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Article

An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia

1
Hydrosolutions Ltd., 8006 Zurich, Switzerland
2
Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(11), 1823; https://doi.org/10.3390/rs10111823
Received: 19 October 2018 / Revised: 12 November 2018 / Accepted: 14 November 2018 / Published: 17 November 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Sound water resources planning and management requires adequate data with sufficient spatial and temporal resolution. This is especially true in the context of irrigated agriculture, which is one of the main consumptive users of the world’s freshwater resources. Existing remote sensing methods for the management of irrigated agricultural systems are often based on empirical cropland data that are difficult to obtain, and that put into question the transferability of mapping algorithms in space and time. Here we implement an automatic irrigation mapping procedure in Google Earth Engine that uses surface reflectance satellite imagery from different sensors. The method is based on unsupervised training of a pixel-by-pixel classification algorithm within image regions identified through unsupervised object-based segmentation, followed by multi-temporal image analysis to distinguish productive irrigated fields from non-productive and non-irrigated areas. Ground-based data are not required. The final output of the mapping algorithm are monthly and annual irrigation maps (30 m resolution). The novel method is applied to the Central Asian Chu and Talas River Basins that are shared between upstream Kyrgyzstan and downstream Kazakhstan. We calculate the development of irrigated areas from 2000 to 2017 and assess the classification results in terms of robustness and accuracy. Based on seven available validation scenes (in total more than 2.5 million pixels) the classification accuracy is 77–96%. We show that on the Kyrgyz side of the Talas basin, the identified increasing trends over the years are highly significant (23% area increase between 2000 and 2017). In the Kazakh parts of the basins the irrigated acreages are relatively stable over time, but the average irrigation frequency within Soviet-era irrigation perimeters is very low, which points to a poor physical condition of the irrigation infrastructure and inadequate water supply. View Full-Text
Keywords: mapping irrigated area; multi-spectral satellite imagery; unsupervised classification; multi-temporal classification; central asia; google earth engine mapping irrigated area; multi-spectral satellite imagery; unsupervised classification; multi-temporal classification; central asia; google earth engine
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MDPI and ACS Style

Ragettli, S.; Herberz, T.; Siegfried, T. An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia. Remote Sens. 2018, 10, 1823. https://doi.org/10.3390/rs10111823

AMA Style

Ragettli S, Herberz T, Siegfried T. An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia. Remote Sensing. 2018; 10(11):1823. https://doi.org/10.3390/rs10111823

Chicago/Turabian Style

Ragettli, Silvan, Timo Herberz, and Tobias Siegfried. 2018. "An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia" Remote Sensing 10, no. 11: 1823. https://doi.org/10.3390/rs10111823

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