Using MODIS Data to Predict Regional Corn Yields

Ban, Ho-Young; Kim, K. S.; Park, No-Wook; Lee, Byun-Woo

doi:10.20944/preprints201610.0044.v1

Cited by 11 publications

(1 citation statement)

References 49 publications

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“…Experiments have shown that phenological dynamic information can solve this heterogeneity issue and improve the yield prediction or estimation accuracy [ 25 , 26 ]. For example, the accumulative leaf area index (LAI) in a specific GP had the highest correlation with the regional crop yield [ 27 ], and the time series index, combined with phenological date information, can effectively improve the yield prediction accuracy [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Pan

Zhu

et al. 2021

Sensors

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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-R2). Duration and rate represent crop growth days and rate, respectively. Max-R2 is the NDVI value with the most significant correlation with corn yield in the NDVI time series. We built three groups of yield regression models, including univariate models using phenological metrics and Max-R2, and multivariate models using phenological metrics, and multivariate models using phenological metrics combined with Max-R2 in the whole, semi-arid, and non-semi-arid regions, respectively, and compared the performance of these models. The results show that most phenological metrics had a statistically significant (p < 0.05) relationship with corn yield (maximum R2 = 0.44). Models established with phenological metrics realized yield prediction before harvest in the three regions with R2 = 0.64, 0.67, and 0.72. Compared with the univariate Max-R2 models, the accuracy of models built with Max-R2 and phenology metrics improved. Thus, the phenology metrics obtained from MODIS-NDVI accurately reflect the corn characteristics and can be used for large-scale yield prediction. Overall, this study showed that phenology metrics derived from remote sensing vegetation indexes could be used as crop yield prediction variables and provide a reference for data organization and yield prediction with physical crop significance.

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Section: Introductionmentioning

confidence: 99%

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Pan

Zhu

et al. 2021

Sensors

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Can Yield Prediction Be Fully Digitilized? A Systematic Review

Darra,

Anastasiou,

Kriezi

et al. 2023

Agronomy

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Going beyond previous work, this paper presents a systematic literature review that explores the deployment of satellites, drones, and ground-based sensors for yield prediction in agriculture. It covers multiple aspects of the topic, including crop types, key sensor platforms, data analysis techniques, and performance in estimating yield. To this end, datasets from Scopus and Web of Science were analyzed, resulting in the full review of 269 out of 1429 retrieved publications. Our study revealed that China (93 articles, >1800 citations) and the USA (58 articles, >1600 citations) are prominent contributors in this field; while satellites were the primary remote sensing platform (62%), followed by airborne (30%) and proximal sensors (27%). Additionally, statistical methods were used in 157 articles, and model-based approaches were utilized in 60 articles, while machine learning and deep learning were employed in 142 articles and 62 articles, respectively. When comparing methods, machine learning and deep learning methods exhibited high accuracy in crop yield prediction, while other techniques also demonstrated success, contingent on the specific crop platform and method employed. The findings of this study serve as a comprehensive roadmap for researchers and farmers, enabling them to make data-driven decisions and optimize agricultural practices, paving the way towards a fully digitized yield prediction.

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Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model

et al. 2019

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Integrating remote sensing data into crop models offers opportunities for improved crop yield estimation. To compare site-specific yield estimation accuracy of a stand-alone crop model with a data-integration approach, a study was conducted in 2016–2017 with nitrogen (N)-fertilized and unfertilized treatments across a heterogeneous 7-ha maize field. For each treatment, yield data were grouped into five classes resulting in 109 spatial zones. In each zone, the Crop Environment Resource Synthesis (CERES)-Maize model was run using the GeoSim plugin within Quantum GIS. In the data integration approach, maize biomass values estimated using satellite imagery at the five (V5) and ten (V10) leaf collar stages were used to optimize the total soil nitrogen concentration (SLNI) and soil fertility factor (SLPF) in CERES-Maize. Without integration, maize yield was simulated with root mean square error (RMSE) of 1264 kg ha−1. Optimization of SLNI improved yield simulations at both V5 and V10. However, better simulations were obtained from optimization at V10 (RMSE 1026 kg ha−1) as compared to V5 (RMSE 1158 kg ha−1). Optimization of SLPF together with SLNI did not further improve the yield simulations. This study shows that integrating remote sensing data into a crop model can improve site-specific maize yield estimations as compared to the stand-alone crop modeling approach.

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Using MODIS Data to Predict Regional Corn Yields

Cited by 11 publications

References 49 publications

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Can Yield Prediction Be Fully Digitilized? A Systematic Review

Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model

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