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Browse FSI scholarship on geopolitics, global health, energy, cybersecurity and more.

Featured Publications

image of a bowl of cartoon frogs with the word Gab on the front

Gabufacturing Dissent: An In-depth Analysis of Gab

Gab was founded in 2016 as an uncensored alternative to mainstream social media platforms. Stanford Internet Observatory’s latest report looks at behaviors and dynamics across the platform.
Liberalism and Its Discontents by Francis Fukuyama

Liberalism and Its Discontents

It's no secret that liberalism hasn't always lived up to its own ideals. But in this short, clear account, Francis Fukuyama offers an essential defense of a revitalized liberalism for the twenty-first century.
Everything Counts: Building a Control Regime for Nonstrategic Nuclear Warheads in Europe

Building a Control Regime for Nonstrategic Nuclear Warheads in Europe

A new report led by Rose Gottemoeller on non-strategic nuclear warhead policies in Europe, particulary in light of Russia's changing status in the global nuclear community.

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David Lobell
Journal Articles

Evaluating maize yield response to fertilizer and soil in Mexico using ground and satellite approaches

Jake Campolo, David Lobell
Field Crops Research, 2021 December 13, 2021
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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

Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions

David Lobell
Remote Sensing of Environment, 2021 September 1, 2021

Crop type mapping at the field level is critical for a variety of applications in agricultural monitoring, and satellite imagery is becoming an increasingly abundant and useful raw input from which to create crop type maps. Still, in many regions crop type mapping with satellite data remains constrained by a scarcity of field-level crop labels for training supervised classification models. When training data is not available in one region, classifiers trained in similar regions can be transferred, but shifts in the distribution of crop types as well as transformations of the features between regions lead to reduced classification accuracy. We present a methodology that uses aggregate-level crop statistics to correct the classifier by accounting for these two types of shifts. To adjust for shifts in the crop type composition we present a scheme for properly reweighting the posterior probabilities of each class that are output by the classifier. To adjust for shifts in features we propose a method to estimate and remove linear shifts in the mean feature vector. We demonstrate that this methodology leads to substantial improvements in overall classification accuracy when using Linear Discriminant Analysis (LDA) to map crop types in Occitanie, France and in Western Province, Kenya. When using LDA as our base classifier, we found that in France our methodology led to percent reductions in misclassifications ranging from 2.8% to 42.2% (mean = 21.9%) over eleven different training departments, and in Kenya the percent reductions in misclassification were 6.6%, 28.4%, and 42.7% for three training regions. While our methodology was statistically motivated by the LDA classifier, it can be applied to any type of classifier. As an example, we demonstrate its successful application to improve a Random Forest classifier.

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Journal Articles

Cleaner air has contributed one-fifth of US maize and soybean yield gains since 1999

David Lobell, Jennifer Burney
Environmental Research Letters, 2021 July 14, 2021

Crop productivity is potentially affected by several air pollutants, although these are usually studied in isolation. A significant challenge to understanding the effects of multiple pollutants in many regions is the dearth of air quality data near agricultural fields. Here we empirically estimate the effect of four key pollutants (ozone (O3), particulate matter (PM), sulfur dioxide (SO2), and nitrogen dioxide (NO2)) on maize and soybean yields in the United States using a combination of administrative data and satellite-derived yield estimates. We identify clear negative effects of exposure to O3, PM, and SO2 in both crops, using yields measured in the vicinity of monitoring stations. We also show that while stations measuring NO2 are too sparse to reliably estimate a yield effect, the strong gradient of NO2 concentrations near power plants allows us to more precisely estimate NO2 effects using satellite measured yield gradients. The presence of some powerplants that turn on and others that shut down during the study period are particularly useful for attributing yield gradients to pollution. We estimate that total yield losses from these pollutants averaged roughly 5% for both maize and soybean over the past two decades. While all four pollutants have statistically significant effects, PM and NO2 appear more damaging to crops at current levels than O3 and SO2. Finally, we find that the significant improvement in air quality since 1999 has halved the impact of poor air quality on major crops and contributed to yield increases that represent roughly 20% of overall yield gains over that period.

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Journal Articles

Using satellite imagery to understand and promote sustainable development

Marshall Burke, David Lobell
Science, 2021 March 19, 2021

Recent years have witnessed rapid growth in satellite-based approaches to quantifying aspects of land use, especially those monitoring the outcomes of sustainable development programs. Burke et al. reviewed this recent progress with a particular focus on machine-learning approaches and artificial intelligence methods. Drawing on examples mostly from Africa, they conclude that satellite-based methods enhance rather than replace ground-based data collection, and progress depends on a combined approach.

