Abstract
Despite substantial research and policy interest in pixel level cropland allocation data, few sources are available that span a large geographic area. The data used for much of this research are derived from complex modeling techniques that may include model simulation and other data processing. We develop a transparent econometric framework that uses pixel level biophysical measurements and aggregate cropland statistics to develop pixel level cropland allocation predictions. Such pixel level land use data can be used to investigate the impact of human activities on the environment. Validation exercises show that our approach is effective at downscaling cropland allocation to multiple levels of resolution.
Highlights
Agricultural productivity and environmental sustainability are central focuses for policymakers and academics
Downscaling of natl crop area statistics using drivers of cropland productivity measured at fine resolutions regions that researchers desire; it limits the reliability of high spatial resolution environmental models that use land use data as inputs
We extend Eq (2), which is defined at the pixel level, to the administrative unit level via aggregation–i.e., the predicted fraction of land in crop k in administrative unit j is equal to the average pixel fraction weighted by area
Summary
Downscaling of natl crop area statistics using drivers of cropland productivity measured at fine resolutions regions that researchers desire; it limits the reliability of high spatial resolution environmental models that use land use data as inputs. To address these issues, researchers have turned to combining census data with satellite imagery [8] or simulation methods [9] to allocate harvested areas from aggregate to pixel-level.
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