3. Method of Transforming the Digital Number into Backscatter Coefficient
The following procedures are used to extract the value of the radar brightness for pixels in processed image for detected product of path image. If DN
j is the digital number, which represents the magnitude of the jth pixel from the start of a range line in the detected image data, then the corresponding value of radar brightness for the pixel is given by;
b0j = 10*log10
[(DNj2 + A3) / A2j] db (1)
Where A2
j is the scaling gain value for the j_th pixel, and A3 is the fixed offset. These values are obtained from Radiometric Data Records.
Radar brightness data may be converted to radar backscatter coefficient using the following equation;
s0j =
b0j +
10*log10(sin Ij) db (2)
Where I
j is the incidence angle at the j_th range pixel. This formula assumes that the earth is a smooth elllipsoid at sea level. Following procedures were then conducted to complete the analysis.
- Geocoding: RADARSAT data were projected onto 1: 50,000 scale topographical maps. Used map projection of these images was UTM and map system was Bessel. The resample images were prepared using nearest neighbor method. Resampled pixels were set to 12.5m.
- Selection of training fields: Using 1:50,000 map and Landsat TM, training fields of land use were selected.
- Transforming into backscatter coefficient: Using (1) and (2) equation, digital number were transformed into backscatter coefficient.
- Relationship between backscatter coefficient of rice and rice growth: Rice growth index of fresh weight, dry weight, LAI (Leaf Area Index) and height were measured and compared to backscatter coefficient.
4. Methods of Estimating the Paddy Field Area
The verification of the accuracy of estimation of paddy field areas was conducted as follows.
- Geocoding: Landsat TM data and RADARSAT data were projected onto 1: 50,000 scale topographical maps. Used map projection of these images was UTM and map system was Bessel. Nearest neighbor method was used for resampling. Resample pixels were set to 25m in Landsat TM data and 12.5m in RADARST data.
- Land cover classification: Respective Landsat TM data with bands 2, 3, 4, 5 and 7 were classified by an unsupervised method (using ISODATA method), in which 60 initial classes and 95 per cent convergence limit were used as parameters. After processing the data, 60 classes were given to grand truth data and re-coded to 8 classes.
- Combining two lands cover classified images: Using two classified images of Landsat TM, new classes were set. The new classes were defined as in Table 1.
- Extracting open water: Pixels labeled as open water were extracted from the land cover classification.
- Reduction of classes: Pixels isolated in the forest area and some classes of forest were re-coded to forest.
- Selection of paddy field candidates: Paddy field candidates were selected from the image processed by (3), (4) and (5). Paddy field candidates were selected previously.
- Extracting paddy field candidates: Water was extracted from the Paddy field candidates using processed image as given in (6).
- Processing district identification file: District boundaries were read from 1: 50,000 maps and its coverage file are created by Arc/Info. Each districts was identified by ID codes. This vector file was transformed in to raster file.
- Speckle noise suppression: Three methods of speckle noise suppression were tested and evaluated. These methods are Lee filter, Frost filter and MAP refined filter.
- Decision of threshold vale in paddy field: After processing speckle noise suppression, histograms of land use were calculated and threshold vale of paddy field was decided from them.
- Selecting paddy field areas from RADARSAT: Using threshold vale of paddy field, paddy field candidates were selected from RADARSAT. By overlaid two paddy field candidates that were processed from RADARSAT and Landsat TM, paddy field areas were selected.
- Sum up the paddy field areas for each district: Paddy field areas were summed up for each district using district file.
- Selecting paddy field areas from Landsat TM and comparing: Land cover classification obtained by processing Landsat TM data acquired on 17 June, 1997 was used for this purpose.
5. Results and Discussions
5.1 Monitoring for Paddy Field
Characteristics of backscatter coefficient were shown in Fig. 2. In general, urban areas had high coefficient throughout. High backscatter urban areas were urban1 and urban2 and in descending data set (July 28) they were high (about 5 dB) while in ascending data sets (May 17 and June 27) they were low (about -5dB). On the other hand, urban3 and urban4 had high backscatter in ascending data sets and while low backscater in descending data. This might have occurred due to the direction of houses or buildings.
Fig. 2 Backscatter coefficient of land use in 1998. Paddy1 and urban4 show standard deviation extents.
Although, backscatter coefficients of paddy field were increased from May 17 to June 27 it remain almost unchanged from June 27 to July 28. River and pond had low backscatter coefficients in all three data sets. The C band of SAR data showed that it is easy to distinguish paddy field from other land covers, when we use data obtained during flooded seasons and growing periods.
Fig. 3 Relationship between Backscatter and rice growth
Backscatter coefficient and rice growth index is related, especially backscatter and LAI is observed to lineal relationship (Fig3).
The study fields had complex land usage due to a mixture corps and growing at various stages. Backscatter coefficient of field area were decreased from May to July as the crop grows, ploughed field would be covered with crop covers.
