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Remotely sensed soil moisture to estimate savannah NDVI
Satellite derived normalized difference vegetation index (NDVI) is a common data source for monitoring regional and global ecosystem properties. In dry lands it has contributed to estimation of inter-annual and seasonal vegetation dynamics and phenology. However, due to the spectral properties of ND...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6040715/ https://www.ncbi.nlm.nih.gov/pubmed/29995901 http://dx.doi.org/10.1371/journal.pone.0200328 |
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author | Boke-Olén, Niklas Ardö, Jonas Eklundh, Lars Holst, Thomas Lehsten, Veiko |
author_facet | Boke-Olén, Niklas Ardö, Jonas Eklundh, Lars Holst, Thomas Lehsten, Veiko |
author_sort | Boke-Olén, Niklas |
collection | PubMed |
description | Satellite derived normalized difference vegetation index (NDVI) is a common data source for monitoring regional and global ecosystem properties. In dry lands it has contributed to estimation of inter-annual and seasonal vegetation dynamics and phenology. However, due to the spectral properties of NDVI it can be affected by clouds which can introduce missing data in the time series. Remotely sensed soil moisture has in contrast to NDVI the benefit of being unaffected by clouds due to the measurements being made in the microwave domain. There is therefore a potential in combining the remotely sensed NDVI with remotely sensed soil moisture to enhance the quality and estimate the missing data. We present a step towards the usage of remotely sensed soil moisture for estimation of savannah NDVI. This was done by evaluating the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture and three of its individual products with respect to their relative performance. The individual products are from the advance scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), and the Land Parameter Retrieval Model-Advanced Microwave Scanning Radiometer-Earth Observing System (LPRM-AMSR-E). Each dataset was used to simulate NDVI, which was subsequently compared to remotely sensed NDVI from MODIS. Differences in their ability to estimate NDVI indicated that, on average, CCI soil moisture differs from its individual products by showing a higher average correlation with measured NDVI. Overall NDVI modelled from CCI soil moisture gave an average correlation of 0.81 to remotely sensed NDVI which indicates its potential to be used to estimate seasonal variations in savannah NDVI. Our result shows promise for further development in using CCI soil moisture to estimate NDVI. The modelled NDVI can potentially be used together with other remotely sensed vegetation datasets to enhance the phenological information that can be acquired, thereby, improving the estimates of savannah vegetation phenology. |
format | Online Article Text |
id | pubmed-6040715 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-60407152018-07-19 Remotely sensed soil moisture to estimate savannah NDVI Boke-Olén, Niklas Ardö, Jonas Eklundh, Lars Holst, Thomas Lehsten, Veiko PLoS One Research Article Satellite derived normalized difference vegetation index (NDVI) is a common data source for monitoring regional and global ecosystem properties. In dry lands it has contributed to estimation of inter-annual and seasonal vegetation dynamics and phenology. However, due to the spectral properties of NDVI it can be affected by clouds which can introduce missing data in the time series. Remotely sensed soil moisture has in contrast to NDVI the benefit of being unaffected by clouds due to the measurements being made in the microwave domain. There is therefore a potential in combining the remotely sensed NDVI with remotely sensed soil moisture to enhance the quality and estimate the missing data. We present a step towards the usage of remotely sensed soil moisture for estimation of savannah NDVI. This was done by evaluating the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture and three of its individual products with respect to their relative performance. The individual products are from the advance scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), and the Land Parameter Retrieval Model-Advanced Microwave Scanning Radiometer-Earth Observing System (LPRM-AMSR-E). Each dataset was used to simulate NDVI, which was subsequently compared to remotely sensed NDVI from MODIS. Differences in their ability to estimate NDVI indicated that, on average, CCI soil moisture differs from its individual products by showing a higher average correlation with measured NDVI. Overall NDVI modelled from CCI soil moisture gave an average correlation of 0.81 to remotely sensed NDVI which indicates its potential to be used to estimate seasonal variations in savannah NDVI. Our result shows promise for further development in using CCI soil moisture to estimate NDVI. The modelled NDVI can potentially be used together with other remotely sensed vegetation datasets to enhance the phenological information that can be acquired, thereby, improving the estimates of savannah vegetation phenology. Public Library of Science 2018-07-11 /pmc/articles/PMC6040715/ /pubmed/29995901 http://dx.doi.org/10.1371/journal.pone.0200328 Text en © 2018 Boke-Olén et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Boke-Olén, Niklas Ardö, Jonas Eklundh, Lars Holst, Thomas Lehsten, Veiko Remotely sensed soil moisture to estimate savannah NDVI |
title | Remotely sensed soil moisture to estimate savannah NDVI |
title_full | Remotely sensed soil moisture to estimate savannah NDVI |
title_fullStr | Remotely sensed soil moisture to estimate savannah NDVI |
title_full_unstemmed | Remotely sensed soil moisture to estimate savannah NDVI |
title_short | Remotely sensed soil moisture to estimate savannah NDVI |
title_sort | remotely sensed soil moisture to estimate savannah ndvi |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6040715/ https://www.ncbi.nlm.nih.gov/pubmed/29995901 http://dx.doi.org/10.1371/journal.pone.0200328 |
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