Spatial snow water equivalent estimation for mountainous areas using wireless-sensor networks and remote-sensing products

We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k-nearest neighbors (k-NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and eva...

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Published in:Remote sensing of environment 2018-09, Vol.215, p.44-56
Main Authors: Zheng, Zeshi, Molotch, Noah P., Oroza, Carlos A., Conklin, Martha H., Bales, Roger C.
Format: Article
Language:English
Subjects:
Algorithms
Bias
Computer simulation
Depth measurement
Ecosystems
Equivalence
Estimates
Gaussian process
Hydrology
Interpolation
k-Nearest neighbors
K-nearest neighbors algorithm
Lidar
MODIS
Mountain hydrology
Mountainous areas
Real time
Reconstructions
Regression models
Remote sensing
Remote sensors
River basins
Rivers
Sensors
Sierra Nevada
Snow
Snow accumulation
Snow depth
Snow water equivalent
Snowmelt
Spatial data
Spatial discrimination learning
Spatial distribution
Training
Water resources
Water resources management
Wireless sensor networks
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cited_by cdi_FETCH-LOGICAL-c368t-d55647cd5d63d4c58e920057a19bc74eb1df755bb441f58063d0387c71bf6d353
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creator Zheng, Zeshi
Molotch, Noah P.
Oroza, Carlos A.
Conklin, Martha H.
Bales, Roger C.
description We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k-nearest neighbors (k-NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and evaluation. In the central-Sierra American River basin, we used a k-NN algorithm to interpolate data from continuous snow-depth measurements in 10 sensor clusters by fusing them with 14 years of daily 500-m resolution SWE-reconstruction maps. Accurate SWE estimation over the melt season shows the potential for providing daily, near real-time distributed snowmelt estimates. Further south, in the Merced-Tuolumne basins, we evaluated the potential of k-NN approach to improve real-time SWE estimates. Lacking dense ground-measurement networks, we simulated k-NN interpolation of sensor data using selected pixels of a bi-weekly Lidar-derived snow water equivalent product. k-NN extrapolations underestimate the Lidar-derived SWE, with a maximum bias of −10 cm at elevations below 3000 m and +15 cm above 3000 m. This bias was reduced by using a Gaussian-process regression model to spatially distribute residuals. Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k-NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20–25 cm to 10–15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. Blending continuous spatially representative ground-based sensors with a historical library of SWE reconstructions over the same basin can provide real-time spatial SWE maps that accurately represents Lidar-measured snow depth; and the estimates can be improved by using historical Lidar scans instead of SWE reconstructions. •k-Nearest neighbors can blend ground measurements and space observations of snowpack.•Topographic variables can be used to reduce bias in k-NN estimates.•The accuracy of historical spatial data is important for k-NN approach.
doi_str_mv 10.1016/j.rse.2018.05.029
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Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k-NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20–25 cm to 10–15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. 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source ScienceDirect Journals
subjects Algorithms
Bias
Computer simulation
Depth measurement
Ecosystems
Equivalence
Estimates
Gaussian process
Hydrology
Interpolation
k-Nearest neighbors
K-nearest neighbors algorithm
Lidar
MODIS
Mountain hydrology
Mountainous areas
Real time
Reconstructions
Regression models
Remote sensing
Remote sensors
River basins
Rivers
Sensors
Sierra Nevada
Snow
Snow accumulation
Snow depth
Snow water equivalent
Snowmelt
Spatial data
Spatial discrimination learning
Spatial distribution
Training
Water resources
Water resources management
Wireless sensor networks
title Spatial snow water equivalent estimation for mountainous areas using wireless-sensor networks and remote-sensing products
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