Rice
paddy mapping with optical remote sensing is challenging in Bangladesh due to
the heterogeneous cropping pattern, fragmented field size and cloud cover during the growing period. The
high-resolution Synthetic Aperture Radar (SAR) sensor is the potential
alternate to mapping rice area in Bangladesh.
The L-band SAR sensor onboard Advanced Land Observing Satellite (ALOS) acquires multi-polarization and
multi-temporal images are a very useful tool for rice area mapping. In
this study, we used ALOS-2 ScanSAR dual (HH+HV)
polarized time series data in the study area. We used orthorectification and
slope corrected backscatter (sigma-naught) images and median filtering (3 × 3)
window for image processing. The unsupervised classification with the k-means++
algorithm is used for initial clustering (20 categories) of images over the
study area. The GPS location of rice paddy field with cropping pattern over
study area uses for classifying the different rice-growing season from the
k-means clustering data. The result is compared with the moderate resolution
imaging spectroradiometer (MODIS) based rice area and national statistical
agricultural yearbook statistics. The results show that, based on the MODIS
based rice map, the rice fields can be mapped with a conditional Kappa value of
0.68 and at user’s and producer’s accuracies of 86% and 90%, respectively. The
large commission error primarily came from confusion between wet season Aus
rice and others crop, Aus-Amon and Boro-Aus-Amon cropping pattern because of their
similar backscatter amplitudes and temporal similarities in the rice growing
season. The relatively high rice mapping accuracy in this study indicates that
the ALOS/PALSAR-2 data could provide useful information in rice cropping
management in subtropical regions such Bangladesh.
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