The application of Google Earth Engine to monitor sugarcane development in India.

The Google Earth Engine platform has transformed access to long term scientific datasets as more than thirty years of image data are readily available for analysis on the cloud. Despite this rapid access to long term data and cloud computing resources of GEE, there is still the requirement to remove noise (e.g., from cloud and haze) and correct for the effects of calibration between image types to process raw data into information about changes in the Earth’s surface. Using the Python Earth Engine API, we developed an algorithm to combine timeseries datasets from high spatial resolution sensors (Landsat-8 and Sentinel-2) and filter noise whilst still retaining high temporal resolution. A second algorithm was then developed to automate the decomposition of pre-processed timeseries data into individual agricultural seasons for the extraction phenological stages for sugarcane fields across India. Our approach was developed and validated on over 800 sugar cane field parcels and overcomes the challenge of previous machine-learning methods for sugarcane monitoring using remote sensing that rely on information on planting and harvesting. Fully automated monitoring of sugarcane is possible over wide areas without the need to download image datasets or process time series data locally. This approach can significantly improve the sustainability of sugarcane production by optimising the harvest to maintain efficiency in the supply of sugarcane to mills during the crushing season and reduce waste by avoiding the harvest of immature cane. The use of GEE means that this approach can be easily modified for use with other crops and in other geographical areas to improve satellite-based monitoring of crops. These tools are essential for realising the goals of sustainable development, and time-series analysis can be used to help producers demonstrate commodities have not come from recently deforested land (for example, the EU regulation on deforestation-free products).