Bin Peng

Towards a multiscale crop modelling framework for climate change adaptation assessment

Nature Plants · 2020 · 309 citations

From the Ground to Space: Using Solar‐Induced Chlorophyll Fluorescence to Estimate Crop Productivity

Geophysical Research Letters · 2020 · 162 citations

Abstract Timely and accurate monitoring of crops is essential for food security. Here, we examine how well solar‐induced chlorophyll fluorescence (SIF) can inform crop productivity across the United States. Based on tower‐level observations and process‐based modeling, we find highly linear gross primary production (GPP):SIF relationships for C4 crops, while C3 crops show some saturation of GPP at high light when SIF continues to increase. C4 crops yield higher GPP:SIF ratios (30–50%) primarily b…

Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction

International Journal of Applied Earth Observation and Geoinformation · 2020 · 158 citations

Large-scale crop yield prediction is critical for early warning of food insecurity, agricultural supply chain management, and economic market. Satellite-based Solar-Induced Chlorophyll Fluorescence (SIF) products have revealed hot spots of photosynthesis over global croplands, such as in the U.S. Midwest. However, to what extent these satellite-based SIF products can enhance the performance of crop yield prediction when benchmarking against other existing satellite data remains unclear. Here we…

Assessing the impacts of cover crops on maize and soybean yield in the U.S. Midwestern agroecosystems

Field Crops Research · 2021 · 93 citations

Recent Rapid Increase of Cover Crop Adoption Across the U.S. Midwest Detected by Fusing Multi‐Source Satellite Data

Geophysical Research Letters · 2022 · 76 citations

Abstract Cover crops have critical significance for agroecosystem sustainability and have long been promoted in the U.S. Midwest. Knowledge of cover cropping variations and impacts of government policies remains very limited. We developed an accurate and cost‐effective approach utilizing satellite fusion data, environmental variables, and machine learning to quantify cover cropping in corn and soybean fields from 2000 to 2021 in the U.S. Midwest. We found that cover crop adoption in most countie…