Land Cover Mapping Using Sentinel-1 Sar Data

Abdikan, Saygın; Sanli, F. B.; Üstüner, Mustafa; Calò, F.

doi:10.5194/isprsarchives-xli-b7-757-2016

Cited by 84 publications

(64 citation statements)

References 11 publications

(13 reference statements)

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“…Sentinel-1A was launched on 3 April 2014, while Sentinel-1B was launched on 25 April 2016 [3]. Sentinel-1A provides C-band images in both singular and dual polarization with a 12-day repeat cycle [36], and has four imaging modes: strip map (SM), interferometric wide swath (IW), extra wide swath (EW), and wave (WV), each with different resolutions. In this study, six Sentinel-1A images in IW mode with a dual polarization scheme (single-look complex (SLC) products) were acquired from the ESA (European Space Agency), as shown in Table 1.…”

Section: Study Area and Datasetsmentioning

confidence: 99%

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

Zhou

Pan

Zhang

et al. 2017

Sensors

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Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat mapping and investigating potential classification improvement by using SAR (Synthetic Aperture Radar) images, optical images, and the integration of both types of data in urban agricultural regions with complex planting structures in Southern China. Both SAR (Sentinel-1A) and optical (Landsat-8) data were acquired, and classification using different combinations of Sentinel-1A-derived information and optical images was performed using a support vector machine (SVM) and a random forest (RF) method. The interference coherence and texture images were obtained and used to assess the effect of adding them to the backscatter intensity images on the classification accuracy. The results showed that the use of four Sentinel-1A images acquired before the jointing period of winter wheat can provide satisfactory winter wheat classification accuracy, with an F1 measure of 87.89%. The combination of SAR and optical images for winter wheat mapping achieved the best F1 measure–up to 98.06%. The SVM was superior to RF in terms of the overall accuracy and the kappa coefficient, and was faster than RF, while the RF classifier was slightly better than SVM in terms of the F1 measure. In addition, the classification accuracy can be effectively improved by adding the texture and coherence images to the backscatter intensity data.

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Section: Study Area and Datasetsmentioning

confidence: 99%

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

Zhou

Pan

Zhang

et al. 2017

Sensors

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“…The broad use of the former SAR systems was, however, quite limited due to high cost and low temporal resolution. The new SAR system as Sentinel-1A provides polarimetric data in high spatial and temporal resolution for allowing land cover mapping with a quite high accuracy up to 93.28% (Abdikan, 2016;Heiko, 2015;Wagner et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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

Hang¹,

Vu²,

Duong³

et al. 2017

TCCKHVTD

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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

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“…The freely available sentinel satellite product provided by the European Space Agency (ESA) has facilitated research on mapping earth's dynamic surface coverage from urban landcover (Abdikan et al, 2016;Fonteh et al, 2016) to crop areas across different seasons (Lavreniuk et al, 2016), and even crop specific monitoring (Chen et al, 2016). The above studies have used the standard image classification methods onto the pixel values to show various features of interest.…”

Section: Introductionmentioning

confidence: 99%

“…The above studies have used the standard image classification methods onto the pixel values to show various features of interest. There has been the use of the single polarization data sets (VH or VV) as well as the dual polarization (combining VH and VV) and in comparison proven the latter resulted to better classification accuracy (Abdikan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Impacts of El Niño on the Extent of Cultivated Fields Using Sar Data Around the Agricultural Region of the Free State, South Africa

Ngie

Tesfamichael

Ahmed

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:There is continuous challenge in crop monitoring from erratic climatic phenomena such as irregular rainfall episodes during required farming seasons or cloud cover. Remote sensing has offered vital support in the monitoring of such scenarios and informs relevant authorities for better decision making. While optical sensors measure the greenness of vegetation to enable monitoring of its status, their usage is constrained by the continuous cloud cover during crop growth seasons in sub Saharan Africa. Synthetic aperture radar data (SAR) are on the other hand capable of penetrating clouds and are sensitive to the structure and moisture content of target features, thereby providing complementary information for monitoring crop cultivated fields. This study sought to evaluate the sensitivity of Sentinel -1 SAR data to the status of cultivated crop fields that experienced varying rainfall amounts between 2015/2016 and 2016/2017 growing seasons as a result of El Niño induced drought in 2015. Dual polarization composites per season were classified and through sample farms delineated from Google Earth image, backscatter values were extracted for statistical comparisons. The two sample t-test was applied to test significance of the differences between the two seasons at the level of farm status. Results showed an overall significant difference (p-value of 0.003< 0.005) in SAR backscatter sensitivity to cultivated crop fields during and after the El Niño phenomenon. While these results are encouraging for areas that experience clouds during growing seasons, further improvements can be expected by factoring in other variables such as topographic and moisture conditions of farms.

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Land Cover Mapping Using Sentinel-1 Sar Data

Cited by 84 publications

References 11 publications

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

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

Monitoring the Impacts of El Niño on the Extent of Cultivated Fields Using Sar Data Around the Agricultural Region of the Free State, South Africa

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