The 10-m crop type maps in Northeast China during 2017–2019

You, Nanshan; Dong, Jinwei; Huang, Jianxi; Du, Guoming; Zhang, Geli; He, Yingli; Yang, Tong; Di, Yuanyuan; Xiao, Xiangming

doi:10.1038/s41597-021-00827-9

Cited by 225 publications

(166 citation statements)

References 48 publications

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“…In recent studies on cotton classification research, the texture was added to the classification of Tiangong-2 images to improve the extraction accuracy of cotton [44]. You et al [45] used optimized features from spectral, temporal, and texture characteristics of the land surface to create a crop map of Northeast China, and the estimates agreed well with the statistical data for most of the municipalities.…”

Section: Author Study Area Smallest Unit Classifier Satellitementioning

confidence: 99%

Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier

et al. 2022

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Accurate cotton maps are crucial for monitoring cotton growth and precision management. The paper proposed a county-scale cotton mapping method by using random forest (RF) feature selection algorithm and classifier based on selecting multi-features, including spectral, vegetation indices, and texture features. The contribution of texture features to cotton classification accuracy was also explored in addition to spectral features and vegetation index. In addition, the optimal classification time, feature importance, and the best classifier on the cotton extraction accuracy were evaluated. The results showed that the texture feature named the gray level co-occurrence matrix (GLCM) is effective for improving classification accuracy, ranking second in contribution among all studied spectral, VI, and texture features. Among the three classifiers, the RF showed higher accuracy and better stability than support vector machines (SVM) and artificial neural networks (ANN). The average overall accuracy (OA) of the classification combining multiple features was 93.36%, 7.33% higher than the average OA of the single-time spectrum, and 2.05% higher than the average OA of the multi-time spectrum. The classification accuracy after feature selection by RF can still reach 92.12%, showing high accuracy and efficiency. Combining multiple features and random forest methods may be a promising county-scale cotton classification method.

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Section: Author Study Area Smallest Unit Classifier Satellitementioning

confidence: 99%

Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier

et al. 2022

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“…Using the reflectance of the red-edge region to calculate a vegetation index can thus improve the classification accuracy [41]. Therefore, a red edge normalization index (RENDVI) and a red edge position index (REP) were also constructed by using the red-edge bands of Sentinel-2 data [42]. The formulae for calculating the different indexes are listed in Table 3.…”

Section: Feature Constructionmentioning

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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“…Maize maps from multiple resources were adopted to constrain the region of crop phenology mapping. The distribution map of maize in Northeast China was derived using Sentinel-2 data (You et al, 2021), and the used maize map was the classification result in 2019. While in other provinces, the maize maps were obtained using the method in Dong et al (2020) to process Landsat images in 2020.…”

Section: Study Area and Datasetsmentioning

confidence: 99%

“…Accurate and timely crop phenology information, which contains multi-phase growth information from sowing to harvest, is highly required by precision agriculture management (Gao and Zhang, 2021;Zeng et al, 2020), such as irrigation schedules and pest control. The agriculture management schemes should be precisely scheduled according to different growth phases, during which period the water requirements and the possibilities of pest and disease events are different (Yang et al, 2021;Xiao et al, 2020). Besides, the effect of climate change on crop phenology has been widely reported (Abbas et al, 2017;Zhang and Tao, 2013;Tao et al, 2012), given that the altered growth phases of crops will influence crop production.…”

Section: Introductionmentioning

confidence: 99%

A 30-m annual maize phenology dataset from 1985 to 2020 in China

Niu

Huang

et al. 2022

Preprint

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Abstract. Crop phenology information provides essential information on crop growth phases, which are highly required for agroecosystem management and yield estimation. Previous crop phenology studies were mainly conducted using coarse-resolution (e.g., 500 m) satellite data, such as the moderate resolution imaging spectroradiometer (MODIS) data. However, precision agriculture requires higher resolution phenology information of crops for better agroecosystem management, and this requirement can be met by long-term and fine-resolution Landsat observations. In this study, we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using all available Landsat images on the Google Earth Engine (GEE) platform. First, we extracted long-term mean phenological indicators using the harmonic model, including the v3 (i.e., the date when the third leaf is fully expanded) and the maturity phases (i.e., when the dry weight of maize grains first reaches the maximum). Second, we identified the annual dynamics of phenological indicators by measuring the difference of dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived maize phenology datasets agree with in-situ observations from the agricultural meteorological stations and the PhenoCam network. Besides, the derived fine-resolution phenology dataset agrees well with the MODIS phenology product regarding their spatial patterns and temporal dynamics. We observed a noticeable difference in maize phenology temporal trends before and after 2000, which is likely attributable to the change of temperature and precipitation, which further alter the farming activities. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the response of agroecosystem to global warming in the future. The data are available at https://doi.org/10.6084/m9.figshare.16437054 (Niu et al., 2021).

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The 10-m crop type maps in Northeast China during 2017–2019

Cited by 225 publications

References 48 publications

Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier

Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier

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

A 30-m annual maize phenology dataset from 1985 to 2020 in China

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