High-resolution crop and maize area mapping for Malawi

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Linked to the research conducted under the Methods and Tools Component of the 50x2030 Initiative (https://www.50x2030.org/), this data deposit includes 10-meter spatial resolution maps for (i) areas cultivated with any crops, and (ii) areas cultivated with maize across Malawi for each rainy season during the period of 2016-2019. The maps are a product of the analyses conducted by Azzari et al. (2021), as part of the collaboration between the World Bank and Atlas AI, in support of one of the objectives of the 50x2030 Initiative to create guidelines for the collection of minimum-required survey data for training and validating remote sensing models for high-resolution crop type mapping and crop yield estimation. Azzari et al. (2021) integrate Sentinel-2 satellite imagery and complementary geospatial data with georeferenced plot-level data from national household surveys that were conducted by the Malawi National Statistical Office and the Central Statistical Agency of Ethiopia during the period of 2018-2020 in order to identify the optimal approach to collecting survey data for training a machine learning model to identify areas cultivated with maize. The best performing model estimated by Azzari et al. (2021) has been used to generate the 10-meter spatial resolution maps that are being made available here. For more information, please see the accompanying Basic Information Document and Azzari et al. (2021).

Reference: Azzari, G., Jain, S., Jeffries, G., Kilic, T., and Murray, S. (2021). "Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa." World Bank Policy Research Working Paper No. 9609, LSMS Washington, DC: World Bank.

Type: 
Geospatial
Languages Supported: 
English
Topics: 
Agriculture and Food Security
GP & CCSAs: 
Agriculture
WB Project ID: 
P172771
Geographical Coverage: 
Malawi
Economy Coverage: 
Economy Coverage not specified
Number of Economies: 
1
Periodicity: 
Annual

Update Frequency

Update Frequency: 
No fixed schedule
Access Options:
Download
Other Producer (s) Name, Affiliation, Role: 
Atlas AI is a contractor for the World Bank, with funding from the 50x2030 Initiative, that supports the implementation of the World Bank-led research program to define guidelines for collecting the minimum-required survey data for training and validating remote sensing models for high-resolution crop type mapping and crop yield estimation in smallholder farming systems.
Other Acknowledgments: 
This is a publication of 50x2030 Initiative to Close the Agricultural Data Gap, a multi-partner program that seeks to bridge the global agricultural data gap by transforming data systems in 50 countries in Africa, Asia, the Middle East and Latin America by 2030. For more information on the Initiative, please visit https://www.50x2030.org/.
Time Periods: 
April, 2021

No Visualizations Available.

Linked to the research conducted under the Methods and Tools Component of the 50x2030 Initiative (https://www.50x2030.org/), this data deposit includes 10-meter spatial resolution maps for (i) areas cultivated with any crops, and (ii) areas cultivated with maize across Malawi for each rainy season during the period of 2016-2019. The maps are a product of the analyses conducted by Azzari et al. (2021), as part of the collaboration between the World Bank and Atlas AI, in support of one of the objectives of the 50x2030 Initiative to create guidelines for the collection of minimum-required survey data for training and validating remote sensing models for high-resolution crop type mapping and crop yield estimation. Azzari et al. (2021) integrate Sentinel-2 satellite imagery and complementary geospatial data with georeferenced plot-level data from national household surveys that were conducted by the Malawi National Statistical Office and the Central Statistical Agency of Ethiopia during the period of 2018-2020 in order to identify the optimal approach to collecting survey data for training a machine learning model to identify areas cultivated with maize. The best performing model estimated by Azzari et al. (2021) has been used to generate the 10-meter spatial resolution maps that are being made available here. For more information, please see the accompanying Basic Information Document and Azzari et al. (2021).

Reference: Azzari, G., Jain, S., Jeffries, G., Kilic, T., and Murray, S. (2021). "Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa." World Bank Policy Research Working Paper No. 9609, LSMS Washington, DC: World Bank.

FieldValue
Modified Date
2021-04-13
Release Date
Periodicity
Identifier
4e06be16-42c9-4ec9-b142-809440d6e15a
License
License Not Specified
Contact Email
Public Access Level
Public
Rating: 
0
No votes yet
Type: 
Languages Supported: 
Time Periods: 
April, 2021
Economy Coverage: 
Other Acknowledgments: 
This is a publication of 50x2030 Initiative to Close the Agricultural Data Gap, a multi-partner program that seeks to bridge the global agricultural data gap by transforming data systems in 50 countries in Africa, Asia, the Middle East and Latin America by 2030. For more information on the Initiative, please visit https://www.50x2030.org/.
Other Producer (s) Name, Affiliation, Role: 
Atlas AI is a contractor for the World Bank, with funding from the 50x2030 Initiative, that supports the implementation of the World Bank-led research program to define guidelines for collecting the minimum-required survey data for training and validating remote sensing models for high-resolution crop type mapping and crop yield estimation in smallholder farming systems.
GP & CCSAs: 
Number of Economies: 
1
Update Frequency: 
Is this dataset a subscription: 
No
Geographical Coverage: 
Data Classification of a Dataset: 
WB Project ID: 
P172771
DEC
Programatic Region: 
Modified date: 
99
Primary Dataset: 
Yes

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This dataset is licensed under CC-BY 4.0

CC-BY 4.0

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