Evaluation of global land cover maps for cropland area estimation in the conterminous United States

Abstract

Global land cover data could provide continuously updated cropland acreage and distribution information, which is essential to a wide range of applications over large geographical regions. Cropland area estimates were evaluated in the conterminous USA from four recent global land cover products: MODIS land cover (MODISLC) at 500-m resolution in 2010, GlobCover at 300-m resolution in 2009, FROM-GLC and FROM-GLC-agg at 30-m resolution based on Landsat imagery circa 2010 against the US Department of Agriculture survey data. Ratio estimators derived from the 30-m resolution Cropland Data Layer were applied to MODIS and GlobCover land cover products, which greatly improved the estimation accuracy of MODISLC by enhancing the correlation and decreasing mean deviation (MDev) and RMSE, but were less effective on GlobCover product. We found that, in the USA, the CDL adjusted MODISLC was more suitable for applications that concern about the aggregated county cropland acreage, while FROM-GLC-agg gave the least deviation from the survey at the state level. Correlation between land cover map estimates and survey estimates is significant, but stronger at the state level than at the county level. In regions where most mismatches happen at the county level, MODIS tends to underestimate, whereas MERIS and Landsat images incline to overestimate. Those uncertainties should be taken into consideration in relevant applications. Excluding interannual and seasonal effects, R2 of the FROM-GLC regression model increased from 0.1 to 0.4, and the slope is much closer to one. Our analysis shows that images acquired in growing season are most suitable for Landsat-based cropland mapping in the conterminous USA.