Parcel-Based Sugarcane Mapping Using Smoothed Sentinel-1 Time Series Data Article Swipe
Hongzhong Li
,
Zhengxin Wang
,
Luyi Sun
,
Longlong Zhao
,
Yelong Zhao
,
Xiaoli Li
,
Yu Han
,
Shouzhen Liang
,
Jinsong Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16152785
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16152785
The timely and accurate mapping of sugarcane cultivation is significant to ensure the sustainability of the sugarcane industry, including sugarcane production, rural society, sugar futures, and crop insurance. Synthetic aperture radar (SAR), due to its all-weather and all-time imaging capability, plays an important role in mapping sugarcane cultivation in cloudy areas. However, the inherent speckle noise of SAR data worsens the “salt and pepper” effect in the sugarcane map. Therefore, in previous studies, an additional land cover map or optical image was still required. This study proposes a new application paradigm of time series SAR data for sugarcane mapping to tackle this limitation. First, the locally estimated scatterplot smoothing (LOESS) smoothing technique was exploited to reconstruct time series SAR data and reduce SAR noise in the time domain. Second, temporal importance was evaluated using RF MDA ranking, and basic parcel units were obtained only based on multi-temporal SAR images with high importance values. Lastly, the parcel-based classification method, combining time series smoothing SAR data, RF classifier, and basic parcel units, was used to generate a sugarcane extent map without unreasonable sugarcane spots. The proposed paradigm was applied to map sugarcane cultivation in Suixi County, China. Results showed that the proposed paradigm was able to produce an accurate sugarcane cultivation map with an overall accuracy of 96.09% and a Kappa coefficient of 0.91. Compared with the pixel-based classification result with original time series SAR data, the new paradigm performed much better in reducing the “salt and pepper” spots and improving the completeness of the sugarcane plots. In particular, the unreasonable non-vegetation spots in the sugarcane map were eliminated. The results demonstrated the efficacy of the new paradigm for mapping sugarcane cultivation. Unlike traditional methods that rely on optical remote sensing data, the new paradigm offers a high level of practicality for mapping sugarcane in large regions. This is particularly beneficial in cloudy areas where optical remote sensing data is frequently unavailable.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs16152785
- https://www.mdpi.com/2072-4292/16/15/2785/pdf?version=1722333967
- OA Status
- gold
- Cited By
- 4
- References
- 82
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401107843
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401107843Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs16152785Digital Object Identifier
- Title
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Parcel-Based Sugarcane Mapping Using Smoothed Sentinel-1 Time Series DataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-07-30Full publication date if available
- Authors
-
Hongzhong Li, Zhengxin Wang, Luyi Sun, Longlong Zhao, Yelong Zhao, Xiaoli Li, Yu Han, Shouzhen Liang, Jinsong ChenList of authors in order
- Landing page
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https://doi.org/10.3390/rs16152785Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/16/15/2785/pdf?version=1722333967Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/16/15/2785/pdf?version=1722333967Direct OA link when available
- Concepts
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Synthetic aperture radar, Smoothing, Computer science, Remote sensing, Data mining, Artificial intelligence, Environmental science, Computer vision, GeographyTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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82Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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