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Stefania Di Tomasso
Journal Articles

Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops

Stefania Di Tomasso, David Lobell, Sherrie Wang
Environmental Research Letters, 2021 November 2, 2021

High resolution crop type maps are an important tool for improving food security, and remote sensing is increasingly used to create such maps in regions that possess ground truth labels for model training. However, these labels are absent in many regions, and models trained in other regions on typical satellite features, such as those from optical sensors, often exhibit low performance when transferred. Here we explore the use of NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar instrument, combined with Sentinel-2 optical data, for crop type mapping. Using data from three major cropped regions (in China, France, and the United States) we first demonstrate that GEDI energy profiles are capable of reliably distinguishing maize, a crop typically above 2m in height, from crops like rice and soybean that are shorter. We further show that these GEDI profiles provide much more invariant features across geographies compared to spectral and phenological features detected by passive optical sensors. GEDI is able to distinguish maize from other crops within each region with accuracies higher than 84\%, and able to transfer across regions with accuracies higher than 82\% compared to 64\% for transfer of optical features. Finally, we show that GEDI profiles can be used to generate training labels for models based on optical imagery from Sentinel-2, thereby enabling the creation of 10m wall-to-wall maps of tall versus short crops in label-scarce regions. As maize is the second most widely grown crop in the world and often the only tall crop grown within a landscape, we conclude that GEDI offers great promise for improving global crop type maps.

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Journal Articles

Changes in the drought sensitivity of US maize yields

David Lobell, Jillian Deines, Stefania Di Tomasso
Nature Food, 2020 November 1, 2020

As climate change leads to increased frequency and severity of drought in many agricultural regions, a prominent adaptation goal is to reduce the drought sensitivity of crop yields. Yet many of the sources of average yield gains are more effective in good weather, leading to heightened drought sensitivity. Here we consider two empirical strategies for detecting changes in drought sensitivity and apply them to maize in the United States, a crop that has experienced myriad management changes including recent adoption of drought-tolerant varieties.

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Journal Articles

Mapping Crop Types in Southeast India with Smartphone Crowdsourcing and Deep Learning

David Lobell, Sherrie Wang, Stefania Di Tomasso
Remote Sensing, 2020 September 11, 2020

High resolution satellite imagery and modern machine learning methods hold the potential to fill existing data gaps in where crops are grown around the world at a sub-field level. However, high resolution crop type maps have remained challenging to create in developing regions due to a lack of ground truth labels for model development. In this work, we explore the use of crowdsourced data, Sentinel-2 and DigitalGlobe imagery, and convolutional neural networks (CNNs) for crop type mapping in India.

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Journal Articles

Sight for Sorghums: Comparisons of Satellite- and Ground-Based Sorghum Yield Estimates in Mali

David Lobell, Stefania Di Tomasso, Marshall Burke
Remote Sensing MDPI, 2019 December 27, 2019

The advent of multiple satellite systems capable of resolving smallholder agricultural plots raises possibilities for significant advances in measuring and understanding agricultural productivity in smallholder systems. However, since only imperfect yield data are typically available for model training and validation, assessing the accuracy of satellite-based estimates remains a central challenge.

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Journal Articles

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

Zhenong Jin, George Azzari, Calum You, Stefania Di Tomasso, Stephen Aston, Marshall Burke, David Lobell
Remote Sensing of Environment, 2019 May 1, 2019

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.

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