Automatic calibration of DSSAT and APEX models for maize yield simulation and economic optimum nitrogen rate determination
(Xiaoxing Zhen, Yuxin Miao, Yanbo Huang, Zhengwei Yang, Gary Feng, Fabián G. Fernández, Curtis J. Ransom, Pang-Wei Liu, Rajat Bindlish, Jessica Erlingis, Meijian Yang)
|
2026
|
Optimizing On-Farm Corn Yield Prediction by a Multi-Source Data Fusion Approach Using Remote Sensing and Machine Learning
(Aamir Raza, Yuxin Miao, Yanbo Huang, Kirk Stueve, Junjun Lu, Zhengwei Yang, Rajat Bindlish)
|
2025
|
High-resolution surface and rootzone soil moisture over US cropland: A novel framework assimilating multi-source remote sensing data, machine learning, and the Layered Green and Ampt Infiltration with Redistribution model
(Shuohao Cai, Yijia Xu, Zhengwei Yang, Wade T. Crow, Zhou Zhang, Jiali Shang, Jiangui Liu, Peter La Follette, Chris Reberg-Horton, Harry Schomberg, Steven Mirsky, Brian Davis, Sarah Seehaver, Alexis Correira, Andrea Basche, Ashley Waggoner, Charles Ellis, Dara Park, DanielleD. Treadwell, David Campbell, Deann Presley, Esleyther L. Henriquez Inoa, Heather Darby, Jared Adam, Jarrod Miller, Joseph Haymaker, John Wallace, Julia Gaskin, Kipling S. Balkcom, Lindsey Ruhl, Mark Reiter, Matthew Ruark, Michael Flessner, Cynthia Sias, Payton Davis, Peter Tomlinson, Richard G. Smith, Nicholas D. Warren, Ryan Dierking, Shalamar Armstrong, Tauana Almeida, Jingyi Huang)
|
2025
|
Hydro-Topographic Contribution to In-Field Crop Yield Variation Using High-Resolution Surface and GPR-Derived Subsurface DEMs
(Chang, J. G., Anderson, M., Gao, F., Russ, A., Zhao, H., Cirone, R., Pachepsky, Y., and Johnson, D. M.)
|
2025
|
VegGAN: A Generative Adversarial Network for Downscaling JPSS/VIIRS Vegetation Indices
(Yifan Yang, Haonan Chen, Changyong Cao, Zhengwei Yang, Qi Chen, Zhe Li, Rick Mueller, and Noa K. Lincoln)
|
2025
|
Robust and timely within-season conterminous United States crop type mapping using Landsat Sentinel-2 time series and the transformer architecture
(Hankui K. Zhang, Yu Shen, Xiaoyang Zhang, Junjie Li, Zhengwei Yang, Yijia Xu, Chen Zhang, Liping Di, David P. Roy)
|
2025
|
Learning county from pixels: corn yield prediction with attention-weighted multiple instance learning
(Wang, X., Ma, Y., Xu, Y., Huang, Q., Yang, Z., and Zhang, Z)
|
2025
|
A multimodal deep learning approach for soil moisture downscaling using remote sensing and weather data
(Xu, Y., Cai, S., Huang, J., Liu, J., Shang, J.,Yang, Z., and Zhang, Z)
|
2025
|
Automated in-season crop-type data layer mapping without ground truth for the Conterminous United States based on multisource satellite imagery
(Hui Li, M.D.; Liping Di; Chen Zhang; Li Lin; Liying Guo; Eugene G. Yu; Zhengwei Yang)
|
2024
|
A Machine Learning-Based High-Resolution Soil Moisture Mapping and Spatial–Temporal Analysis: The mlhrsm Package
(Yuliang Peng, Zhengwei Yang, Jingyi Huang, Zhou Zhang)
|
2024
|
Cloud-Powered Agricultural Mapping: A Revolution Toward 10m Resolution Cropland Data Layers
(Z. Li, R. Mueller, Z. Yang, D. Johnson and P. Willis)
|
2024
|
