Next Article in Journal
Oceanic Mesoscale Eddy Detection Method Based on Deep Learning
Next Article in Special Issue
Refugee Camp Monitoring and Environmental Change Assessment of Kutupalong, Bangladesh, Based on Radar Imagery of Sentinel-1 and ALOS-2
Previous Article in Journal
Comparison of LiDAR and Digital Aerial Photogrammetry for Characterizing Canopy Openings in the Boreal Forest of Northern Alberta
Open AccessArticle

Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan
Remote Sens. 2019, 11(16), 1920; https://doi.org/10.3390/rs11161920
Received: 24 July 2019 / Revised: 13 August 2019 / Accepted: 14 August 2019 / Published: 16 August 2019
The Advanced Satellite with New system ARchitecture for Observation-2 (ASNARO-2), which carries the X-band Synthetic Aperture Radar (XSAR), was launched on 17 January 2018 and is expected to be used to supplement data provided by larger satellites. Land cover classification is one of the most common applications of remote sensing, and the results provide a reliable resource for agricultural field management and estimating potential harvests. This paper describes the results of the first experiments in which ASNARO-2 XSAR data were applied for agricultural crop classification. In previous studies, Sentinel-1 C-SAR data have been widely utilized to identify crop types. Comparisons between ASNARO-2 XSAR and Sentinel-1 C-SAR using data obtained in June and August 2018 were conducted to identify five crop types (beans, beetroot, maize, potato, and winter wheat), and the combination of these data was also tested. To assess the potential for accurate crop classification, some radar vegetation indices were calculated from the backscattering coefficients for two dates. In addition, the potential of each type of SAR data was evaluated using four popular supervised learning models: Support vector machine (SVM), random forest (RF), multilayer feedforward neural network (FNN), and kernel-based extreme learning machine (KELM). The combination of ASNARO-2 XSAR and Sentinel-1 C-SAR data was effective, and overall classification accuracies of 85.4 ± 1.8% were achieved using SVM. View Full-Text
Keywords: ASNARO-2; crop; C-band; Sentinel-1; machine learning algorithm; X-band ASNARO-2; crop; C-band; Sentinel-1; machine learning algorithm; X-band
Show Figures

Figure 1

MDPI and ACS Style

Sonobe, R. Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping. Remote Sens. 2019, 11, 1920. https://doi.org/10.3390/rs11161920

AMA Style

Sonobe R. Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping. Remote Sensing. 2019; 11(16):1920. https://doi.org/10.3390/rs11161920

Chicago/Turabian Style

Sonobe, Rei. 2019. "Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping" Remote Sens. 11, no. 16: 1920. https://doi.org/10.3390/rs11161920

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop