Attenuation of Radar Signal by a Boreal Forest Canopy in Winter

Lemmetyinen, Juha; Ruiz, Jorge Jorge; Cohen, Juval; Haapamaa, Jouko; Kontu, Anna; Pulliainen, Jouni; Praks, Jaan

doi:10.1109/lgrs.2022.3187295

Cited by 9 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2. Furthermore, since the attenuation of forest vegetation has been found to correlate with air temperature at L to X bands [46], it is expected to observe an increase in the volumetric decorrelation for the high temperatures.…”

Section: A Coherence Conservation In Snowmentioning

confidence: 98%

“…Furthermore, vegetation typically presents poor conservation of coherence, as it is heavily affected by wind [36]. Recent research indicates that in winter, the transmissivity and backscatter from vegetation may strongly vary as a result of temperature changes [44]- [46]. Some atmospheric phenomena produce temporal decorrelation over snow covered surfaces, as they produce changes in the snow pack properties.…”

Section: A Coherence Conservation In Snowmentioning

confidence: 99%

See 1 more Smart Citation

Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval

Ruiz

Lemmetyinen

Kontu

et al. 2022

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Interferometric Synthetic Aperture Radar (InSAR) is a promising tool for the retrieval of Snow Water Equivalent (SWE) from space. Due to refraction, the interferometric phase changes with snow depth and density, which is exploited by the InSAR method. While the method was first proposed two decades ago, qualitative research using experimental data analyzing factors affecting retrieval performance remains scarce. In this work a tower-based 1-10 GHz, fully polarimetric SAR with InSAR capabilities was used to analyze the effect of meteorological events (air temperature, precipitation intensity, and wind) on the observed temporal decorrelation of interferometric image pairs, at L-, S-, C-and X-bands. These factors were found to be causes of decorrelation in snow, being the temperature the critical variable in the case of snowmelt events. Of the analyzed bands, L-band presented the best coherence conservation properties. Additionally, the phase change between pairs with sufficient coherence was applied to generate estimates of changes in SWE, studying the retrieval errors at different bands and over different temporal baselines. SWE accumulation was calculated from 6 hours up to 12 days temporal baseline over a non-vegetated area. SWE accumulation profiles were successfully reconstructed for short temporal baselines and low frequencies, while an increase in the retrieval error was observed for high frequencies and long temporal baselines, indicating the limitations of higher frequencies for repeat-pass InSAR retrieval. The analysis was also reproduced over a forested area at L-band with similar results as to the non-vegetated area.

show abstract

Section: A Coherence Conservation In Snowmentioning

confidence: 98%

Section: A Coherence Conservation In Snowmentioning

confidence: 99%

Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval

Ruiz

Lemmetyinen

Kontu

et al. 2022

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Radar backscatter measurements are sensitive to SWE, but are also impacted by other environmental parameters, such as forest canopies (Lemmetyinen et al, 2022), snow microstructure (King et al, 2018;Rutter et al, 2019;Sandells et al, 2021) and soil moisture and roughenss (Zhu et al, 2022). These "nuisance parameters" motivate the introduction of a priori information to help constrain SWE retrieval (Tsang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Retrieval of SWE from dual-frequency radar measurements: Usingtimeseries to overcome the need for accurate a priori information

Durand,

Johnson,

Dechow

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. Measurements of radar backscatter are sensitive to snow water equivalent (SWE) across a wide range of frequencies, motivating proposals for satellite missions to measure global distributions of SWE. However, radar backscatter measurements are also sensitive to snow stratigraphy, microstructure and to surface roughness, complicating SWE retrieval. A number of recent advances have created new tools and datasets with which to address the retrieval problem, including a parameterized relationship between SWE, microstructure, and radar backscatter, and methods to characterize surface scattering. Although many algorithms also introduce external (prior) information on SWE or snow microstructure, the precision of the prior datasets used must be high in some cases in order to achieve accurate SWE retrieval. We hypothesize that a time series of radar measurements can be used to solve this problem, and demonstrate that SWE retrieval with acceptable error characteristics is achievable by using previous retrievals as priors for subsequent retrievals. We demonstrate the accuracy of three configurations of the prior information: using a global SWE model, using the previously retrieved SWE, and using a weighted average of the model and the previous retrieval. We assess the robustness of the approach by quantifying the sensitivity of the SWE retrieval accuracy to SWE biases artificially introduced in the prior. We find that the retrieval with the weighted averaged prior demonstrates SWE accuracy better than than 20 %, and an error increase of only 3 % relative RMSE per 10 % change in prior bias; the algorithm is thus both accurate and robust. This finding strengthens the case for future radar-based satellite missions to map SWE globally.

show abstract

“…Forests pose an important limitation on the applicability of the technique, and recent studies have helped refine estimates of forest conditions under which SWE may be estimated (e.g. Macelloni et al, 2017;Lemmetyinen et al, 2022). In this paper, we focus on retrieval issues posed by snow microstructure.…”

mentioning

confidence: 99%

Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method

Pan

Durand

Lemmetyinen

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. Radar at high frequency is a promising technique for fine-resolution snow water equivalent (SWE) mapping. In this paper, we extend the Bayesian-based Algorithm for SWE Estimation (BASE) from passive to active microwave (AM) application and test it using ground-based backscattering measurements at three frequencies (X- and dual Ku-bands, 10.2, 13.3 and 16.7 GHz), VV polarization obtained at 50° incidence angle from the Nordic Snow Radar Experiment (NoSREx) in Sodankylä, Finland. We assume only an uninformative prior for snow microstructure, in contrast with an accurate prior required in previous studies. Starting from a biased SWE prior from land surface model simulation, two-layer snow state variables and single-layer soil variables are iterated until their posterior distribution could stably reproduce the observed microwave signals. The observation model is the Microwave Emission Model of Layered Snowpacks 3 and Active (MEMLS3&a). Results show that BASE-AM achieved a RMSE of ~10 cm for snow depth (SD) and less than 30 mm for SWE, compared with the RMSE of ~20 cm SD and ~50 mm SWE from priors. Retrieval errors are significantly larger when BASE-AM is run using a single snow layer. The results support the potential of X- and Ku-band radar for SWE retrieval and shows that providing a fully-unbiased snow microstructure prior is not the only promise to obtain accurate SWE retrievals.

show abstract

Attenuation of Radar Signal by a Boreal Forest Canopy in Winter

Cited by 9 publications

References 14 publications

Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval

Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval

Retrieval of SWE from dual-frequency radar measurements: Usingtimeseries to overcome the need for accurate a priori information

Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method

Contact Info

Product

Resources

About