Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique

Feng, Ping; Wang, Bin; Liu, De Li; Waters, Cathy; Xiao, Dengpan; Shi, Liwei; Yu, Qiang

doi:10.1016/j.agrformet.2020.107922

Cited by 92 publications

(53 citation statements)

References 49 publications

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“…Finally, this study provides a method reference for establishing the yield prediction model of other crops. The R 2 between the yield and NDVI rate of GP1, GP2, and GP4 ranged from 0.27 to 0.44, indicating that it may be more beneficial to organize time series data parameters based on the GP [ 25 ] and provide support for dynamic yield predictions with the growth stages as a time unit [ 26 ]. The combination of the duration and rate in each GP can simply simulate the crop growth process.…”

Section: Discussionmentioning

confidence: 99%

“…This condition leads to spatial-temporal heterogeneity between the yield prediction variables. 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%

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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%

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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“…For example, Pede et al [36] assessed the potential benefits of LST derived by satellite for maize yield prediction across the US Corn Belt from 2010 to 2016 by using metrics of killing degree day (KDD) and growing degree day (GDD). Feng et al [37] developed a hybrid yield forecasting approach by combing climate extremes, NDVI, crop-process model simulated biomass, and the Standardized Precipitation Evapotranspiration Index (SPEI) with a regression model (RF or multiple linear regression (MLR)). They found that the forecasting system based on RF was better than that based on MLR at each forecasting event.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

et al. 2021

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Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

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“…A reliable statistical algorithm that remote sensing data can depend on is critical for the estimation of crop yield. As one of the challenging problems in agriculture, many previous studies have tried to find a more accurate way between diversified regression models [23][24][25][26]. Machine learning algorithms have become an important decision support tool in massive crop yield estimation [27,28].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

Tang

et al. 2020

Remote Sensing

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Sugarcane is a multifunctional crop mainly used for sugar and renewable bioenergy production. Accurate and timely estimation of the sugarcane yield before harvest plays a particularly important role in the management of agroecosystems. The rapid development of remote sensing technologies, especially Light Detecting and Ranging (LiDAR), significantly enhances aboveground fresh weight (AFW) estimations. In our study, we evaluated the capability of LiDAR mounted on an Unmanned Aerial Vehicle (UAV) in estimating the sugarcane AFW in Fusui county, Chongzuo city of Guangxi province, China. We measured the height and the fresh weight of sugarcane plants in 105 sampling plots, and eight variables were extracted from the field-based measurements. Six regression algorithms were used to build the sugarcane AFW model: multiple linear regression (MLR), stepwise multiple regression (SMR), generalized linear model (GLM), generalized boosted model (GBM), kernel-based regularized least squares (KRLS), and random forest regression (RFR). The results demonstrate that RFR (R2 = 0.96, RMSE = 1.27 kg m−2) performs better than other models in terms of prediction accuracy. The final fitted sugarcane AFW distribution maps exhibited good agreement with the observed values (R2 = 0.97, RMSE = 1.33 kg m−2). Canopy cover, the distance to the road, and tillage methods all have an impact on sugarcane AFW. Our study provides guidance for calculating the optimum planting density, reducing the negative impact of human activities, and selecting suitable tillage methods in actual cultivation and production.

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Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique

Cited by 92 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

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

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