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Journal Articles

A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt

Jillian Deines, David Lobell
Remote Sensing of Environment, 2021 February 1, 2021

Crop yield maps estimated from satellite data increasingly are used to understand drivers of yield trends and variability, yet satellite-derived regional maps are rarely compared with location-specific yields due to the difficulty of acquiring sub-field ground truth data at scale. In commercial agricultural systems, combine harvesters with onboard yield monitors collect real-time yield data during harvest with high spatial resolution, generating a large ground dataset. Here, we leveraged a yield monitor dataset of over one million maize field observations across the United States Corn Belt from 2008 to 2018 to evaluate the Scalable Crop Yield Mapper (SCYM). SCYM uses region-specific crop model simulations and climate data to interpret vegetation indices from satellite observations, thus enabling efficient sub-field yield estimation across large regions and multiple years without reliance on ground data calibration. We used the ground dataset to compare alternative SCYM model implementations, define minimum required satellite observation criteria, and evaluate the sensitivity of satellite-based yield estimates to on-the-ground variation in management, soil, and annual weather. We found that smoothing annual time series data with harmonic regression increased 30 m pixel-level accuracy from r2 = 0.31 to 0.40 and reduced dependency on specific satellite observation timing, enabling robust yield estimation on 97% of annual maize area using only Landsat data. Agreement improved as the assessment was aggregated to field-level (r2 = 0.45) and county-level (r2 = 0.69) scales, demonstrating the need for fine-resolution ground truth data to better assess sub-field level accuracy in high resolution products. We found that SCYM and ground data showed a similar yield response to management and environmental variation, particularly capturing linear and nonlinear responses to sowing density, soil water holding capacity, and growing season precipitation. However, sensitivity to factors like soil quality and planting date was muted for SCYM estimates compared to ground-based yields. Random forest models were able to match SCYM performance when trained on at least 1000 ground observations, but performed poorly when tested on years and locations not represented in the training data. Our results demonstrate that satellite yield maps can provide much-needed information on multidecadal yield trends and inform yield gap analyses.

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Journal Articles

Uniting remote sensing, crop modelling and economics for agricultural risk management

David Lobell
Nature Reviews Earth & Environment, 2021 January 19, 2021

The increasing availability of satellite data at higher spatial, temporal and spectral resolutions is enabling new applications in agriculture and economic development, including agricultural insurance. Yet, effectively using satellite data in this context requires blending technical knowledge about their capabilities and limitations with an understanding of their influence on the value of risk-reduction programmes. In this Review, we discuss how approaches to estimate agricultural losses for index insurance have evolved from costly field-sampling-based campaigns towards lower-cost techniques using weather and satellite data. We identify advances in remote sensing and crop modelling for assessing agricultural conditions, but reliably and cheaply assessing production losses remains challenging in complex landscapes. We illustrate how an economic framework can be used to gauge and enhance the value of insurance based on earth-observation data, emphasizing that even as yield-estimation techniques improve, the value of an index insurance contract for the insured depends largely on how well it captures the losses when people suffer most. Strategically improving the collection and accessibility of reliable ground-reference data on crop types and production would facilitate this task. Audits to account for inevitable misestimation complement efforts to detect and protect against large losses.

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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

High-Resolution Soybean Yield Mapping Across the US Midwest Using Subfield Harvester Data

Walter Dado, Jillian Deines, David Lobell
Remote Sensing, 2020 October 22, 2020

Cloud computing and freely available, high-resolution satellite data have enabled recent progress in crop yield mapping at fine scales. However, extensive validation data at a matching resolution remain uncommon or infeasible due to data availability. This has limited the ability to evaluate different yield estimation models and improve understanding of key features useful for yield estimation in both data-rich and data-poor contexts.

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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

On the role of anthropogenic climate change in the emerging food crisis in southern Africa in the 2019–2020 growing season

David Lobell
Global Change Biology, 2020 February 19, 2020

Researchers including David Lobell analyze how human-caused climate change has impacted a water deficit in Southern Africa and might contribute to a rising food security crisis in the region.

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Commentary

Viewpoint: Principles and priorities for one CGIAR

David Lobell
Food Policy, 2020 January 16, 2020
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Journal Articles

Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery

Sherrie Wang, George Azzari, David Lobell
Remote Sensing MDPI, 2020 January 7, 2020

Accurate automated segmentation of remote sensing data could benefit applications from land cover mapping and agricultural monitoring to urban development surveyal and disaster damage assessment. While convolutional neural networks (CNNs) achieve state-of-the-art accuracy when segmenting natural images with huge labeled datasets, their successful translation to remote sensing tasks has been limited by low quantities of ground truth labels, especially fully segmented ones, in the remote sensing domain.

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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

Satellites reveal a small positive yield effect from conservation tillage across the US Corn Belt

Jillian Deines, David Lobell, Sherrie Wang
Environmental Research Letters, 2019 December 6, 2019

Machine learning and satellite data of crops shows that farms that till the soil less can increase yields of corn and soybeans and improve the health of the soil. Farmers have resisted a switch to reduced tilling because it was believed to reduce yields. Instead, it may increase yields while lowering production costs and reducing soil erosion.

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Journal Articles

Eyes in the Sky, Boots on the Ground: Assessing Satellite- and Ground-Based Approaches to Crop Yield Measurement and Analysis

David Lobell, Marshall Burke, George Azzari, Sydney Gourlay, Zhenong Jin, Talip Kilic, Siobhan Murray
American Journal of Agricultural Economics, 2019 October 26, 2019

Understanding the determinants of agricultural productivity requires accurate measurement of crop output and yield. In smallholder production systems across low- and middle-income countries, crop yields have traditionally been assessed based on farmer-reported production and land areas in household/farm surveys, occasionally by objective crop cuts for a sub-section of a farmer’s plot, and rarely using full-plot harvests. In parallel, satellite data continue to improve in terms of spatial, temporal, and spectral resolution needed to discern performance on smallholder plots.

