ArticleSensors (Basel, Switzerland)2021
Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- RGB-UAV-based phenotyping reveals the influence of planting density on physiological traits, biomass, and yield in buckwheat.BMC plant biology · 2026Article
- Evaluating Geostationary Satellite-Based Approaches for NDVI Gap Filling in Polar-Orbiting Satellite Observations.Sensors (Basel, Switzerland) · 2026Article
- A review of remote sensing-based crop yield estimation: machine learning techniques and environmental, algorithmic, and hardware limitations.Frontiers in plant science · 2026Review
- Rice yield prediction through integration of biophysical parameters with SAR and optical remote sensing data using machine learning models.Scientific reports · 2024Article
- Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture.Plants (Basel, Switzerland) · 2024Article
- Multi-source information fusion-driven corn yield prediction using the Random Forest from the perspective of Agricultural and Forestry Economic Management.Scientific reports · 2024Article
- A Lightweight Recognition Method for Rice Growth Period Based on Improved YOLOv5s.Sensors (Basel, Switzerland) · 2023Article
- Predicting Maize Theoretical Methane Yield in Combination with Ground and UAV Remote Data Using Machine Learning.Plants (Basel, Switzerland) · 2023Article
- Analysis of Corn Yield Prediction Potential at Various Growth Phases Using a Process-Based Model and Deep Learning.Plants (Basel, Switzerland) · 2023Article
- A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh.PloS one · 2023Article
- Article
- Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method.Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided into semi-arid and non-semi-arid regions. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product MOD09Q1 was used to calculate the normalized difference vegetation index (NDVI) time series. According to the NDVI time series, we divided the corn growing season into four growth phases, calculated phenological information metrics (duration and rate) for each growth phase, and obtained the maximum correlation NDVI (Max-R
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