Data filter includes: “data filter,” “ROI extraction,” and “classification” modules.
Data filter: Filter the data of the target image according to the custom setting. After clicking the button, the calculation dialog box pops up (Figure 2-57). Select the loaded satellite data and set the logical relationship between the operations, value, and condition (single condition also works) of the filter condition. Click “OK” to start data filtering. Data filtering result automatically displayed on 3D geosphere (Figure 2-58).
Figure 2‑57. Satellite data filtering. The data of the monthly average salinity of the SMAP data set in August 2017, with values greater than 5 and less than 31 being selected.
Figure 2‑58. Data filtering results: (a) February 2017, (b) May 2017, (c) August 2017, and (d) November 2017. The color scale indicates the areas with salinity greater than 5 and less than or equal to 31. By filtering the salinity data, the Changjiang River plume in different months can be seen clearly.
ROI extraction: Extract the area in the target layer according to the custom setting. Different from the previous function, the extracted region is only a single value image.
Users can restrict conditions for the target layer of single image, or select interest areas for different images and conditions. After clicking the button, the calculation dialog box pops up
(Figure 2-59). On the right, [+] or [-] can increase or decrease the number of conditions, respectively. Select the loaded satellite data, set the logical relationship between the symbols, values, and conditions of the selection, and then click “OK” to extract the region of interest. It automatically displays the extracted result on the 3D geosphere (Figure 2-60).
Figure 2‑59. ROI extract. The chlorophyll concentration above 1 mg/m3 on November 8, 2005 in the study area is extracted from the ESACCI data set.
Figure 2‑60. Images of (a) November 8, 2015; (c) November 9, 2015; (e) November 10, 2015 and (g) November 12, 2015, showing the 4-km resolution chlorophyll concentration in the western South China Sea from the ESACCI data set. ROI extraction results of (b) November 8, 2015; (d) November 9, 2015; (f) November 10, 2015; and (h) November 12, 2015, showing the area where the algal bloom occurred. The red color represents the area where chlorophyll concentration is greater than 1mg/m3. The evolution of algal blooms from explosion to extinction can be observed by the extraction of interest area, and the area of algal blooms can be calculated by counting the areas of ROI extraction results.
Classification: the classification of satellite data is mainly based on a customized classification standard for one or more images. After clicking on the button, dialog box pops up (Figure 2-61). [+] [-] on the right side can increase or decrease in number of classification criteria. Choose the loaded satellite data, in turn, fill in the interval value, set up the standard logic relationship, and click on “OK” to start data classification. It will automatically show results on the 3D geosphere (Figure 2-62).
Figure 2‑61. Data classification setting. In Hirata et al. (2008), chlorophyll concentration data were used to distinguish the dominant phytoplankton species: chlorophyll concentration ranging 0-0.25mg /m3 is dominated by pico-phytoplankton (0.2-2 μm); chlorophyll concentration ranging 0.25-1.3mg /m3 is dominated by nano-phytoplankton (2-20μm); chlorophyll concentration greater than 1.3 mg/m3 is dominated by micro-phytoplankton (50-1000μm). After Hirata et al (2008). [An absorption model to determine phytoplankton size classes from satellite ocean colour. Satellite of Environment, 112(2008), 3153-3159]
Figure 2‑62. Classification results of major phytoplankton categories in March 2004. The original data are from monthly average chlorophyll concentration data of 4-km resolution from ESACCI. The red color represents micro-phytoplankton dominant area, the green color represents the nano-phytoplankton dominant area, and the blue color is for pico-phytoplankton dominant area.
