Decoding the inconsistency of six cropland maps in China

Accurate cropland information is critical for agricultural planning and production,especially in foodstressed countries like China.Although widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades,considerable discrepancies exist among these products both in total area and in spatial distribution of croplands,impeding further applications of these datasets.The factors influencing their inconsistency are also unknown.In this study,we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020,including three state-of-the-art 10-m products(i.e.,Google Dynamic World,ESRI Land Cover,and ESA WorldCover)and three 30-m ones(i.e.,GLC_FCS30,GlobeLand 30,and CLCD).We also investigated the effects of landscape fragmentation,climate,and agricultural management.Validation using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy(92.3%).These maps collectively overestimated Chinese cropland area by up to 56%.Up to 37%of the land showed spatial inconsistency among the maps,concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps,cropland fragmentation and management practices such as irrigation.Our work shed light on the promotion of future cropland mapping efforts,especially in highly inconsistent regions.

Using a global reference sample set and a cropland map for area estimation in China

A technically transparent and freely available reference sample set for validation of global land cover mapping was recently established to assess the accuracies of land cover maps with multiple resolutions.This sample set can be used to estimate areas because of its equal-area hexagon-based sampling design.The capabilities of these sample set-based area estimates for cropland were investigated in this paper.A 30-m cropland map for China was consolidated using three thematic maps(cropland,forest and wetland maps)to reduce confusion between cropland and forest/wetland.We compared three area estimation methods using the sample set and the 30 m cropland map.The methods investigated were:(1)pixel counting from a complete coverage map,(2)direct estimation from reference samples,and(3)model-assisted estimation combining the map with samples.Our results indicated that all three methods produced generally consistent estimates which agreed with cropland area measured from an independent national land use dataset.Areas estimated from the reference sample set were less biased by comparing with a National Land Use Dataset of China(NLUD-C).This study indicates that the reference sample set can be used as an alternative source to estimate areas over large regions.