Smallholder maize area and yield mapping at national scales with Google Earth Engine

Zhenong Jin, George Azzari, Calum You, Stefania Di Tommaso, Stephen Aston, Marshall Burke, David B. Lobell

Research output: Contribution to journalArticlepeer-review

65 Scopus citations

Abstract

Accurate measurements of maize yields at field or subfield scales are useful for guiding agronomic practices and investments and policies for improving food security. Data on smallholder maize systems are currently sparse, but satellite remote sensing offers promise for accelerating learning about these systems. Here we document the use of Google Earth Engine (GEE) to build “wall-to-wall” 10 m resolution maps of (i) cropland presence, (ii) maize presence, and (iii) maize yields for the main 2017 maize season in Kenya and Tanzania. Mapping these outcomes at this scale is extremely challenging because of very heterogeneous landscapes, lack of cloud-free satellite imagery, and the low quantity of quality ground-based data in these regions. First, we computed seasonal median composites of Sentinel-1 radar backscatter and Sentinel-2 optical reflectance measures for each pixel in the region, and used them to build both crop/non-crop and maize/non-maize Random Forest (RF) classifiers. Several thousand crop/non-crop labels were collected through an in-house GEE labeler, and thousands of crop type labels from the 2015–2017 growing seasons were obtained from various sources. Results show that the crop/non-crop classifier successfully identified cropland with over 85% out-of-sample accuracy in both countries, with Sentinel-1 being particularly useful for prediction. Among the cropped pixels, the maize/non-maize classier had an accuracy of 79% in Tanzania and 63% in Kenya. To map maize yields, we build on past work using a scalable crop yield mapper (SCYM) that utilizes simulations from a crop model to train a regression that predicts yields from observations. Here we advance past approaches by (i) grouping simulations by Global Agro-Environmental Stratification (GAES) zones across the two countries, in order to account for landscape heterogeneity, (ii) utilizing gridded datasets on soil and sowing and harvest dates to setup model simulations in a scalable way; and (iii) utilizing all available satellite observations during the growing season in a parsimonious way by using harmonic regression fits implemented in GEE. SCYM estimates were able to capture about 50% of the variation in the yields at the district level in Western Kenya as measured by objective ground-based crop cuts. Finally, we illustrated the utility of our yield maps with two case studies. First, we document the magnitude and interannual variability of spatial heterogeneity of yields in each district, and how it varies for different parts of the region. Second, we combine our estimates with recently released soil databases in the region to investigate the most important soil constraints in the region. Soil factors explain a high fraction (72%) of variation in predicted yields, with the predominant factor being soil nitrogen levels. Overall, this study illustrates the power of combining Sentinel-1 and Sentinel-2 imagery, the GEE platform, and advanced classification and yield mapping algorithms to advance understanding of smallholder agricultural systems.

Original languageEnglish (US)
Pages (from-to)115-128
Number of pages14
JournalRemote Sensing of Environment
Volume228
DOIs
StatePublished - Jul 2019
Externally publishedYes

Bibliographical note

Funding Information:
This study is made possible by the support of the American People provided to the Feed the Future Innovation Lab for Sustainable Intensification through the United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Program activities are funded by the USAID under Cooperative Agreement No. AID-OAA-L-14-00006 . Funding was also provided by NASA Harvest Consortium grant 54308-Z6059203 to DBL, USAID grant 740681-74171D to MB and DBL and Global Innovation Fund to MB. The authors acknowledge Jake Campolo for early assistance on the maize simulations, AfSIS, One Acre Fund and TAMASA for providing data and the Bill and Melinda Gates Foundation for supporting those efforts in data collection and dissemination. Code and data generated from this paper will be made available to public upon publication.

Funding Information:
This study is made possible by the support of the American People provided to the Feed the Future Innovation Lab for Sustainable Intensification through the United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Program activities are funded by the USAID under Cooperative Agreement No. AID-OAA-L-14-00006. Funding was also provided by NASA Harvest Consortium grant 54308-Z6059203 to DBL, USAID grant 740681-74171D to MB and DBL and Global Innovation Fund to MB. The authors acknowledge Jake Campolo for early assistance on the maize simulations, AfSIS, One Acre Fund and TAMASA for providing data and the Bill and Melinda Gates Foundation for supporting those efforts in data collection and dissemination. Code and data generated from this paper will be made available to public upon publication.

Publisher Copyright:
© 2019 Elsevier Inc.

Keywords

  • Cropland classification
  • Data fusion
  • Sentinel-1
  • Sentinel-2
  • Yield mapping

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