4.7 Article

High-frequency remote monitoring of large lakes with MODIS 500 m imagery

期刊

REMOTE SENSING OF ENVIRONMENT
卷 124, 期 -, 页码 234-241

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2012.05.018

关键词

Satellite imagery; Water quality; Maine; New England; Assessment tool

资金

  1. University of Maine
  2. United States Geological Survey (USGS) Maine Cooperative Fish and Wildlife Research Unit
  3. Maine Department of Environmental Protection (MDEP)

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Satellite-based remote monitoring programs of regional lake water quality largely have relied on Landsat Thematic Mapper (TM) owing to its long image archive, moderate spatial resolution (30 m), and wide sensitivity in the visible portion of the electromagnetic spectrum, despite some notable limitations such as temporal resolution (i.e., 16 days), data pre-processing requirements to improve data quality, and aging satellites. Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors on Aqua/Terra platforms compensate for these shortcomings, although at the expense of spatial resolution. We developed and evaluated a remote monitoring protocol for water clarity of large lakes using MODIS 500 m data and compared MODIS utility to Landsat-based methods. MODIS images captured during May-September 2001, 2004 and 2010 were analyzed with linear regression to identify the relationship between lake water clarity and satellite-measured surface reflectance. Correlations were strong (R-2 = 0.72-0.94) throughout the study period; however, they were the most consistent in August, reflecting seasonally unstable lake conditions and inter-annual differences in algal productivity during the other months. The utility of MODIS data in remote water quality estimation lies in intra-annual monitoring of lake water clarity in inaccessible, large lakes, whereas Landsat is more appropriate for inter-annual, regional trend analyses of lakes >= 8 ha. Model accuracy is improved when ancillary variables are included to reflect seasonal lake dynamics and weather patterns that influence lake clarity. The identification of landscape-scale drivers of regional water quality is a useful way to supplement satellite-based remote monitoring programs relying on spectral data alone. (c) 2012 Elsevier Inc. All rights reserved.

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