The global distribution of cropping intensity (CI) is essential to our understanding of agricultural land use management on Earth. Optical remote sensing has revolutionized our ability to map CI over large areas in a repeated and cost-efficient manner. Previous studies have mainly focused on investigating the spatiotemporal patterns of CI ranging from regions to the entire globe with the use of coarse-resolution data, which are inadequate for characterizing farming practices within heterogeneous landscapes. To fill this knowledge gap, in this study, we utilized multiple satellite data to develop a global, spatially continuous CI map dataset at 30 m resolution (GCI30). Accuracy assessments indicated that GCI30 exhibited high agreement with visually interpreted validation samples and in situ observations from the PhenoCam network. We carried out both statistical and spatial comparisons of GCI30 with six existing global CI estimates. Based on GCI30, we estimated that the global average annual CI during 2016–2018 was 1.05, which is close to the mean (1.09) and median (1.07) CI values of the existing six global CI estimates, although the spatial resolution and temporal coverage vary significantly among products. A spatial comparison with two satellite-based land surface phenology products further suggested that GCI30 was not only capable of capturing the overall pattern of global CI but also provided many spatial details. GCI30 indicated that single cropping was the primary agricultural system on Earth, accounting for 81.57 % (12.28×10⁶ km²) of the world's cropland extent. Multiple-cropping systems, on the other hand, were commonly observed in South America and Asia. We found large variations across countries and agroecological zones, reflecting the joint control of natural and anthropogenic drivers on regulating cropping practices. As the first global-coverage, fine-resolution CI product, GCI30 is expected to fill the data gap for promoting sustainable agriculture by depicting worldwide diversity of agricultural land use intensity. The GCI30 dataset is available on Harvard Dataverse: https://doi.org/10.7910/DVN/86M4PO (Zhang et al., 2020).

Having updated knowledge of cropland extent is essential for crop monitoring and food security early warning. Previous research has proposed different methods and adopted various datasets for mapping cropland areas at regional to global scales. However, most approaches did not consider the characteristics of farming systems and apply the same classification method in different agroecological zones (AEZs). Furthermore, the acquisition of in situ samples for classification training remains challenging. To address these knowledge gaps and challenges, this study applied a zone-specific classification by comparing four classifiers (random forest, the support vector machine (SVM), the classification and regression tree (CART) and minimum distance) for cropland mapping over four different AEZs in the Zambezi River basin (ZRB). Landsat-8 and Sentinel-2 data and derived indices were used and synthesized to generate thirty-five layers for classification on the Google Earth Engine platform. Training samples were derived from three existing landcover datasets to minimize the cost of sample acquisitions over the large area. The final cropland map was generated at a 10 m resolution. The performance of the four classifiers and the viability of training samples were analysed. All classifiers presented higher accuracy in cool AEZs than in warm AEZs, which may be attributed to field size and lower confusion between cropland and grassland classes. This indicates that agricultural landscape may impact classification results regardless of the classifiers. Random forest was found to be the most stable and accurate classifier across different agricultural systems, with an overall accuracy of 84% and a kappa coefficient of 0.67. Samples extracted over the full agreement areas among existing datasets reduced uncertainty and provided reliable calibration sets as a replacement of costly in situ measurements. The methodology proposed by this study can be used to generate periodical high-resolution cropland maps in ZRB, which is helpful for the analysis of cropland extension and abandonment as well as intensity changes in response to the escalating population and food insecurity.

Despite its essential importance to various spatial agriculture and environmental applications, the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote-sensing products. Each of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping, which leads to high spatial discrepancies among remote sensing cropland products. Since no dataset could cope with all limitations, multiple datasets initially derived from various remote sensing sensors and classification techniques must be integrated into a more accurate cropland product than individual layers. Here, in the current study, four cropland products, produced initially from multiple sensors (e.g. Landsat-8 OLI, Sentinel-2 MSI, and PROBA–V) to cover the period (2015–2017), were integrated based on their cropland mapping accuracy to build a more accurate cropland layer. The four cropland layers’ accuracy was assessed at Agro-ecological zones units via an intensive reference dataset (17,592 samples). The most accurate cropland layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over Africa. As a result, the new layer was produced in higher cropland mapping accuracy (overall accuracy = 91.64% and cropland’s F-score = 0.75). The layer mapped the African cropland area as 282 Mha (9.38% of the Continent area). Compared to earlier cropland synergy layers, the constructed cropland mask showed a considerable improvement in its spatial resolution (30 m instead of 250 m), mapping quality, and closeness to official statistics (R² = 0.853 and RMSE = 2.85 Mha). The final layer can be downloaded as described under the “Data Availability Statement” section.