Pre-Processing of Synthetic Aperture Radar Sentinel-1 Images for Agricultural Land
The precise measurement of agriculture land cover is significant for food security, economic stability, and environmental conditions. Remote sensing is a process of collecting information of object, area or phenomena using airplanes, satellite systems with subsequent processing and interpretation. SAR is efficient for gathering crop measurements, even in the regions of agricultural land covered with clouds, where collection of clear optical imagery is not effective. In this research, C-band imagery from the ASF Data Search Vertex Sentinel-1B satellite from a small region of North Dakota is used to perform the pre-processing. The satellite provides repeat coverage approximately every 12 days. Here, L1 Detected High-Resolution Dual Pol (GRD-HD), (VV/VH) dual-polarized, ascending direction imagery is pre-processed within SNAP. Pre-processing includes calibration, speckle filtering and terrain correction on the selected subset image. The pre-processed image is exported to the RGB image in Google Earth KMZ (Keyhole Markup language Zipped) for further analysis.
Keywords: SAR, Sentinel-1, Remote Sensing, Agriculture, Cropland Data Layer