Challenges in using land use and land cover data for global change studies

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Mapping cropland-use intensity across Europe using MODIS NDVI time series (2016, background): “Unfortunately, existing data on cropland-use intensity are mostly coarse in scale, heavily rely on uncertain croplandmaps (Fritz et al 2011, Fritz et al 2013), or are based on national statistics, which themselves may contain uncertainties (Verburg et al 2011, Zaks and Kucharik 2011).”
Global patterns of agricultural land‐use intensity and vertebrate diversity (2015, methodology): “to inconsistent input data and limitations with processing algorithms and positional accuracy which is exacerbated by a lack of formal validation (Verburg et al., 2011).”
SoilGrids1km — Global Soil Information Based on Automated Mapping (2014, background): “temporal variability due to changes in land use and/or land cover [59];”
Joint Deep Learning for land cover and land use classification (2019, methodology): “Pixel-based methods are used widely to classify individual pixels into particular LC categories based purely on spectral reflectance, without considering neighbouring pixels (Verburg et al., 2011).”
A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring (2016, background): “which are paramount to monitoring the environmental and societal impacts of land use [30,31], remains understudied [32].”