Cropland Abandonment Mapping at Sub-Pixel Scales Using Crop Phenological Information and MODIS Time-Series Images

Abstract

Cropland abandonment is a common land-use change with mixed impacts on the environment and rural economic development. Prevalent small family farms and excessive land fragmentation result in cropland abandonment processes that are often gradual and spatially dispersed. These difficulties limit the ability to apply previous remote sensing mapping methods of cropland abandonment to areas with complex underlying surfaces, where mixed pixels and spectral aliasing problems arise. To meet the demands of broad-scale and high-accuracy abandoned cropland mapping, we developed the PCRRSBS–CV model, which combines information regarding crop phenology with Moderate Resolution Imaging Spectroradiometer (MODIS) time-series images. Considering the inter-annual variations in spectral metrics of abandoned cropland, the phenology-based cropland retirement remote sensing (PCRRS) model was developed to estimate changes in spectral metrics parameters during the process of cropland abandonment. Additionally, the dead fuel index and normalized difference vegetation index (NDVI) were employed to estimate the tilled soil fraction (BS) in mixed pixels (according to crop type) that would produce a clear soil signal at a certain time during the year. We predicted that use of the BS as the weighting coefficient for the PCRRS model would reduce interference from the mixed pixel problem, while spatial heterogeneity could be reduced by dividing the research area into regional units. Use of the coefficient of variation (CV) of the NDVI time series with phenological information highlights the volatility of crop growth periods and helps to eliminate disturbances associated with woodlands and grasslands. Finally, we demonstrated this method in the Loess Plateau of China and a portion of central Europe; we verified its accuracy using high-resolution images from Google Earth. Our algorithm demonstrated overall accuracy of 82.2% and can extract cropland abandonment information as little as 20% of a pixel. The overall root mean square error (RMSE) was controlled below 15%, and the RMSE of abandoned croplands for an individual crop type was approximately 10%. The Loess Plateau and a portion of central Europe have nearly 4.36Mha and 13.1Mha of abandoned cropland from 2000 to 2015, respectively. In summary, the high accuracy achieved by this method enables the monitoring of cropland abandonment dynamics on a large scale.

Keywords: Cropland abandonment, Phenology, Sub-pixel, MODIS time series, Change detection