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

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Big Earth Data 2022年第1期6卷 54-76页

作者： Mohsen Nabil Miao Zhang Bingfang Wu Jose Bofana Abdelrazek Elnashar State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of Sciences(AIRCAS)BeijingChina College of Resources and Environment University of Chinese Academy of Sciences(UCAS)BeijingChina Division of Agriculture Applications Soilsand Marine(AASMD)National Authority for Remote Sensing&Space Sciences(NARSS)CairoEgypt Faculty of Agricultural Sciences Catholic University of MozambiqueCuambaMozambique Department of Natural Resources Faculty of African Postgraduate StudiesCairo UniversityGizaEgypt

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 *** of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping,which leads to high spatial discre-pancies among remote sensing cropland *** 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 ***,in the current study,four cropland products,produced initially from multiple sensors(***-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 *** four cropland layers’accuracy was assessed at Agro-ecological zones units via an inten-sive reference dataset(17,592 samples).The most accurate crop-land layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over *** 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 crop-land 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 down-loaded as described under the“Data Availability Statement”section.

关键词： Cropland mapping synergy mapping land cover accuracy assessment multisource data fusion

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