysical variables and topsoil properties by using:

● inversion of Radiative Transfer models (e.g., PROSPECT-PRO and SAIL) by applying Spectral Indices or Machine Learning Regression or LUT-based algorithms.

● multivariate regression and machine learning algorithms to retrieve topsoil properties (soil texture and SOC content).

 

The 3 years PhD will be carried out at Dip. Di Ingegneria Astronautica, Elettrica ed Energetica (Via Salaria 851, Roma)

PhD is reserved to EU candidate.

Deadline for the application 20 July 2021.

 

Eligibility and desired competences:

● Master level degree in Engineering Mathematics, Physics, Informatics, Statistics, Agricultural Sciences or similar

● Master thesis work on remote sensing

● Good coding skills in Matlab or Python and/or IDL or R

● Expertise in the use and processing of satellite remote sensing imagery. Experience in ML and PLSR techniques is preferred

● Fluency in English

● Adaptation capability, flexibility, willingness to travel

 

 

For any question write to:

 

giovanni.laneve@uniroma1.it or

 

stefano.pignatti@imaa.cnr.it