Cold Regions Hydrology High-Resolution Observatory for Snow and Cold Land Processes

Rott, Helmut; Yueh, Simon; Cline, D.; Duguay, Claude R.; Essery, Richard; Haas, Christian; Hélière, Florence; Kern, Michael; Macelloni, Giovanni; Malnes, Eirik; Nägler, Thomas; Pulliainen, Jouni; Rebhan, H.; Thompson, A. K.

doi:10.1109/jproc.2009.2038947

Cited by 167 publications

(140 citation statements)

References 33 publications

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“…The primary objective of CLPX-2 was to acquire snow volume backscatter measurements from NASA's POLSCAT (POLarimetric SCATterometer) airborne Ku-band radar system and the necessary ground truth measurements ) for validation of the proposed NASA Snow and Cold Land Processes (SCLP) and ESA Cold Regions Hydrology Highresolution Observatory (CoreH 2 O) satellite missions (Rott et al, 2010). The airborne lidar portion of the campaign was intended to be an ancillary validation data set for the radar measurements.…”

Section: Study Areamentioning

confidence: 99%

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

et al. 2015

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Abstract. Repeated light detection and ranging (lidar) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km 2 lidar-derived data set of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically based energy-and-mass-balance snow model with satellite, airborne and automated groundbased observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the conterminous United States. Independent validation data are scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation data set with substantial geographic coverage.Within 12 distinctive 500 × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 lidar acquisitions. This supplied a data set for constraining the uncertainty of upscaled lidar estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled lidar snow depths were then compared to the SNODAS estimates over the entire study area for the dates of the lidar flights. The remotely sensed snow depths provided a more spatially continuous comparison data set and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between lidar observations and SNODAS estimates were most drastic, providing insight into the causal influences of natural processes on model uncertainty.

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Section: Study Areamentioning

confidence: 99%

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

et al. 2015

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“…The COld REgions Hydrology High-resolution Observatory (CoReH2O) is one of the three satellite missions selected for scientific and technical feasibility studies (Phase-A) within the Earth Explorer Program of the European Space Agency (ESA) [ESA, 2008], [Rott et al, 2010]. The principal objective of the CoReH2O mission is to carry out the frequent spatially-detailed measurements of snow and ice in order to advance our knowledge and prediction of the water cycle in cold regions and to improve the representation of the cryosphere in climate models.…”

Section: Introductionmentioning

confidence: 99%

Effect of forests on the retrieval of snow parameters from backscatter measurements

Macelloni

Brogioni

Montomoli

et al. 2012

European Journal of Remote Sensing

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In preparation for the CoReH2O satellite mission, one of the three missions selected for scientific and technical feasibility studies within the Earth Explorer Programme of the ESA, experimental and theoretical studies have been under way in order to improve methods for the retrieval of snow physical properties from SAR data. The aim of this paper is to investigate the impact of vegetation in the retrieval of snow parameters from microwave backscattering measurements. ARTT model capable of simulating scattering from a snowcovered vegetated terrain was developed and implemented to study the sensitivity to snow and vegetation parameters. A procedure for correcting the vegetation effect in the SWE retrieval algorithm has been suggested.

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“…the mapping of wet snow cover in Alpine environments -e.g. with CoReH2O [8]. The ESA Sentinel-1 satellites [17] and the Radarsat Constellation Mission [3] (in isolation or in combination) should offer sufficiently frequent revisit times that some temporal resolution could conceivably be traded for improved image properties in higher level composite backscatter products.…”

Section: Discussionmentioning

confidence: 99%

SAR backscatter multitemporal compositing via local resolution weighting

Small

2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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A method is presented for generating composite SAR imagery from a set of multiple radiometrically terrain-corrected (RTC) backscatter images and their co-registered local illuminated areas. The process is implemented in map geometry: the resolution of each image is estimated locally as the inverse of the local contributing area and used to weight the contributions from all available observations. Some potential contributors could be out-of-swath or occluded by radar shadow. The resulting composite backscatter image can trade off temporal resolution for improved local spatial resolution while simultaneously increasing the local number of looks. The composite image is characterized by more homogenous properties (resolution, noise) in comparison to single acquisitions. The technique is demonstrated using ScanSAR data from ENVISAT ASAR and Radarsat-2. SAR BACKSCATTER MULTITEMPORAL COMPOSITING VIA LOCAL RESOLUTION WEIGHTING David SmallRemote Sensing Laboratories, Dept. of Geography, University of Zurich Winterthurerstrasse 190, CH-8057 Zurich, Switzerland; E-mail: david.small@geo.uzh.ch ABSTRACT A method is presented for generating composite SAR imagery from a set of multiple radiometrically terraincorrected (RTC) backscatter images and their co-registered local illuminated areas. The process is implemented in map geometry: the resolution of each image is estimated locally as the inverse of the local contributing area and used to weight the contributions from all available observations. Some potential contributors could be out-of-swath or occluded by radar shadow.The resulting composite backscatter image can trade off temporal resolution for improved local spatial resolution while simultaneously increasing the local number of looks. The composite image is characterized by more homogenous properties (resolution, noise) in comparison to single acquisitions. The technique is demonstrated using ScanSAR data from ENVISAT ASAR and Radarsat-2.

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Cold Regions Hydrology High-Resolution Observatory for Snow and Cold Land Processes

Cited by 167 publications

References 33 publications

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Effect of forests on the retrieval of snow parameters from backscatter measurements

SAR backscatter multitemporal compositing via local resolution weighting

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