The researchers employed more than 1700 multispectral satellite images (covering not just the visible wavelengths but those that are not visible to the human eye such as infrared), from early ones acquired in the 1980s up to 2013. These images were analysed using Google Earth Engine’s cloud high performance computing infrastructure and algorithms purposely developed to discriminate palaeorivers. These use the seasonal contrast between vegetation, soils with different moisture levels, bare terrain and soil mineral content to overcome visibility issues related to cloud cover, seasonal cultivation patterns, recent crop selection, extensive irrigation and long-term land-use patterns.

The results create a step change in our current knowledge of the Ancient Indus hydrology and the environmental conditions in which this Bronze Age civilisation operated. They also provide important insights on the influence of climate change in its eventual demise.

The code employed for the development of this study has been publicly released and is available as supplementary material to the published article. By doing so, the authors hope that their innovative method can be employed by other researchers tackling climate change or interested in past environmental conditions.1

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Large-Scale, Multi-Temporal Remote Sensing of Palaeo-River Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation

Remote sensing has considerable potential to contribute to the identification and reconstruction of lost hydrological systems and networks. Remote sensing-based reconstructions of palaeo-river networks have commonly employed single or limited time-span imagery, which limits their capacity to identify features in complex and varied landscape contexts. This paper presents a seasonal multi-temporal approach to the detection of palaeo-rivers over large areas based on long-term vegetation dynamics and spectral decomposition techniques. Twenty-eight years of Landsat 5 data, a total of 1711 multi-spectral images, have been bulk processed using Google Earth Engine© Code Editor and cloud computing infrastructure. The use of multi-temporal data has allowed us to overcome seasonal cultivation patterns and long-term visibility issues related to recent crop selection, extensive irrigation and land-use patterns. The application of this approach on the Sutlej-Yamuna interfluve (northwest India), a core area for the Bronze Age Indus Civilisation, has enabled the reconstruction of an unsuspectedly complex palaeo-river network comprising more than 8000 km of palaeo-channels. It has also enabled the definition of the morphology of these relict courses, which provides insights into the environmental conditions in which they operated. These new data will contribute to a better understanding of the settlement distribution and environmental settings in which this, often considered riverine, civilisation operated. 

Keywords: multi-temporal; seasonal; vegetation; palaeo-river; Indus Civilisation; archaeology