Intercomparison on four irrigated cropland maps in Mainland China

Abstract

Wide-coverage spatial information on irrigated croplands is a vital foundation for food security and water resources studies at the regional level. Several global irrigated-cropland maps have been released to the public over the past decade due to the efforts of the remote sensing community. However, the consistency and discrepancy between these maps is largely unknown because of a lack of comparative studies, limiting their use and improvement. To close this knowledge gap, we compared the latest four irrigated-cropland datasets (GMIA, GRIPC, GlobCover, and GFSAD) in mainland China. First, the four maps were compared quantitatively and neutral regionaland provincial-level statistics of the relative proportions of irrigated land were obtained through regression analysis. Second, we compared the similarities and discrepancies of the datasets on spatial grids. Furthermore, the contributions of mosaic cropland pixels in GlobCover and GFSAD were also analyzed because of their extensive distribution and ambiguous content. Results showed that GMIA has the lowest dispersion and best statistical correlation followed by GRIPC, while the corresponding features of GlobCover and GFSAD are approximately equal. Spatial agreement of the four maps is higher in eastern than western China, and disagreement is contributed mostly by GlobCover and GFSAD. However, divergence exists in the ratios of the different agreement levels, as well as their sources, on a regional scale. Mosaic pixels provide more than half of the irrigated areas for GlobCover and GFSAD, and they include both correct and incorrect information. Our results indicate a need for a uniform quantitative classification system and for greater focus on heterogeneous regions. Furthermore, the results demonstrate the advantage of numerical restriction in the calculations. Therefore, special attention should be paid to integrating databases and to exploring remote sensing features and methods for spatial reconstruction and identification of untypical irrigation areas.