An Automatic Decision-Level Fusion Rice Mapping Method of Optical and SAR Images Based on Cloud Coverage
Timely and accurate mapping of paddy rice cultivation is crucial for estimating rice production and optimizing land utilization. Optical images are essential data source for paddy rice mapping, but it is susceptible to cloud contamination. Existing methods struggle to effectively utilize clear-sky p...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10836781/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Timely and accurate mapping of paddy rice cultivation is crucial for estimating rice production and optimizing land utilization. Optical images are essential data source for paddy rice mapping, but it is susceptible to cloud contamination. Existing methods struggle to effectively utilize clear-sky pixel information in optical images containing clouds, which impacts the accuracy of paddy rice mapping under cloudy conditions. To address the abovementioned problems, we propose an automatic decision-level fusion rice mapping method of optical and synthetic aperture radar (SAR) images based on cloud coverage (the Auto-OSDF method). The method effectively utilizes clear-sky pixels in images containing clouds and leverages the advantages of SAR features in heavily clouded regions. We tested and validated the Auto-OSDF method in Xiangyin County, Hunan Province, and analyzed the impact of different cloud coverage levels (10%–50%) on the accuracy of rice mapping based on this method. The results indicate that, as cloud coverage increases, the rice mapping accuracy of the Auto-OSDF method is not significantly affected, with overall accuracy and Kappa coefficients both above 93% and 0.90, respectively. To show the value of the proposed method in large-scale applications, we further mapped paddy rice in the entire Hunan Province, and the overall accuracy and Kappa coefficient were 92.47% and 0.87, respectively. The results obtained by the Auto-OSDF method show an average R<sup>2</sup> of 0.926 compared to municipal-level statistical planting areas. The abovementioned study demonstrates that the Auto-OSDF method is capable of achieving stable and high-precision rice mapping under cloud contamination interference. |
|---|---|
| ISSN: | 1939-1404 2151-1535 |