Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics Article Swipe
Fadhlullah Ramadhani
,
Reddy Pullanagari
,
Gábor Kereszturi
,
Jonathan Procter
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13081498
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13081498
Monitoring rice production is essential for securing food security against climate change threats, such as drought and flood events becoming more intense and frequent. The current practice to survey an area of rice production manually and in near real-time is expensive and involves a high workload for local statisticians. Remote sensing technology with satellite-based sensors has grown in popularity in recent decades as an alternative approach, reducing the cost and time required for spatial analysis over a wide area. However, cloud-free pixels of optical imagery are required to produce accurate outputs for agriculture applications. Thus, in this study, we propose an integration of optical (PROBA-V) and radar (Sentinel-1) imagery for temporal mapping of rice growth stages, including bare land, vegetative, reproductive, and ripening stages. We have built classification models for both sensors and combined them into 12-day periodical rice growth-stage maps from January 2017 to September 2018 at the sub-district level over Java Island, the top rice production area in Indonesia. The accuracy measurement was based on the test dataset and the predicted cross-correlated with monthly local statistics. The overall accuracy of the rice growth-stage model of PROBA-V was 83.87%, and the Sentinel-1 model was 71.74% with the Support Vector Machine classifier. The temporal maps were comparable with local statistics, with an average correlation between the vegetative area (remote sensing) and harvested area (local statistics) is 0.50, and lag time 89.5 days (n = 91). This result was similar to local statistics data, which correlate planting and the harvested area at 0.61, and the lag time as 90.4 days, respectively. Moreover, the cross-correlation between the predicted rice growth stage was also consistent with rice development in the area (r > 0.52, p < 0.01). This novel method is straightforward, easy to replicate and apply to other areas, and can be scaled up to the national and regional level to be used by stakeholders to support improved agricultural policies for sustainable rice production.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs13081498
- https://www.mdpi.com/2072-4292/13/8/1498/pdf?version=1618469686
- OA Status
- gold
- Cited By
- 15
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3155326714
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3155326714Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs13081498Digital Object Identifier
- Title
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Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District StatisticsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-04-13Full publication date if available
- Authors
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Fadhlullah Ramadhani, Reddy Pullanagari, Gábor Kereszturi, Jonathan ProcterList of authors in order
- Landing page
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https://doi.org/10.3390/rs13081498Publisher landing page
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https://www.mdpi.com/2072-4292/13/8/1498/pdf?version=1618469686Direct 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
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https://www.mdpi.com/2072-4292/13/8/1498/pdf?version=1618469686Direct OA link when available
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Remote sensing, Environmental science, Computer science, Cloud computing, Pixel, Statistics, Geography, Mathematics, Artificial intelligence, Operating systemTop concepts (fields/topics) attached by OpenAlex
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15Total citation count in OpenAlex
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2025: 3, 2024: 6, 2023: 2, 2022: 3, 2021: 1Per-year citation counts (last 5 years)
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65Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2021-04-13 |
| publication_year | 2021 |
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