Page 384 - Special Topic Session (STS) - Volume 4

STS2319 Lakshman N. R. et al.
level yield data for Thai Binh. Results show that while the Landsat–MODIS
fused data does not necessarily show benefits for paddy rice mapping, it has
provided great benefits for crop yield estimation. Only through the fusion data
from Landsat and MODIS can we recover the peak growth trajectory of
vegetation indexes. This information is the most critical input for our current
algorithm. Our results also confirm the value of optical data for crop yield
estimation if the cloudiness issue can be alleviated or overcome to some
degree. We recognize that the current fusion approach still has room for
improvement as has been reviewed by Gao et al. (2015), and as is being further
improved by Zhu et al. (2016).
One possible issue here is how to best utilize the Landsat–MODIS fused
data and original Landsat data. More advanced smoothing or weighted
regression approaches are needed to deal with the possible discrepancy
between the fused and original data. Meanwhile, emerging new datasets of
surface reflectance, such as Sentinel-2 (20 m resolution, 16-day revisiting
frequency) and Project for On-Board Autonomy - Vegetation (PROBA-V) from
Satellite Pour l'Observation de la Terre- VEGETATION (SPOT-VGT) (100 m
resolution, 16-day revisiting frequency), can further improve the temporal and
spatial samplings to alleviate cloudiness issue in tropics. New fusion
algorithms thus should consider multiple sources of data for fusion, instead of
only for Landsat and MODIS.

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