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Open AccessArticle

Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
2
Department of Polar Remote Sensing, Korea Polar Research Institute, Incheon 21990, Korea
3
Climate Research Department, APEC Climate Center, Busan 48058, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 447; https://doi.org/10.3390/rs10030447
Received: 24 January 2018 / Revised: 27 February 2018 / Accepted: 9 March 2018 / Published: 12 March 2018
(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)
Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach. View Full-Text
Keywords: paddy rice; Landsat; data fusion; paddy rice mapping index (PMI); ALOS PALSAR; RADARSAT-1; time-series analysis paddy rice; Landsat; data fusion; paddy rice mapping index (PMI); ALOS PALSAR; RADARSAT-1; time-series analysis
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MDPI and ACS Style

Park, S.; Im, J.; Park, S.; Yoo, C.; Han, H.; Rhee, J. Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data. Remote Sens. 2018, 10, 447.

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