Mid‐season county‐level corn yield forecast for US Corn Belt integrating satellite imagery and weather variables

Schwalbert, Raíssa; Amado, Telmo Jorge Carneiro; Nieto, Luciana; Corassa, Geomar Mateus; Rice, Charles W.; Peralta, Nahuel Raúl; Schauberger, Bernhard; Gornott, Christoph; Ciampitti, Ignacio A.

doi:10.1002/csc2.20053

Cited by 32 publications

(15 citation statements)

References 49 publications

(89 reference statements)

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“…The yield prediction method using phenological metrics works best in the semi-arid region ( Table 2 , Figure 7 ). The United States suffered a drought in 2012, resulting in severe crop yield losses [ 55 ]. For the whole and non-semi-arid region, the explanatory power (R 2 ) for models constructed with data including 2012 (average R 2 = 0.64, 0.67) was higher than that of models built without 2012 data (R 2 = 0.59, 0.59).…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…the RF algorithm provided better results than other ML techniques. Schwalbert et al [32] evaluated the contribution of weather variables to estimate corn yield based on RS data and RF algorithm, which resulted in a mean absolute error (MAE) of about 0.89 Mg ha −1 . Thus, considering the previous experiences, the purpose of this investigation was to assess spectral bands and derived-VIs from orbital images to develop predictive sugarcane yield models based on RF and MLR (Multiple Linear Regression) algorithms.…”

Section: Study Sitementioning

confidence: 99%

“…These studies verified that the RF algorithm provided better results than other ML techniques. Schwalbert et al [32] evaluated the contribution of weather variables to estimate corn yield based on RS data and RF algorithm, which resulted in a mean absolute error (MAE) of about 0.89 Mg ha −1 .…”

Section: Introductionmentioning

confidence: 99%

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

et al. 2021

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Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.

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“…Considering the hybrid models, some of the developed models provided predictions with RRMSE values as small as 6-7%. This indicates that the developed models outperform the corn yield prediction models developed in the literature 24,[72][73][74][75] .…”

Section: Discussionmentioning

confidence: 72%

Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt

Shahhosseini

Huber

et al. 2021

Sci Rep

216

100

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This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.

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Mid‐season county‐level corn yield forecast for US Corn Belt integrating satellite imagery and weather variables

Cited by 32 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

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt

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