High resolution, annual maps of the characteristics of smallholder-dominated croplands at national scales

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Lyndon D Estes, Su Ye, Lei Song, Boka Luo, J. Ronald Eastman, Zhenhua Meng, Qi Zhang, Dennis McRitchie, Stephanie R. Debats, Justus Muhando, Angeline H. Amukoa, Brian W. Kaloo, Jackson Makuru, Ben K. Mbatia, Isaac M. Muasa, Julius Mucha, Adelide M. Mugami, Judith M. Mugami, Francis W. Muinde, Fredrick M. Mwawaza, Jeff Ochieng, Charles J. Oduol, Purent Oduor, Thuo Wanjiku, Joseph G. Wanyoike, Ryan Avery, Kelly Caylor


Understanding agricultural change requires reliable, frequently updated maps that describe the characteristics of croplands. Such data are often unavailable for regions dominated by smallholder agricultural systems, which are particularly challenging for remote sensing. To overcome these challenges, we designed a system to minimize several sources of error that arise when mapping smallholder croplands. To overcome errors caused by mismatches between image resolution and cropland scales, as well as persistent cloud cover, the system converts daily, 3.7 m PlanetScope imagery into two seasonal composites within a single agricultural year. To reduce errors that occur when training classifiers, we built a labelling platform that rigorously assesses label accuracy, and creates more accurate consensus labels that train a Random Forests model. The labelling platform and model interact within an active learning process that boosts the accuracy of the resulting cropland probability map, which is used in a segmentation process to delineate individual field boundaries. We applied this system to map Ghana’s croplands for the year 2018. We divided Ghana into 16 mapping regions (12,160-23,535 km^2), training separate models for each using a total of 6,299 labels, plus 1,600 for validation. Using an independent map reference sample (n=1,207), we found that overall accuracies of the resulting cropland probability and field boundary maps were 88% and 86.7%, respectively, with User’s accuracies for the cropland class of 61.2% and 78.9%, and Producer’s accuracies of 67.3% and 58.2%. Croplands covered 16.1-23.2% of the mapped area, comprising 1,131,146 total fields with an average size of 3.92 ha. Estimates based on the map reference sample indicate the cropland percentage is 17.1% (15.4-18.9%) or 17.6% (15.6-19.6%), depending on the map used to estimate the standard error. Using the labellers’ digitized field boundaries to estimate biases in field boundary statistics, we calculated an adjusted mean field size of 1.73 ha and total field count of 1,662,281. Although the cropland class contained substantial errors, the system was effective in mitigating error and quantifying resulting performance gains. By minimizing training errors, consensus labelling improved the model’s F1 scores by up to 25%, while 3 iterations of active learning increased the F1 score by 9.1%, on average, which was 2.3% higher than training models with randomly selected labels. Map accuracy can be improved by replacing Random Forests with a convolutional neural network. These results demonstrate a readily adapted, transferrable framework for developing high resolution, annual, nation-scale maps that provide important details about smallholder-dominated croplands.




Agriculture, Environmental Monitoring, Environmental Sciences, Geographic Information Sciences, Geography, Life Sciences, Natural Resources and Conservation, Physical and Environmental Geography, Physical Sciences and Mathematics, Remote Sensing, Spatial Science, Sustainability


machine learning, Africa, active learning, CubeSat, PlanetScope, smallholder, cropland, field size, consensus labels, Ghana


Published: 2021-03-12 07:05

Last Updated: 2021-03-23 12:01

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CC BY Attribution 4.0 International

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