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Journal Articles

The impact of agricultural interventions can be doubled by using satellite data

Meha Jain, Balwinder-Singh, Preeti Rao, Amit K. Srivastava, Shishpal Poonia, Jennifer Blesh, George Azzari, Andrew J. McDonald, David Lobell
Nature Sustainability, 2019 October 7, 2019

Feeding a growing population while reducing negative environmental impacts is one of the greatest challenges of the coming decades. We show that microsatellite data can be used to detect the impact of sustainable intensification interventions at large scales and to target the fields that would benefit the most, thereby doubling yield gains.

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Journal Articles

The role of irrigation in changing wheat yields and heat sensitivity in India

Esha Zaveri, David Lobell
Nature Communications, 2019 September 12, 2019

Irrigation has been pivotal in wheat’s rise as a major crop in India and is likely to be increasingly important as an adaptation response to climate change. Here we use historical data across 40 years to quantify the contribution of irrigation to wheat yield increases and the extent to which irrigation reduces sensitivity to heat.

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Journal Articles

Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches

Yaping Cai, Kaiyu Guan, David Lobell, Andries B.Potgieter, Shaowen Wanga, Jian Peng, Tianfang Xu, Senthold Assen, Yongguang Zhang, Liangzhi You, Bin Peng
Agricultural and Forest Meteorology , 2019 May 15, 2019

Wheat is the most important staple crop grown in Australia, and Australia is one of the top wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is important for regional and global food security. Prior studies use either climate data, or satellite data, or a combination of these two to build empirical models to predict crop yield. However, though the performance of yield prediction using empirical methods is improved by combining the use of climate and satellite data, the contributions from different data sources are still not clear.

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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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Journal Articles

Water Use Efficiency as a Constraint and Target for Improving the Resilience and Productivity of C3 and C4 Crops

Andrew D.B. Leakey, John N. Ferguson, Charles P. Pignon, Alex Wu, Zhenong Jin, Graeme L. Hammer, David Lobell
Annual Review of Plant Biology , 2019 April 30, 2019

The ratio of plant carbon gain to water use, known as water use efficiency (WUE), has long been recognized as a key constraint on crop production and an important target for crop improvement. WUE is a physiologically and genetically complex trait that can be defined at a range of scales. Many component traits directly influence WUE, including photosynthesis, stomatal and mesophyll conductances, and canopy structure. Interactions of carbon and water relations with diverse aspects of the environment and crop development also modulate WUE.

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Journal Articles

Strengthened scientific support for the Endangerment Finding for atmospheric greenhouse gases

Philip B. Duffy, Christopher B. Field, Noah Diffenbaugh, Scott C. Doney, Zoe Dutton, Sherri Goodman, Lisa Heinzerling, Solomon Hsiang, David Lobell, Loretta J. Mickley, Samuel Myers, Susan M. Natali, Camille Parmesan, Susan Tierney, A. Park Williams
Science, 2019 March 7, 2019

We assess scientific evidence that has emerged since the U.S. Environmental Protection Agency’s 2009 Endangerment Finding for six well-mixed greenhouse gases and find that this new evidence lends increased support to the conclusion that these gases pose a danger to public health and welfare.

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Working Papers

Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery

Wenjie Hu, Jay Harshadbhai Patel, Zoe-Alanah Robert, Paul Novosad, Samuel Asher, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon
AAAI/ACM Conference , 2019 February 20, 2019

Millions of people worldwide are absent from their country’s census. Accurate, current, and granular population metrics are critical to improving government allocation of resources, to measuring disease control, to responding to natural disasters, and to studying any aspect of human life in these communities. Satellite imagery can provide sufficient information to build a population map without the cost and time of a government census.

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Commentary

Strengthened scientific support for the Endangerment Finding for atmospheric greenhouse gases

Philip B. Duffy, Christopher B. Field, Noah Diffenbaugh, Scott C. Doney, Zoe Dutton, Sherri Goodman, Lisa Heinzerling, Solomon Hsiang, David Lobell, Loretta J. Mickley, Samuel Myers, Susan M. Natali, Camille Parmesan, Susan Tierney, A. Park Williams
Science, 2019 February 8, 2019

We assess scientific evidence that has emerged since the U.S. Environmental Protection Agency’s 2009 Endangerment Finding for six well-mixed greenhouse gases and find that this new evidence lends increased support to the conclusion that these gases pose a danger to public health and welfare.

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Journal Articles

Satellite mapping of tillage practices in the North Central US region from 2005 to 2016

George Azzari, Patricio Grassini, Juan Edreira, Shawn Conley, Spyridon Mourtzinis, David Lobell
Remote Sensing of Environment, 2019 February 1, 2019

Low-intensity tillage has become more popular among farmers in the United States and many other regions.

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