Abstract
Sentinel-1 provides an extraordinary opportunity to explore the temporal behavior of backscattering of C-band synthetic aperture radar (SAR) due to its unique capability of successive observations every 12 days. This study reported new findings on the critical temporal features of Sentinel-1 backscattering over mountainous forested areas in northeast China and their application in regional forest mapping. Two interesting phenomena were discovered through the analysis of 450 scenes of images acquired by Sentinel-1A or Sentinel-1B over an area of 318,898.62 km2. The first phenomenon was that the dates of the largest drops of backscattering coefficients over forest and non-forest plots were different during the transition from autumn to winter. The largest drop of non-forest plots occurred around the date of the minimum temperature decreasing below 0 °C, while that of forest plots occurred around the date of the maximum temperature decreasing below 0 °C. The second phenomenon was that at the dates where these two drops occurred, the magnitude of the drop was negatively correlated with the forest canopy coverage for the first date and positively correlated for the second date. Based on these two phenomena, two methods for the forest mapping, referred to as the direct method and the indirect method, were proposed using only three dates of Sentinel-1 images, i.e., Date1: before the minimum temperature decreased below 0 °C, Date2: after the minimum temperature decreased below 0 °C but before the maximum temperature decreased below 0 °C, and Date3: after the maximum temperature decreased below 0 °C. The results showed that the overall accuracy of the forest map produced by the direct method was 93.60%, while that by the indirect method was 93.80%. Their accuracies were comparable with those of forest maps derived from publicly released land cover maps, which was approximately 94.42% for the best one. This study proposed a new way to do large-scale forest mapping in annually frozen regions using as few Sentinel-1 SAR images as possible.
Highlights
Forests play a significant role in the global carbon cycle through continuously absorbing carbon dioxide (CO2) to reduce the concentration of atmospheric greenhouse gases [1,2]
In this study, based on observations of 450 images covering an area of 318,898.62 km2 acquired by Sentinel-1A from June 2015 to July 2016 and by Sentinel-1B from August 2018 to December 2018 over mountainous forested areas in northeast China, we reported new findings on the critical temporal features of C-band backscattering coefficients and Remote Sens. 2020, 12, 1485 proposed corresponding methods for the regional forest mapping
The Sentinel-1A images used as Date3 are all acquired within November, while those for Date2 are all within October
Summary
Forests play a significant role in the global carbon cycle through continuously absorbing carbon dioxide (CO2) to reduce the concentration of atmospheric greenhouse gases [1,2]. They are of great importance for all living beings on the planet by providing habitats, foods, and other materials. It is essential to make the timely monitoring of forest resources using consistent and updated observations at a regional or even global scale provided by spaceborne remote sensing sensors. The generation and updating of these products tend to be limited by the cloud coverage (or weather conditions) [9], especially for fine-resolution maps
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
