Discrimination of rice crop grown under different cultural practices using temporal ERS-1 synthetic aperture radar data
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Cited by (70)
Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data
2019, International Journal of Applied Earth Observation and GeoinformationCitation Excerpt :In addition to labour- and water- savings, DS reduces methane emissions in rice, reduces the cost of cultivation, and allows timely planting of a subsequent crop, and hence is expected to further expand as labour and water costs increase in future, and improved varieties and crop management practices become available making DS more attractive to farmers (Kumar and Ladha, 2011). The differences in crop establishment practices in terms of presence and duration of water in the fields, water depth, rice canopy structure and growth duration can have a direct effect on the SAR backscatter (Chakraborty et al., 1997; Lam-Dao et al., 2009; Nelson et al., 2014). Spatial and temporal information on establishment methods can improve the accuracy of remote sensing-based rice crop growth monitoring, and yield estimation since crop establishment method affects water use, weed presence, and incidence of disease (Singh et al., 2011; Chauhan et al., 2015; Setiyono et al., 2017).
Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data
2018, International Journal of Applied Earth Observation and GeoinformationCitation Excerpt :Active microwave sensors are much less affected by cloud cover and due to their all-weather, day and night imaging capabilities and have been used since the 1990s to map rice areas. Studies using time series data from C- and X-band sensors have shown their potential for rice mapping, the European Remote Sensing satellites (ERS) 1 and 2 (Aschbacher et al., 1995; Kurosu et al., 1995; Patel et al., 1995; Chakraborty et al., 1997; Le Toan et al., 1997; Panigrahy et al., 1997; Liew et al., 1998b; McNairn and Brisco, 2004; Diuk-Wasser et al., 2006), Radarsat (Liew et al., 1998a; Panigrahy et al., 1999; Ribbes, 1999; Shao et al., 2001; Li et al., 2003; Choudhury and Chakraborty, 2006; Yonezawa et al., 2012; Yang et al., 2016; Zhang et al., 2016), Envisat ASAR (Advanced Synthetic Aperture Radar) (Bouvet et al., 2009; Bouvet and Le Toan, 2011; Karila et al., 2014; Nguyen et al., 2015), TerraSAR-X (Lopez-Sanchez et al., 2011; Pei et al., 2011; Asilo et al., 2014; Nelson et al., 2014), COSMO-SkyMed (Asilo et al., 2014; Nelson et al., 2014; Corcione et al., 2016; Busetto et al., 2017; Boschetti et al., 2017; Phan et al., 2018) and more recently Sentinel-1 (Clauss et al., 2017; Torbick et al., 2017; Onojeghuo et al., 2018; Nguyen et al., 2016; Son et al., 2017). These studies focussed on mapping rice areas, which are flooded prior to transplanting or seeding and achieved accuracies between 78% and 98%.