Prof. Huijin Yang Profile

Prof. Huijin Yang

at Henan Univ

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Publications (3)

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SPIE Journal Paper | 15 November 2024

Winter wheat yield prediction based on convolutional neural network–bidirectional long short-term memory–attention model using time series synthetic aperture radar images

Ziyang Zheng, Jianhui Zhao, Huijin Yang, Ning Li, Jike Chang

JARS, Vol. 18, Issue 04, 044517, (November 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.044517

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Timely and accurate prediction of winter wheat yield is crucial for national food security. In the field of crop yield prediction, deep learning techniques are playing an increasingly important role. However, many existing methods mainly utilize convolutional neural network (CNN) or long short-term memory (LSTM) network, failing to fully exploit the spatiotemporal information in remote sensing data. To address this issue, a CNN–bidirectional long short-term memory (BiLSTM)–attention model for winter wheat yield prediction was proposed using time series Sentinel-1A synthetic aperture radar images. The histogram dimension reduction technique was employed to generate the samples. The CNN was used to extract the spatial–spectral features from the image samples, and the BiLSTM network was adopted to learn the temporal features of winter wheat growth stages from the time series samples. Furthermore, an attention mechanism was introduced to make the networks learn important features more efficiently to improve the accuracy of yield prediction. The time series Sentinel-1A synthetic aperture radar images covering Weishi County, Kaifeng city, Henan province, China, were used for model training and validation. The experimental results demonstrated that the proposed model exhibited good accuracy in yield prediction for the study area, with a coefficient of determination of 0.79, a root mean square error of 583.53 kg/ha, and a mean absolute error of 458.41 kg/ha. The proposed method has a promising application in crop yield prediction and provides a useful reference for similar crop yield prediction.

Proceedings Article | 19 January 2024 Paper

Winter wheat phenology classification using stacking ensemble learning algorithm based on Sentinel-1A SAR images

Haoxin Duan, Huijin Yang, Jianhui Zhao, Ning Li

Proceedings Volume 12980, 1298010 (2024) https://doi.org/10.1117/12.3020989

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Crop monitoring and phenology estimation based on the satellite systems have become an important research area due to high demand on crops. Synthetic Aperture Radar (SAR) is a kind of microwave remote sensing equipment, which has the advantage of all-weather and all-day, and can realize large-scale and periodic crop phenological monitoring. Besides, thanks to the high temporal resolution of new generation space-based sensors, it has been possible to monitor growth cycle of crops by classification algorithms. A stacking ensemble learning algorithm using time series Sentinel-1A SAR images for winter wheat phenology classification was proposed in this paper based on multiple machine learning models, including Random Forest (RF), Support Vector Machine (SVM), K-nearest Neighbor(K-NN), Naive Bayes (NB) and BP Neural Network (BP) models. The experimental results showed that, comparing with each single model, the stacking ensemble learning algorithm proposed in this paper had the optimal performance, with the highest overall recognition accuracy of 81.40%, demonstrating its effectiveness and application potential for winter wheat phenology identification.

Proceedings Article | 15 November 2023 Paper

Regional winter wheat yield estimation based on CASA model and improved harvest index using remote sensing images

Qian Li, Jianhui Zhao, Huijin Yang, Ning Li

Proceedings Volume 12815, 128150Q (2023) https://doi.org/10.1117/12.3010380

KEYWORDS: Data modeling, Yield improvement, Remote sensing, Vegetation, Agriculture, Solar radiation models, Solar radiation, Atmospheric modeling, Process modeling, Meteorology

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Accurately and timely grasping agricultural information at the regional scale helps to solve food security issues and formulate agricultural policies. Remote sensing images have the advantages of wide monitoring range and the ability to eliminate human interference. In recent years, they have been increasingly valued in crop yield estimation. The CASA model is used to estimate the net primary productivity (NPP) of crops and then combine it with the harvest index (HI) to estimate crop yields. However, most studies use a fixed HI for yield estimation, which can lead to low accuracy. In this context, the HI is studied and improved for winter wheat yield prediction in this research. The main contents are as follows: Firstly, time-series Sentinel-2 optical data and meteorological data were used as inputs of the CASA model to calculate the NPP of winter wheat. Then, HI was calculated using the fieldwork data and a method was proposed to improve it due to its geographic location-related characteristics. Finally, the calculated NPP and improved HI were used to estimate the winter wheat yield. The experimental results prove that the proposed method has higher accuracy than original methods, with determination coefficient(R²) of 0.595, root mean square error(RMSE) of 793.4 kg/ha, and mean absolute error(MAE) of 659.53 kg/ha.

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