Constructing a 30m African Cropland Layer for 2016 by Integrating Multiple Remote sensing, crowdsourced, and Auxiliary Datasets

Abstract

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 (R 2 = 0.853 and RMSE = 2.85 Mha). The final layer can be downloaded as described under the "Data Availability Statement" section.

Validation and refinement of cropland map in southwestern China by harnessing ten contemporary datasets

“…This study also demonstrates the feasibility of generating optimized new data from existing data in an era when open-sourced cropland maps are becoming increasingly abundant [65][66][67] . For further improvements in data fusion, it is possible to generate higher-precision cropland maps by optimizing and integrating datasets based on geographic subdivisions and data-driven alogrithms 68,69 .…”

Characterizing Cropland Patterns Across North‐East Africa Using Time Series Vegetation Indices

“…The spatial and temporal studies related to cropland patterns at regional scales are limited. Particularly, no study on cropland phenology and its dynamics is available for North‐East Africa where the largest share of cropland area exists among regions of Africa (Nabil et al., 2021). The changes in agricultural land use, precipitation, and land surface temperature (LST) may result in dramatic impacts on the productivity and phenology of the croplands, leading to agroecosystem shifts.…”

Large-area mapping of active cropland and short-term fallows in smallholder landscapes using PlanetScope data

“…Pre-processing approaches that aggregate images across multiple years may help to mitigate these limitations, but these may obfuscate the spatio-temporal patterns of cropland distribution due to the rapid changes. Lastly, fallows are often included in generic cropland definitions of existing map products ( Nabil et al, 2021 ), likely due to limitations in spatial resolution and minimum mapping units, spectral-temporal similarities between fallows and other land covers, and challenges for reference data collection. Consequently, knowledge about the shares and spatial distribution of fallow land in smallholder systems of SSA is currently scarce.…”