Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USA

Remote sensing of ice phenology for small lakes is hindered by a lack of satellite observations with both high temporal and spatial resolutions. By merging multi-source satellite data over individual lakes, we present a new algorithm that successfully estimates ice freeze and thaw timing for lakes w...

Full description

Bibliographic Details
Main Authors: Shuai Zhang, Tamlin M. Pavelsky
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/11/14/1718
id doaj-92d2ffd41aae4cb887052787c6bd56c5
record_format Article
spelling doaj-92d2ffd41aae4cb887052787c6bd56c52020-11-24T21:35:13ZengMDPI AGRemote Sensing2072-42922019-07-011114171810.3390/rs11141718rs11141718Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USAShuai Zhang0Tamlin M. Pavelsky1Department of Geological Sciences, University of North Carolina at Chapel Hill, 104 South Rd, Chapel Hill, NC 27599, USADepartment of Geological Sciences, University of North Carolina at Chapel Hill, 104 South Rd, Chapel Hill, NC 27599, USARemote sensing of ice phenology for small lakes is hindered by a lack of satellite observations with both high temporal and spatial resolutions. By merging multi-source satellite data over individual lakes, we present a new algorithm that successfully estimates ice freeze and thaw timing for lakes with surface areas as small as 0.13 km<sup>2</sup> and obtains consistent results across a range of lake sizes. We have developed an approach for classifying ice pixels based on the red reflectance band of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, with a threshold calibrated against ice fraction from Landsat Fmask over each lake. Using a filter derived from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) surface air temperature product, we removed outliers in the time series of lake ice fraction. The time series of lake ice fraction was then applied to identify lake ice breakup and freezeup dates. Validation results from over 296 lakes in Maine indicate that the satellite-based lake ice timing detection algorithm perform well, with mean absolute error (MAE) of 5.54 days for breakup dates and 7.31 days for freezeup dates. This algorithm can be applied to lakes worldwide, including the nearly two million lakes with surface area between 0.1 and 1 km<sup>2</sup>.https://www.mdpi.com/2072-4292/11/14/1718limnologylake icesmall lakes
collection DOAJ
language English
format Article
sources DOAJ
author Shuai Zhang
Tamlin M. Pavelsky
spellingShingle Shuai Zhang
Tamlin M. Pavelsky
Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USA
Remote Sensing
limnology
lake ice
small lakes
author_facet Shuai Zhang
Tamlin M. Pavelsky
author_sort Shuai Zhang
title Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USA
title_short Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USA
title_full Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USA
title_fullStr Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USA
title_full_unstemmed Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USA
title_sort remote sensing of lake ice phenology across a range of lakes sizes, me, usa
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2019-07-01
description Remote sensing of ice phenology for small lakes is hindered by a lack of satellite observations with both high temporal and spatial resolutions. By merging multi-source satellite data over individual lakes, we present a new algorithm that successfully estimates ice freeze and thaw timing for lakes with surface areas as small as 0.13 km<sup>2</sup> and obtains consistent results across a range of lake sizes. We have developed an approach for classifying ice pixels based on the red reflectance band of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, with a threshold calibrated against ice fraction from Landsat Fmask over each lake. Using a filter derived from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) surface air temperature product, we removed outliers in the time series of lake ice fraction. The time series of lake ice fraction was then applied to identify lake ice breakup and freezeup dates. Validation results from over 296 lakes in Maine indicate that the satellite-based lake ice timing detection algorithm perform well, with mean absolute error (MAE) of 5.54 days for breakup dates and 7.31 days for freezeup dates. This algorithm can be applied to lakes worldwide, including the nearly two million lakes with surface area between 0.1 and 1 km<sup>2</sup>.
topic limnology
lake ice
small lakes
url https://www.mdpi.com/2072-4292/11/14/1718
work_keys_str_mv AT shuaizhang remotesensingoflakeicephenologyacrossarangeoflakessizesmeusa
AT tamlinmpavelsky remotesensingoflakeicephenologyacrossarangeoflakessizesmeusa
_version_ 1725945943751131136