Empirical Inferences Under Bayesian Framework to Identify Cellwise Outliers
(Luca Sartore, Lu Chen, Valbona Bejleri)
|
2024
|
Identifying Anomalous Data Entries in Repeated Surveys
(Luca Sartore, Lu Chen, Justin van Wart, Andrew Dau, Valbona Bejleri)
|
2024
|
Crop Sequence Boundaries Using USDA National Agricultural Statistics Service Historic Cropland Data Layers
(Kevin A. Hunt, Jake Abernethy, Peter C. Beeson, Maria Bowman, Steven Wallander, Ryan Williams)
|
2024
|
Data Reconciliation and Estimation in an Agricultural Survey
(Habtamu K. Benecha, Denise A. Abreu, Rachael Jennings, Linda J. Young)
|
2023
|
An Assessment of Crop-Specific Land Cover Predictions Using High-Order Markov Chains and Deep Neural Networks
(Luca Sartore, Claire Boryan, Andrew Dau, Patrick Willis)
|
2023
|
Preseason Crop Type Prediction Using Crop Sequence Boundaries
(Jonathon Abernethy, Peter Beeson, Claire Boryan, Kevin Hunt, Luca Sartore)
|
2023
|
Multisource Maximum Predictor Discrepancy for Unsupervised Domain Adaptation on Corn Yield Prediction
(Ma, Yuchi, Zhengwei Yang, Zhou Zhang)
|
2023
|
Assessing Machine Leaning Algorithms on Crop Yield Forecasts Using Functional Covariates Derived from Remotely Sensed Data
(Luca Sartore, Arthur N. Rosales, David M. Johnson, Clifford H. Spiegelman)
|
2022
|
Capture-Recapture Estimation of Characteristics of U.S. Local Food Farms Using a Web-Scraped List Frame
(Michael Hyman, Luca Satore, Linda J. Young)
|
2022
|
Using Small Area Estimation to Produce Official Statistics
(Linda J. Young, Lu Chen)
|
2022
|
Smoothing County-Level Sampling Variances to Improve Small
Area Models’ Outputs
(Lu Chen, Luca Sartore, Habtamu Benecha, Valbona Bejleri, Balgobin Nandram)
|
2022
|
Hierarchical Bayesian Model with Inequality Constraints for US County Estimates
(Lu Chen, Balgobin Nandram, Nathan B. Cruze)
|
2022
|
Model-Based Estimates for Farm Labor Quantities
(Lu Chen, Nathan B. Cruze, Linda J. Young)
|
2022
|
Modeling Swine Population Dynamics at a Finer
Temporal Resolution
(Luca Sartore, Yijun Wei, Emilola Abayomi, Seth Riggins, Gavin Corral, Valbona Bejleri, Clifford Spiegelman)
|
2020
|
NASS Geospatial Applications from the Cropland Data Layer.
(Sandborn, A., R. Mueller, C. Boryan, D. Johnson, Z. Yang, L. Ebinger, A. Rosales, P. Willis, R. Seffrin, R. Jennings, M. Deaton, and H. Hamer)
|
2019
|
Early Season Winter Wheat Identification Using Sentinel -1 Synthetic Aperture Radar (SAR) and Optical Data
(Boryan, C., Z. Yang, P. Willis, and A. Sandborn)
|
2019
|
Impact of Non-Proportional Training Sampling of Imbalanced Classes on Land Cover Classification Accuracy With See5 Decision Tree
(Yang, Z. and C. Boryan)
|
2019
|
Fusion of Moderate Resolution Earth Observations for Operational Crop Type Mapping.
(Torbick, N.; X. Huang, B. Ziniti, D. Johnson, J. Masek, and M. Reba)
|
2018
|
Developing Integer Calibration Weights for Census of Agriculture
(Luca Sartore, Kelly Toppin, Linda Young, Clifford Spiegelman)
|
2018
|
Real-time Monitoring of Crop Phenology in the Midwestern United States using VIIRS Observations.
