So I am conducting a research on changes in NO2 and aerosol index during a certain time period of 1 year. I am using sentinel-5 data. Following is the link:
s5phub.copernicus.eu
I used anaconda(spyder) to analyze the data, creating a map for each day. So in total, there are like more than 30 images. A made a collage of these for my manuscript but it doesn't look quite neat. And is a bit difficult to comprehend.
Is there any way I can integrate these images into one i.e. one image per month that reveals the average. Any tool or software that is acceptable for research purpose. I really need help with this.
Hey there Mika Mika,
Absolutely, integrating multiple air quality map images into a single image to depict the average is a great approach for your research. Here's a method using Python and a library called Matplotlib:
1. **Data Preparation**: First, you'll Mika Mika need to download the images from the Sentinel-5 website and organize them properly.
2. **Python Script**: Write a Python script to read all the images, calculate the average, and create a new image.
```python
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
# Function to read an image and convert it to numpy array
def read_image(image_path):
img = Image.open(image_path)
return np.array(img)
# Function to calculate the average of images
def average_images(image_paths):
total_images = len(image_paths)
first_image = read_image(image_paths[0])
sum_image = first_image.astype(np.float64)
# Summing up all images
for path in image_paths[1:]:
img = read_image(path)
sum_image += img.astype(np.float64)
# Calculating the average
average_image = (sum_image / total_images).astype(np.uint8)
return average_image
# List of paths to your daily images
daily_images_paths = ["path_to_your_images/image1.png", "path_to_your_images/image2.png", ...]
# Call the function to get the average image
average_img = average_images(daily_images_paths)
# Save the average image
plt.imsave("average_image.png", average_img)
```
3. **Visualization**: You Mika Mika can also visualize the average image using Matplotlib.
```python
plt.imshow(average_img)
plt.axis('off')
plt.show()
```
This script will generate an average image from all the daily images you Mika Mika have. It's important to ensure that the images are aligned and have the same dimensions. You Mika Mika can adjust the script accordingly if your images have different sizes.
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Feel free to reach out if you Mika Mika need further assistance!
With one year of image data on NO2 and aerosols, you can produce much more specific statistics than just an average yearly value per pixel.
What about P99, P95 percentiles? I figger your imagery must contain different strata, hence produce percentiles for these strata, and you will learn much more about pollution behavior than just with a yearly average value. Instead of using Matplotlib, it is better to use ENVI and Statistica combined to do the job.
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