Cargando…

Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France

The objective of this paper is to present an analysis of Sentinel-1 derived surface soil moisture maps (S1-SSM) produced with high spatial resolution (at plot scale) and a revisit time of six days for the Occitanie region located in the South of France as a function of precipitation data, in order t...

Descripción completa

Detalles Bibliográficos
Autores principales: Bazzi, Hassan, Baghdadi, Nicolas, El Hajj, Mohammad, Zribi, Mehrez
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412719/
https://www.ncbi.nlm.nih.gov/pubmed/30781451
http://dx.doi.org/10.3390/s19040802
_version_ 1783402670752530432
author Bazzi, Hassan
Baghdadi, Nicolas
El Hajj, Mohammad
Zribi, Mehrez
author_facet Bazzi, Hassan
Baghdadi, Nicolas
El Hajj, Mohammad
Zribi, Mehrez
author_sort Bazzi, Hassan
collection PubMed
description The objective of this paper is to present an analysis of Sentinel-1 derived surface soil moisture maps (S1-SSM) produced with high spatial resolution (at plot scale) and a revisit time of six days for the Occitanie region located in the South of France as a function of precipitation data, in order to investigate the potential of S1-SSM maps for detecting heavy rainfalls. First, the correlation between S1-SSM maps and rainfall maps provided by the Global Precipitation Mission (GPM) was investigated. Then, we analyzed the effect of the S1-SSM temporal resolution on detecting heavy rainfall events and the impact of these events on S1-SSM values as a function of the number of days that separated the heavy rainfall and the S1 acquisition date (cumulative rainfall more than 60 mm in 24 hours or 80 mm in 48 hours). The results showed that the six-day temporal resolution of the S1-SSM map doesn’t always permit the detection of an extreme rainfall event, because confusion will appear between high S1-SSM values due to extreme rainfall events occurring six days before the acquisition of S1-SSM, and high S1-SSM values due to light rain a few hours before the acquisition of Sentinel-1 images. Moreover, the monitoring of extreme rain events using only soil moisture maps remains difficult, since many environmental parameters could affect the value of SSM, and synthetic aperture radar (SAR) doesn’t allow the estimation of very high soil moistures (higher than 35 vol.%).
format Online
Article
Text
id pubmed-6412719
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-64127192019-04-03 Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France Bazzi, Hassan Baghdadi, Nicolas El Hajj, Mohammad Zribi, Mehrez Sensors (Basel) Letter The objective of this paper is to present an analysis of Sentinel-1 derived surface soil moisture maps (S1-SSM) produced with high spatial resolution (at plot scale) and a revisit time of six days for the Occitanie region located in the South of France as a function of precipitation data, in order to investigate the potential of S1-SSM maps for detecting heavy rainfalls. First, the correlation between S1-SSM maps and rainfall maps provided by the Global Precipitation Mission (GPM) was investigated. Then, we analyzed the effect of the S1-SSM temporal resolution on detecting heavy rainfall events and the impact of these events on S1-SSM values as a function of the number of days that separated the heavy rainfall and the S1 acquisition date (cumulative rainfall more than 60 mm in 24 hours or 80 mm in 48 hours). The results showed that the six-day temporal resolution of the S1-SSM map doesn’t always permit the detection of an extreme rainfall event, because confusion will appear between high S1-SSM values due to extreme rainfall events occurring six days before the acquisition of S1-SSM, and high S1-SSM values due to light rain a few hours before the acquisition of Sentinel-1 images. Moreover, the monitoring of extreme rain events using only soil moisture maps remains difficult, since many environmental parameters could affect the value of SSM, and synthetic aperture radar (SAR) doesn’t allow the estimation of very high soil moistures (higher than 35 vol.%). MDPI 2019-02-16 /pmc/articles/PMC6412719/ /pubmed/30781451 http://dx.doi.org/10.3390/s19040802 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Letter
Bazzi, Hassan
Baghdadi, Nicolas
El Hajj, Mohammad
Zribi, Mehrez
Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France
title Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France
title_full Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France
title_fullStr Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France
title_full_unstemmed Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France
title_short Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France
title_sort potential of sentinel-1 surface soil moisture product for detecting heavy rainfall in the south of france
topic Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412719/
https://www.ncbi.nlm.nih.gov/pubmed/30781451
http://dx.doi.org/10.3390/s19040802
work_keys_str_mv AT bazzihassan potentialofsentinel1surfacesoilmoistureproductfordetectingheavyrainfallinthesouthoffrance
AT baghdadinicolas potentialofsentinel1surfacesoilmoistureproductfordetectingheavyrainfallinthesouthoffrance
AT elhajjmohammad potentialofsentinel1surfacesoilmoistureproductfordetectingheavyrainfallinthesouthoffrance
AT zribimehrez potentialofsentinel1surfacesoilmoistureproductfordetectingheavyrainfallinthesouthoffrance