- Home
- Publications
- Publication Search
- Publication Details
Title
Using MODIS Data to Predict Regional Corn Yields
Authors
Keywords
-
Journal
Remote Sensing
Volume 9, Issue 1, Pages 16
Publisher
MDPI AG
Online
2016-12-29
DOI
10.3390/rs9010016
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Using temporal stability to estimate soya bean yield: a case study in Paraná state, Brazil
- (2016) Gleyce K. D. A. Figueiredo et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data
- (2016) Yang Shao et al. REMOTE SENSING OF ENVIRONMENT
- An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data
- (2015) Yang Shao et al. International Journal of Applied Earth Observation and Geoinformation
- Crop yield prediction from remotely sensed vegetation indices and primary productivity in arid and semi-arid lands
- (2015) Hadi H. Jaafar et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- An approach for evaluating the impact of gaps and measurement errors on satellite land surface phenology algorithms: Application to 20year NOAA AVHRR data over Canada
- (2015) Sivasathivel Kandasamy et al. REMOTE SENSING OF ENVIRONMENT
- Empirical Regression Models for Estimating Multiyear Leaf Area Index of Rice from Several Vegetation Indices at the Field Scale
- (2014) Masayasu Maki et al. Remote Sensing
- 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
- Prediction of rice crop yield using MODIS EVI−LAI data in the Mekong Delta, Vietnam
- (2013) N. T. Son et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR
- (2013) Jingfeng Huang et al. PLoS One
- 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
- Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection
- (2013) Felix Rembold et al. Remote Sensing
- Green Leaf Area Index Estimation in Maize and Soybean: Combining Vegetation Indices to Achieve Maximal Sensitivity
- (2012) Anthony Nguy-Robertson et al. AGRONOMY JOURNAL
- The use of satellite data for crop yield gap analysis
- (2012) David B. Lobell FIELD CROPS RESEARCH
- Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data
- (2012) Louis Kouadio et al. International Journal of Applied Earth Observation and Geoinformation
- Seasonal variation in MODIS LAI for a boreal forest area in Finland
- (2012) Janne Heiskanen et al. REMOTE SENSING OF ENVIRONMENT
- Detecting Spatiotemporal Changes of Corn Developmental Stages in the U.S. Corn Belt Using MODIS WDRVI Data
- (2011) Toshihiro Sakamoto et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Assessment of Potato Phenological Characteristics Using MODIS-Derived NDVI and LAI Information
- (2009) Akm Saiful Islam et al. GIScience & Remote Sensing
- The Concordance Correlation Coefficient for Repeated Measures Estimated by Variance Components
- (2009) Josep L. Carrasco et al. Journal of Biopharmaceutical Statistics
- Grid and sensor web technologies for environmental monitoring
- (2009) Nataliia Kussul et al. Earth Science Informatics
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started