TY - GEN
T1 - Researth on the Detection Method of Antarctic Ice Sheet Freezing and Thawing Based on Gee and Sentinel-1 Data
AU - Yun, Cheng
AU - Lu, Zhang
AU - Huiqian, Chen
AU - Bing, Sun
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Based on Sentinel-1 EW mode data and GEE platform, this paper proposes an Antarctic ice sheet freezing and thawing detection method based on change detection and decision tree. On the GEE platform, first of all, the median value of the Sentinel-1 data in the winter months of June, July and August is as a base image set, then the difference between the summer and the base image of the same orbit is detected. At last, the thresholds of both the elevation and the backscattering coefficient difference are used to judge the freezing state of the Antarctic ice sheet. Use this method, the monthly update information on the freezing and thawing of the Antarctic ice sheet from October 2016 to March 2017, and from October 2017 to March 2018 are obtained. For the detection results, the accuracy of the self-selected samples and automatic weather station data is used for verification. The average overall accuracy of the self-selected sample verification is 93%, the average Kappa coefficient is 0.90, and the average accuracy of the automatic weather station verification is 83.86%.
AB - Based on Sentinel-1 EW mode data and GEE platform, this paper proposes an Antarctic ice sheet freezing and thawing detection method based on change detection and decision tree. On the GEE platform, first of all, the median value of the Sentinel-1 data in the winter months of June, July and August is as a base image set, then the difference between the summer and the base image of the same orbit is detected. At last, the thresholds of both the elevation and the backscattering coefficient difference are used to judge the freezing state of the Antarctic ice sheet. Use this method, the monthly update information on the freezing and thawing of the Antarctic ice sheet from October 2016 to March 2017, and from October 2017 to March 2018 are obtained. For the detection results, the accuracy of the self-selected samples and automatic weather station data is used for verification. The average overall accuracy of the self-selected sample verification is 93%, the average Kappa coefficient is 0.90, and the average accuracy of the automatic weather station verification is 83.86%.
KW - Antarctic ice-sheet
KW - GEE
KW - Sentinel-1
KW - freezing and thawing
UR - https://www.scopus.com/pages/publications/85077707455
U2 - 10.1109/IGARSS.2019.8898788
DO - 10.1109/IGARSS.2019.8898788
M3 - 会议稿件
AN - SCOPUS:85077707455
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4141
EP - 4144
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -