Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea

Jeong, Seungtaek; Ko, Jonghan; Yeom, Jong-Min

doi:10.1016/j.scitotenv.2021.149726

Cited by 69 publications

(29 citation statements)

References 74 publications

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“…We found that LSTM consistently outperformed RF despite of years and regions, which was well supported by many previous studies (Jeong et al, 2022;Luo et al, 2022a;Schwalbert et al, 2020;Tian al., 2021). The strengths of the LSTM model are its recurrent neural network structure, which had been proved to successfully capture cumulative and complex nonlinear 240 relationships between crop yields and climatic factors (Jiang et al, 2019;.…”

Section: Advantages Of Globalwheatyield4kmsupporting

confidence: 91%

“…For instance, Kang et al (2020) compared the performances of a set of statistical and ML methods and indicated that 45 all ML models achieved better accuracy in predicting county-level maize yield. Emerging breakthroughs in algorithms such as deep learning (DL) approaches have accomplished more accurate crop yield estimation (Jeong et al, 2022;. For example, the long short-term memory (LSTM) model adopts a recurrent neural network structure that can recognize sequential information for long time periods and capture sophisticated nonlinear relationships, showing superior performance over ML models in yield prediction (Jiang et al, 2019;Tian et al, 2021).…”

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confidence: 99%

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GlobalWheatYield4km: a global wheat yield dataset at 4-km resolution during 1982–2020 based on deep learning approaches

Luo

Zhang

Cao

et al. 2022

Preprint

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Abstract. Accurate and spatially explicit information on crop yield over large areas is paramount for ensuring global food security and guiding policy-making. However, most public datasets are coarse resolution in both space and time. Here, we used data-driven models to develop a 4-km dataset of global wheat yield (GlobalWheatYield4km) from 1982 to 2020. First, we proposed a phenology-based approach to map spatial distribution. Then we determined the optimal grid-scale yield estimation model by comparing the performance of two data-driven models (i.e., Random Forest (RF) and Long Short-Term Memory (LSTM)), with publicly available data (i.e., satellite and climatic data from the Google Earth Engine (GEE) platform, soil properties, and subnational statistics covering ~11000 political units). The results showed that GlobalWheatYield4km captured 82 % of yield variations with RMSE of 619.8 kg/ha. In addition, our dataset had a higher accuracy (R2 ~0.73) as compared with Spatial Production Allocation Model (R2 ~ 0.49) across all regions and years. The GlobalWheatYield4km dataset will play important roles in modelling crop system and assessing climate impact over larger areas ((DOI of the referenced dataset: https://doi.org/10.6084/m9.figshare.10025006; Luo et al., 2022b).

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Section: Advantages Of Globalwheatyield4kmsupporting

confidence: 91%

mentioning

confidence: 99%

GlobalWheatYield4km: a global wheat yield dataset at 4-km resolution during 1982–2020 based on deep learning approaches

Luo

Zhang

Cao

et al. 2022

Preprint

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“…However, since it is difficult to interpret the meaning of the weights, there is a disadvantage in that the results are also difficult to interpret. In addition, when fewer training datasets are collected, the performance of the ML models mentioned above can be better 53 .…”

Section: Methodsmentioning

confidence: 99%

Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth

Jeong

Shin

et al. 2022

Sci Rep

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Machine learning (ML) and deep neural network (DNN) techniques are promising tools. These can advance mathematical crop modelling methodologies that can integrate these schemes into a process-based crop model capable of reproducing or simulating crop growth. In this study, an innovative hybrid approach for estimating the leaf area index (LAI) of paddy rice using climate data was developed using ML and DNN regression methodologies. First, we investigated suitable ML regressors to explore the LAI estimation of rice based on the relationship between the LAI and three climate factors in two administrative rice-growing regions of South Korea. We found that of the 10 ML regressors explored, the random forest regressor was the most effective LAI estimator, and it even outperformed the DNN regressor, with model efficiencies of 0.88 in Cheorwon and 0.82 in Paju. In addition, we demonstrated that it would be feasible to simulate the LAI using climate factors based on the integration of the ML and DNN regressors in a process-based crop model. Therefore, we assume that the advancements presented in this study can enhance crop growth and productivity monitoring practices by incorporating a crop model with ML and DNN plans.

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“…A modified version of CNNs is 1D CNNs. Compared with CNNs, they accept 1D data as input, so they need less computing power [16] and less data [17] to get fitted, and can support city-scale study. 1D CNNs are mainly used to deal with image and signal processing problems [30] in previous studies.…”

Section: D Convolutional Neural Networkmentioning

confidence: 99%

“…Among them, ANNs are the most popular benchmark algorithm applied in the literature [8]. In recent years, hybrid models [14] and deep learning [15] (e.g., convolutional neural networks (CNNs) [16,17], long short-term memory) are emerging with a high performance on assessments of flood events [8] as a representation of the state of the art in machine learning. We chose the NB, perceptron, ANNs, and CNNs with an aim to investigate the capacity of CNNs compared to more conventional machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Assessing Surface Water Flood Risks in Urban Areas Using Machine Learning

Liu

Luo

et al. 2021

Water

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Urban flooding is a devastating natural hazard for cities around the world. Flood risk mapping is a key tool in flood management. However, it is computationally expensive to produce flood risk maps using hydrodynamic models. To this end, this paper investigates the use of machine learning for the assessment of surface water flood risks in urban areas. The factors that are considered in machine learning models include coordinates, elevation, slope gradient, imperviousness, land use, land cover, soil type, substrate, distance to river, distance to road, and normalized difference vegetation index. The machine learning models are tested using the case study of Exeter, UK. The performance of machine learning algorithms, including naïve Bayes, perceptron, artificial neural networks (ANNs), and convolutional neural networks (CNNs), is compared based on a spectrum of indicators, e.g., accuracy, F-beta score, and receiver operating characteristic curve. The results obtained from the case study show that the flood risk maps can be accurately generated by the machine learning models. The performance of models on the 30-year flood event is better than 100-year and 1000-year flood events. The CNNs and ANNs outperform the other machine learning algorithms tested. This study shows that machine learning can help provide rapid flood mapping, and contribute to urban flood risk assessment and management.

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Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea

Cited by 69 publications

References 74 publications

GlobalWheatYield4km: a global wheat yield dataset at 4-km resolution during 1982–2020 based on deep learning approaches

GlobalWheatYield4km: a global wheat yield dataset at 4-km resolution during 1982–2020 based on deep learning approaches

Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth

Assessing Surface Water Flood Risks in Urban Areas Using Machine Learning

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