Integrating climate and satellite remote sensing data for predicting county-level wheat yield in China using machine learning methods

Zhou, Weimo; Liu, Yujie; Ata-Ul-Karim, Syed Tahir; Ge, Quansheng; Li, Xing; Xiao, Jingfeng

doi:10.1016/j.jag.2022.102861

Cited by 30 publications

(18 citation statements)

References 53 publications

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“…By using RS-derived NDVI with the amount of solar radiation the crop is absorbing (i.e., APAR), the LUE model can use these variables for its calibration and validation, which makes it more accurate in predicting crop yields [104,105]. The performance of the LUE model in forecasting crop yields also shows consistency with other studies [5,9,106]. Yuan et al [103] successfully validated the crop yields using the satellite-based LUE model at 36 crop sites.…”

Section: Importance Of Linking Crop Growth Models With Rs In Crop Yie...mentioning

confidence: 66%

Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany

et al. 2023

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The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km2) for the year 2019. For WW and OSR, the synthetic products’ high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R2 = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R2 = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world.

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Section: Importance Of Linking Crop Growth Models With Rs In Crop Yie...mentioning

confidence: 66%

Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany

et al. 2023

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“…forecast models make use of both meteorological forcing data and vegetation remote sensing (Jeong et al, 2022;Kuwata and Shibasaki, 2015;Paudel et al, 2022;Sun et al, 2020;Zhou et al, 2022), and therefore cannot be used for future impact assessment when remotely sensed vegetation index is not available. North Korea using satellite vegetation indices, meteorological and geospatial data as predictors.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model

Liu

Yang

Mohammadi

et al. 2022

Artificial Intelligence for the Earth Systems

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A major challenge for food security worldwide is the large inter-annual variability of crop yield, and climate change is expected to further exacerbate this volatility. Accurate prediction of the crop response to climate variability and change is critical for short-term management and long-term planning in multiple sectors. In this study, using maize in the U.S. Corn Belt as an example, we train and validate multiple machine learning (ML) models predicting crop yield based on meteorological variables and soil properties using the leaving-one-year-out approach, and compare their performance with that of a widely used process-based crop model (PBM). Our proposed Long Short-Term Memory model with attention (LSTMatt) outperforms other ML models (including other variations of LSTM developed in this study), and explains 73% of the spatiotemporal variance of the observed maize yield, in contrast to 16% explained by the regionally calibrated PBM; the magnitude of yield prediction errors in LSTMatt is about one-third of that in the PBM. When applied to the extreme drought year 2012 that has no counterpart in the training data, the LSTMatt performance drops but still shows advantage over the PBM. Findings from this study suggest a great potential for out-of-sample application of the 𝐿𝑆𝑇𝑀𝑎𝑡𝑡 model to predict crop yieldunder a changing climate.

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“…In order to evaluate the validation, we randomly divided 10% of the sample data into a testing set using for Leave-One-Out validation, and the remaining sample data was used as a training set 36 , 37 . Among the training set, we chose 10% of the data randomly at one time for 10-fold cross-validation to avoid over-fitting 38 , 39 . Two evaluation indicators were used to evaluate the performance of the simulation, including the determination coefficients ( R 2 ) and the root mean square error (RMSE).…”

Section: Methodsmentioning

confidence: 99%

County-level intensity of carbon emissions from crop farming in China during 2000–2019

Li,

Jia,

et al. 2024

Sci Data

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Agriculture is an important contributor to global carbon emissions. With the implementation of the Sustainable Development Goals of the United Nations and China’s carbon neutral strategy, accurate estimation of carbon emissions from crop farming is essential to reduce agricultural carbon emissions and promote sustainable food production systems in China. However, previous long-term time series estimates in China have mainly focused on the national and provincial levels, which are insufficient to characterize regional heterogeneity. Here, we selected the county-level administrative district as the basic geographical unit and then generated a county-level dataset on the intensity of carbon emissions from crop farming in China during 2000–2019, using random forest regression with multi-source data. This dataset can be used to delineate spatio-temporal changes in carbon emissions from crop farming in China, providing an important basis for decision makers and researchers to design agricultural carbon reduction strategies in China.

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Integrating climate and satellite remote sensing data for predicting county-level wheat yield in China using machine learning methods

Cited by 30 publications

References 53 publications

Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany

Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany

Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model

County-level intensity of carbon emissions from crop farming in China during 2000–2019

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