A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region
<p>Detailed knowledge on surface water distribution and its changes is of high importance for water management and biodiversity conservation. Landsat-based assessments of surface water, such as the Global Surface Water (GSW) dataset developed by the European Commission Joint Research Centre (J...
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Format: | Article |
Language: | English |
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Copernicus Publications
2019-07-01
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Series: | Hydrology and Earth System Sciences |
Online Access: | https://www.hydrol-earth-syst-sci.net/23/3037/2019/hess-23-3037-2019.pdf |
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author | L. Li A. Skidmore A. Skidmore A. Vrieling T. Wang |
author_facet | L. Li A. Skidmore A. Skidmore A. Vrieling T. Wang |
author_sort | L. Li |
collection | DOAJ |
description | <p>Detailed knowledge on surface water distribution and its
changes is of high importance for water management and biodiversity
conservation. Landsat-based assessments of surface water, such as the Global
Surface Water (GSW) dataset developed by the European Commission Joint
Research Centre (JRC), may not capture important changes in surface water
during months with considerable cloud cover. This results in large temporal
gaps in the Landsat record that prevent the accurate assessment of surface water
dynamics. Here we show that the frequent global acquisitions by the Moderate
Resolution Imaging Spectrometer (MODIS) sensors can compensate for this
shortcoming, and in addition allow for the examination of surface water changes at fine temporal resolution. To account for water bodies smaller than a MODIS
cell, we developed a global rule-based regression model for estimating the
surface water fraction from a 500 m nadir reflectance product from MODIS
(MCD43A4). The model was trained and evaluated with the GSW monthly water
history dataset. A high estimation accuracy (<span class="inline-formula"><i>R</i><sup>2</sup>=0.91</span>, RMSE <span class="inline-formula">=11.41</span> %, and MAE <span class="inline-formula">=6.39</span> %) was achieved. We then applied the algorithm to
18 years of MODIS data (2000–2017) to generate a time series of surface
water fraction maps at an 8 d interval for the Mediterranean. From these maps
we derived metrics including the mean annual maximum, the standard deviation, and the seasonality of surface water. The dynamic surface water extent estimates
from MODIS were compared with the results from GSW and water level data
measured in situ or by satellite altimetry, yielding similar temporal
patterns. Our dataset complements surface water products at a fine spatial
resolution by adding more temporal detail, which permits the effective
monitoring and assessment of the seasonal, inter-annual, and long-term
variability of water resources, inclusive of small water bodies.</p> |
first_indexed | 2024-12-16T08:23:48Z |
format | Article |
id | doaj.art-b8ad51930fc94511a898ca03a1fb522a |
institution | Directory Open Access Journal |
issn | 1027-5606 1607-7938 |
language | English |
last_indexed | 2024-12-16T08:23:48Z |
publishDate | 2019-07-01 |
publisher | Copernicus Publications |
record_format | Article |
series | Hydrology and Earth System Sciences |
spelling | doaj.art-b8ad51930fc94511a898ca03a1fb522a2022-12-21T22:38:03ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382019-07-01233037305610.5194/hess-23-3037-2019A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean regionL. Li0A. Skidmore1A. Skidmore2A. Vrieling3T. Wang4Faculty of Geo-information Science and Earth Observation, University of Twente, Enschede, the NetherlandsFaculty of Geo-information Science and Earth Observation, University of Twente, Enschede, the NetherlandsDepartment of Environmental Sciences, Macquarie University, NSW, AustraliaFaculty of Geo-information Science and Earth Observation, University of Twente, Enschede, the NetherlandsFaculty of Geo-information Science and Earth Observation, University of Twente, Enschede, the Netherlands<p>Detailed knowledge on surface water distribution and its changes is of high importance for water management and biodiversity conservation. Landsat-based assessments of surface water, such as the Global Surface Water (GSW) dataset developed by the European Commission Joint Research Centre (JRC), may not capture important changes in surface water during months with considerable cloud cover. This results in large temporal gaps in the Landsat record that prevent the accurate assessment of surface water dynamics. Here we show that the frequent global acquisitions by the Moderate Resolution Imaging Spectrometer (MODIS) sensors can compensate for this shortcoming, and in addition allow for the examination of surface water changes at fine temporal resolution. To account for water bodies smaller than a MODIS cell, we developed a global rule-based regression model for estimating the surface water fraction from a 500 m nadir reflectance product from MODIS (MCD43A4). The model was trained and evaluated with the GSW monthly water history dataset. A high estimation accuracy (<span class="inline-formula"><i>R</i><sup>2</sup>=0.91</span>, RMSE <span class="inline-formula">=11.41</span> %, and MAE <span class="inline-formula">=6.39</span> %) was achieved. We then applied the algorithm to 18 years of MODIS data (2000–2017) to generate a time series of surface water fraction maps at an 8 d interval for the Mediterranean. From these maps we derived metrics including the mean annual maximum, the standard deviation, and the seasonality of surface water. The dynamic surface water extent estimates from MODIS were compared with the results from GSW and water level data measured in situ or by satellite altimetry, yielding similar temporal patterns. Our dataset complements surface water products at a fine spatial resolution by adding more temporal detail, which permits the effective monitoring and assessment of the seasonal, inter-annual, and long-term variability of water resources, inclusive of small water bodies.</p>https://www.hydrol-earth-syst-sci.net/23/3037/2019/hess-23-3037-2019.pdf |
spellingShingle | L. Li A. Skidmore A. Skidmore A. Vrieling T. Wang A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region Hydrology and Earth System Sciences |
title | A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region |
title_full | A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region |
title_fullStr | A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region |
title_full_unstemmed | A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region |
title_short | A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region |
title_sort | new dense 18 year time series of surface water fraction estimates from modis for the mediterranean region |
url | https://www.hydrol-earth-syst-sci.net/23/3037/2019/hess-23-3037-2019.pdf |
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