Mapping winter wheat in Kaifeng, China using Sentinel-1A time-series images

Li, Ning; Li, Heping; Zhao, Jianhui; Guo, Zhengwei; Yang, Huige

doi:10.1080/2150704x.2022.2046888

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…The development of modern agriculture requires the support of remote sensing information, but local agricultural planning and management personnel have limited knowledge of remote sensing and require simple-tooperate methods. Our findings suggest that the accuracy of crop classification and extraction is increasing, but the spatial and temporal fusion of the data concerned [26], the processing and application of aperture radar data [4,38], all of which require very specialized remote sensing knowledge, and the analysis and processing are relatively complex and require professional training to be applied correctly, which is not conducive to the application of the data in the planning and management of modern agriculture. Compared with Qu et al [16] who constructed a winter wheat identification index using the multiplication of key phenological period difference indices, the user accuracy of winter wheatsummer corn identification in this study reached a minimum of 86.96%, slightly higher than the user accuracy of 86.03% of Qu et al for wheat identification in Beijing.…”

Section: Advantages Of the Methodologymentioning

confidence: 95%

“…3), the max-min judgment method (Eq. 2) [4] was employed to identify the corresponding peak and valley phenological where NDVIT i is the time corresponding to the key phenological stage, and NDVI i is the NDVI value for each period beginning from Day 289 in 2020 to Day 289 in 2021 in order of 1, 2, 3, ..., 24.…”

Section: Key Phenological Stage Differencementioning

confidence: 99%

“…SAR is not affected by meteorological factors such as clouds and rain and can recognize structural features on the crop surface; however, it is time-consuming and costly, thereby limiting its application in research. Current crop classification and extraction remain primarily based on optical satellites [4]. With the development and application of remote sensing information technology, the accuracy of crop classification and extraction has improved; hence, the application of multi-source multitemporal remote sensing data and time series remote sensing data has become popular in research.…”

mentioning

confidence: 99%

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Spatial Extraction of Wheat-Corn Rotation Areas in Sushui River Basin Based on NDVI Differences in Key Phenological Stages

Jing,

Zhu,

Zhang

et al. 2024

Pol. J. Environ. Stud.

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Food security is the cornerstone of the sustainable development of a country, and ensuring sufficient arable land is crucial for achieving this. However, owing to socio-economic development since the 1990s, the proportion of grain cultivation has decreased. In contrast, the number of cultivation areas for fruits, vegetables, and other economic crops has increased, indicating a substantial shift in the acreage and distribution of grain cultivation. In 2022, it was proposed that fertile farmland be dedicated to grain cultivation, as stated in the "Various economic crops shall not compete with grain for land" policy, to further

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Section: Advantages Of the Methodologymentioning

confidence: 95%

Section: Key Phenological Stage Differencementioning

confidence: 99%

mentioning

confidence: 99%

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Spatial Extraction of Wheat-Corn Rotation Areas in Sushui River Basin Based on NDVI Differences in Key Phenological Stages

Jing,

Zhu,

Zhang

et al. 2024

Pol. J. Environ. Stud.

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“…In order to prove the effectiveness of the model combining geodesic distance spectral similarity measure and one-dimensional convolutional neural network (GDSSM-CNN), the crop recognition method using GDSSM and the crop recognition method using the 1D-CNN model are compared. The use of similarity and threshold methods to classify crops has been proved to have a good effect [48,49]. In addition, in order to study the influence of different thresholds (in the training data generation part) on the classification results, GDSSM-CNN methods with thresholds of 0.95, 0.9, 0.85 and 0.8 are used for comparison.…”

Section: Crop Classificationmentioning

confidence: 99%

Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data

Tian

et al. 2022

Remote Sensing

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Crop classification is an important part of crop management and yield estimation. In recent years, neural networks have made great progress in synthetic aperture radar (SAR) crop classification. However, the insufficient number of labeled samples limits the classification performance of neural networks. In order to solve this problem, a new crop classification method combining geodesic distance spectral similarity measurement and a one-dimensional convolutional neural network (GDSSM-CNN) is proposed in this study. The method consisted of: (1) the geodesic distance spectral similarity method (GDSSM) for obtaining similarity and (2) the one-dimensional convolutional neural network model for crop classification. Thereinto, a large number of training data are extracted by GDSSM and the generalized volume scattering model which is based on radar vegetation index (GRVI), and then classified by 1D-CNN. In order to prove the effectiveness of the GDSSM-CNN method, the GDSSM method and 1D-CNN method are compared in the case of a limited sample. In terms of evaluation and verification of methods, the GDSSM-CNN method has the highest accuracy, with an accuracy rate of 91.2%, which is 19.94% and 23.91% higher than the GDSSM method and the 1D-CNN method, respectively. In general, the GDSSM-CNN method uses a small number of ground measurement samples, and it uses the rich polarity information in multi-temporal fully polarized SAR data to obtain a large number of training samples, which can quickly improve the accuracy of classification in a short time, which has more new inspiration for crop classification.

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“…This process is not only time-consuming and labor-intensive but also subject to subjective factors. Data updating is slow, which can greatly limit its practical application [5,6]. Furthermore, in most cases, the data obtained through traditional survey methods can only describe the rice-planting area through tabular digits and cannot provide an intuitive spatial distribution map of rice.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Method for Rice Mapping Based on Phenological Features with Sentinel-1 Time-Series Images

Tian

et al. 2023

Remote Sensing

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Rice is one of the most important staple foods in the world, feeding more than 50% of the global population. However, rice is also a significant emitter of greenhouse gases and plays a role in global climate change. As a result, quickly and accurately obtaining the rice mapping is crucial for ensuring global food security and mitigating global warming. In this study, we proposed an automated rice mapping method called automated rice mapping using V-shaped phenological features of rice (Auto-RMVPF) based on the time-series Sentinel-1A images, which are composed of four main steps. First, the dynamic threshold method automatically extracts abundant rice samples by flooding signals. Second, the second-order difference method automatically extracts the phenological period of rice based on the scattering feature of rice samples. Then, the key “V” feature of the VH backscatter time series, which rises before and after rice transplanting due to flooding, is used for rice mapping. Finally, the farmland mask is extracted to avoid interference from non-farmland features on the rice map, and the median filter is applied to remove noise from the rice map and obtain the final spatial distribution of rice. The results show that the Auto-RMVPF method not only can automatically obtain abundant rice samples but also can extract the accurate phenological period of rice. At the same time, the accuracy of rice mapping is also satisfactory, with an overall accuracy is more than 95% and an F1 score of over 0.91. The overall accuracy of the Auto-RMVPF method is improved by 2.8–12.2% compared with support vector machine (SVM) with an overall accuracy of 89.9% (25 training samples) and 92.2% (124 training samples), random forest (RF) with an overall accuracy of 82.8% (25 training samples) and 88.3% (124 training samples), and automated rice mapping using synthetic aperture radar flooding signals (ARM-SARFS) with an overall accuracy of 89.9%. Altogether, these experimental results suggest that the Auto-RMVPF method has broad prospects for automatic rice mapping, especially for mountainous regions where ground samples are often not easily accessible.

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Mapping winter wheat in Kaifeng, China using Sentinel-1A time-series images

Cited by 6 publications

References 10 publications

Spatial Extraction of Wheat-Corn Rotation Areas in Sushui River Basin Based on NDVI Differences in Key Phenological Stages

Spatial Extraction of Wheat-Corn Rotation Areas in Sushui River Basin Based on NDVI Differences in Key Phenological Stages

Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data

An Automatic Method for Rice Mapping Based on Phenological Features with Sentinel-1 Time-Series Images

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