(Liu, L., X. Zhang, Y. Yu, F. Gao, and Z. Yang)
|
2018
|
Operational Agricultural Flood Monitoring With Sentinel-1 Synthetic Aperture Radar
(Boryan, C., Z. Yang, A. Sandborn, P. Willis, and B. Haack)
|
2018
|
Evaluation of Sentinel-1A C-Band Synthetic Aperature Radar for Citrus Crop Classification in Florida, United States
(Boryan, C., Z. Yang, and B. Haack)
|
2018
|
The USDA NASS Cropland Data Layer Program Transition from Research to Operations (2006-2009)
(Boryan, C.)
|
2018
|
Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery
(Gao, F., M. Anderson, C.Daughtry, and D. Johnson)
|
2018
|
Integration of the Cropland Data Layer Based Automatic Stratification Method into the Traditional Area Frame Construction Process
(Boryan, C., and Z. Yang)
|
2017
|
Measuring land-use and land-cover change using the U.S. department of agriculture’s cropland data layer: Cautions and recommendations
(Lark, T., Mueller, R., Johnson, D., and H. Gibbs)
|
2017
|
Developing Crop Specific Area Frame Stratifications based on Geospatial Crop Frequency and Cultivation Data Layers
(Boryan, C., Z. Yang, P. Willis, and L. Di)
|
2017
|
Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought
(Otkin, J., M. Anderson, C. Hain, M. Svobodad, D. Johnson, R. Mueller, T. Tadesse, B. Wardlow, J. Brown)
|
2016
|
A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products
(Johnson, D.)
|
2016
|
The role of remote sensing for regional monitoring of U.S. crop condition and yield, Evaluation of drought and drought impacts through interdisciplinary methods
(Johnson, D.)
|
2015
|
The utility of the Cropland Data Layer for monitoring US grassland extent
(Johnson, D., R. Mueller, and P. Willis)
|
2015
|
Assessing bioenergy-driven agricultural land use change and biomass quantities with MODIS time series in the U.S. Midwest
(Wang, C., C. Zhong, and Z. Yang)
|
2014
|
Making Cropland Data Layer Data Accessible and Actionable in GIS Education
(Han, W., Z. Yang, L. Di, A. Yagci, and S. Han)
|
2014
|
A Geospatial Web Service Approach For Creating On-Demand Cropland Data Layer Thematic Maps
(Han, W., Z. Yang, L. Di, and P. Yue)
|
2014
|
A Literature Review of Crop Area Estimation" Food and Agriculture Organization
(Craig, Michael and Dale Atkinson)
|
2013
|
Reported Uses of CropScape and the National Cropland Data Layer Program
(Mueller R., and J. Harris)
|
2013
|
Evaluating the Accuracy Assessment Methods of a Thematic Raster through SAS Resampling Techniques and GTL Visualizations
(Seffrin, R.)
|
2013
|
Exploring Continuous Corn Cropping Patterns and Their Relationship with Geographic Factors
(Yang, Z.)
|
2013
|
US National Cropland Soil Moisture Monitoring Using SMAP
(Yang, Z., R. Mueller, and W. Crowe)
|
2013
|
An Innovative Approach to Integrating SAS Macros with GIS Software Products to Produce County-Level Accuracy Assessments
(Zakzeski, A., and R. Seffrin)
|
2013
|
CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support.
(Han, W., Z. Yang, L. Di, and R. Mueller)
|
2012
|
Monitoring US Agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program
(Boryan, C., Z. Yang, R. Mueller, and M. Craig)
|
2011
|
Phenology-Based Assessment of Perennial Energy Crops in North American Tallgrass Prairie
(Wang, C., F. Fritschib, G. Staceyc and Z. Yang)
|
2011
|
The 2009 Cropland Data Layer
(Johnson, D., and R. Mueller)
|
2010
|
Area Frame Design for Agricultural Surveys
(Jim Cotter, Carrie Davies, Jack Nealon, Ray Roberts)
|
2010
|
