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Modelling of surface roughness on agriculture area using Radarsat-2 satellite

Nurtyawan R.a, Ferdiyanti G.A.a, Budiharto A.a, Wikantika K.a

a Department of Geodesy Engineering, National Institute of Technology, Bandung, 40124, Indonesia

[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 IOP Publishing Ltd. All rights reserved.This research aims to model surface roughness for agricultural land using RADARSAT-2 satellite imagery. To obtain the model of surface roughness, the digital number of radar image are converted into backscattering coefficient and used as basis of surface roughness modeling. Furthermore, using the relationship between the backscattering coefficient, local incidence angle, and the wavelength was made initial models for surface roughness. The new equation that can model surface roughness on agricultural land is obtained by performing calibration between the value of the surface roughness from initial models and combinations of initials models with the value of surface roughness from field measurement that the equation resulted y=-591987×5+(9.106)x4-(6.107)x3+(2.108)x2-(3.108)x+(2.108) with RMSE value = 0.31 cm, where y is the value of surface roughness modeled (cm) and x is the surface roughness value of the combination of initial models used during the calibration process (cm). The combination of HV, VH polaristation and local incident angle (h0HV+h0VH)x cos produces a correlation value (R2) = 0.804.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Agricultural land,Backscattering coefficients,Calibration process,Correlation value,Field measurement,Incidence angles,Modeling of surface roughness,Surface roughness model[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1755-1315/500/1/012080[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]