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Articles | Volume XLIII-B5-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2021, 37–41, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B5-2021-37-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2021, 37–41, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B5-2021-37-2021

  30 Jun 2021

30 Jun 2021

COHERENCE and BACKSCATTER BASED CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 with DYNAMIC TIME WARPING

O. G. Narin1, S. Abdikan2, C. Bayik3, A. Sekertekin4, A. Delen5, and F. Balik Sanli6 O. G. Narin et al.
  • 1Department of Geomatics Engineering, Afyon Kocatepe University, Turkey
  • 2Department of Geomatics Engineering, Hacettepe University, Turkey
  • 3Department of Geomatics Engineering, Zonguldak Bulent Ecevit University, Turkey
  • 4Department of Geomatics Engineering, Cukurova University, Turkey
  • 5Department of Geomatics Engineering, Tokat Gaziosmanpasa University, Turkey
  • 6Department of Geomatics Engineering, Yildiz Technical University, Turkey

Keywords: Cropland Mapping, Sentinel-1, Coherence, Backscatter, Dynamic Time-Warping

Abstract. Cropland mapping is an important inventory for food security and decision making operated by governments. Crop mapping is used to identify the croplands and their spatial distribution. For a reliable analysis and forecast for projection, multi-temporal data play a key role. Even current open and frequent optical satellite data such as Sentinel-2 and Landsat support monitoring, they are not always operational due to atmospheric conditions (rain, cloud cover, haze, etc.). On the other hand, Synthetic Aperture Radar (SAR) satellites provide alternative data sets compared to optical satellites since they can acquire images under all weather conditions. In this study, an annual cropland monitoring study is conducted using Sentinel-1 SAR. For the investigation, Tokat Province an agricultural region of Turkey, where the main source of income is agriculture, was selected. There are 4 different vegetation species (wheat, sunflower, sugar beet, corn) in the study area. Sentinel-1 data was used to generate time-series of each class and phenological structures of the crops. In this context, backscatter images of both vertical-vertical (VV) and vertical-horizontal (VH) polarized data, and coherence of both VV and VH were produced from Sentinel-1 data. Time-Weighted Dynamic Time-Warping (TWDTW) classification approach was used over cropland. The produced time-series are classified under different scenarios. The results showed that only coherence has provided higher accuracies about 81% compared to using only backscatter images as 49%.