Extraction of points from satellite data using KML files
Satellite Data:
- AQUARIUS L2 SCI
- AMSR2 - GW1AM2 Level-1B
- SMAP L1B, L1_S0_LORES and L1C
Description:
- read_HDF_using_KML
This script reads the satellite images in H5 format, extracts the environmental variables required within the box of the study area. Within the study area, the values of the variables are extracted in the points (Lat, Lon) of the flight lines received through KML files.
Generates .csv files for each H5 satellite file, which contains the coordinates (Lat, Lon) of the HDF pixel and the KML point, the distance between them and the sensed variables required, as shown:
Point_name / Coordinates_KML / Coordinates_HDF / Distance[degree] / tb_h / tb_v
- createBigDataSet
Script that reads all files .CSV by MONTHS within an initial file corresponding to a YEAR. Each file has .CSV files (each file corresponds to a satellite pass per hour) for all days of the month, having more than one pass per day. It generates a pandas object that owns all the .CSV files and organizes them by date. Then, generate a single .CSV file.
- plotBigDataSet
Script that reads the CSV file. with the complete data set and performs the plot of the variables. For this, the mean and the deviation are calculated using panda group operations.
- functions
It contains the necessary functions for extracting points such as: reading satellite images in H5 format, reading the KML files, calculating the pixels closest to the points using the Euclidean distance and finally extracting the the physical variables in said pixels.
- MATfilesConvertion
Script that reads the .MAT files and loads the parameters in a pandas object, it is necessary to know what the coordinates of the points are. Then, create a .KML file with the required data.
- read_AMSR2_GW1AM2, read_Aquarius_L2_SCI, read_SMAP_L1B, read_SMAP_L1B_S0_LORES
These functions read the satellite images and plot the selected variables using the basemap library. Each type of satellite image needs its particular reading function due to the organization of the .H5 file, for example: the location and format of the variables, in addition, in some cases the variables have different samplings according to the frequency of the band.
Dependences:
python - Geopandas
python - H5py
python - Pykml
python - Simplekml
python - Shapely
python - Pandas
python - NumPy
python - Scipy
python - Matplolib
python - Basemap