The area extraction of winter wheat in mixed planting area based on Sentinel-2 a remote sensing satellite images

Wei, Mengfan; Qiao, Baojun; Zhao, Jianhui; Zuo, Xianyu

doi:10.1080/17445760.2019.1597084

Cited by 21 publications

(15 citation statements)

References 10 publications

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“…Combining methods using object-based and pixelbased methods has been proposed to classify crops planting areas [38][39][40]. However, these methods are difficult to achieve breakthroughs in automatic classification and visual recognition of crop planting areas simply from the spectral information of remote sensing images in the scattered and small planting areas [41][42][43][44]. Combining the parcel data with remote sensing image to extract the structure information of crop planting can make up for the deficiency of the classification from remote sensing images simply and obtain better classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images

Xie

Zhang

et al. 2021

Journal of Mathematics

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In the complex planting area with scattered parcels, combining the parcel vector data with remote sensing images to extract the winter wheat planting information can make up for the deficiency of the classification from remote sensing images simply. It is a feasible direction for precision agricultural subsidies, but it is difficult to collect large-scale parcel data and obtain high spatial resolution or time-series remote sensing images in mass production. It is a beneficial exploration of making use of existing parcel data generated by the ground survey and medium-resolution remote sensing images with suitable time and spatial resolution to extract winter wheat planting areas for large-scale precision agricultural subsidies. Therefore, this paper proposes a new algorithm to extract winter wheat planting areas based on ownership parcel data and medium-resolution remote sensing images for improving classification accuracy. Initially, the segmentation of the image is carried out. To this end, the parcel data is used to generate the region of interest (ROI) of each parcel. Second, the homogeneity of each ROI is detected by its statistical indices (mean value and standard deviation). Third, the parallelepiped classifier and rule-based feature extraction classification methods are utilized to conduct the homogeneous and nonhomogeneous ROIs. Finally, two classification results are combined as the final classification result. The new algorithm was applied to a complex planting area of 103.60 km2 in central China based on the ownership parcel data and Gaofen-1 PMS and WFV remote sensing images in this paper. The experimental results show that the new algorithm can effectively extract winter wheat planting area, eliminate the problem of salt-and-pepper noise, and obtain high-precision classification results (kappa = 0.9279, overall accuracy = 96.41%, user’s accuracy = 99.16%, producer’s accuracy = 93.39%, commission errors = 0.84%, and omission errors = 6.61%) when the size of ownership parcels matches the spatial resolution of remote sensing images.

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Section: Introductionmentioning

confidence: 99%

A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images

Xie

Zhang

et al. 2021

Journal of Mathematics

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“…Remote sensing has the advantages of large-scale applicability, high speed, and traceable history (Wei et al 2020). The use of remote sensing for monitoring the surface-water quality of the Xiong'an New Area can quickly and accurately reflect the effect of treatment on the sewage treatment ponds.…”

Section: Introductionmentioning

confidence: 99%

Time-Series Analysis of Surface-Water Quality in Xiong’an New Area, 2016–2019

Wang

Yao

Shen

et al. 2020

J Indian Soc Remote Sens

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The Xiong'an New Area was established by the Chinese government on 1 April 2017 as a new district in the Hebei Province, and soon after its establishment, investigations on the waterbodies of the district began to initiate governance. In this study, data from 105 Sentinel-2 cloudless images for the period April 2016–September 2019 were analysed to understand the changes in the concentrations of chlorophyll-a and water turbidity in surface waterbodies of the Xiong'an New Area. The results suggest that (1) the water quality shows an overall improving trend owing to surface-water pollution control measures by the Chinese government in the region; (2) the turbidity of the surface waterbody declined gradually particularly after the spring of 2017; (3) the concentration of chlorophyll-a shows a weak decreasing trend, with a low value in spring and a high value in summer with a four-year average of 31.3 μg/L; and (4) by comparing the results of the water quality of two ponds inside and outside the Lake Baiyangdian and the overall water quality of Xiong'an New Area, the changes in turbidity and chlorophyll-a concentration during the study period were consistent.

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“…These studies still had some shortcomings: (1) They were limited to municipal regions and small areas because high-resolution images are limited by their coverage and easily affected by clouds, and it is difficult to obtain high-quality images that cover the whole growth cycle of winter wheat over large areas. (2) They are based on remote sensing images acquired on specific dates and few attempts have been made to explore the accuracy of the identification of winter wheat in different growth periods [23]. (3) Although many algorithms have been applied to winter wheat identification, few methods discuss the differences of winter wheat identification at different growth periods.…”

Section: Introductionmentioning

confidence: 99%

The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing

et al. 2022

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The aim of this study was to explore the differences in the accuracy of winter wheat identification using remote sensing data at different growth stages using the same methods. Part of northern Henan Province, China was taken as the study area, and the winter wheat growth cycle was divided into five periods (seeding‒tillering, overwintering, reviving, jointing‒heading, and flowering‒maturing) based on monitoring data obtained from agrometeorological stations. With the help of the Google Earth Engine (GEE) platform, the separability between winter wheat and other land cover types was analyzed and compared using the Jeffries‒Matusita (J‒M) distance method. Spectral features, vegetation index, water index, building index, texture features, and terrain features were generated from Sentinel-2 remote sensing images at different growth periods, and then were used to establish a random forest classification and extraction model. A deep U-Net semantic segmentation model based on the red, green, blue, and near-infrared bands of Sentinel-2 imagery was also established. By combining models with field data, the identification of winter wheat was carried out and the difference between the accuracy of the identification in the five growth periods was analyzed. The experimental results show that, using the random forest classification method, the best separability between winter wheat and the other land cover types was achieved during the jointing‒heading period: the overall identification accuracy for the winter wheat was then highest at 96.90% and the kappa coefficient was 0.96. Using the deep-learning classification method, it was also found that the semantic segmentation accuracy of winter wheat and the model performance were best during the jointing‒heading period: a precision, recall, F1 score, accuracy, and IoU of 0.94, 0.93, 0.93, and 0.88, respectively, were achieved for this period. Based on municipal statistical data for winter wheat, the accuracy of the extraction of the winter wheat area using the two methods was 96.72% and 88.44%, respectively. Both methods show that the jointing‒heading period is the best period for identifying winter wheat using remote sensing and that the identification made during this period is reliable. The results of this study provide a scientific basis for accurately obtaining the area planted with winter wheat and for further studies into winter wheat growth monitoring and yield estimation.

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The area extraction of winter wheat in mixed planting area based on Sentinel-2 a remote sensing satellite images

Cited by 21 publications

References 10 publications

A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images

A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images

Time-Series Analysis of Surface-Water Quality in Xiong’an New Area, 2016–2019

The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing

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