Salinity stress and water availability inferred from satellite leaf area index assimilated into a water-energy-crop model
N. Paciolla, C. Corbari, G. Rossi
Politecnico Di Milano, Milan, Italy
D. Gabrieli, F. Morari
Dept. of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Legnaro, Italy
E. Zancanaro, P. Teatini
Dept. of Civil, Environmental and Architectural Engineering,
University of Padova, Padova, Italy
D. Skokovic, J. Sobrino
Universitat de Valencia, Valencia, Spain
ABSTRACT
Purpose Climate change, increasing aridity, water scarcity and population growth, enhancing food demand and
irrigated land expansion, are expected to increase the extent of salinity-affected areas. This study aims to combine
the crop-energy-water balance model FEST-EWB-SAFY with Leaf Area Index (LAI) and Land Surface Temperature (LST) data
from remote sensing to monitor maize development in a field with a shallow water table and highly affected by salinity.
Methods The FEST-EWB-SAFY model couples the distributed energy-water balance FEST-EWB model, which computes
time-continuous soil moisture and evapotranspiration, and the SAFY crop model for yield prediction. The model was
employed in synergy with satellite observations of LST and LAI. LST was used for the calibration/validation of the water
and energy balances, whereas LAI was used both for the calibration of crop parameters and a data assimilation scheme.
Results The data assimilation scheme was able to reproduce the observed spatial heterogeneity in crop development,
associated to the uneven water table depth and salinity distribution, as these effects were picked up from satellite.
A good correspondence was also found between modelled yield and the distributed samplings from a combined harvester
equipped with a yield monitor.
Conclusion The results, comparable to those obtained with the a posteriori calibration, show that data
assimilation of remote sensing observations allow to improve the model as the agricultural season progresses,
including information which is difficult to monitor continuously in-situ.
