Context and objectives
The overall objective of the research was to develop a regional crop growth model with a profound hydrological basis, which is frequently updated using remote sensing data. Currently, the potential of remote sensing in crop growth modelling has not been fully explored, and is mainly restricted to the growing conditions monitoring of all the crops mixed (1-km data). The challenge was to extend the use of remote sensing to crop modelling through assimilation of biophysical parameters observed from multiple sensors including radar systems. As crop development and yield strongly depend on the availability of water in the upper layers of the soil on one hand and the crop remote sensing can take advantage of moisture information on the other hand, a joint effort from both the hydrological and agricultural communities was needed. For this reason, special attention was given to the coupling of the crop model to a spatially distributed hydrological model in order to obtain good estimates of the crop parameters and of the soil water content available for the different crops.
Expected scientific results
The key findings of the STEREOCROP approach with regards to regional crop monitoring are as follows:
(i) a set of large parcels better observed by remote sensing systems can be representative of the entire regional distribution of fields allowing the monitoring to focus only on these;
(ii) the soil roughness can hardly be measured at regional scale in operational conditions in spite of proposed improvements for its measurements (model of 4-m profiles, photogrammetric restitution) and therefore, the soil roughness uncertainty has to be integrated in the retrieval process (possibilitic approach, etc);
(iii) only the relative temporal evolution of the soil moisture content can be monitored using catchment average soil moisture monitoring sites;
(iv) the top soil moisture can be estimated thanks to the coupling of a hydrological model and a crop growth model providing critical information for LAI retrieval from SAR;
(v) the accuracy of LAI retrieval from optical and from SAR data is of the same order of magnitude and SAR systems can effectively complement the optical time series, in particular for the critical fast-growing period;
(vi) the coupling of the crop growth model with the hydrological model allows retrieving efficiently the crop LAI from SAR without any soil measurement but using only the hourly rainfall distribution;
(vii) the Ensemble Kalman Filter method is an efficient method for the data assimilation in the TOPCROP model allowing to take into account the respective accuracy of the different sources of information.
|Project leader(s):||UCL - Environmental Sciences|