{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T04:25:23Z","timestamp":1768710323081,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper provides recommendations on how large-scale household surveys should be conducted to generate the data needed to train models for satellite-based crop type mapping in smallholder farming systems. The analysis focuses on maize cultivation in Malawi and Ethiopia, and leverages rich, georeferenced plot-level data from national household surveys that were conducted in 2018\u201320 and integrated with Sentinel-2 satellite imagery and complementary geospatial data. To identify the approach to survey data collection that yields optimal data for training remote sensing models, 26,250 in silico experiments are simulated within a machine learning framework. The best model is then applied to map seasonal maize cultivation from 2016 to 2019 at 10-m resolution in both countries. The analysis reveals that smallholder plots with maize cultivation can be identified with up to 75% accuracy. Collecting full plot boundaries or complete plot corner points provides the best quality of information for model training. Classification performance peaks with slightly less than 60% of the training data. Seemingly little erosion in accuracy under less preferable approaches to georeferencing plots results in the total area under maize cultivation being overestimated by 0.16\u20130.47 million hectares (8\u201324%) in Malawi.<\/jats:p>","DOI":"10.3390\/rs13234749","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4749","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa"],"prefix":"10.3390","volume":"13","author":[{"given":"George","family":"Azzari","sequence":"first","affiliation":[{"name":"Atlas AI, Palo Alto, CA 94301, USA"}]},{"given":"Shruti","family":"Jain","sequence":"additional","affiliation":[{"name":"Atlas AI, Palo Alto, CA 94301, USA"}]},{"given":"Graham","family":"Jeffries","sequence":"additional","affiliation":[{"name":"Farmers Business Network, Rockland, ME 04841, USA"}]},{"given":"Talip","family":"Kilic","sequence":"additional","affiliation":[{"name":"Development Data Group, World Bank, Washington, DC 20433, USA"}]},{"given":"Siobhan","family":"Murray","sequence":"additional","affiliation":[{"name":"Development Data Group, World Bank, Washington, DC 20433, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.foodpol.2016.09.018","article-title":"Are African households (not) leaving agriculture? patterns of households\u2019 income sources in rural Sub-Saharan Africa","volume":"67","author":"Davis","year":"2017","journal-title":"Food Policy"},{"key":"ref_2","first-page":"11553","article-title":"Strengthening agricultural decisions in countries at risk of food insecurity: The GEOGLAM Crop Monitor for Early Warning","volume":"237","author":"Justice","year":"2020","journal-title":"Remote Sens. 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