IEEE Transactions on Geoscience and Remote Sensing, Volume 60
 Anthology ID:
 G2268
 Month:
 Year:
 2022
 Address:
 Venue:
 GWF
 SIG:
 Publisher:
 Institute of Electrical and Electronics Engineers (IEEE)
 URL:
 https://gwfuwaterloo.github.io/gwfpublications/G2268
 DOI:
Investigating the Effect of Lake Ice Properties on Multifrequency Backscatter Using the Snow Microwave Radiative Transfer Model
Justin Murfitt

Claude R. Duguay

Ghislain Picard

Grant E. Gunn
Recent investigations using polarimetric decomposition and numerical models have helped to improve understanding of how radar signals interact with lake ice. However, further research is needed on how radar signals are impacted by varying lake ice properties. Radiative transfer models provide one method of improving this understanding. These are the first published experiments using the Snow Microwave Radiative Transfer (SMRT) model to investigate the response of different imaging SAR frequencies (L, C, and Xband) at HH and VV polarizations using various incidence angles (20°, 30°, and 40°) to changes in ice thickness, porosity, bubble radius, and icewater interface roughness. This is also the first use of SMRT in combination with a thermodynamic lake ice model. Experiments were for a lake with tubular bubbles and one without tubular bubbles under difference scenarios. Analysis of the backscatter response to different properties indicate that increasing ice thickness and layer porosity have little impact on backscatter from lake ice. Xband backscatter shows increased response to surface ice layer bubble radius; however, this was limited for other frequencies except at shallower incidence angles (40°). All three frequencies display the largest response to increasing RMS height at the icewater interface, which supports surface scattering at the icewater interface as being the dominant scattering mechanism. These results demonstrate that SMRT is a valuable tool for understanding the response of SAR data to changes in freshwater lake ice properties and could be used in the development of inversion models.
Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A MachineLearning Assessment
Kiana Zolfaghari

Nima Pahlevan

Caren Binding

Daniela Gurlin

Stefan Simis

Antonio Ruiz Verdú

Lin Li

Christopher J. Crawford

Andrea Vander Woude

Reagan M. Errera

Arthur Zastepa

Claude R. Duguay
Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of bestavailable multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large ( <inlineformula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <texmath notation="LaTeX">$N =905$ </texmath></inlineformula> ) database of colocated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> radiometric spectra and PC are employed. We first examine the performance of selected widely used machinelearning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( <inlineformula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <texmath notation="LaTeX">$R_{\mathrm {rs}}$ </texmath></inlineformula> ) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a fullwidth at halfmaximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to <inlineformula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <texmath notation="LaTeX">$R_{\mathrm {rs}}$ </texmath></inlineformula> spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between <inlineformula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <texmath notation="LaTeX">$\sim 73$ </texmath></inlineformula> % and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.
Evaluation of SMAP Soil Moisture Retrieval Accuracy Over a Boreal Forest Region
Jaison Thomas Ambadan

Heather C. MacRae

Andreas Colliander

Erica Tetlock

Warren Helgason

Ze’ev Gedalof

Aaron Berg
Estimating soil moisture (SM) over the circumpolar boreal forest would have numerous applications including wildfire risk detection, and weather prediction. Evaluation of satellite derived SM retrievals in boreal ecoregions is hindered by available in situ SM observation networks. To address this, an SM monitoring network was established in a boreal forest region in Saskatchewan, Canada. The network is unique as there are no other SM network of similar size in the boreal forest. The network consisted of 17 SM stations within a single Soil Moisture Active Passive (SMAP) satellite observation pixel ( <inlineformula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <texmath notation="LaTeX">$33\times 33$ </texmath></inlineformula> km). We present an analysis of the sensitivity and accuracy of SMAP SM products in a boreal forest environment over a twoyear period in 2018 and 2019. Results show current SMAP radiometerbased L2 SM products have higher correlation with the in situ lower mineral layer SM than with the top organic layer, although the overall correlation is low. Correlations between in situ mineral layer SM and SMAP brightnesstemperature (TB) products are higher than those observed with the SMAP SM product, suggesting current SMAP SM retrieval from the TB using the <inlineformula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <texmath notation="LaTeX">$\tau $ </texmath></inlineformula> – <inlineformula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <texmath notation="LaTeX">$\omega $ </texmath></inlineformula> model introduces large uncertainties in the SM estimation, possibly from uncertain vegetation and surface parameters in the retrieval model. Results show SM can be retrieved using the <inlineformula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <texmath notation="LaTeX">$\tau $ </texmath></inlineformula> – <inlineformula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <texmath notation="LaTeX">$\omega $ </texmath></inlineformula> model with reasonable accuracy over the boreal forest provided the vegetation and soil parameters are optimized. The SM retrieval using a dual channel <inlineformula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <texmath notation="LaTeX">$\tau $ </texmath></inlineformula> – <inlineformula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <texmath notation="LaTeX">$\omega $ </texmath></inlineformula> model, which utilize both horizontally and vertically polarized SMAP TB, performs better than that with a single channel algorithm (SCA), using optimized parameters.