Improved prediction of rice yield at field and county levels by synergistic use of SAR, optical and meteorological data

Yu, Weiguo; Yang, Gaoxiang; Li, Dong; Zheng, Hengbiao; Yao, Xia; Zhu, Yan; Cao, Weixing; Qiu, Lin; Cheng, Tao

doi:10.1016/j.agrformet.2023.109729

Cited by 13 publications

(3 citation statements)

References 69 publications

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“…[37]. In some instances, researchers employ multimodal satellite imagery, incorporating Sentinel-2 multispectral data alongside Sentinel-1 radar data [24][30][34] [35]. Concurrently, other studies [33] make use of multispectral data from both MODIS and Sentinel-2.…”

Section: Features and Image Collectionmentioning

confidence: 99%

Curating Multimodal Satellite Imagery for Precision Agriculture Datasets with Google Earth Engine

Wijaya,

Munir,

Utama

2023

icdsos

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In the era of modern agriculture, satellite imagery has been widely used to monitor crops, one of which is paddy. This paper tries to describe the vegetation indices, climate, and soil index features related to paddy plants and curates a collection of satellite imagery on the Google Earth Engine (GEE). This paper reveals how GEE can be used to collect and process multimodal satellite imagery to form a precision agriculture dataset. The objective of this study is to establish a comprehensive precision agriculture dataset by leveraging multimodal satellite imagery to monitor paddy crops. The data collected as a dataset originates from 306 locations in Karawang Regency, Indonesia, during the 2019-2020 period. In the first step, we identify the relevant features essential for paddy crop analysis. Subsequently, we carefully select image collections within GEE based on these features. Afterward, we perform data acquisition and necessary preprocessing through the Google Colab environment. The results showed that satellite imagery from Sentinel-2 outperforms Landsat 8 in terms of spatial and temporal resolution. Apart from that, the generated dataset successfully captures the growth patterns of paddy plants.

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Section: Features and Image Collectionmentioning

confidence: 99%

Curating Multimodal Satellite Imagery for Precision Agriculture Datasets with Google Earth Engine

Wijaya,

Munir,

Utama

2023

icdsos

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“…However, with a limited training dataset, both ML and DL were prone to overfitting [22][23][24][25]. The ensemble model can combine the prediction of several weak learners to make the final prediction, which can obtain better prediction performance [26][27][28][29]. Li et al [30] integrated multiple ML models with a Bayesian average model to improve the prediction accuracy of maize yield in Northeast China.…”

Section: Introductionmentioning

confidence: 99%

Wheat Yield Robust Prediction in the Huang-Huai-Hai Plain by Coupling Multi-Source Data with Ensemble Model under Different Irrigation and Extreme Weather Events

Zhao,

He,

Yao

et al. 2024

Remote Sensing

Self Cite

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The timely and robust prediction of wheat yield is very significant for grain trade and food security. In this study, the yield prediction model was developed by coupling an ensemble model with multi-source data, including vegetation indices (VIs) and meteorological data. The results showed that green chlorophyll vegetation index (GCVI) is the optimal remote sensing (RS) variable for predicting wheat yield compared with other VIs. The accuracy of the adaptive boosting- long short-term memory (AdaBoost-LSTM) ensemble model was higher than the LSTM model. AdaBoost-LSTM coupled with optimal input data had the best performance. The AdaBoost-LSTM model had strong robustness for predicting wheat yield under different irrigation and extreme weather events in general. Additionally, the accuracy of AdaBoost-LSTM for rainfed counties was higher than that for irrigation counties in most years except extreme years. The yield prediction model developed with the characteristic variables of the window from February to April had higher accuracy and smaller data requirements, which was the best prediction window. Therefore, wheat yield can be accurately predicted by the AdaBoost-LSTM model one to two months of lead time before maturity in the HHHP. Overall, the AdaBoost-LSTM model can achieve accurate and robust yield prediction in large-scale regions.

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“…Deep learning (DL) model is the more advanced ML model that transform raw input data over stacked nonlinear layers to improve model performance 34 – 36 . Among them, long short-term memory (LSTM) has wide application and better performance in yield prediction researches 12 , 37 , 38 . However, ML and DL have a large demand for training samples, and it is costly to obtain enough data samples in the large region 7 , 39 .…”

Section: Introductionmentioning

confidence: 99%

Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS

Zhang,

Roslan,

Shafri

et al. 2024

Sci Rep

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To obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, the quantity and quality of available images and the selection of prediction variables often limit the performance of yield prediction. In our study, the synthesized images of Landsat and MODIS were used to provide remote sensing (RS) variables, which can fill the missing values of Landsat images well and cover the study area completely. The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). The results showed that kernel NDVI (kNDVI) and near-infrared reflectance (NIRv) slightly outperform normalized difference vegetation index (NDVI) in yield prediction. And the regression algorithm had a more prominent effect on yield prediction, while the yield prediction model using Long Short-Term Memory (LSTM) outperformed the yield prediction model using Light Gradient Boosting Machine (LGBM). The model combining LSTM algorithm and NIRv had the best prediction effect and relatively stable performance in single year. The optimal model was then used to generate 30 m resolution wheat yield maps in the past 20 years, with higher overall accuracy. In addition, we can define the optimum prediction time at April, which can consider simultaneously the performance and lead time. In general, we expect that this prediction model can provide important information to understand and ensure food security.

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Improved prediction of rice yield at field and county levels by synergistic use of SAR, optical and meteorological data

Cited by 13 publications

References 69 publications

Curating Multimodal Satellite Imagery for Precision Agriculture Datasets with Google Earth Engine

Curating Multimodal Satellite Imagery for Precision Agriculture Datasets with Google Earth Engine

Wheat Yield Robust Prediction in the Huang-Huai-Hai Plain by Coupling Multi-Source Data with Ensemble Model under Different Irrigation and Extreme Weather Events

Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS

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