Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation

Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environ...

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Bibliographic Details
Published inAgronomy (Basel) Vol. 9; no. 5; p. 255
Main Authors Novelli, Francesco, Spiegel, Heide, Sandén, Taru, Vuolo, Francesco
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2019
Subjects
Accuracy
Agricultural production
Calibration
Cereal crops
Crop growth
crop growth model
Crop yield
Crops
Data assimilation
Data collection
Environmental conditions
Environmental policy
Environmental testing
EPIC model
Fertilization
Growing season
Growth models
Leaf area
Leaf Area Index
Leaves
Nondestructive testing
Optimization
Physiology
Precipitation
Radiation
Remote sensing
Satellites
Sentinel-2
Variables
Vegetation
Wheat
Winter wheat
yield estimation
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Summary:Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.
ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy9050255