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...
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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 |
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