1. Sub-hectare agriculture fields mapped for food security programs
Christopher S.R. Neigh1, Mark Carroll1,2, Margret Wooten1,2, Jessica McCarty3,
Bristol Powell4, Markus Enenkel4, Greg Husak5, Christopher Hain6 and Daniel Osgood4
1Biospheric Sciences Lab., NASA GSFC , 2Science Systems Applications Inc., 3Miami University, 4IRI Columbia Univ., 5Univ. Cali. Santa Barbra, 6NASA MSFC
Figure 1
300 m N
Figure 2
©DigitalGlobe NextView 2014
Global food production in the developing world occurs within sub-hectare fields that are difficult to identify with moderate resolution satellite imagery. Knowledge about the distribution of these fields is critical to food security programs. We developed a semi-automated high-performance computational methodology to rapidly extract cropped area from WorldView and Landsat.
Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics

2. Name: Christopher S.R. Neigh, NASA/GSFC
Phone:
References:
McCarty, J.L., Neigh, C.S.R., Carroll, M.L., & Wooten, M.R. (2017). Extracting smallholder cropped area in Tigray, Ethiopia with wall-to-wall sub-meter WorldView and moderate resolution Landsat 8 imagery Remote Sensing of Environment, in revision.
See, L.D., Fritz, S., You, L.Z., Ramankutty, N., Herrero, M., Justice, C., Becker-Reshef, I., Thornton, P., Erb, K., Gong, P., Tang, H.J., van der Velde, M., Ericksen, P., McCallum, I., Kraxner, F., & Obersteiner, M. (2015). Improved global cropland data as an essential ingredient for food security. Global Food Security-Agriculture Policy Economics and Environment, 4,
Neigh, C.S.R., Masek, J.G., & Nickeson, J. (2013). High-Resolution Satellite Data Open for Government Research. EOS Transactions, 94,
Data Sources: Sub-meter commercial imagery from WorldView-1, -2, and -3, moderate resolution optical imagery from Landsat 8 OLI, and digital elevation model from SRTM for orthorectification. Validation data derived from GPS watermarked photography from IRI.
Technical Description of Figures: The images show in-situ validation photography with the resultant crop area (CA) map overlaid in dark red upon 1 m resolution WorldView-1 imagery. Existing CA maps from moderate resolution Landsat and MODIS disagree and sub-meter data are needed to resolve fields that are often less than 50 × 50 m in dimension.
Figure 1: (Upper left) Multispectral Google Earth image for the same region as figure 2. Color photographs were taken by Bristol Powell in August 2016 located with a recreational grade GPS (± 5 m) in cardinal directions (North-N, South-S, East-E, and West-W) and ground with the photo location indicated by the yellow star in figure 2.
Figure 2: Example of WV-1 1 m resampled image with segmentation classification result overlaid in dark red for an area near the village of Ruba Felege in Tigray, Ethiopia. The inset Earth is centered on the study domain. The crop area map was derived from 1 m image texture using ‘Otsu’ multi-level histogram thresholding.
Scientific significance, societal relevance, and relationships to future missions: Agriculture systems in Sub-Saharan Africa have been difficult to monitor for famine early warning systems due to sub-hectare field sizes and lack of adequate in-situ and high-resolution remote sensing data for wall-to-wall CA mapping. We resolved this deficiency by developing a semi-automated image segmentation approach using wall-to-wall sub-meter imagery with GSFC’s high-performance computing to map CA throughout Tigray, Ethiopia that encompasses more than 41,000 km2. Over 6,000 WorldView images were processed wall-to-wall in less than one week to map CA. This empirical, simple, and low direct cost approach, could be a viable big-data high-end computing methodology to extract wall-to-wall CA for other regions of the world that have smallholder agriculture that is difficult to map with moderate resolution satellite imagery. Crop area information is vital to the success of food security programs that use index-insurance.
Earth Sciences Division – Hydrosphere, Biosphere, and Geophysics 2