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Rice cropping systems classification using time-series Landsat images and Phenology-based algorithms in Supan Buri, Thailand |
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| รหัสดีโอไอ | |
| Creator | Kritchayan Intarat |
| Title | Rice cropping systems classification using time-series Landsat images and Phenology-based algorithms in Supan Buri, Thailand |
| Contributor | Chattida Singkawat |
| Publisher | Asia-Pacific Journal of Science and Technology |
| Publication Year | 2567 |
| Journal Title | Asia-Pacific Journal of Science and Technology |
| Journal Vol. | 29 |
| Journal No. | 1 |
| Page no. | 10 |
| Keyword | Rice phenology, Remote sensing, Time-series analysis, Rice cropping systems, Google Earth Engine |
| URL Website | https://so01.tci-thaijo.org/index.php/APST |
| Website title | https://so01.tci-thaijo.org/index.php/APST/article/view/261491 |
| ISSN | 2539-6293 |
| Abstract | Suphan Buri, a province in the central region of Thailand, is essentially a rice producing and exporting area of Thailand. This study aims to classify rice cropping systems, applying a phenological and pixel-based paddy rice mapping (PPPM) algorithm along with the cutting-edge Google Earth Engine (GEE) cloud platform. Four cropping systems i.e., single rice crop (SCR), double rice crop (DCR), two and a half rice crop (THCR), and triple rice crop (TCR), are duly investigated. To support agricultural policies and irrigation, rice cropping systems can provide vital information. Such an approach can analyze the heading-period rice's phenology using the Enhanced Vegetation Index (EVI) retrieved from the Landsat 8 time-series images. Statistical assessments are employed to evaluate the rice cropping systems, revealing the high performance of the PPPM model. Overall results are seen to be highly successful, attaining an accuracy of 0.91; Kappa statistics reach 0.80. GEE reveals many advantages in geospatial analysis. |
