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

[1]  George Alan Blackburn,et al.  High resolution wheat yield mapping using Sentinel-2 , 2019, Remote Sensing of Environment.

[2]  Bruno Basso,et al.  Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone. , 2016, The Science of the total environment.

[3]  Chris Murphy,et al.  APSIM - Evolution towards a new generation of agricultural systems simulation , 2014, Environ. Model. Softw..

[4]  D. Lobell,et al.  A scalable satellite-based crop yield mapper , 2015 .

[5]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[6]  C. Woodcock,et al.  Continuous change detection and classification of land cover using all available Landsat data , 2014 .

[7]  Zhengwei Yang,et al.  Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program , 2011 .

[8]  Francesco Pirotti,et al.  Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques , 2019, Remote. Sens..

[9]  C. Jordan.  Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[10]  Jesús Álvarez-Mozos,et al.  Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[11]  Jerome H. Friedman,et al.  Recent Advances in Predictive (Machine) Learning , 2006, J. Classif..

[12]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[13]  J. Abatzoglou.  Development of gridded surface meteorological data for ecological applications and modelling , 2013 .

[14]  W. Wiebold,et al.  Climate-induced reduction in US-wide soybean yields underpinned by region- and in-season-specific responses , 2015, Nature Plants.

[15]  Inbal Becker-Reshef,et al.  Forecasting wheat yield from weather data and MODIS NDVI using Random Forests for Punjab province, Pakistan , 2017 .

[16]  David B. Lobell,et al.  The use of satellite data for crop yield gap analysis , 2013 .

[17]  Tiziana Simoniello,et al.  A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses , 2018, Remote Sensing of Environment.

[18]  David B. Lobell,et al.  Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data , 2016, Remote. Sens..

[19]  I. Ciampitti,et al.  Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil , 2020 .

[20]  Yanghui Kang,et al.  Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach , 2019, Remote Sensing of Environment.

[21]  J. Dash,et al.  The MERIS terrestrial chlorophyll index , 2004 .

[22]  Senthold Asseng,et al.  Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches , 2018, Agricultural and Forest Meteorology.

[23]  D. F. Heermann,et al.  Frequency Analysis of Yield for Delineating Yield Response Zones , 2004, Precision Agriculture.

[24]  Flavio Esposito,et al.  Soybean yield prediction from UAV using multimodal data fusion and deep learning , 2020 .

[25]  Jie Sun,et al.  County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model , 2019, Sensors.

[26]  Gilles Louppe,et al.  Scikit-learn: Machine Learning Without Learning the Machinery , 2015, GETMBL.

[27]  Jesús Álvarez-Mozos,et al.  Inter-Comparison of Atmospheric Correction Methods on Sentinel-2 Images Applied to Croplands , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[28]  David B. Lobell,et al.  Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques , 2019, Remote Sensing of Environment.

[29]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[30]  Belinda A. Margono,et al.  Mapping and monitoring deforestation and forest degradation in Sumatra (Indonesia) using Landsat time series data sets from 1990 to 2010 , 2012 .

[31]  Siobhan Murray,et al.  Eyes in the Sky, Boots on the Ground: Assessing Satellite‐ and Ground‐Based Approaches to Crop Yield Measurement and Analysis , 2018, American Journal of Agricultural Economics.

[32]  R. G. V. Bramley,et al.  Adoption of variable rate fertiliser application in the Australian grains industry: status, issues and prospects , 2011, Precision Agriculture.

[33]  C. Field,et al.  Canopy near-infrared reflectance and terrestrial photosynthesis , 2017, Science Advances.

[34]  A. Viña,et al.  Green leaf area index estimation in maize and soybean: Combining vegetation indices to achieve maximal sensitivity , 2012 .

[35]  Mohsen Azadbakht,et al.  Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[36]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[37]  A. Gitelson,et al.  Informative spectral bands for remote green LAI estimation in C3 and C4 crops , 2016 .

[38]  David B Lobell,et al.  Increasing drought and diminishing benefits of elevated carbon dioxide for soybean yields across the US Midwest , 2018, Global change biology.

[39]  Damir Medak,et al.  Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers , 2020, ISPRS Int. J. Geo Inf..

[40]  Christopher O. Justice,et al.  Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations , 2015 .

[41]  Xiaodong Yang,et al.  Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data , 2019, Plant Methods.

[42]  D. Lobell,et al.  Improving the accuracy of satellite-based high-resolution yield estimation: A test of multiple scalable approaches , 2017 .

[43]  Guijun Yang,et al.  A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy , 2018, Remote. Sens..

[44]  José Moreno,et al.  Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI) , 2019, Sensors.

[45]  D. Lobell,et al.  Satellite detection of rising maize yield heterogeneity in the U.S. Midwest , 2017 .

[46]  David B. Lobell,et al.  Sight for Sorghums: Comparisons of Satellite- and Ground-Based Sorghum Yield Estimates in Mali , 2019, Remote. Sens..

[47]  Anatoly A. Gitelson,et al.  Assessment of Canopy Chlorophyll Content Retrieval in Maize and Soybean: Implications of Hysteresis on the Development of Generic Algorithms , 2017, Remote. Sens..

[48]  François Waldner,et al.  High temporal resolution of leaf area data improves empirical estimation of grain yield , 2019, Scientific Reports.

[49]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[50]  David B. Lobell,et al.  The impact of agricultural interventions can be doubled by using satellite data , 2019, Nature Sustainability.

[51]  A. Gitelson,et al.  Quantitative estimation of chlorophyll-a using reflectance spectra : experiments with autumn chestnut and maple leaves , 1994 .

[52]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[53]  Bruno Basso,et al.  Drivers of within-field spatial and temporal variability of crop yield across the US Midwest , 2018, Scientific Reports.

[54]  A. Viña,et al.  Remote estimation of leaf area index and green leaf biomass in maize canopies , 2003 .

[55]  Jerry Adriani Johann,et al.  Using phenology-based enhanced vegetation index and machine learning for soybean yield estimation in Paraná State, Brazil , 2018, Journal of Applied Remote Sensing.

[56]  D. Good,et al.  Weather, Technology, and Corn and Soybean Yields in the U.S. Corn Belt , 2008 .

[57]  Craig S. T. Daughtry,et al.  Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery , 2018, Remote. Sens..

[58]  D. Lobell,et al.  Satellite-based assessment of yield variation and its determinants in smallholder African systems , 2017, Proceedings of the National Academy of Sciences.

[59]  Zhe Zhu,et al.  Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data , 2016 .

[60]  Luis Alonso,et al.  Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content , 2011, Sensors.

[61]  Yuanheng Sun,et al.  Red-Edge Band Vegetation Indices for Leaf Area Index Estimation From Sentinel-2/MSI Imagery , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[62]  David B. Lobell,et al.  Smallholder maize area and yield mapping at national scales with Google Earth Engine , 2019, Remote Sensing of Environment.

[63]  Venkatraman Srinivasan,et al.  Decreasing, not increasing, leaf area will raise crop yields under global atmospheric change , 2016, Global change biology.

[64]  Jan G. P. W. Clevers,et al.  Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[65]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[66]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[67]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..