Remote Sensing of Environment, Volume 215
 Anthology ID:
 G1860
 Month:
 Year:
 2018
 Address:
 Venue:
 GWF
 SIG:
 Publisher:
 Elsevier BV
 URL:
 https://gwfuwaterloo.github.io/gwfpublications/G1860
 DOI:
The influence of snow microstructure on dualfrequency radar measurements in a tundra environment
Joshua King

Chris Derksen

Peter Toose

Alexandre Langlois

C. F. Larsen

Juha Lemmetyinen

P. Marsh

Benoît Montpetit

Alexandre Roy

Nick Rutter

Matthew Sturm
Abstract Recent advancement in the understanding of snowmicrowave interactions has helped to isolate the considerable potential for radarbased retrieval of snow water equivalent (SWE). There are however, few datasets available to address spatial uncertainties, such as the influence of snow microstructure, at scales relevant to spaceborne application. In this study we introduce measurements from SnowSAR, an airborne, dualfrequency (9.6 and 17.2 GHz) synthetic aperture radar (SAR), to evaluate high resolution (10 m) backscatter within a snowcovered tundra basin. Coincident in situ surveys at two sites characterize a generally thin snowpack (50 cm) interspersed with deeper drift features. Structure of the snowpack is found to be predominantly wind slab (65%) with smaller proportions of depth hoar underlain (35%). Objective estimates of snow microstructure (exponential correlation length; lex), show the slab layers to be 2.8 times smaller than the basal depth hoar. In situ measurements are used to parametrize the Microwave Emission Model of Layered Snowpacks (MEMLS3&a) and compare against collocated SnowSAR backscatter. The evaluation shows a scaling factor (ϕ) between 1.37 and 1.08, when applied to input of lex, minimizes MEMLS root mean squared error to
Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory
Jan Písek

Henning Buddenbaum

Fernando Camacho

Joachim Hill

Jennifer Jensen

Holger Lange

Zhili Liu

Arndt Piayda

Yonghua Qu

Olivier Roupsard

Shawn Serbin

Svein Solberg

Oliver Sonnentag

Anne Thimonier

Francesco Vuolo
Abstract Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODISbased CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability (‘ptheory’), along with raw LAI2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types. The ptheory describes the probability (pvalue) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy. We show that empiricallybased CI maps can be integrated with the MODIS LAI product. Our results indicate that it is feasible to derive approximate pvalues for any location solely from Earth Observation data. This approximation is relevant for future applications of the photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data.