ReaLSAT, a global dataset of reservoir and lake surface area variations

Reservoir and Lake Surface Area Timeseries (ReaLSAT) dataset provides an unprecedented reconstruction of surface area variations of lakes and reservoirs at a global scale using Earth Observation (EO) data and novel machine learning techniques. The dataset provides monthly scale surface area variations (1984 to 2015) of 681,137 water bodies below 50°N and sizes greater than 0.1 square kilometers.

 The dataset contains the following files:

1) ReaLSAT.zip: A shapefile that contains the reference shape of waterbodies in the dataset.

2) monthly_timeseries.zip: contains one CSV file for each water body. The CSV file provides monthly surface area variation values. The CSV files are stored in a subfolder corresponding to each 10 degree by 10 degree cell. For example, monthly_timeseries_60_-50 folders contain CSV files of lakes that lie between 60 E and 70 E longitude, and 50S and 40 S. 

3) monthly_shapes_<bottom_left_lon>_<bottom_left_lat>.zip: contains shapefile for each water body that lie within the 10 degree by 10 degree cell. The shapefile provides monthly shapes together with their time and area attributes. 

4) ReaLSAT.html: a readme python notebook that provides information about reading and visualizing the dataset. The notebook also contains the code to download the data to reduce the overhead of downloading each file manually.

5) evaluation_data.zip: contains the random subsets of the dataset used for evaluation. The zip file contains a README file that describes the evaluation data.

6) generate_realsat_timeseries.ipynb: a Google Colab notebook that provides the code to generate timerseries and surface extent maps for any waterbody.

7) large_lakes.zip: The zip file contains timeseries csv as well as monthly shapes of 1120 large lakes.

Please refer to the following papers to learn more about the processing pipeline used to create ReaLSAT dataset:

[1] Khandelwal, Ankush, Anuj Karpatne, Praveen Ravirathinam, Rahul Ghosh, Zhihao Wei, Hilary A. Dugan, Paul C. Hanson, and Vipin Kumar. "ReaLSAT, a global dataset of reservoir and lake surface area variations." Scientific data 9, no. 1 (2022): 1-12.

[2] Khandelwal, Ankush. "ORBIT (Ordering Based Information Transfer): A Physics Guided Machine Learning Framework to Monitor the Dynamics of Water Bodies at a Global Scale." (2019).

Version Updates

Version 1.4: added 1120 large lakes to the dataset and removed partial lakes that overlapped with these large lakes.

Version 1.3: fixed visualization related bug in generate_realsat_timeseries.ipynb

Version 1.2: added a Google Colab notebook that provides the code to generate timerseries and surface extent maps for any waterbody in ReaLSAT database.