Rice paddy mapping with optical remote sensing is challenging in Bangladesh due to the heterogeneous cropping pattern, fragmented field size and cloud </span><span style="font-family:Verdana;">co...Rice paddy mapping with optical remote sensing is challenging in Bangladesh due to the heterogeneous cropping pattern, fragmented field size and cloud </span><span style="font-family:Verdana;">cover during the growing period. The high-resolution Synthetic Aperture</span><span style="font-family:Verdana;"> Radar (SAR) sensor is the potential alternate to mapping rice area in Bangla</span><span style="font-family:Verdana;">desh. The L-band SAR sensor onboard Advanced Land Observing Satellit</span><span style="font-family:Verdana;">e (</span><span style="font-family:Verdana;">ALOS) acquires multi-polarization and multi-temporal images are </span><span style="font-family:Verdana;">a very useful tool for rice area mapping. In this study, we used ALOS-2 ScanSAR dual (HH</span></span><span style="font-family:""> </span><span style="font-family:Verdana;">+</span><span style="font-family:""> </span><span style="font-family:Verdana;">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 rel展开更多
文摘Rice paddy mapping with optical remote sensing is challenging in Bangladesh due to the heterogeneous cropping pattern, fragmented field size and cloud </span><span style="font-family:Verdana;">cover during the growing period. The high-resolution Synthetic Aperture</span><span style="font-family:Verdana;"> Radar (SAR) sensor is the potential alternate to mapping rice area in Bangla</span><span style="font-family:Verdana;">desh. The L-band SAR sensor onboard Advanced Land Observing Satellit</span><span style="font-family:Verdana;">e (</span><span style="font-family:Verdana;">ALOS) acquires multi-polarization and multi-temporal images are </span><span style="font-family:Verdana;">a very useful tool for rice area mapping. In this study, we used ALOS-2 ScanSAR dual (HH</span></span><span style="font-family:""> </span><span style="font-family:Verdana;">+</span><span style="font-family:""> </span><span style="font-family:Verdana;">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 rel
