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Open Access

Monitoring Changes to Small-Sized Lakes Using Medium Spatial and Temporal Satellite Imagery in the Badain Jaran Desert from 2015 to 2020

Qinyu Zhao1, Luyan Ji2,3,4( ), Yonggang Su1, Kai Yu2,3,4, Yongchao Zhao2,3,4,5
College of Electronic and Information Engineering, Hebei University, Baoding 071000, China
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Key Laboratory of Technology in Geo-Spatial information Processing and Application System, Beijing 100190, China
Key Laboratory of Target Cognition and Application Technology (TCAT), Beijing 100094, China
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract

The Badain Jaran Desert is the second-largest desert in China, and its lakes, which are generally small-sized and highly dynamic, play a significant role for plants and animals in this arid region. Therefore, long-term monitoring of the distribution of lakes in the Badain Jaran Desert with high spatial and temporal resolution is of great importance. However, due to the tradeoff between pixel size and swath width, currently no single satellite sensor can provide such a time series. Thereby, in this study, we focus on applying the deep learning based spatiotemporal fusion method (super-resolution based spatial fusion with Generative Adversarial Network (GAN)) to a low spatial yet high temporal resolution data (i.e., MODIS 250 m daily reflectance time series) and a high spatial yet low temporal resolution data (i.e., Landsat 30 m 16-day reflectance time series) to generate a daily 30 m time series for 37 selected lakes in the Badain Jaran Desert. Then, an automatic water extraction algorithm is proposed, and a daily 30 m water mapping production is generated for our study area from 2015 to 2020. The overall accuracy can reach 0.92, while the average error of lake areas is less than 9.21%, which is much higher than that derived from the MODIS time series. Moreover, based on our daily high spatial resolution results, it is possible to analyze the water phenology for all sizes of lakes in the Badain Jaran Desert. We have performed a detailed analysis of interannual variability and seasonal changes for the selected 37 lakes in the Badain Jaran Desert. The results show that from 2015 to 2020, the shrinkage of the small lakes (<0.5 km2) is more severe than lakes with a larger size. As for seasonal changes, the lake area can be divided into four stages: quick increase due to ice melting from winter to spring, slow decrease due to evaporation from spring to summer, moderate recovery due to the arrival of the rainy season from summer to autumn, and quick decrease due to lake freezing from autumn to winter. Therefore, it is feasible to use spatiotemporal fusion algorithms to generate long-term time series for monitoring the dynamic changes of small lakes in desert areas.

Keywords

Badain Jaran Desertarid lakesspatiotemporal fusionwater mappingseasonality

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International Journal of Crowd Science
Volume 9 Issue 2,
April 2025
Pages 96-109
DOI: 10.26599/IJCS.2024.9100025

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Cite this article:
Zhao Q, Ji L, Su Y, et al. Monitoring Changes to Small-Sized Lakes Using Medium Spatial and Temporal Satellite Imagery in the Badain Jaran Desert from 2015 to 2020. International Journal of Crowd Science, 2025, 9(2): 96-109. https://doi.org/10.26599/IJCS.2024.9100025

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Received: 08 November 2023
Revised: 03 July 2024
Accepted: 08 August 2024
Published: 13 May 2025
© The author(s) 2025.

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