Mapping land cover using multi-temporal sentinel-1a data: A case study in Hanoi

Abstract

Land cover mapping is one of the most important applications of both optical and microwave remote sensing. The optical remote sensing recognizes land cover objects using spectral reflectance of the material constituting the land cover. The microwave remote sensing recognizes ground objects using backscatter, of which the intensity depends on the roughness of the ground's surface. Therefore, the multi temporal SAR images owning a lot of phenology information of land cover are the potential ideal data source for land cover mapping, in particular in the urban area. In this article, the authors present a new approach to the classification of land cover by using multi-temporal Sentinel-1A data. The experience data are single-pole (VV) in Interferometric Wide Swath mode (IW) collected from December 2014 to October 2015 along descending orbit over Hanoi, Vietnam. Decision tree method is applied base on analyzing threshold of standard deviation, mean backscatter value of land cover patterns, and combining double-crop rice classification image. The double-crop rice image is classified by rice phenology using multi-temporal Sentinel-1A images. The threshold in decision tree method is analyzed by field surveying data. The resulting classified image has been assessed using the test points in high-resolution images of Google Earth and field data. The accuracy of proposed method achieved 84.7%.Optical satellite imagery plays an important part in mapping land cover. Recognition of the land cover is based on spectral reflectance characteristics of land cover categories (Abdalla and Abdulaziz, 2012;Nguyen Dinh Duong et al., 2014;Li et al., 2004) or NDVI time series (Lambin et al., 1999;Myneni et al., 1995). However, optical imagery has many disadvantages due to weather condition and cloudiness. This is apparent

Accuracy Assessment of different classifiers for Sustainable Development in Landuse and Landcover mapping using Sentinel SAR and Landsat-8 data

“…The input training pixels were created for the classes like vegetation, waterbodies, settlement, and barren land. Generally, the water pixels have lower backscattering values [23], and the vegetation pixels have higher backscattering values in the processed SAR image [24]. The classified images were obtained from the various classifiers.…”

Integration of differential interferometric synthetic aperture radar and persistent scatterer interferometric approaches to assess deformation in enshi city, hubei, China

“…To overcome the limitations of the DInSAR technique, the persistent Scatterers InSAR (PS-InSAR) method was proposed (Ferretti et al, 2001;Prati et al, 2010). To modify the measurement accuracy of surface deformation in conventional InSAR, the PS-InSAR practices certain features of anomalies were initiated due to the atmospheric delay and backscattering of some Persistent Scatterers (PS) on the land surface (Hang et al, 2017;Khan et al, 2021;Makabayi et al, 2021). The PS-InSAR can be used in regional and large-scale area research and this method provides measurements of surface deformation with accuracy up to millimeter precision (Kim et al, 2007;Wasowski et al, 2008;Prati et al, 2010;Ali and Pan 2021;Wang et al, 2018) (Kim et al, 2007;Wasowski et al, 2008;Prati et al, 2010;Ali and Pan 2021;Wang et al, 2018).…”

Urban Classification Using Multi-temporal Sentinel-1 Data Based On Coherence Characteristics

“…SAR data mainly use InSAR techniques to research as DEM [16], subsidence land [17,18], or use to classify rice [19,20]. Hang et al (2016) [21] proposed to use backscatter intensity changes on multi-temporal Sentinel-1A images to determine land-cover features. This study proved that the backscatter of urban areas in SAR images is high and stable on multitemporal data.…”