Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

(2020) Sotirios V. Archontoulis et al.   CROP SCIENCE

Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil

(2020) Raí A. Schwalbert et al.   AGRICULTURAL AND FOREST METEOROLOGY

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

(2020) Yanghui Kang et al.   Environmental Research Letters

Deep learning in environmental remote sensing: Achievements and challenges

(2020) Qiangqiang Yuan et al.   REMOTE SENSING OF ENVIRONMENT

Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States

(2020) Yumiao Wang et al.   Remote Sensing

Impacts of climate change on the optimum planting date of different maize cultivars in the central US Corn Belt

(2020) Mitch E. Baum et al.   EUROPEAN JOURNAL OF AGRONOMY

Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States

(2019) Yan Li et al.   GLOBAL CHANGE BIOLOGY

Crop Yield Prediction Using Deep Neural Networks

(2019) Saeed Khaki et al.   Frontiers in Plant Science

Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach

(2019) Yanghui Kang et al.   REMOTE SENSING OF ENVIRONMENT

Increased atmospheric vapor pressure deficit reduces global vegetation growth

(2019) Wenping Yuan et al.   Science Advances

Comparison of GLUE and DREAM for the estimation of cultivar parameters in the APSIM-maize model

(2019) Meiling Sheng et al.   AGRICULTURAL AND FOREST METEOROLOGY

Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods

(2019) Elisa Kamir et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Soybean yield prediction from UAV using multimodal data fusion and deep learning

(2019) Maitiniyazi Maimaitijiang et al.   REMOTE SENSING OF ENVIRONMENT

Deep learning in agriculture: A survey

(2018) Andreas Kamilaris et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Adapting the CROPGRO Model to Simulate Alfalfa Growth and Yield

(2018) Wafa Malik et al.   AGRONOMY JOURNAL

Machine learning methods for crop yield prediction and climate change impact assessment in agriculture

(2018) Andrew Crane-Droesch   Environmental Research Letters

Why Jupyter is data scientists’ computational notebook of choice

(2018) Jeffrey M. Perkel   NATURE

Distribution-Free Predictive Inference for Regression

(2017) Jing Lei et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields

(2017) Kaiyu Guan et al.   REMOTE SENSING OF ENVIRONMENT

Google Earth Engine: Planetary-scale geospatial analysis for everyone

(2017) Noel Gorelick et al.   REMOTE SENSING OF ENVIRONMENT

Drought effects on US maize and soybean production: spatiotemporal patterns and historical changes

(2016) Samuel C Zipper et al.   Environmental Research Letters

Deep learning

(2015) Yann LeCun et al.   NATURE

Heat stress in temperate and tropical maize hybrids: Kernel growth, water relations and assimilate availability for grain filling

(2014) Juan I. Rattalino Edreira et al.   FIELD CROPS RESEARCH

How do various maize crop models vary in their responses to climate change factors?

(2014) Simona Bassu et al.   GLOBAL CHANGE BIOLOGY

Greater Sensitivity to Drought Accompanies Maize Yield Increase in the U.S. Midwest

(2014) D. B. Lobell et al.   SCIENCE

Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics

(2013) Douglas K. Bolton et al.   AGRICULTURAL AND FOREST METEOROLOGY

Testing Remote Sensing Approaches for Assessing Yield Variability among Maize Fields

(2013) Adam M. Sibley et al.   AGRONOMY JOURNAL

MODIS-based corn grain yield estimation model incorporating crop phenology information

(2013) Toshihiro Sakamoto et al.   REMOTE SENSING OF ENVIRONMENT

An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States

(2013) David M. Johnson   REMOTE SENSING OF ENVIRONMENT

The critical role of extreme heat for maize production in the United States

(2013) David B. Lobell et al.   Nature Climate Change

Heat stress in temperate and tropical maize hybrids: Differences in crop growth, biomass partitioning and reserves use

(2012) Juan I. Rattalino Edreira et al.   FIELD CROPS RESEARCH

Crop yield forecasting on the Canadian Prairies using MODIS NDVI data

(2011) M.S. Mkhabela et al.   AGRICULTURAL AND FOREST METEOROLOGY

The potential of the satellite derived green chlorophyll index for estimating midday light use efficiency in maize, coniferous forest and grassland

(2011) Chaoyang Wu et al.   ECOLOGICAL INDICATORS

Crop Yield Gaps: Their Importance, Magnitudes, and Causes

(2009) David B. Lobell et al.   Annual Review of Environment and Resources

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

(2009) Mark A. Friedl et al.   REMOTE SENSING OF ENVIRONMENT

Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States

(2008) Christopher Daly et al.   INTERNATIONAL JOURNAL OF CLIMATOLOGY

Development of a two-band enhanced vegetation index without a blue band

(2008) Z JIANG et al.   REMOTE SENSING OF ENVIRONMENT