Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches

Cai, Yaping; Guan, Kaiyu; Lobell, David B.; Potgieter, Andries; Wang, Shaowen; Peng, Jian; Xu, Tianfang; Asseng, Senthold; Zhang, Yongguang; You, Liangzhi; Peng, Bin

doi:10.1016/j.agrformet.2019.03.010

Cited by 380 publications

(278 citation statements)

References 92 publications

Supporting

Mentioning

200

Contrasting

Unclassified

Order By: Relevance

“…Combining SIF with all environmental data (i.e., climate data, soil properties, and irrigation ratio) significantly improved the predicted R 2 s (ranging from 0.19 to 0.32) depending on the methods (Figure 8a). Moreover, the peak SIF consistently showed more contribution than the other two stages, which was in agreement with previous studies [17,29].…”

Section: The Spatial Patterns Of Predicted Yieldsupporting

confidence: 93%

“…In agreement with previous studies [17,64,65], we found both SIF and EVI were positively correlated with yield across AEZs, and the correlation coefficients at the peak stage for SIF were generally higher than that for EVI, illustrating SIF was more sensitive to a high photosynthesis rate. However, we noticed the predicted R 2 for two data sources were comparable on the national scale irrelative to methods.…”

Section: Comparing the Performances Of Evi And Sif In Predicting Cropsupporting

confidence: 92%

See 1 more Smart Citation

Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches

Zhang

Luo

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Maize is an extremely important grain crop, and the demand has increased sharply throughout the world. China contributes nearly one-fifth of the total production alone with its decreasing arable land. Timely and accurate prediction of maize yield in China is critical for ensuring global food security. Previous studies primarily used either visible or near-infrared (NIR) based vegetation indices (VIs), or climate data, or both to predict crop yield. However, other satellite data from different spectral bands have been underutilized, which contain unique information on crop growth and yield. In addition, although a joint application of multi-source data significantly improves crop yield prediction, the combinations of input variables that could achieve the best results have not been well investigated. Here we integrated optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield across four agro-ecological zones (AEZs) in China using a regression-based method (LASSO), two machine learning (ML) methods (RF and XGBoost), and deep learning (DL) network (LSTM). The results showed that combining multi-source data explained more than 75% of yield variation. Satellite data at the silking stage contributed more information than other variables, and solar-induced chlorophyll fluorescence (SIF) had an almost equivalent performance with the enhanced vegetation index (EVI) largely due to the low signal to noise ratio and coarse spatial resolution. The extremely high temperature and vapor pressure deficit during the reproductive period were the most important climate variables affecting maize production in China. Soil properties and management factors contained extra information on crop growth conditions that cannot be fully captured by satellite and climate data. We found that ML and DL approaches definitely outperformed regression-based methods, and ML had more computational efficiency and easier generalizations relative to DL. Our study is an important effort to combine multi-source remote sensed and environmental data for large-scale yield prediction. The proposed methodology provides a paradigm for other crop yield predictions and in other regions.

show abstract

Section: The Spatial Patterns Of Predicted Yieldsupporting

confidence: 93%

Section: Comparing the Performances Of Evi And Sif In Predicting Cropsupporting

confidence: 92%

Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches

Zhang

Luo

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…DCN provides the best estimation results with RMSE equals 0.82 Mg ha −1 as compared with LASSO and RF ( figure 4). LASSO provides the lowest estimation accuracy with RMSE equals 1.14 Mg ha −1 , agreeing with the discovery in previous study that linear methods are limited in simulating the relationship between crop yield and meteorological factors relative to nonlinear approaches (Cai et al 2018). RF provides a better result than LASSO with RMSE equals 1.05 Mg ha −1 .…”

Section: Performance Comparison Among Dcn Lasso and Rfsupporting

confidence: 89%

DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

Lin

Zhong

Wang

et al. 2020

Environ. Res. Lett.

View full text Add to dashboard Cite

Large-scale crop yield estimation is critical for understanding the dynamics of global food security. Understanding and quantifying the temporal cumulative effect of crop growth and spatial variances across different regions remains challenging for large-scale crop yield estimation. In this study, a deep spatial-temporal learning framework, named DeepCropNet (DCN), has been developed to hierarchically capture the features for county-level corn yield estimation. The temporal features are learned by an attention-based long short-term memory network and the spatial features are learned by the multi-task learning (MTL) output layers. The DCN model has been applied to quantify the relationship between meteorological factors and the county-level corn yield in the US Corn Belt from 1981 to 2016. Three meteorological factors, including growing degree days, killing degree days, and precipitation, are used as time-series inputs. The results show that DCN provides an improved estimation accuracy (RMSE=0.82 Mg ha −1 ) as compared to that of conventional methods such as LASSO (RMSE=1.14 Mg ha −1 ) and Random Forest (RMSE=1.05 Mg ha −1 ). Temporally, the attention values computed from the temporal learning module indicate that DCN captures the temporal cumulative effect and this temporal pattern is consistent across all states. Spatially, the spatial learning module improves the estimation accuracy based on the regional specific features captured by the MTL mechanism. The study highlights that the DCN model provides a promising spatial-temporal learning framework for corn yield estimation under changing meteorological conditions across large spatial regions.

show abstract

“…Recently, machine-learning (ML) has emerged as a powerful tool for environmental analysis (Chlingaryan et al 2018), as well as climate impact assessment on crop yield (Jeong et al 2016, Johnson et al 2016, Feng et al 2018, Cai et al 2019, Vogel et al 2019. ML often shows better performance compared to conventional linear regression models (Feng et al 2018), as it can capture non-linear relationships, handle the interactions among predictors and do not assume a certain shape of response function (e.g.…”

Section: Introductionmentioning

confidence: 99%

Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models

Leng

Hall

2020

Environ. Res. Lett.

103

View full text Add to dashboard Cite

Pervious assessments of crop yield response to climate change are mainly aided with either process-based models or statistical models, with a focus on predicting the changes in average yields, whilst there is growing interest in yield variability and extremes. In this study, we simulate US maize yield using process-based models, traditional regression model and a machine-learning algorithm, and importantly, identify the weakness and strength of each method in simulating the average, variability and extremes of maize yield across the country. We show that both regression and machine learning models can well reproduce the observed pattern of yield averages, while large bias is found for process-based crop models even fed with harmonized parameters. As for the probability distribution of yields, machine learning shows the best skill, followed by regression model and process-based models. For the country as a whole, machine learning can explain 93% of observed yield variability, followed by regression model (51%) and process-based models (42%). Based on the improved capability of the machine learning algorithm, we estimate that US maize yield is projected to decrease by 13.5% under the 2 • C global warming scenario (by~2050 s). Yields less than or equal to the 10th percentile in the yield distribution for the baseline period are predicted to occur in 19% and 25% of years in 1.5 • C (by~2040 s) and 2 • C global warming scenarios, with potentially significant implications for food supply, prices and trade. The machine learning and regression methods are computationally much more efficient than process-based models, making it feasible to do probabilistic risk analysis of climate impacts on crop production for a wide range of future scenarios.

show abstract

Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches

Cited by 380 publications

References 92 publications

Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches

Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches

DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models

Contact Info

Product

Resources

About