@inproceedings{18850b49555e4abf964183b38979f26c,
title = "A Physical-Statistical Retrieval Framework to Estimate SWE from X and Ku-Band SAR Observations",
abstract = "A physical-statistical framework to estimate Snow Water Equivalent (SWE) and Snow depth (SD) from SAR measurements was implemented and applied to SnowSAR flight-line data collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA and averaged to 90 m resolution. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, SWE and SD and physical model parameters. To reduce the number of physical parameters, the snowpack is represented by two layers only. Retrievals compare well with pit observations with good performance in deep snow and residual errors less than 8% for SnowSAR incidence angles > 30°.",
keywords = "BASE-AM, Grand Mesa, MEMLS, MSHM, SWE, SnowEx'2017",
author = "Siddharth Singh and Michael Durand and Edward Kim and Jinmei Pan and Kang, {Do Hyuk} and Barros, {Ana P.}",
note = "This research was funded in part by NASA{\textquoteright}s Terrestrial Hydrology Program under NASA grant NNX17AL44G with the corresponding author$ and through the NOAA Cooperative Institute for Research to Operations in Hydrology (CIROH) agreement NA22NWS4320003. The authors appreciate the National Snow & Ice Data Center for providing the SnowEx{\textquoteright}2017 data and all the colleagues who worked on the field campaign to collect the valuable datasets.; 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 ; Conference date: 16-07-2023 Through 21-07-2023",
year = "2023",
doi = "10.1109/IGARSS52108.2023.10281838",
language = "English (US)",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "17--20",
booktitle = "IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "United States",
}