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A crop type dataset for consistent land cover classification in Central Asia
Ruben Remelgado1,2, Sherzod Zaitov3, Shavkat Kenjabaev3
1Institute of Geography and Geology, Julius Maximilian University Wuerzburg, Wuerzburg, Germany. ruben.remelgado@idiv.de.
Scientific Data
|July 30, 2020
Summary
Accurate crop type mapping is crucial for managing water resources and food security in arid regions like the Aral Sea Basin. This study provides essential ground-truth data for remote sensing, addressing a critical data gap.
Area of Science:
- Environmental science
- Agricultural science
- Remote sensing
Background:
- Land cover, particularly crop type, is vital for understanding water usage and food security, especially in arid irrigated regions like the Aral Sea Basin (ASB).
- Accurate crop mapping using remote sensing is hindered by a lack of consistent ground-truth data in the ASB.
- Addressing this data deficiency is critical for regional water resource management and mitigating food insecurity risks.
Purpose of the Study:
- To establish a comprehensive, validated dataset of crop types in the Aral Sea Basin to support remote sensing-based land cover analysis.
- To provide essential ground-truth data for improving the accuracy of crop type mapping in Uzbekistan and Tajikistan.
- To develop transferable, open-source workflows for consistent future data collection.
Main Methods:
- Collection of thousands of crop type polygons, with the majority in Uzbekistan and a smaller portion in Tajikistan.
- Compilation of 8,196 ground-truth samples across multiple years (2008, 2011, 2015-2018) covering 40 distinct crop types.
- Validation of collected data using expert knowledge and remote sensing, employing open-source and transferable methodologies.
Main Results:
- The dataset is dominated by "cotton" (40%) and "wheat" (25%) samples, reflecting regional agricultural importance.
- The collected data represent a significant advancement in ground-truth availability for the Aral Sea Basin.
- Validated data and established workflows ensure reliability and consistency for future research and applications.
Conclusions:
- The generated crop type dataset and associated workflows are essential for accurate land cover mapping and water resource management in the Aral Sea Basin.
- This study addresses a critical data gap, enabling better prediction of water scarcity and food insecurity risks in the region.
- The open-source nature of the workflows promotes future data collection and enhances the sustainability of remote sensing applications in Central Asia.
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