Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery

Tian, Haifeng; Yong-Jiu, Wang; Cui, Ting; Zhang, Lijun; Qin, Yaochen

doi:10.3390/rs13193822

Cited by 66 publications

(32 citation statements)

References 32 publications

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“…The red, green, blue, and NIR wavebands have a spatial resolution of 10 m. The four red-edge wavebands and two shortwave infrared wavebands have a spatial resolution of 20 m. The spatial resolution of the other three wavebands is 60 m. Their revisit period is 10 d. In this study, the red, green, blue, and NIR wavebands were used because their spatial resolution is high. In addition, according to previous research [17,32], the red-edge wavebands with a spatial resolution of 20 m contribute little to improving the identification accuracy of canola.…”

Section: Sentinel-2 Imagerymentioning

confidence: 89%

“…Enhanced imagery of crop characteristics gleaned from their phenological information helps detect and map crops automatically [14,15]. These characteristics are exclusive and steady because most crops show a unique phenological pattern, even though some have a similar growing season [16,17]. Hence, once an automated algorithm derived from these characteristics is completed and published, it can provide training rules that can be used directly and repeated year after year without retraining [13,18].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery

Tian

Chen

et al. 2022

Remote Sensing

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Because canola is a major oilseed crop, accurately determining its planting areas is crucial for ensuring food security and achieving UN 2030 sustainable development goals. However, when canola is extracted using remote-sensing data, winter wheat causes serious interference because it has a similar growth cycle and spectral reflectance characteristics. This interference seriously limits the classification accuracy of canola, especially in mixed planting areas. Here, a novel canola flower index (CFI) is proposed based on the red, green, blue, and near-infrared bands of Sentinel-2 images to improve the accuracy of canola mapping, based on the finding that spectral reflectance of canola on the red and green bands is higher than that of winter wheat during the canola flowering period. To investigate the potential of the CFI for extracting canola, the IsoData, support vector machine (SVM), and random forest (RF) classification methods were used to extract canola based on Sentinel-2 raw images and CFI images. The results show that the average overall accuracy and kappa coefficient based on CFI images were 94.77% and 0.89, respectively, which were 1.05% and 0.02, respectively, higher than those of the Sentinel-2 raw images. Then we found that a threshold of 0.14 on the CFI image could accurately distinguish canola from non-canola vegetation, which provides a solution for automatic mapping of canola. The overall classification accuracy and kappa coefficient of this threshold method were 96.02% and 0.92, which were very similar to those of the SVM and RF methods. Moreover, the advantage of the threshold classification method is that it reduces the dependence on training samples and has good robustness and high classification efficiency. Overall, this study shows that CFI and Sentinel-2 images provide a solution for automatic and accurate canola extraction.

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Section: Sentinel-2 Imagerymentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery

Tian

Chen

et al. 2022

Remote Sensing

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“…For social transformation in China, government innovation can act as an essential tool for improving the ability, systems, and allocation of resources in the local administration. Local governments have adjusted the economic and administrative power of market allocation of resources, including financial subsidies, tax incentives, business license issuance, and investment restrictions (Tian et al, 2021a;Tian et al, 2021b;Tan et al, 2022). Of these expenditures, government financial science and technology expenditure is an important instrument for the government to participate in regional innovation activities.…”

Section: Literature Reviewmentioning

confidence: 99%

Government innovation support for green development efficiency in China: A regional analysis of key factors based on the dynamic GMM model

Khattak

Khan

Sun

et al. 2022

Front. Environ. Sci.

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Local government innovation support (LGIS) is an exogenous pulling force for high-quality economic development. Accurately identifying the mechanism by which LGIS affects green development remains practically and theoretically significant for building a long-term framework for driving green development. Based on provincial panel data (2010–2019), this paper uses the Super-SBM model to measure the green development efficiency (GDE) of different provinces in China. A dynamic panel GMM model is constructed to evaluate the significant relationship between LGIS and GDE and to explore the potential impact mechanism of other key factors such as fixed asset investment (FAI), environmental regulation (ER), industrial structure (IS), and foreign direct investment (FDI) on GDE. The estimation showed that LGIS had a positive impact on GDE. The data analysis indicated that GDE in the lag period significantly affected the GDE in the current period. More so, as per the results, the promoting effect of GDE in the second lag period gradually weakened due to the decline of LGIS and ER restrictions. The current model also revealed that FAI and ER inhibited the GDE progress, while IS and FAI had improved the state of GDE in the region. The research findings imply that China should further increase the government innovation expenditure, FDI utilization, and IS upgrade to promote high-quality economic development.

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“…is platform provides scientific analysis of geographic data based on big data and cloud computing. With high-performance computing resources, it is a good tool for processing huge amounts of geospatial data [22][23][24]. GEE platform contains archived remote sensing data, which is supported by NASA, U.S. Geological Survey, and NOAA, as well as PB data.…”

Section: Overview Of the Study Areamentioning

confidence: 99%

Blue-Green Space Changes of Baiyangdian Wetland in Xiong’an New Area, China

Zhao

Shuan²,

Meng

et al. 2022

Advances in Meteorology

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As a regulator of ecological environment, Baiyangdian Wetland is in a pivotal position in constructing the blue-green space (BGS) of Xiong’an New Area in China. This study aims to reveal the spatiotemporal changes of the BGS in Baiyangdian Wetland from 2016 to 2021. It uses Google Earth Engine (GEE) to calculate NDVI and NDWI based on Sentinel-2 Satellite remote sensing data and extracts the blue-green space by a classification model driven by NDVI and NDWI. Moreover, the land-use transfer matrix and landscape pattern indices are applied for evaluating the BGS changes in the wetland. According to the results, vegetation in the wetland shows no obvious spatial transfer. From 2016 to 2020, the BGS proportion in the wetland showed a stable increase, with the blue space getting larger by 10.8%. The indicators of the Number of Patches (NP), Patch Density (PD), Largest Patch Index (LPI), Contagion, and Landscape Shape Index (LSI) of the wetland decreased, suggesting a better ecological environment since the establishment of Xiong’an New Area in 2017. Based on the results, the author makes the following conclusion: the construction of BGS in Baiyangdian Wetland results in a well-organized ecological environment. The study provides a reference for building Xiong’an New Area and monitoring BGS changes in other regions.

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Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery

Cited by 66 publications

References 32 publications

A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery

A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery

Government innovation support for green development efficiency in China: A regional analysis of key factors based on the dynamic GMM model

Blue-Green Space Changes of Baiyangdian Wetland in Xiong’an New Area, China

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