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Guan, K., Li, Z., Rao, L.N., Gao, F., Xie, D., Hien, N.T. and Zeng, Z. (2018) Mapping Paddy Rice Area and Yields over Thai Binh Province in Vietnam from MODIS, Landsat, and ALOS-2/PALSAR-2. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 2238-2252.
https://doi.org/10.1109/JSTARS.2018.2834383

has been cited by the following article:

• TITLE: Crop Calendar Mapping of Bangladesh Rice Paddy Field with ALOS-2 ScanSAR Data

  AUTHORS: Md. Rahedul Islam

  KEYWORDS: K-Means++, Unsupervised Classification, Moderate Resolution Imaging Spectroradiometer (MODIS), Backscatter Co-Efficient, Field Data

  JOURNAL NAME: Advances in Remote Sensing, Vol.10 No.3, September 30, 2021

  ABSTRACT: 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.