High-Resolution Ratoon Rice Monitoring under Cloudy Conditions with Fused Time-Series Optical Dataset and Threshold Model
Ratoon rice, an effective rice cultivation system, allows paddy rice to be harvested twice from the same stubble, playing an important role in ensuring food security and adapting to climate change with its unique growth characteristics. However, there is an absence of research related to remote-sens...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/17/4167 |
| _version_ | 1827727863679287296 |
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| author | Rongkun Zhao Yue Wang Yuechen Li |
| author_facet | Rongkun Zhao Yue Wang Yuechen Li |
| author_sort | Rongkun Zhao |
| collection | DOAJ |
| description | Ratoon rice, an effective rice cultivation system, allows paddy rice to be harvested twice from the same stubble, playing an important role in ensuring food security and adapting to climate change with its unique growth characteristics. However, there is an absence of research related to remote-sensing monitoring of ratoon rice, and the presence of other rice cropping systems (e.g., double-season rice) with similar characteristics poses a hindrance to the accurate identification of ratoon rice. Furthermore, cloudy and rainy regions have limited available remote-sensing images, meaning that remote-sensing monitoring is limited. To address this issue, taking Yongchuan District, a typical cloud-prone region in Chongqing, China, as an example, this study proposed the construction of a time-series optical dataset using the Modified Neighborhood Similar Pixel Interpolator (MNSPI) method for cloud-removal interpolation and the Flexible Spatiotemporal DAta Fusion (FSDAF) model for fusing multi-source optical remote-sensing data, in combination with vegetation index features and phenological information to build a threshold model to map ratoon rice at high-resolution (10 m). The mapping performance of ratoon rice was evaluated using independent field samples to obtain the overall accuracy and kappa coefficient. The findings indicate that the combination of the MNSPI method and FSDAF model had a stable and effective performance, characterized by high correlation coefficient (r) values and low root mean square error (RMSE) values between the restored/predicted images and the true images. Notably, it was possible to effectively capture the distinct characteristics of ratoon rice in cloudy and rainy regions using the proposed threshold model. Specifically, the identified area of ratoon rice in the study region was 194.17 km<sup>2</sup>, which was close to the official data (158–180 km<sup>2</sup>), and the overall accuracy and kappa coefficient of ratoon rice identification result were 90.73% and 0.81, respectively. These results demonstrate that our proposed threshold model can effectively distinguish ratoon rice during vital phenological stages from other crop types, enrich the technical system of rice remote-sensing monitoring, and provide a reference for agricultural remote-sensing applications in cloudy and rainy regions. |
| first_indexed | 2024-03-10T23:14:02Z |
| format | Article |
| id | doaj.art-12fd294585244dfdb74335689fb25af0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:14:02Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-12fd294585244dfdb74335689fb25af02023-11-19T08:45:22ZengMDPI AGRemote Sensing2072-42922023-08-011517416710.3390/rs15174167High-Resolution Ratoon Rice Monitoring under Cloudy Conditions with Fused Time-Series Optical Dataset and Threshold ModelRongkun Zhao0Yue Wang1Yuechen Li2Chongqing Engineering Research Center for Remote Sensing Big Data Application, Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaChongqing Engineering Research Center for Remote Sensing Big Data Application, Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaChongqing Engineering Research Center for Remote Sensing Big Data Application, Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaRatoon rice, an effective rice cultivation system, allows paddy rice to be harvested twice from the same stubble, playing an important role in ensuring food security and adapting to climate change with its unique growth characteristics. However, there is an absence of research related to remote-sensing monitoring of ratoon rice, and the presence of other rice cropping systems (e.g., double-season rice) with similar characteristics poses a hindrance to the accurate identification of ratoon rice. Furthermore, cloudy and rainy regions have limited available remote-sensing images, meaning that remote-sensing monitoring is limited. To address this issue, taking Yongchuan District, a typical cloud-prone region in Chongqing, China, as an example, this study proposed the construction of a time-series optical dataset using the Modified Neighborhood Similar Pixel Interpolator (MNSPI) method for cloud-removal interpolation and the Flexible Spatiotemporal DAta Fusion (FSDAF) model for fusing multi-source optical remote-sensing data, in combination with vegetation index features and phenological information to build a threshold model to map ratoon rice at high-resolution (10 m). The mapping performance of ratoon rice was evaluated using independent field samples to obtain the overall accuracy and kappa coefficient. The findings indicate that the combination of the MNSPI method and FSDAF model had a stable and effective performance, characterized by high correlation coefficient (r) values and low root mean square error (RMSE) values between the restored/predicted images and the true images. Notably, it was possible to effectively capture the distinct characteristics of ratoon rice in cloudy and rainy regions using the proposed threshold model. Specifically, the identified area of ratoon rice in the study region was 194.17 km<sup>2</sup>, which was close to the official data (158–180 km<sup>2</sup>), and the overall accuracy and kappa coefficient of ratoon rice identification result were 90.73% and 0.81, respectively. These results demonstrate that our proposed threshold model can effectively distinguish ratoon rice during vital phenological stages from other crop types, enrich the technical system of rice remote-sensing monitoring, and provide a reference for agricultural remote-sensing applications in cloudy and rainy regions.https://www.mdpi.com/2072-4292/15/17/4167ratoon ricecloudy and rainy regionsflexible spatiotemporal data fusionthreshold model |
| spellingShingle | Rongkun Zhao Yue Wang Yuechen Li High-Resolution Ratoon Rice Monitoring under Cloudy Conditions with Fused Time-Series Optical Dataset and Threshold Model Remote Sensing ratoon rice cloudy and rainy regions flexible spatiotemporal data fusion threshold model |
| title | High-Resolution Ratoon Rice Monitoring under Cloudy Conditions with Fused Time-Series Optical Dataset and Threshold Model |
| title_full | High-Resolution Ratoon Rice Monitoring under Cloudy Conditions with Fused Time-Series Optical Dataset and Threshold Model |
| title_fullStr | High-Resolution Ratoon Rice Monitoring under Cloudy Conditions with Fused Time-Series Optical Dataset and Threshold Model |
| title_full_unstemmed | High-Resolution Ratoon Rice Monitoring under Cloudy Conditions with Fused Time-Series Optical Dataset and Threshold Model |
| title_short | High-Resolution Ratoon Rice Monitoring under Cloudy Conditions with Fused Time-Series Optical Dataset and Threshold Model |
| title_sort | high resolution ratoon rice monitoring under cloudy conditions with fused time series optical dataset and threshold model |
| topic | ratoon rice cloudy and rainy regions flexible spatiotemporal data fusion threshold model |
| url | https://www.mdpi.com/2072-4292/15/17/4167 |
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