Sino - Italian
“Development of analysis and assimilation techniques of new advanced Earth observation satellite data into crop models: the case study of cereal crops in Italy and China”.
The project is participated by an Italian and Chinese team pertaining to Universities and research Institutions covering different expertise from optical to radar remote sensing processing for agronomical sciences, and aims to exploit the combined use of optical and radar satellite data to retrieve bio-physical and/or bio-chemical characteristics of soil and crops.
Over recent years the availability of data for Earth Observation (EO) multisource / platform has greatly increased. The availability of EO data is enabling the improvement of techniques for retrieving environmental variables (quantitative remote sensing). These variables, however, rarely are used in crop models to derive proxy variables (e.g. yield, consumption of nitrates, proteins content etc.) of strong operational value. To date, in fact, the methods for monitoring crops and in particular of cereal crops (e.g. wheat, rice, maize), both at the local and regional level based on the use of remote sensing data, are still far from being effective and operational. This is because they are not yet able to monitor the various stages of agricultural activities, detect the crops temporal dynamics in a systematic way to be functional for defining corrective actions at field scale during the growing phase.
The synergic use of radar and multispectral optical data (considering the lack of hyperspectral data set until 2017/2020) will be addressed to produce a set of variables functional to train/assimilate in dynamic mode crop growing models (DSSAT will be primarily used) to derive useful information through the growing season (e.g. fertilization) and post harvesting (e.g. nitrate consumption, proteins etc.).
The project is focussed not only on the development of novel techniques for the retrieval of vegetation variables useful for early stress detection or on the validation of optical and radar processing/retrieval techniques, but also on assessing the impact that these agronomical variables have when applied to crop growing models configured at the field scale.