Next Article in Journal
A Feasibility Study of Sea Ice Motion and Deformation Measurements Using Multi-Sensor High-Resolution Optical Satellite Images
Next Article in Special Issue
A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture
Previous Article in Journal
Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data
Previous Article in Special Issue
A Small UAV Based Multi-Temporal Image Registration for Dynamic Agricultural Terrace Monitoring
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessEditor’s ChoiceArticle
Remote Sens. 2017, 9(9), 931; https://doi.org/10.3390/rs9090931

Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa

1
Department of Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA
2
National Bureau of Economic Research, Cambridge, MA 02138, USA
3
One Acre Fund, Kigali, Rwanda
*
Author to whom correspondence should be addressed.
Received: 3 August 2017 / Revised: 1 September 2017 / Accepted: 4 September 2017 / Published: 8 September 2017
Full-Text   |   PDF [7788 KB, uploaded 8 September 2017]   |  

Abstract

Accurate measurements of crop production in smallholder farming systems are critical to the understanding of yield constraints and, thus, setting the appropriate agronomic investments and policies for improving food security and reducing poverty. Nevertheless, mapping the yields of smallholder farms is challenging because of factors such as small field sizes and heterogeneous landscapes. Recent advances in fine-resolution satellite sensors offer promise for monitoring and characterizing the production of smallholder farms. In this study, we investigated the utility of different sensors, including the commercial Skysat and RapidEye satellites and the publicly accessible Sentinel-2, for tracking smallholder maize yield variation throughout a ~40,000 km2 western Kenya region. We tested the potential of two types of multiple regression models for predicting yield: (i) a “calibrated model”, which required ground-measured yield and weather data for calibration, and (ii) an “uncalibrated model”, which used a process-based crop model to generate daily vegetation index and end-of-season biomass and/or yield as pseudo training samples. Model performance was evaluated at the field, division, and district scales using a combination of farmer surveys and crop cuts across thousands of smallholder plots in western Kenya. Results show that the “calibrated” approach captured a significant fraction (R2 between 0.3 and 0.6) of yield variations at aggregated administrative units (e.g., districts and divisions), while the “uncalibrated” approach performed only slightly worse. For both approaches, we found that predictions using the MERIS Terrestrial Chlorophyll Index (MTCI), which included the red edge band available in RapidEye and Sentinel-2, were superior to those made using other commonly used vegetation indices. We also found that multiple refinements to the crop simulation procedures led to improvements in the “uncalibrated” approach. We identified the prevalence of small field sizes, intercropping management, and cloudy satellite images as major challenges to improve the model performance. Overall, this study suggested that high-resolution satellite imagery can be used to map yields of smallholder farming systems, and the methodology presented in this study could serve as a good foundation for other smallholder farming systems in the world. View Full-Text
Keywords: smallholder farms; yield; Africa; remote sensing; maize; agriculture smallholder farms; yield; Africa; remote sensing; maize; agriculture
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Jin, Z.; Azzari, G.; Burke, M.; Aston, S.; Lobell, D.B. Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa. Remote Sens. 2017, 9, 931.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top