# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 2 Study Area and Data
### Satellite Data  
In this study, two types of time series satellite data (optical and SAR) were used. Multispectral Landsat 5, 7, and 8 level-1 images were acquired from the United States Geological Survey (USGS)  
Figure 1: Study area (Landsat 5 true color composition). (**a**) Site A is Sutter County in California State with the Landsat image collected on 26 July 2009 and (**b**) site B is Dangjin in South Korea with the Landsat image collected on 21 September 2003. Figure 2: Rice planting and harvest schedules, and satellite data availability. (path/row 44/33 (for site A) and 116/34 (for site B)). Three visible bands, Blue (0.45-0.53 \(\upmu\)m), Green (0.52-0.60 \(\upmu\)m), and Red (0.63-0.69 \(\upmu\)m), and three Infrared (IR) bands, near-infrared (NIR) (0.76-0.90 \(\upmu\)m), shortwave-infrared 1 (SWIR1) (1.55-1.75 \(\upmu\)m), and SWIR2 (2.08-2.35 \(\upmu\)m), at 30 m spatial resolution were used. A total of 16 Landsat 5 images from 2007 to 2009 for site A, and 11 images from 2003 to 2005 at site B were acquired (Tables 1 and 2). Unlike site A, where enough cloud free images for every month are available, it is hard to obtain clear images at site B during the rainy season (June to July). Figure 2 shows data availability with crop schedules by month. Atmospheric correction using ENVI Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) to Landsat images were conducted to produce scaled reflectance data. Then, Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) were calculated using Green, Red, and NIR reflectance data (Equations (1) and (2)). \[\mathrm{NDWI}=\frac{\mathrm{Green-NIR}}{\mathrm{Green+NIR}} \tag{1}\]  
\[\mathrm{NDVI}=\frac{\mathrm{NIR-Red}}{\mathrm{NIR+Red}} \tag{2}\]  
Multi temporal SAR images from the Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR) (site A) and RADARSAT-1 (site B) for horizontal transmit and horizontal receive (HH) polarization data were used. According to [PERSON] et al. [32] and [PERSON] et al. [33], among the SAR signals at different polarizations, horizontal (HH) and vertical (VV) polarizations, the HH backscattered signals are higher and are more dominated by specular reflection for the rice canopy and water surfaces than the VV backscattered ones. HH polarization data have been used in many paddy rice mapping studies [17; 32; 34; 35]. ALOS PALSAR L band fine beam data with HH polarization at 6.25 m spatial resolution were used. A total of 10 images from ALOS PALSAR between 2006 and 2009 were used for site A (Table 1). A total of 6 images collected from the RADARSAT-1 C band standard mode with HH polarization at 30 m spatial resolution between 2003 and 2005 were used for site B (Table 2). Preprocessing of SAR data was carried out with Alaska Satellite Facility (ASF) Map Ready software ([[http://www.asf.alaska.edu](http://www.asf.alaska.edu)]([http://www.asf.alaska.edu](http://www.asf.alaska.edu))), and the backscatter coefficient \(\sigma\)0 (dB) was calculated through Equation (3) [36]. \[\sigma\mathrm{0}=10\times\mathrm{log[DN]2+CF} \tag{3}\] \begin{table}
\begin{tabular}{c c} \hline
**ALOS PALSAR** & ** Landsat 5** \\ \hline
29 December 2007 & 15 January 2009 \\  & 31 January 2009 \\
26 March 2007 & 20 March 2009 \\
28 March 2008 & 2 April 2008 \\  & 5 April 2009 \\  & 21 April 2009 \\
13 May 2008 & 7 May 2009 \\  & 23 May 2009 \\  & 8 June 2009 \\  & 24 June 2009 \\
1 July 2009 & 10 July 2009 \\  & 26 July 2009 \\
13 August 2006 & 27 August 2009 \\
26 September 2007 & 7 September 2007 \\
1 October 2009 & 30 October 2009 \\
13 November 2008 & 15 November 2009 \\
27 December 2008 & \\ \hline \end{tabular}
\end{table} 
Table 1: Acquisition dates of satellite data at site A.  
where DN is the digital number of a SAR image and CF is a calibration factor. SAR data were resampled to a 30 m pixel size and were projected to the Universal Transverse Mercator (UTM) coordinate system to collocate with Landsat data.