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Global validation of the Advanced Change Detection and Short-Term Change Detections (STCDs)

This repository is for a global validaton of soil moisture retrieval algorithms

Part I: Data preparation

In-situ soil moisture

Ground soil moisture measurements are available at https://ismn.geo.tuwien.ac.at/en/.

A jupyter notebook (Preprocessing_ISMN_Raw_Data.ipynb) was built for the preprocessing of the raw data

The outputs include:

  • Site-specific files named as network-station including the available daily averaged soil moisutre at 0 - 5 cm of each station;
  • A csv file containing the details of each station.

SMAP data

The SMAP data is available at https://nsidc.org/data/SPL3SMP. A pyhton script can be generated automatically for batch download

Use Extract the SMAP soil moisture.ipynb to extract the soil moisutre over each station.

Remote sensing data from Google Earth Engine (GEE)

Setup

An google developer account is required to access the GEE

The https://github.com/giswqs/geemap is suggested for the setup of GEE

Sentinel-1 and MODIS NDVI

Use Extract GEE data.ipynb to download Sentinel-1 and MODIS NDVI

Landcover

Use Extract static auxiliary data.ipynb to download landcover from GEE

Part II: A python version of advanced change detection and short term change detection methods

comming soon

Update on Dec. 23 2022: The author is struggling with his KPI and obviously the python version is not comming shortly. You may request a MATLAB version instead by sending to liujun.zhu@hhu.edu.cn

Reference

Liujun Zhu, Rui Si, Xiaoji Shen & Jeffrey P. Walker (2022) An advanced change detection method for time-series soil moisture retrieval from Sentinel-1, Remote Sensing of Environment

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This repository is for a global validaton of soil moisture retrieval algorithms

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