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Journal Articles

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

Field Crops Research, 2021

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

Environmental Research Letters, 2021

Journal Articles

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

Remote Sensing of Environment, 2021

Journal Articles

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

Environmental Research Letters, 2021

Journal Articles

Using satellite imagery to understand and promote sustainable development

Science, 2021

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

Remote Sensing of Environment, 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.

Journal Articles

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

Global Change Biology, 2020

Commentary

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

American Journal of Agricultural Economics, 2019

Journal Articles

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

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

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

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

Using satellite imagery to understand and promote sustainable development

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

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

Changes in the drought sensitivity of US maize yields

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

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

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

Viewpoint: Principles and priorities for one CGIAR

Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery

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

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

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

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

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

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

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

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

Strengthened scientific support for the Endangerment Finding for atmospheric greenhouse gases

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

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

Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques

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