Geographic and cartographic inconsistency factors among different cropland classification datasets: A field validation case in Cambodia

Abstract

Abstract Cropland cover datasets is of great significance for research on agricultural monitoring. The existing investigations on the inconsistency of different cropland datasets have mainly focused on first-class cropland and only analyzed the causes of this inconsistency in terms of cartography. To date, investigations have neglected the importance of fine cropland types in studies such as global food security assessment, and a comprehensive analysis of the causes of inconsistency from the perspectives of both cartography and geography is lacking. Moreover, the verification samples of existing studies have primarily been collected based on Google Earth. So, we examined the cropland resources of Cambodia using areal, spatial consistency, elevation classification, and field survey data assessment methods for the Global Food Security-support Analysis Data at 30 m for Southeast Asia, Global Land Cover Fine Surface Cover30-2015, Finer Resolution Observation and Monitoring of Global Land Cover2015, and SERVIR-Mekong datasets and comprehensively investigated the causes of inconsistency in terms of geography and cartography. The results revealed that the consistency of the extracted areas of first-class cropland among the four datasets was high. But, the cropland areas and statistical results from the Food and Agriculture Organization (FAO) of the United Nations are quite different. The overall accuracy (OA) for the first-class cropland of GFSAD30SEACE, GLC_FCS30-2015, and SERVIR-Mekong datasets were >82%. For fine cropland types, however, the OA of the SERVIR-Mekong dataset was relatively high, at 74.87%, while the accuracy levels of the global-scale GLC_FCS30-2015 and FROM_GLC2015 datasets were <50% due to the influence of scale size on mapping accuracy. In addition, in the eastern and northern portions of Cambodia with elevations of 50–200 m, the spatial consistency of the four datasets was low due to the serious confusion between cropland and forest, grassland, and shrub types. Therefore, land cover producers should adopt a zonal stratification strategy, focusing on remote sensing extraction techniques for confusing types in areas with high inconsistency to improve the accuracy of cropland.