Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics

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dc.citation.issue8
dc.citation.volume13
dc.contributor.authorRamadhani F
dc.contributor.authorPullanagari R
dc.contributor.authorKereszturi G
dc.contributor.authorProcter J
dc.contributor.editorFarooque AA
dc.date.accessioned2024-04-17T20:51:48Z
dc.date.accessioned2024-07-25T06:51:09Z
dc.date.available2021-04-13
dc.date.available2024-04-17T20:51:48Z
dc.date.available2024-07-25T06:51:09Z
dc.date.issued2021-04-13
dc.description.abstractMonitoring 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 pro-duce 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.
dc.description.confidentialfalse
dc.edition.editionApril 2021
dc.identifier.author-urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000644667000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifier.citationRamadhani F, Pullanagari R, Kereszturi G, Procter J. (2021). Mapping a cloud-free rice growth stages using the integration of proba-v and sentinel-1 and its temporal correlation with sub-district statistics. Remote Sensing. 13. 8.
dc.identifier.doi10.3390/rs13081498
dc.identifier.eissn2072-4292
dc.identifier.elements-typejournal-article
dc.identifier.numberARTN 1498
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71010
dc.languageEnglish
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttps://www.mdpi.com/2072-4292/13/8/1498
dc.relation.isPartOfRemote Sensing
dc.rights(c) 2021 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectrice growth stages
dc.subjectmachine learning
dc.subjectmonitoring
dc.subjectPROBA-V
dc.subjectSentinel-1
dc.subjectSVM
dc.titleMapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics
dc.typeJournal article
pubs.elements-id444765
pubs.organisational-groupOther
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