Product in use: SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 7
We perform time-series analysis of soil moisture for Bengaluru city.
The bounds assumed for this analysis is N_lat = 13.07 ,S_lat = 12.82 , W_lon = 77.1 , E_lon = 77.9. We then take average of the pixels which intersect the bounds to calculate the soil moisture.
The three main process we perform are:
- Download the SMAP L3 data for the latest one month ( August 2021 here).
- Extraction of the soil moisture values from SMAP L3 data over Lat, Lon of Bangalore in python.
- Plot the time series plot for the extracted soil moisture values for the latest one month.
To see it in action run the colab notebook and follow the instructions there
In this section we look at the different files inside the repository as well as an explanation about their functionality
| File Name | Explanation / Function |
|---|---|
colab_wrapper.ipynb |
Colab wrapper to download, run and visualise the time series python scripts |
main.py |
contains the class and function to extract info from the downloaded files |
download_SPL3.py |
script by NASA to download data. can be modified to change the date of download for SMAP L3 |
EASE2_M36km.lats.964x406x1.double |
EASE grid latidues to intersection with bounds |
EASE2_M36km.lons.964x406x1.double |
EASE grid longitudes to intersection with bounds |
- Python with Jupyter/ colab
Data courtesy: O'Neill, P. E., S. Chan, E. G. Njoku, T. Jackson, R. Bindlish, and J. Chaubell. 2020. SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 7. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/HH4SZ2PXSP6A. [31st August, 2021].