Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop
growth assessment
Abstract
Accurate and high-resolution spatio-temporal information about crop
phenology obtained from Synthetic Aperture Radar (SAR) data is an
essential component for crop management and yield estimation at a local
scale. Crop growth monitoring studies seldom exploit complete
polarimetric information contained in dual-pol GRD SAR data. In this
study, we propose three polarimetric descriptors: the pseudo
scattering-type parameter (θc), the pseudo scattering entropy parameter
(Hc), and the co-pol purity parameter (mc) from dual-pol S1 GRD SAR
data. We also introduce a novel unsupervised clustering framework using
Hc and θc with six clustering zones to represent various scattering
mechanisms. We implemented the proposed algorithm on the cloud-based
Google Earth Engine (GEE) platform for Sentinel-1 SAR data. We have
shown the sensitivity of these descriptors over a time series of data
for wheat and canola crops at a test site in Canada. From the leaf
development stage to the flowering stage for both crops, the pseudo
scattering-type parameter θc changes by approximately 17°. Moreover,
within the entire phenology window, both mc and Hc varies by about 0.6.
The effectiveness of θc and Hc to cluster the phenological stages for
the two crops is also evident from the clustering plot. During the leaf
development stage, about 90 % of the sampling points were clustered
into the low to medium entropy scattering zone for both the crops.
Throughout the flowering stage, the entire cluster shifted into the high
entropy vegetation scattering zone. Finally, during the ripening stage,
the clusters of sample points were split between the high entropy
vegetation scattering zone and the high entropy distributed scattering
zone, with > 55 % of the sampling points in the high
entropy distributed scattering zone. This innovative clustering
framework will facilitate
the operational use of S1 GRD SAR data for agricultural applications.
This article is submitted to ISPRS Journal of Photogrammetry and Remote
Sensing