Chandrashekhar Biradar (ICARDA) co-authored a paper on the mapping of annual cropland in Central Asia. Published in the Remote Sensing journal, the study used a reference time-series-based mapping method (RBM) to create binary cropland vs. non-cropland maps using irregular Landsat time series. This method was applied in seven distinct agricultural landscapes in Xinjiang, China, and the Aral Sea Basin. The authors found that the accuracy of this study was higher than 85% and also significantly more accurate than existing products, such as GLC30 and FROM–GLC.

Abstract

Mapping the spatial and temporal dynamics of cropland is an important prerequisite for regular crop condition monitoring, management of land and water resources, or tracing and understanding the environmental impacts of agriculture. Analyzing archives of satellite earth observations is a proven means to accurately identify and map croplands. However, existing maps of the annual cropland extent either have a low spatial resolution (e.g., 250–1000 m from Advanced Very High Resolution Radiometer (AVHRR) to Moderate-resolution Imaging Spectroradiometer (MODIS); and existing high-resolution maps (such as 30 m from Landsat) are not provided frequently (for example, on a regular, annual basis) because of the lack of in situ reference data, irregular timing of the Landsat and Sentinel-2 image time series, the huge amount of data for processing, and the need to have a regionally or globally consistent methodology. Against this backdrop, we propose a reference time-series-based mapping method (RBM) and create binary cropland vs. non-cropland maps using irregular Landsat time series and RBM. As a test case, we created and evaluated annual cropland maps at 30 m in seven distinct agricultural landscapes in Xinjiang, China, and the Aral Sea Basin. The results revealed that RBM could accurately identify cropland annually, with producer’s accuracies (PA) and user’s accuracies (UA) higher than 85% between 2006 and 2016. In addition, cropland maps by RBM were significantly more accurate than the two existing products, namely GlobaLand30 and Finer Resolution Observation and Monitoring of Global Land Cover (FROM–GLC).

Hao, P., Löw, F. and Biradar, C., 2018. Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia. Remote Sensing, 10(12), p.2057.

https://doi.org/10.3390/rs10122057