| dc.description.abstract |
Rice is staple food and an economically important export crop of Thailand. Thailand, therefore, is one of the largest rice producers and exporters in the world. With the increase in population and climate change, rice monitoring has become an essential issue around the world. The rice monitoring in Thailand is highly significant and indispensable, as rice is the country’s most important crop, and around 60 percent of Thai farmers mainly plant rice. The rice monitoring is also necessary for economic and ecological development planning. Traditionally, statistics data from the field survey was used to map rice area and estimate rice production. However, the field surveying is time consuming and costly. Thus, remote sensing monitoring has been widely used instead due to its higher effectiveness in rice mapping, using the optical image or synthetic aperture radar (SAR) image. Following the rice crop calendar, there are two rice growing seasons (wet season and dry season) in the Central of Thailand, a very important rice production region in the country. Rice growing season is in the rainy season with cloudy weather which limits the optical sensors usually affected by cloud cover. Hence, the optical sensors are difficult to apply for the rice monitoring in cloudy wet-season. Recently, SAR imagery has played a more important role in rice crop mapping. Many studies indicated that SAR has been a suitable tool for monitoring rice cultivated in cloudy and rainy weather because it uses microwave radiation which can pass through clouds.
The aim of this study is to map the rice-growing areas in Ayutthaya province, Central Thailand using Sentinel-1 VH-polarized SAR images during the wet season (May-November) in 2015 and the dry season (November-April) in 2016. Four essential steps were required to conduct the study: (1) image pre-processing, including radiometric correction, geometric correction, and speckle noise removal, (2) Dynamic Normalized Difference Sigma Naught Index (NDSI) calculation, using time series SAR images to better detect rice fields when they have different growing time, (3) rice growing area classification with applying a threshold derived from expectation–maximization (EM) algorithm, and (4) mapping accuracy assessment, by comparing with ground reference data. The classification result for the wet-season rice indicated the overall accuracy of 80.1% and Kappa coefficient of 0.60. For the dry-season rice, the overall accuracy and kappa coefficient were 83.3% and 0.67, respectively. This study demonstrates the potential of the applicability of Sentinel-1 VH-polarized SAR imagery for rice area mapping in Central Thailand. | en_US |