Abstract
Understanding crop phenology is crucial for predicting crop yields and identifying potential risks to food security. The objective was to investigate the effectiveness of satellite sensor data, compared to field observations and proximal sensing, in detecting crop phenological stages. Time series data from 122 winter wheat, 99 silage maize, and 77 late potato fields were analyzed during 2015–2017. The spectral signals derived from Digital Hemispherical Photographs (DHP), Disaster Monitoring Constellation (DMC), and Sentinel-2 (S2) were crop-specific and sensor-independent. Models fitted to sensor-derived fAPAR (fraction of absorbed photosynthetically active radiation) demonstrated a higher goodness of fit as compared to fCover (fraction of vegetation cover), with the best model fits obtained for maize, followed by wheat and potato. S2-derived fAPAR showed decreasing variability as the growing season progressed. The use of a double sigmoid model fit allowed defining inflection points corresponding to stem elongation (upward sigmoid) and senescence (downward sigmoid), while the upward endpoint corresponded to canopy closure and the maximum values to flowering and fruit development. Furthermore, increasing the frequency of sensor revisits is beneficial for detecting short-duration crop phenological stages. The results have implications for data assimilation to improve crop yield forecasting and agri-environmental modeling.
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Claims 7
Claim · model-written summary
Spectral signals were crop specific and sensor independent.
Not reportedTaxon
Verbatim
e0“The spectral signals derived from Digital Hemispherical Photographs (DHP), Disaster Monitoring Constellation (DMC), and Sentinel-2 (S2) were crop-specific and sensor-independent.”
Claim · model-written summary
fAPAR models fitted better than fCover models.
Not reportedTaxon
Verbatim
e0“Models fitted to sensor-derived fAPAR (fraction of absorbed photosynthetically active radiation) demonstrated a higher goodness of fit as compared to fCover (fraction of vegetation cover)”
Claim · model-written summary
Model fits were best for maize, then wheat and potato.
Zea maysOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“with the best model fits obtained for maize, followed by wheat and potato”
Claim · model-written summary
S2 fAPAR variability decreased through the growing season.
Not reportedTaxon
Verbatim
e0“S2-derived fAPAR showed decreasing variability as the growing season progressed.”
Claim · model-written summary
Double sigmoid inflection points corresponded to stem elongation and senescence.
- Outcome
- inflection pointse0
- Comparator
- stem elongation (upward sigmoid) and senescence (downward sigmoid)e0
Not reportedTaxonDirection
Verbatim
e0“The use of a double sigmoid model fit allowed defining inflection points corresponding to stem elongation (upward sigmoid) and senescence (downward sigmoid)”
Claim · model-written summary
The upward endpoint corresponded to canopy closure, and maxima to flowering and fruit development.
Not reportedTaxonDirection
Verbatim
e0“while the upward endpoint corresponded to canopy closure and the maximum values to flowering and fruit development”
Claim · model-written summary
More frequent revisits were beneficial for detecting short crop stages.
- Treatment
- increasing the frequency of sensor revisitse0
- Outcome
- detecting short-duration crop phenological stagese0
Not reportedTaxon
Verbatim
e0“increasing the frequency of sensor revisits is beneficial for detecting short-duration crop phenological stages”